The Proceedings of the 2nd European Conference on Intellectual Capital ISCTE Lisbon University Institute, Lisbon, Portugal and Polytechnic Institute of Leiria, Portugal 29-30 March 2010
Edited by Susana Cristina Serrano Fernandes Rodrigues ISCTE Lisbon University Institute Portugal
Copyright The Authors, 2010. All Rights Reserved. No reproduction, copy or transmission may be made without written permission from the individual authors. Papers have been double-blind peer reviewed before final submission to the conference. Initially, paper abstracts were read and selected by the conference panel for submission as possible papers for the conference. Many thanks to the reviewers who helped ensure the quality of the full papers. These Conference Proceeding have been submitted to the Thomson ISI for indexing. Further copies of this book and previous year’s proceedings can be purchased from http://academic-conferences.org/2-proceedings.htm ISBN: 978-1-906638-59-7 CD
Published by Academic Publishing Limited Reading UK 44-118-972-4148 www.academic-publishing.org
Contents Paper Title
Page No.
Author(s)
Preface
vii
Biographies of Conference Chairs, Programme Chair, Keynote Speaker and Minitrack Chairs
ix
Biographies of contributing authors
xi 1
2
Intellectual Capital and Knowledge Management in Collaborative Networks
Carmen Agüero and Paloma Sánchez 1 ITESM, Monterrey, Mexico 2 UAM, Madrid, Spain
Intellectual Capital and Value Creation in the Machinery and Equipment Industry
João Francisco de Aguiar, Leonardo Fernando Cruz Basso and Herbert Kimura Mackenzie Presbyterian University, São Paulo, Brasil
10
Implementing Knowledge Management Through IT Opportunities: Definition of a Theoretical Model Based on Tools and Processes Classification
Elena Alberghini1, Livio Cricelli2 and Michele Grimaldi2 1 University of Rome Tor Vergata, Rome, Italy 2 University of Cassino, Italy
21
Intellectual Capital in Polish Corporate Groups Current Trends and Future Challenges
Maria Aluchna and Beata Mierzejewska Warsaw School of Economics, Warsaw, Poland
34
Intellectual Capital Development by Means of Knowledge Conversions
Eckhard Ammann Reutlingen University, Germany
44
Transformational Leadership as a Tool of Knowledge Dynamics
Sorin Anagnoste, Simona Agoston and Ramona Puia The Academy of Economic Studies, Bucharest, Romania
54
The Aptness of Knowledge Related Metaphors: a Research Agenda
Daniel Andriessen INHolland University of Applied Sciences, Hoofddorp, The Netherlands
59
Using e-Portfolios to Evaluate Intellectual Capital of Online Learners
Bob Barrett American Public University, Charles Town, USA
67
Intellectual Capital and Value Creation in the Production and Assembly of Vehicles and Auto-Parts Sector in Brazil
Leonardo Fernando Cruz Basso, Herbert Kimura and João Francisco de Aguiar Mackenzie Presbyterian University, São Paulo, Brasil
77
Managing Intellectual Capital in Hungarian Universites – the Case of Corvinus University of Budapest
Viktória Bodnár, Tamás Harangozó, Tamás Tirnitz, Éva Révész and Gergely Kováts Corvinus University of Budapest, Hungary
89
Distributed Artificial Intelligence in Organisational Management
Stefan Adrian Boronea, Mihai Horia Zaharia and Gabriela Atanasiu “Gheorghe Asachi” Techincal University, Iasi, Romania
100
Tacit Knowledge Sharing in Organizational Knowledge Dynamics
Constantin Bratianu and Ivona Orzea Academy of Economic Studies, Bucharest, Romania
107
A Critical Analysis of Nonaka’s Model of Knowledge Dynamics
Constantin Bratianu Academy of Economic Studies, Bucharest, Romania
115
i
1
Paper Title
Author(s)
Knowledge Dynamics - a Metaphorical Approach
Stefan Bratosin1 and Mihaela Alexandra Ionescu2 1 Paul Sabatier University of Toulouse 3, France 2 National School of Political Studies and Public Administration, Bucharest, Romania
121
Knowledge Management in Local Government Sector: the Role of the Quality Certification
Elisabeth Brito1, Leonor Cardoso2 and Catarina Ramalho2 1 University of Aveiro, Portugal 2 University of Coimbra, Portugal
127
On the Importance of Managing Intangible Assets as Part of Corporate Strategy
Annie Brooking Anglia Ruskin University, Cambridge, UK
137
Sharing Knowledge in a Knowledge City Using CoPs
Sheryl Buckley and Apostolos Giannakopoulos University of Johannesburg, South Africa
144
Non-Rational Thinking in the Decision Making Process
Aurel Burciu and Cristian Valentin Hapenciuc “Ştefan cel Mare” University of Suceava, Romania
152
A Strategic Model for Intellectual Capital Reporting: Study of Service Industry in Serbia
Sladjana Cabrilo1 and Leposava GrubicNesic2 1 Educons University, Novi Sad, Serbia 2 University of Novi Sad, Novi Sad, Serbia
161
The Role of Creative Industries in Stimulating Intellectual Capital in Cities and Regions
Maria Rosário Cabrita1 and Cristina Cabrita2 1 Universidade Nova de Lisboa, Portugal 2 Universitat Politècnica de Catalunya, Spain
171
Emotional Capital for Building Sustainable Business Performance
Dan Candea and Rodica Candea Technical University of Cluj-Napoca, Romania
180
Organizational Commitment, Knowledge Management and Socia Economy: An empirical study
Leonor Cardoso, Andreia Meireles, Joana Marques University of Coimbra, Portugal
189
Empirical Study of the Impact of Organisational Identification and job Involvement on the Processes of Knowledge Management
Leonor Cardoso1, Andreia Meireles1, Rita Moreira1 and Florinda Matos2 1 University of Coimbra, Coimbra, Portugal 2 ISCTE Lisbon University Institute, Portugal
199
Knowledge Management Capabilities Supporting Collaborative Working Environments in a Project Oriented Context
Ruben Costa1, 2, Celson Lima1, 2, João Antunes2, Paulo Figueiras2, Vitor Parada2 1 UNINOVA, Centre of Technology and Systems, Portugal 2 Universidade Nova de Lisboa, Portugal
208
The Anti-Competitive Risk in Knowledge Dynamics: the Case of Tacit Collusion
Alina Mihaela Dima Academy of Economic Studies Bucharest, Romania
217
The Social Dynamics of Generating and Leveraging Intellectual Capital for Innovation
Ken Dovey and Grant Mooney University of Technology Sydney, Australia
225
Knowledge as Open Space
Tiit Elenurm Estonian Business School, Tallinn, Estonia
232
Human Resource Management Practices and Organizational Results
Teresa Esteves¹ and António Caetano² ¹The Portuguese School of Bank Management (ISGB), Lisboa, Portugal ²ISCTE – IUL Lisbon University Institute, Lisboa, Portugal
239
ii
Page No.
Paper Title
Author(s)
Clan, Adhocracy, Market or Hierarchy? Which is the Best for Knowledge Sharing in Hungary?
Zoltán Gaál, Lajos Szabó, Nóra ObermayerKovács, Zoltán Kovács and Anikó Csepregi University of Pannonia, Veszprém, Hungary
249
A Structured Model of Relationship Dynamics Between Organizational Knowledge Management and Organizational Learning
Fernando Luiz Goldman PPED/IE/UFRJ, Rio de Janeiro, Brazil
257
A Natural History of Intellectual Capital
Paul Davis Richard Griffiths Pontifica Universidad Catolica del Peru, Lima, Peru
265
A Case Study of Knowledge Elicitation on Intellectual Capital Performance in the Fund Service Industry
Jie Gu, Rongbin Lee and Cherie Lui The Hong Kong Polytechnic University, HKSAR, China
277
Intellectual Capital Management in Crisis – the Case of a Hungarian Knowledge-Intensive SME
Tamás Harangozó, Viktória Bodnár, Nóra Szűcs and Dávid Dankó Corvinus University of Budapest, Hungary
289
Artistic Organizations in Mexico: Model and Indicators of Intellectual Capital
Laura Verónica Herrera Franco, Patricia Ramírez Hernández and Graciela May Mora Universidad Veracruzana, Orizaba, México
301
Intellectual Poverty – the Bane of African Development
Alban Igwe Lagos State University, Nigeria
309
Rethinking the Learning Organization
Ton Jörg University of Utrecht, The Netherlands
317
Intellectual Capital and Value Creation in the Furniture Manufacturing Sector in Brazil
Herbert Kimura, Leonardo Fernando Cruz Basso and João Francisco Aguiar Mackenzie Presbyterian University, São Paulo, Brazil
326
How Intangibles Affect the Polish Consumers’ Decisions on the Banking Market
Monika Klimontowicz Karol Adamiecki University of Economics, Katowice, Poland
337
Strategic Asset-Base Valuation and Evaluation Framework With an Intellectual Capital Perspective
Mark Koole1 and Eric de Roos1, 2 1 TiasNimbas Business School, Utrecht, Netherlands 2 Bradford University School of Management, Bradford, UK
346
Managing Unstructured Information and Knowledge Flow in Knowledge Work Team
Rongbin Lee, Benny Cheung and Y. Wang The Hong Kong Polytechnic University, Hong Kong
355
Simulating Knowledge Dynamics in Earthquake Risk Estimation
Florin Leon and Gabriela Atanasiu Technical University “Gheorghe Asachi” of Iaşi, Romania
362
The Importance of Social Capital to the Management of Multinational Firms: Relational Networks Among Chinese and American Firms
Cheng-Lung Li1, William Channg2 and Jasper Hsieh3 1 Kun Shan University Tainan, Taiwan 2 Ming Chuan University Taipei, Taiwan 3 Nanhua University, Chiayi, Taiwan
374
What National Intellectual Capital Indices can Tell About the Global Economic Crisis of 20072009?
Carol Yeh-Yun Lin1 and Leif Edvinsson2 1 National Chengchi Univeristy, Taiwan 2 Lund University, Sweden
383
Knowledge Management: Antecedent of Organizational Innovation and Competitiveness
Mehrdad Madhoushi and Abdolrahim Sadati Mazandaran University, Babolsar, Iran
391
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Page No.
Paper Title
Author(s)
Measuring Intangibles in SMEs Having in Mind the Intellectual Capital of the European Union Lisbon Strategy for Growth and Jobs
Nevenka Maher High Business School, Slovenia
399
The Perception and Influence of Romanian Leadership in Generating and Transforming Organizational Knowledge
Anca Mândruleanu Academy of Economic Studies, Bucharest, Romania
407
The Importance of Intellectual Capital in Organisational Sustainability
Isabel Martins1, Ana Martins1, Orlando Petiz2 and Jay McCabe3 1 University of Glamorgan Business School, Pontypridd, UK 2 University of Minho, Braga, Portugal 3 Cardiff University, UK
414
Why Intellectual Capital Management Accreditation is a Tool for Organizational Development?
Florinda Matos1, 2, Albino Lopes1, Susana Rodrigues2 and Nuno Matos3 1 ISCTE - Lisbon University Institute, Portugal 2 ESTG - Polytechnic Institute of Leiria, Portugal 3 PMEConsult, Portugal
422
Beyond Words: Visual Presentation as a Generative Process for Understanding KM
Jane McKenzie and Christine van Winkelen University of Reading Henley on Thames UK
430
The Second Generation of Knowledge Management: an Analysis of the Relationship Between Professional Training and Knowledge Management
Andreia Meireles, Leonor Cardoso and Américo Albuquerque University of Coimbra, Portugal
440
Analysing and Enhancing IC in Business Networks: Results From a Recent Study
Kai Mertins, Markus Will and Cornelia Meyer Fraunhofer IPK, Berlin, Germany
450
Knowledge Management Indicators in Industrial Organizations - an Intra and Inter Sectoral Analysis
Samuel Monteiro and Leonor Cardoso University of Beira Interior, Portugal University of Coimbra, Portugal
457
The Influence of Knowledge Dynamics on Consumer Behaviour
Corina Pelau1, Anca Daniela Vladoi1, Monica Fufezan2, Violeta Mihaela Dinca1 and Valentina Ghinea1 1 Academy of Economic Studies, Bucharest, Romania 2 Moda S.A. Arad, Romania
465
Reinventing a Company the Success Story of IBM, Revived and Driven Forward by its Knowledge Leader CEO
Melinda Plescan, Anca Borza, Ovidiu Bordean and Catalina Mitra Babes-Bolyai University, Cluj Napoca, Romania
472
Knowledge Dynamics in Contemporary Marketing – From Holistic Marketing to Computational Marketing
Nicolae Al. Pop1, Klaus Bruno Schebesch2 and Corina Pelau1 1 Academy of Economic Studies Bucharest, Romania 2 Western University “Vasile Goldis”, Arad, Romania
480
Global Economic Crisis as an Opportunity for Knowledge Based Economy in Bosnia and Herzegovina
Stevo Pucar University of Banja Luka, Bosnia and Herzegovina
490
Leveraging the Quality of Knowledge Sharing By Implementing a Reward Program and Performance Management System
Yuli Purwanti, Nelson Rikardo Pasaribu and Paul Lumbantobing PT. Telekomunikasi Indonesia, Tbk, Indonesia
499
iv
Page No.
Paper Title
Author(s)
Communities of Practice: Finally a Link Between Individual and Organizational Learning in Management Development Programs
Donald Ropes1 and Jürg Thölke2, 3 1 INHolland University of Applied Sciences, Hoofddorp, The Netherlands 2 HAN University of Applied Sciences, Nijmegen, The Netherlands 3 Nyenrode Business Universiteit, Breukelen, The Netherlands
504
A Model to Measure Intellectual Capital Efficiency at National Level: Comparison, Results and Relationships
Victor Raúl López Ruiz1, Domingo Nevado Peña2 and Jose Luis Alfaro Navarro1 1 Universidad de Castilla-La Mancha, Albacete, Spain 2 Universidad de Castilla-La Mancha, Ciudad Real, Spain
513
The Relation Between Network of Collaboration (as a Relational Capital Dimension) and a Firm‘s Innovativeness
Helena Santos-Rodrigues1, Pedro Figueroa Dorrego2, Carlos Maria Jardon3 1 ESTG-IPVC, Viana do Castelo, Portugal 2 Universidade de Vigo, Pontevedra, Spain 3 Universidade de Vigo, Vigo, Spain
521
Ideas and Things: Understanding the Dynamic Dimension of Intellectual Capital
Christiaan Stam INHOLLAND University of Applied Sciences, The Netherlands
523
Romania and Bulgaria-two Similar Countries With Opposite Reactions
Stelian Stancu, Anca Lupu and Cristina Andreescu Academy of Economic Studies, Bucharest, Romania
537
Knowledge as Metaphor: Problems and Perspectives for KM
Gerard Steen VU University, Amsterdam, The Netherlands
545
Knowledge Dynamics: the Learning Cycle and Intellectual Capital. A Basic Framework for Knowledge Internalization
Marta–Christina Suciu, Alexandru BratescuGhitiu, Mina Ivanovici, Ana-Maria Neagu Trocmaer, Remus Avram, Emanuela Avram, Cristiana Bolocan Protopopescu Academy of Economic Studies, Bucharest, Romania
553
The Role of Book in the Formation of Human Capital: a Case Study of Iran
Asmaa Taheri Moghaddar and Yasser Monempour University of Tehran, Tehran, Iran
561
European Policies to Foster Knowledge: the Case of the European Social Fund – an Introductory Study
Eduardo Tomé Universidade Lusíada de Vila Nova de Famalicão, Portugal
566
The Buy-in to Corporate Culture: Creating Internal Emotional Capital Through Workbased Volunteering Schemes
Ann Turner Queen Margaret University, Edinburgh, UK
576
Metaphors of Open Innovation
Marien van den Boom INHOLLAND University of Applied Sciences, The Netherlands
586
An IC-Based Conceptual Framework for Developing Organizational Decision Making Capability
Christine van Winkelen and Jane McKenzie University of Reading, UK
592
Relevance and Importance of Intellectual Capital Reporting for new Technology-Based Ventures
Eleni Magdalini Vasileiadou and Magdalena Mißler-Behr Brandenburg University of Technology, Cottbus, Germany
600
v
Page No.
Paper Title
Author(s)
Page No.
The Professional Network for Education, Research and Training "Learning-KnowledgeDevelopment"
Marin Vlada1 and Adriana Sarah Nica2 1 University of Bucharest, Romania 2 University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania
612
Valuing Knowledge Assets in Renewable Energy SMEs: Some Early Evidence
Maria Weir1, Robert Huggins2, Giovanni Schiuma3, Antonio Lerro3 and Daniel Prokop2 1 Intellectual Assets Centre, Glasgow, UK 2 University of Wales Institute, Cardiff, UK 3 University of Basilicata, Potenza, Italy
622
Do Knowledge Intensive Organizations Rock?
Wes Wierda INHolland University, The Netherlands
632
The Global Economic Crisis of 2007-2009: its Relationship to Intellectual Capital Creation
Piotr Wiśniewski Warsaw School of Economics, Poland
641
Innovation or Imitation? — An Exploratory Study of Bandit Gadgets Made by the Chinese SMEs
Fei Ye Beijing University of Technology, P. R. China
656
Economic Growth and Gender Division of Labor with Creativity, Knowledge Utilization, and Capital Accumulation
Wei-Bin Zhang Ritsumeikan Asia Pacific University, Beppushi, Japan
663
Strengthening Feminist Activism Among Young Polish SME Entrepreneurs
Maria Edith Burke University of Salford, UK
679
Financial Valuation of Intangibles, Real Options and Entrepreneurial Performance
José Domingo García-Merino; Gerardo Arregui-Ayastuy; Arturo RodríguezCastellanos and Lidia García-Zambrano Universidad del País Vasco, Bilbao, Spain
685
Determinants of Regional Innovation: Evidence From Portuguese Regional Innovation Systems
Vítor Hugo dos Santos Ferreira Polytechnic Institute of Leiria, Portugal
695
The Intellectual Capital of the University’s Department
Valentina Maksimova and Natalia Tikhomirova The Moscow State University of Economics, Statistics and Informatics, Russia
705
Creating a Creativity Scoring System
Marisa Silva University of Coimbra, Portugal
714
Summary of Value Added Intellectual Capital (VAIC) and Calculated Intangible Value (CIV) all Over the World: Lessons to be Learnt
Nellija Titova University of Latvia, Riga, Latvia
723
The Regulation of Intellectual Capital in the United States
Margaret Vroman Northern Michigan University, Marquette, USA
734
Measuring International Spread of Knowledge: the Portuguese Technology Balance of Payments
João Miguel Coelho, Carla Ferreira and João Veiga Banco de Portugal, Lisboa, Portugal
743
‘Best Practice’ as Worst Practice: Broken Metaphor, Nude Emperor
James Falconer Eagna Research and Consulting, Toronto, Canada
754
Research in Progress Papers
Practitioner Papers
vi
Preface These proceedings represent the work of presenters at the 2nd European Conference on Intellectual Capital (ECIC 2010). The Conference is jointly hosted this year by ISCTE Lisbon University Institute, Lisbon and the Polytechnic Institute of Leiria, Portugal. The Conference Co Chairs are Susana Rodrigues, CDRSP, CIGS, Polytechnic Institute of Leiria, Florinda Matos, ISCTE-IUL, Lisbon and Albino Lopes, ISCTE-IUL, Lisbon, Portugal. The Conference Co Programme Chairs are Paulo Rita and Alexandra Fernandes, ISCTE-IUL, Lisbon and Vitor Hugo Ferreira and Silvia Ferrão, Polytechnic Institute of Leiria, Portugal. The opening keynote address is given by Göran Roos, Intellectual Capital Services, UK. The second day of the conference will be opened by Dra. Arminda Neves, Deputy Coordinator of the Lisbon Strategy in Portugal who will address the topic of Future prospects of the Lisbon Strategy. A primary aim of this conference is to contribute to the further advancement of IC theory and practice. The conference provides a platform for presenting findings and ideas for the intellectual capital community and associated fields. The range of people, issues, and the mix of approaches followed will ensure an interesting two days. 186 abstracts were received for this conference. After the double blind, peer review process there are 85 papers published in these Conference Proceedings. These papers represent truly global research from some 33 different countries, including Albania, Australia, Boznia and Herzegovina, Brazil, Canada, Chile, China, Czech Republic, Estonia, Germany, Greece, Hong Kong, Hungary, Indonesia, Iran, Israel, Italy, Japan, Latvia, Mexico, The Netherlands, Nigeria, Poland, Portugal, Romania, Russia, Serbia, Slovenia, South Africa, Spain, Taiwan, United Kingdom and USA. We hope that you have an enjoyable conference.
Susana Cristina Serrano Fernandes Rodrigues April 2010
vii
Conference Executive: Prof. Dr Daniel Andriessen, INHolland University of Applied Sciences, The Netherlands Dr Ahmed Bounfour, University Paris-Sud, France Professor Alexandra Fernandes, ISCTE-IUL, Lisbon, Portugal Dr Aino Kianto, Lappeenranta University of Technology, Finland Silvia Ferrão, Polytechnic Institute of Leiria, Portugal Vitor Hugo Ferreira, Polytechnic Institute of Leiria, Portugal Prof. Dr Rongbin W. B Lee, Hong Kong Polytechnic University, PRC Dr Karl-Heinz Leitner, Austrian Reseach Centers, Austria Dr Antti Lönnqvist, Tampere University of Technology, Finland Professor Albino Lopes, ISCTE-IUL, Lisbon, Portugal Professor Bernard Marr, Advanced Performance Institute, UK Florinda Matos, ISCTE Business School, Lisbon University, Portugal Dr Gordon McConnachie, Coordinator Global Association of IC Practitioners, Thailand Professor Paulo Rita, ISCTE-IUL, Lisbon, Portugal Dr Susana Rodrigues, CDRSP, Polytechnic Institute of Leiria, Portugal Dr Christiaan Stam, INHolland University of Applied Sciences, The Netherlands Dr Marien van den Boom, INHolland University of Applied Sciences, The Netherlands Professor Jose Maria Viedma, Polytechnic University of Catalonia, Spain
Conference Committee:
The conference committee consists of key people in the intellectual capital community. The following people have confirmed their participation: Daniel Andriessen, (INHolland University of Applied Sciences, The Netherlands); Derek Asoh (University of Botswana); Ossi Aura, (Elisa Health & Fitness, Helsinki, Finland); Ahmed Bounfour, (University Paris-Sud, France); Constantin Bratianu, (Academy of Economic Studies, Bucharest Romania); Acma Bulent (Anadolu University, Eskisehir, Turkey); Sladjanad Čabrilo, (Public Utility Company “LISJE”, Nova Sadi, Serbia); Leonor Cardoso, (University of Coimbra, Portugal); Daniela Carlucci, (University of Basilicata, Potenza, Italy); Shulien Chang (Ming-Chuan University, Taipei, Taiwan); Yuan-Chieh Chang, (National Tsing Hua University, Hsinchu, Taiwan); Eggert Claessen, (Reykjavik University, Iceland); Hugh Coombs (University of Glamorgan, Pontypridd, UK); Robert de Hoog (University of Twente, The Netherlands); Johne Dumay (University of Sydney, Australia); Susana Elena, (Institute for Prospective Technological Studies and European Commission - Joint Research Centre, Spain); Mihaela Enache, (Universitat Politècnica de Catalunya, Barcelona, Spain); Libor Friedel (Thomas Bata University, Zlin, Czech Republic); Santanu Ghosh (University of Burdwan, India); Stefan Gueldenberg (University of Liechtenstein, Vaduz, Liechtenstein); Markus Hagemeister, (Institute of Applied Business Economics, Spain); Robert Huggins, (University of Wales Institute Cardiff, UK); Melvyn Ingleson (MJI Business Solutions, Scotland, UK); Aki Jaaskelainen (Tampere University of Technology, Finland); Joseph Kessels, (University of Twente, The Netherlands); Aino Kianto, (Lappeenranta University of Technology, Finland); Gan Kin, (MARA University of Technology, Malacca, Malaysia); Mart Kivikas (Clausthal University of Technology, Germany); Marcin Kozak, (Czestochowa University of Technology, Poland); Josephine Lappia (Hogeschool, Rotterdam, The Netherlands); Rongbin Lee, (The Hong Kong Polytechnic University, Hong Kong); Karl-Heinz Leitner, (Austrian Reseach Centers, Austria); Joao Leitau, (Polytechnic Institute of Portalegre, Portugal); Libor Friedel (Faculty of Management and Economics (FaME), Tomas Bata (University in Zlin, Czech Republic); Phillipe Leliaert (Maastricht School of Management, Netherlands); Carol Yeh-Yun Lin, (National Chengchi University, Taipei Taiwan); Antti Lönnqvist, (Tampere University of Technology, Finland); Pauline Lumbantobing, (PT. Telekomunikasi Indonesia, Tbk, Indonesia); Bernard Marr, (Advanced Performance Institute, UK); Florinda Matos (ISCTE, Portugal); Gordon McConnachie, (GM IAM Services, Bankok, Thailand); Kai Mertins (Fraunhofer IPK, Berlin, Germany); Sue Molesworth ,(Keele University, Stoke-on-Trent); Jussi Okkonen (Tampere University of Technology, Finland); Miller Perry, (Open University of Israel, Raanana, Israel); Stephen Pike, (Intellectual Capital Services Ltd, London, UK); Katja Pok (Pok Perspectives, Germandy); Agnieta Pretorius, (Tshwane University of Technology, South Africa); Susana Rodrigues, (Polytechnic Institute of Leiria, Portugal); Donald Ropes, (University of Amsterdam, The Netherlands); Charles Savage, (FOM Fachhochschule für Ökonomie und Management, Germany); Christiaan Stam, (INHolland University of Applied Sciences, The Netherlands); Jukka Surakka (ArcadaUniversity of Applied Science, Helsinki, Finland); Eduardo Tome, (Universidade Lusíada, Famalicão, Portugal); Belén Vallejo, (University of the Basque Country, Bilbao, Spain); Frederik van Buren, (Dow Benelux B.V., The Netherlands); Marien van den Boom, (INHolland University of Applied Sciences, The Netherlands); Mitchell Van der Zahn, (Curtin University of Technology, Perth, Australia); Christine van Winkelen (Henley Business School of the Reading University, Henley, UK); Sergej van Middendorp, (v-work strategy B.V. and Fielding Graduate University, Bilthoven, The Netherlands); Jose Maria Viedma (Polytechnic University of Catalonia, Spain); Vilma Vuori (Tampere University of Technology, Finland); Jui Chi Wang, (Hsing Wu College, Taipei County, Taiwan); Campbell Warden, (Instituto de Astrofisica de Canarias, Tenerife); Maria Weir, (Intellectual Assets Centre, Glasgow, Scotland); Alan Willis, (Independent Consultant, Mississauga, Ontario, Canada); Piotr Wisniewski (Warsay School of Economics, Poland); Inge Wulf (Clausthal Unviersity of Technology, Germany).
viii
Biographies of Conference Chairs, Programme Chairs and Keynote Speakers Conference Chairs Dr Susana Rodrigues is fellow of the Research Centre for Rapid and Sustainable Product Development (CDRSP) and the Research Centre for Sustainable Management (CIGS) at the Polytechnic Institute of Leiria, Portugal. She was awarded a Ph.D. degree in 2002 in Strategic Management by the University of Wolverhampton, UK. She lectures advanced strategic management and marketing courses in management master degree programmes at the School of Management and Technology (ESTG), Polytechnic Institute of Leiria (IPL), Portugal. She is also lectures in strategic management in advanced training courses for executives. She does consultant work in strategic management and strategic marketing. Her research interest comprises a wide range of topics within the field of strategic management, namely business strategy, business failure, innovation, entrepreneurship and knowledge management. Florinda Matos is a Ph.D. student, in the area of Human Resources, at ISCTE Business School, Lisbon University Institute. She is a researcher at the Management Research Center – MRC. She was awarded a Master of Science in Management by ISCTE Business School. Her Doctorate’s research focuses on intellectual Capital Management. Her research interests include business strategy, organisational change, innovation, knowledge and intellectual capital. She is lectures organisational behaviour in the management programme at ISCTE Lisbon University Institute. She has been working as a business consultant, especially in the area of human resources and organisational strategy. Currently, she is leading the development of ICAA – Intellectual Capital Accreditation Association.
Professor Albino Lopes is Associate Professor at ISCTE-IUL Business School – Lisbon, Portugal. He is fellow of the Management Research Center (MRC) at ISCTE-IUL Business School – Lisbon, Portugal. He was awarded a Ph.D. in Psychology at Catholic University of Louvain, Belgium. He has been invited as a conference key note speaker in the area of the human resources management. He lectures in leadership, organisational behaviour and human resources. He is expert in advanced training courses for executives. He is author of a number of books and has published many articles in refereed Journals and in refereed international conferences in the field of human resources and organisational behaviour. He is also a member of the Editorial Board of several academic journals. He has supervised and examined several PhD theses and master dissertations. He has extensive experience as a consultant both in private and in public companies. In recent years, his research has been focused on studying the importance of managing intellectual capital as a driver of organisational innovation.
Programme Chairs Professor Paulo Rita is currently Director of the Management Research Centre (MRC) and Full Professor of Marketing at ISCTE-IUL Business School – Lisbon, Portugal. He is the Coordinator of the Doctoral Programmes of the Management Department at ISCTE-IUL Business School. He obtained his Ph.D. in Marketing at Cardiff Business School, University of Wales, UK, and subsequently a Post Doctorate on Web Marketing at the University of Nevada, Las Vegas, USA. His current research interests are focused on E-Commerce, Business Intelligence, Consumer Behaviour, and Tourism Marketing. He has published several books, namely: “Advances in Doctoral Research in Management” (2006) published by World Scientific, “Expert Systems in Tourism Marketing” (1996) published by the International Thomson Business Press, and “Computer Modelling and Expert Systems in Marketing” (1994) published by Routledge. In addition to presenting numerous refereed papers at international conferences, he also has had a number of book chapters and many articles published in refereed journals. He is also a member of the Editorial Board of five academic journals, has supervised and examined a number of PhD theses and master dissertations. He has taught both at Doctoral and Master programmes, in Portugal, Spain, Czech Republic, Mozambique, Cape Verde and Brazil.
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Professor Alexandra Fernandes was awarded a Ph.D. in Human Resources Management at ISCTE-IUL Business School – Lisbon. Her Phd theme was: Typology of Organizational Learning in Portugal. She is lectures in Management Sciences Department at ISCTE. She is responsible for the Portuguese and English programmes in Human Resources Management, Management and Organizational Behavior in Bachelor’s and Master Degrees and Summer School. Her current research interests are Human Resource Management, Human Capital and Arts Management. She is Coordinator of Arts and Creativity Management and Entrepreneurship Post-Graduation at ISCTE. She is also the Coordinator of Art Markets Management Master at ISCTE/ Faculdade de Letras of Lisbon University. She has published articles in scientific journals and also has books published in Portuguese. She has supervised and examined several PhD and master theses. Vitor Hugo Ferreira is fellow of the Research Centre for Rapid and Sustainable Product Development (CDRSP) at the Polytechnic Institute of Leiria, Portugal. Graduated in Economics by the University of Coimbra, in 2001. He worked as a client manager in a Banking institution in 2002. Since 2002 is working as an assistant Professor at the School of Management and Technology (ESTG), Polytechnic Institute of Leiria (IPL), Portugal. The main subjects taught are Principles of Management and Management and the Economics of Innovation. He works as a business consultant. He finished his Masters in the Technical University of Lisbon, in 2006, presenting a thesis on competences, innovation and strategy in the Portuguese and Chinese mould industry. He is currently finishing his Ph.D on entrepreneurship, innovation and regional growth, and is linked to the DIME and ETIC networks. Silvia Ferrão is graduated in Computer Management, by the Higher Institute of Languages and Management of Leiria – ISLA. She was awarded a postgraduate degree in computer engineering in systems and multimedia on Education, by the University of Coimbra, Portugal. She is currently doing her Ph.D on Knowledge Management, particularly on competences acquisition on Virtual Learning Environments in the University of Extremadura, Badajoz, Spain. She is a lecturer in computer management, at the Polytechnic Institute of Leiria, Portugal. She is Fellow of the Research Centre for Computer and Communications. Her research interests are Knowledge Management, e-Learning, b-Learning, distance learning and virtual learning environments.
Keynote Speakers Göran Roos is Honorary Professor at Warwick Business School in the UK, Visiting Professor of Innovation Management and Business Model Innovation at VTT Technical Research Centre of Finland, Visiting Professor of Intangible Asset Management and Performance Measurement at the Centre for Business Performance at Cranfield University and Visiting Faculty at Helsinki School of Economics Executive Education at both Helsinki and Singapore. Göran is one of the founders of modern intellectual capital science and a recognised world expert in this field and a major contributor to the thinking and practice in the areas of strategy, innovation management and industrial policy. Göran is the author and co-author of over one hundred books, book chapters, papers and articles many of which have been recognised with awards. Göran was named one of the 13 most influential thinkers for the 21st Century by the Spanish business journal “Direccion y Progreso”. Göran has worked as a consultant in most OECD countries and has served in management positions in several European and USbased corporations and presently sits on several corporate advisory boards. Göran has founded and divested several companies and serves as managing director of Intellectual Capital Services Ltd.
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Biographies of contributing authors (in alphabetical order) Carmen Agüero is researcher at the Institute of Technology (ITESM)-Mexico. PhD candidate in Economy, Innovation Management and Technological Policy at Complutense University of Madrid-Spain. She received an MBA degree at Catholic University of Peru. Her research interests are in the areas of Knowledge Management, Intellectual Capital Management, Collaborative Networks, Regional Development and Systems of Innovation. Technical and scientific ITESM coordinator of FP7 project titled “FIRST” (Implementing cooperation on Future Internet and ICT Components between Europe and Latin America). Elena Alberghini works as an IT Project Manager at Eni S.p.A in Rome. She graduated in Information Engineering from the University ‘La Sapienza’ of Rome. She is a PhD student at the faculty of Engineering of the University of Rome ‘Tor Vergata’. Her current research field is focused on knowledge management implementations and enterprise 2.0. Maria Aluchna Ph.D. – assistant professor at Department of Management Theory, Warsaw School of Economics, Poland. She specializes in corporate governance, business groups as well as in strategic management. She was awarded DAAD scholarship (stay at Universität Passau) and Fulbright Commission scholarship(stay at Columbia University). She is an author of many articles and papers for conferences and journals at the national as well as international level. Eckhard Ammann is a professor for computer science at the Reutlingen University, Germany, since 1992.Before that, he spent 8 years with the IBM company doing research and development in parallel systems and system structures. His research interests include knowledge management,intellectual capital, business process modeling, distributed systems,and virtual organisations. Sorin AnagnosteIs a PhD student in my second year at The Academy of Economic Studies in Bucharest and also a member at this university of the Center for Intellectual Capital from the Faculty of Business Administration. I am interested in leadership, knowledge management and intellectual capital in emerging markets. Daniel Andriessen is Professor of intellectual capital at INHolland University of Applied Sciences, The Netherlands, and director of the INHolland Centre for Research in Intellectual Capital, a research group set up to study the impact of the intangible economy on people and organizations. He is especially interested in the role of the unconscious in organizations, as - for example – can be witnessed by the use of metaphors in organizational theory and practice. Bob Barrett is a professor for the School of Business at the American Public University in Charles Town, West Virginia, USA. He lectures both nationally and internationally on the topics of Electronic Portfolios (ePortfolios), Virtual Teams/Management, and Using Online Learning as a Strategic Tool for Students with Disabilities. Stefan Boronea is a graduate student at the Technical University Gheorghe Asachi in Iasi, Romania, currently studying Distributed Systems. His work has been focused primarily in the fields of Artificial Intelligence and Distributed Systems, with applications in human behavior modeling (communication patterns among society members) and optimization problems. Constantin Bratianu is professor of Strategic Management and Knowledge Management at the Faculty of Business Administration, Academy of Economic Studies, Bucharest, Romania. He is currently the Head of the UNESCO Department for Business Administration, and Director of the Research Centre for Intellectual Capital, within the same university. He has been Visiting Professor at the Osaka University, Tokyo University of Science and Technology, Kobe University, Japan, Our Lady of the Lake University, San Antonio, Texas, USA, University of Applied Sciences of Upper Austria at Styer, Austria, and Modern University, Cairo, Egypt. Elisabeth de Jesus Oliveira Brito. Doctorate in Psychology (area of expertise in Organizational Psychology) - Faculty of Psychology and Educational Sciences of the University of Coimbra. Currently, is Professor in Águeda Higher School of Technology and Management, University of Aveiro – ESTGA (Portugal), where also coordinates the Unit for the Promotion of Students’ Success and of Working Students (UPSETE), (inside ESTGA, Águeda Higher School of Technology and Management, University of Aveiro). Annie Brooking has been working as CEO of high technology SMEs for over 25 years in the UK and Silicon Valley. She is author of books on Intellectual Capital and Knowledge Management. Annie teaches part time at ARU Cambridge and is an MBA Mentor at the Judge Business School at Cambridge University UK. xi
Sheryl Buckley is Deputy Head of Department in the Department of Business Information Technology (BIT) at the University of Johannesburg (UJ). Her passion lies in the Information Science discipline. Before joining the University in 2000, Sheryl taught at a High School for ten years and later at a Technical College for ten years. Sheryl is a member of CSSA, IKMS, SAICIT and ISOC-ZA. She is a committee member of a number of international organisations such as ECKM09, ECEI09 and ICICK09 as well as an active peer reviewer. Aurel Burciu is Professor Ph.D. at the Management and Business Administration Department at the Economics and Public Administration Faculty, as part of “Ştefan cel Mare” University of Suceava. Since 2000 he is the vice rector of Suceava University and his areas of interest in teaching and research are Management, International transactions and Comparative management. During 2002-2003 he obtained a Fulbright scholarship at the University of Central Florida Orlando, SUA, project with the title Cyclicity, entropy an chaos in economic life. Maria Edith Burke conducts research in the area of Knowledge and Information Management. She is a Senior Lecturer at the University of Salford’s Business School working with the Information Systems and Organizational Behaviour Centre’s. Dr Burke is also a Visiting Fellow at the Jagiellonian University in Poland and has held visiting posts with the Eotvos Lorand University, Budapest, Budapest University of Technology and the Nicholas Copernicus University in North Poland. Sladjana Cabrilo is an Assistant Professor of Knowledge Management at the University Educons, Novi Sad, Serbia. Her main teaching and research areas are knowledge management, intellectual capital, innovation and change management and business performance measurement and her current interests focus on measuring and reporting of intellectual capital. She has authored and co-authored numerous academic articles and papers and has presented worldwide. She is a member of IC Group at Regional Chamber of Commerce (Serbia) and she also works as a consultant in public sector. Cristina Cabrita is a student of master in architecture at the ETSAB – Escola Tecnica Superior d’Arquitectura de Barcelona and holds its graduation at the Faculty of Architecture of Lisbon (FAL) – Technical University of Lisbon. Her area of research is creative cities related to sustainable architecture, recovery, history of art and urban design. Maria do Rosário Cabrita is an Assistant Professor at the Universidade Nova de Lisboa and an Assistant Professor at the Portuguese Banking Management School in Lisbon. She received her PhD in Business Administration from the Institute of Economics and Business Administration, Lisbon Technical University. She has also several years of experience in various management positions in international banks. António Caetano: PhD on organizational psychology, Full Professor in human resources management at ISCTE/IUL – Lisbon University Institute, and senior researcher at Centro de Investigação e Intervenção Social (CIS/IUL).Research interests: Main areas of current research include: organizational dynamics and change, multilevel analysis of teamwork effectiveness, and human resources management. Dan Candea is Professor of Strategic Management at the Technical University of Cluj-Napoca, Romania. He won a Ford Foundation fellowship to study at MIT where he earned his Ph.D. from MIT Sloan School of Management. His current scientific research interests are in business sustainability. Leonor Cardoso is doctored in Psychology (area of specialization in Work and Organizational Psychology) of the Faculty of Psychology and Educational Sciences of the University of Coimbra, institution where also acquired the master and licentiate degrees. Currently, develops activities as Investigator and Associated Professor in the same institution. Li Cheng Lung is an assistant Professor for Department of Business Administration, Kun Shan University of Taiwan. He had professional experiences including channel marketing and new business development for consumer goods in the multinational corporations such as Exxon Mobil Oil corporation. His recent research interests are on strategic alliance and cross-cultural Management. João Miguel Coelho is responsible for the Current and Capital Accounts Unit at Banco de Portugal. He is graduated in Economics at the Universidade Nova de Lisboa (FE-UNL) and has obtained his undergraduate degree at Universidade Técnica de Lisboa (ISEG-UTL). He is Teaching Assistant at Universidade Católica Portuguesa (FCEE-UCP) since 2003. Ruben Costa Researcher at UNINOVA Institute and PhD student at Universidade Nova de Lisboa. Holds an MSc in Computer Science and a graduation in Electrical and Electronic Engineering. He’s has been xii
collaborating in several EU projects, where his primary area of expertise is related with KSEM (Knowledge Science, Engineering and Management) fundamentals; Business and IT service activities. Anikó Csepregi is a Lecturer at University of Pannonia, Hungary. After completing her Bachelor of Business Administration studies at Budapest Business School, College of Finance and Accountancy in 2004 she started her M.Sc in Economics studies at University of Pannonia, Faculty of Economics. She completed her M.Sc in Economics degree in 2006. Between 2006 and 2009 she was a Ph.D. Student. Since September 2009 she is a Lecturer. Her main fields of interest include knowledge management and knowledge sharing. She has published numerous articles and presented her work at national and international conferences. Mihaela Alina Dima is associate professor at the Academy of Economic Studies from Bucharest, School of Business Administration. She has a PhD. in Economics since 2007 specialization in International Business. The main fields of interest are: International Business, Competition Policy, European integration, Higher Education. She has presented various papers at international conferences in Europe and USA. Ken Dovey is the Director of the Information Technology Management Program (ITMP) at the University of Technology Sydney in Australia. He has lectured at numerous universities and business schools around the world and has been a consultant on leadership, organisational development and change to many global corporations as well as national and supranational organizations. Tiit Elenurm holds the professorship in entrepreneurship at the Estonian Business School. Ph. D. in 1980 for the dissertation “Management of the Process of Implementation of New Organizational Structures”. His vision is to develop synergy between training, consulting and research. Research interests include knowledge management, intellectual capital, change management and international transfer of management knowledge. Teresa Pereira Esteves. Lecturer in human resources management subjects at the Portuguese School of Bank Management (ISGB); Consultant for Organisational Behaviour and Human Resources Management at the Portuguese Bank Training Institute.PhD in Human Resources Management at ISCTE –IUL Lisbon University Institute.Research interests: Human resource management; Organisational Commitment; Distance learning training. James Falconer lives in Toronto and is president of Eagna Research and Consulting. His work generally involves strategic realignment, organizational redesign, process improvement, and stewardship of the resulting transformations. He has been consulting for more than twenty years in a variety of industries. He also engages in research in various conceptual areas and tries to argue business relevance. He can also occasionally be opinionated about urban affairs, the arts, home renovation, coffee, and wine. Ye Fei is an Associated Professor at the School of Economics and Management, Beijing University of Technology. She received a PhD degree in Management Information Systems from University of Maryland, College Park, and an MBA degree in International Finance from St. John’s University. Her current research interests include business strategy, entrepreneurship, innovation, and knowledge management. She has taught undergraduate- and graduate-level courses in strategic management, managerial decision making, organizational behavior and MIS. Vitor Hugo dos Santos Ferreira: PHd student in the technical university of Lisbon, participated in the DIMETICS and Globelics PhD schools.Works as an assistant lecturer in Lecturer in the Polytechnic Institute of Leiria, in the Department of Economics and Management. Is a member of CDRSP. Currently researches on the area of innovation studies and entrepreneurship. Carla Ferreira works in the Current and Capital Accounts Unit, in the Statistics Department of Banco de Portugal. She has a degree in Mathematics Applied to Economics and Business Studies in the Instituto Superior de Economia e Gestão (ISEG – UTL) and Master Degree in Statistics and Information Management in the Instituto Superior de Estatística e Gestão de Informação (ISEGI – UNL). Pedro Figueroa is is a Professor in University of Vigo. He is PhD in Management at the Santiago de Compostela University (Spain). He has worked as teacher of Business and Economics in the Caixavigo Management School, and the University of Santiago de Compostela. He has published many books in Spanish on business and finance, specially referred to SMEs in Galician collaboration with other authors. During the last few years, he has worked on different projects. He has directed numerous doctoral theses too.
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Lidia García-Zambrano As researcher in Financial Economics at The University of the Basque Country, her research activities are oriented towards the fields of the knowledge management: assessment and financial valuation of intangibles. She is co-author of various articles in international scientific magazines. She is doing a Master in Finance while working on her thesis about financial valuation of intangibles. Fernando Luiz Goldman is a brazilian doctoral student in Public Policies, Strategies and Development in Economics Institute of UFRJ. Electrical Engineer by UFRJ. Master in Production Engineering by UFF. It also has a MBA by FGV. Since 2007 President of chapter Rio de Janeiro of Brazilian Society of KM. Tamás Harangozó is graduated as an economist (MA) at the Faculty of Business Administration of the Corvinus University of Budapest in 2007. Currently he is a Ph.D. student at the Institute of Management of same university and his research activities are mainly on management control, intellectual capital management and possible performance-based and behavioral aspects of them. His theoretical and practical interests and activities cover particularly management control (controlling), performance management and organizational behavior. Laura Verónica Herrera Franco. Accountant, academic of the Accounting and Administration Faculty belonging to the Veracruz University (Institution of Superior Education in the State), Mexico. Coordinator of the Master degree in exertion of the Quality, same Institution. Specialist in Finances, Master degree in Small Enterprises Administration, actually studying the Doctor degree in Sciences of Administration. Patricia Ramírez Hernández Lawyer, academic of the Accounting and Administration Faculty belonging to the Veracruz University (Institution of Superior Education in the State), Mexico. Coordinator of the Juridical area of the Department for the Integral Development of the Family, Municipal Government of Orizaba, Veracruz. Master degree in Government and Public Administration, actually studying the Doctor degree in Public Administration. Robert Huggins is Director of the Centre for International Competitiveness at the Cardiff School of Management, University of Wales Institute, Cardiff. As the Co-Founder of the World Knowledge Competitiveness Index and Originator of the European Competitiveness Index and UK Competitiveness Index, his research aims to inform corporate strategy and public policy, especially actions aimed at improving global competitiveness. Mihaela Alexandra Tudor Ionescu is Associate Professor, PhD at the Faculty of Communication and Public Relations, National School of Political Sciences and Public Administration, Bucharest, Romania. She is associated member of LERASS (University of Toulouse 3, France) and she teaches courses on culture and organizational behaviour, employer branding, organizational communication, and research/editing methods in human and social sciences. Her research interests revolve around the role of metaphorical representation and mediation in communication, culture and organizational behaviour, employer branding. Carlos Maria F-Jardón is a Professor of Econometrics in University of Vigo. He is PhD in Economics and Mathematics University of Navarre (Spain). He has worked as teacher of quantitative methods in Business and Economics in the University of Navarre, the Caixavigo Management School, and the University of Santiago de Compostela. He has published many books in Spanish on the quantitative economics applied to the business and finance, specially referred to SMEs in Galician collaboration with other authors. He published many papers in these themes. Ton Jörg Ph.D. studied Physics and Mathematics with Chemistry ('Bachelor' of Science, in 1970) at Utrecht University, before his study of Psychology at the University of Amsterdam. He is a psychologist since 1977 (Master in Psychology). He has been working as an evaluation researcher in different national (e.g. on adult education for disadvantaged older people, and the PLON-project about the innovation of Physics Education in secondary schools), and international projects (SISS-International Science Report). Herbert Kimura Electronics Engineering - Aeronautics Institute of Technology - Brazil Master in Statistics Mathematics and Statistics Institute of University of Sao Paulo - Brazil Doctoral in Business Administration Business, Economics and Accounting School of the University of Sao Paulo – Brazil . Professor: Graduate Business Program - Mackenzie Presbyterian University Monika Klimontowicz is lecturer and a Ph.D. student at K. Adamiecki University of Economics in Katowice. Her Doctorate’s research focuses on the role of intangibles in the process of achieving banks’ competitive advantage. Her research interests include business strategy, innovation, knowledge and intellectual capital. She has been working as a marketing manager and business consultant. xiv
Mark Koole is a post graduate in Finance at TiasNimbas Business School, The Netherlands. Under the supervision of Prof. Dr. Eric de Roos the master thesis was well accepted and contributed to graduating with distinction achievement. Based the master thesis his first research paper was developed for the European Conference on Intellectual Capital Portugal 2010. Beata Kupiec-Teahan is a marketing economist at the Land Economy R&D of Scottish Agricultural College. Her research interests include applied marketing modelling, (food) marketing research and marketing research methodologies. Rongbin Lee is the Chair Professor of and Director of the Knowledge Management Research Centre of The Hong Kong Polytechnic University, and the editor of the Journal of Information and Knowledge Management Systems and the International Journal of Knowledge and Systems Science. His research interest includes manufacturing strategy, knowledge management, organizational learning and intellectual capital management. Florin Leon is a Lecturer with the Department of Computer Science and Engineering of the Technical University of Iasi, Romania. His main research interests are: artificial intelligence, simulations using intelligent agents and data mining. He has been involved in interdisciplinary collaborations regarding the application of artificial intelligence techniques to civil engineering and chemistry problems. Víctor López is Ph.D. in Econometrics (2002). He research in University of Castilla-La Mancha. His research lines are Intellectual Capital Measurement, Regional Economy and Models Applied to Management. He is author of diverse papers, books and documents on these topics. He has given international courses on Intellectual Capital, and obtained several investigation prizes. Anca Domnica Lupu. She graduated the Academy of Economic Studies-Faculty of Cybernetics and Statistics in 2005, took her master degree in Management and then entered the Phd with the thesis Contributions to the evaluation of Intellectual Capital. Mehrdad Madhoushi PhD in Management (in field of systems), University of Mazandaran Associate professor of department of business management. Managing director of jurnal of Humanities & Social Sciences(1999- now). Chief editor of journal of executive management (2008- now) Head of MBA & Business management department (2008-now) Neva Maher graduated in Economics from the University of Ljubljana, in Slovenia. She was a counsellor to the President of Court of Audit of Slovenia. She is expert on accountancy and finance. She has writen several manuals (for auditing, controlling, monitoring, implementing and evaluation). Skilled in training for sound financial management and controls, programmes for SME, action plans for trade – internal EU market and external market, training civil servants and developping training programmes for SME entrepreneurship. Valentina Maksimova, Professor, PhD in Economics.The Head of Economics and Investment Department of the Moscow State University of Economics, Statistics and Informatics. Her research fields are Economics, Knowledge Economy, Investments in Human Capital., Investments. She has published textbooks on Microeconomics and Investment. Her current research is in the field of knowledge management and Intellectual Capital. Florinda Matos is a Ph.D. student, in the area of Human Resources, at ISCTE Business School, Lisbon University Institute. She is a researcher at the Management Research Center – MRC. She was awarded a Master of Science in Management by ISCTE Business School. Her Doctorate’s research focuses on intellectual Capital Management. Her research interests include business strategy, organisational change, innovation, knowledge and intellectual capital. She teaches management areas at ISCTE Lisbon University Institute and at ESTG - Polytechnic Institute of Leiria. She has been working as a business consultant, Jane McKenzie is Director of the Henley Knowledge Management Forum. As Professor of Management Knowledge and Learning at Henley she has been teaching and researches in the area for 10 years. She has published extensively in academic and practitioner journals, co-authoring Understanding the Knowledgeable Organization: Nurturing Knowledge Competence with Dr Chrsitine van Winkelen. Andreia Meireles. Master’s Degree in Psychology, in Work and Organizational Psychology area, by the Faculty of Psychology and Educational Sciences of the University of Coimbra. At this moment, she is a PhD student with scholarship funded by the Foundation for Science and Technology, and is developing her research project on the knowledge management area. At the present, she also has the position of Invited Professor in the Superior School of Education (Polytechnic Institute of Coimbra).
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Kai Mertins is Director of the Division Corporate Management at the Fraunhofer-Institut for Production Systems and Design Technology (IPK), Berlin/Germany since 1988. He studies Control Theory in Hamburg and Economy together with Production Management at the Technical University of Berlin. Since 1998 he is Professor for Global Production Management at the Technical University of Berlin. Ivona Orzea is a doctoral student and teaching assistant of Faculty of Business Administration in Bucharest. She is a member of the Research Center for Intellectual Capital. The research areas of interest are: knowledge dynamics, knowledge management, intellectual capital and change management. Sara Parry is a Lecturer in Marketing at Bangor University. Her current research interests include software and high-tech marketing, Relationship Marketing and B2B marketing. She has previously presented her research at European Marketing Academy conference (EMAC) and the Academy of Marketing conference. Corina Pelau is university asistant at the Academy of Economic Studies, Bucharest Romania, UNESCO Chair for Business Administration. Within the Faculty for Business Administration, in foreign languages, she helds courses and seminars of marketing, customer relationship management and organiztional and consumer behavior in German and English language. Her main reseach interests are marketing-controlling, customer relationship management and consumer behavior. Juli Purwanti. Senior Officer of Employee Knowledge Management at PT Telekomunikasi Indonesia, Tbk. Graduated in Mathematical Science, University of Gadjah Mada Master of Business Administration, Institute of Technology Bandung. Main Activity in company : Developing work environment & policy to motivate employee doing knowledge sharing for supporting company tranformation to be knowledge Enterprise Melinda Plescan graduated from Babes-Bolyai University in 2005, having a Bachelors degree in Economics. In 2006 she got her Masters in Hotel Management, and in the same year she was the beneficiary of a 2 years scholarship to study at National Taiwan Normal University, to get a second Masters degree, this time in Human Resource. After her return in 2008, she started her PhD, on the topic of Knowledge Transfer. She is presently an Assistant Professor at the Faculty of Economics and Business Administration, at BabesBolyai University in Cluj Napoca, Romania, teaching Management. Nicolae Al. Pop is university professor at the Academy of Economic Studies, Bucharest Romania, Chair for Marketing and Dean of the Faculty of Business Administration, in foreign languages. He took part at a post graduate study program in the field of retailing at the “J. W. Goethe” University of Frankfurt a.M., Germany and he was profesor at the University of Applied Sciences in Bochum Germany. He has coordinated several research projects in the field of marketing, international markets and CRM. Helena Santos-Rodrigues is a PhD in social sciences at the University of Vigo (Spain), has a MBA in International Marketing and Finances, graduated at Catholic University-Oporto (Portugal). Is within the Department de Ciências Económicas e Empresariais, Escola Superior de Tecnologia Gestão, Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal, were teaches Marketing, Organization and Strategy. Her research interests are: intellectual Capital, Knowledge Management and innovation. Donald Ropes is a research fellow at the Centre for Research in Intellectual Capital at Inholland University of Applied Sciences and a researcher in knowledge management at the University of Amsterdam in the Faculty of Social and Behavioral Sciences. The focus of his research, and PhD dissertation, is on how communities of practice can be successfully organized as forums for learning and innovation. Marisa Sofia Silva Currently attends a PhD Programme in Management, specialization in Strategy, at the University of Coimbra; Pos-graduate studies in Information Management at the University of Coimbra; Honours Degree in Economics (University of Coimbra and Glasgow University).The professional background was firstly developed on Financial Services, Regional Development and International Cooperation. Christiaan Stam (1965) is Associate Professor at the Centre for Research in Intellectual Capital at INHOLLAND University of Applied Sciences. Central themes in his work are knowledge management, intellectual capital measurement and knowledge productivity. In 1999 he initiated www.intellectualcapital.nl, a startpage for the IC-community. In December 2007 he successfully defended his Ph.D. thesis in Knowledge Productivity at Twente University, The Netherlands. Gerard Steen is professor of Language Use and Cognition at VU University Amsterdam. He has held positions at the University of Utrecht and the University of Tilburg and is director of the Language, Cognition, and Communication research programme at VU University Amsterdam involving over 40 researchers working on the role of language in effective communication. He directs a number of research projects on xvi
metaphor, including two projects sponsored by the Netherlands Organization for Scientific Research, NWO, one vici-project on metaphor in discourse and one Open Competition project on visual metaphor. Professor Steen is associate editor of Metaphor & Symbol and serves on the editorial board of six other international scholarly journals and book series in language and literature. Marta-Christina Suciu, Phd in Economics. Graduate of Cybernetics Faculty, Academy of Economic Studies Bucharest (ASE), 1981. Research fellow, National Institute for Economic Research, Romanian Academy. Since 1993 teaching & research at ASE. Now full professor & PhD supervisor in Economics, ASE. Topic of interest: Knowledge-based society, intellectual capital, KM, creative economy, investing in people and skills. Asmaa Taheri Moghaddar .Profession: Attorney at law and lecturer of Azad university of Rafsanjan. Membership: Iranian bar association, researcher of comparative law institution. Awards: Degree 14 in the entrance exam of universities in Iran Jürg Thölke is a senior program manager for in-company programs at Nyenrode Business University. In this function he also works as consultant, coach and teacher, balancing on the crossroad of strategy, organizational psychology, and technique. He also has a chair for Managing Innovation of Learning Processes in the Organization at the HAN University, Nijmegen. Nellija Titova, working for EU funds projects’ monitoring where invetsment in IC development is one of the primary objective and PhD student of University of Latvia is exploring the issues of Intellectual capital evaluation and comparative analysis of the economic entities. She has published several articles which are getting more and more specific. Eduardo Tomé made his PhD Thesis in Economics at the Technical University in Lisbon in 2001. Since than he has taught in several Portuguese Universities, presented more than 20 papers in International conferences and published 10 papers in referred Journals. He has participated in two ECIC conferences held in Haarlem in 2007 and 2009. Ann Turner is Programme Leader for Undergraduate Public Relations programmes at Queen Margaret University, Edinburgh. She lectures at both undergraduates and postgraduates level and delivers the Chartered Institute of Public Relations Postgraduate Diploma. Her research interests are in the area of corporate social responsibility looking specifically at the motivations and benefits of work-based volunteering schemes. Marien van den Boom carried out numerous research assignments and lectureships on Arab philosophy, cross-cultural communication and social diversity, international communication management and information technology, knowledge processes in Asia. In his current position as fellow of the Centre of Research in Intellectual Capital he focuses on knowledge economies in Asian countries. He regularly lectures at universities and research centers in Southeast Asia and the Middle East on topics about Intellectual capital and Asian cultures. Christine van Winkelen has been has worked with the Henley Knowledge Management Forum since its inception in 2000, project managing and leading collaborative research projects. She was the Director for five years. She has published extensively in academic and practitioner journals, co-authoring Understanding the Knowledgeable Organization: Nurturing Knowledge Competence with Professor Jane McKenzie from Henley. Eleni Magdalini Vasileiadou holds a professional degree in Architecture and a postgraduate degree in Business Studies. She currently lectures Technology and Innovation Management in the Brandenburg University of Technology in Cottbus (Germany). Her Doctorate’s research encompasses intellectual capital and the implementation of methods for its evaluation and management, particularly in innovative and technology-based ventures. João Veiga started his Ph.D in Economics in 2007 at Universidade Nova de Lisboa (FE-UNL) and has obtained his undergraduate degree at Universidade do Minho (Escola de Economia e Gestão). Since 2006, works in the Current and Capital Accounts Unit, in Statistics Department of Banco de Portugal. Margaret Vroman is an Assistant Professor of Business Law at Northern Michigan University. She has also taught at Michigan State University’s College of Law and Western Michigan University. In addition to her JD degree from the University of Toledo, College of Law, she possesses an MA degree in Political Science (International Relations) from Western Michigan University. She recently published a business law textbook xvii
and has authored several book chapters on the use of scientific evidence as well as numerous journal articles. Maria Weir is Operations Manager of the Intellectual Assets Centre in Scotland, and has played a key role in the Centre’s development. Maria’s areas of research interest include partnership development and performance management. Her recent work has focused on the development of intellectual asset management among Scotland’s business community, as well as being a key member of the team responsible for establishing the Intellectual Asset Centre as a leading international think-tank in the field of IA research. Wes Wierda At INHolland University was the manager of the Media and Entertainment Management academy. He started to write his Phd at the University of Leiden. The dissertation concerns the strategic position of the music publishers in the Netherlands, economic value creation was researched upon..As an Associate Lecturer he applied the acquired knowledge in order to create a efficiency and market-bases approach for the clients in the media and entertainment industry. Piotr Wisniewski is Associate Professor of Corporate Finance at the Warsaw School of Economics and is focusing his post-doctoral research on intellectual capital growth in emerging economies. Dr Wisniewski brings over a decade of cross-country executive experience in European financial services; associate membership of the British Chartered Institute for Securities & Investment and membership of the American Professional Risk Managers’ International Association. Dr Wisniewski's academic interests revolve around intangible assets, capital markets, creative accounting, behavioural finance and international taxation. Carol Yeh-Yun Lin is a professor of the Department of Business Administration at National Chengchi University in Taiwan. Dr. Lin has published extensively with over 40 scholarly publications and 60 conference presentations. She has been awarded “Highly Commended Award Winner at the Literati Network Awards for Excellence 2009” for her paper “National intellectual capital: Comparison of the Nordic Countries, Journal of Intellectual Capital, 9(4), 525-545”, co-authored with Prof. Leif Edvinsson. Zhang Wei-Bin. PhD (Sweden), is Professor in Ritsumeikan Asia Pacific University, Japan. His main research fields are nonlinear economic dynamics, growth theory, trade theory, East Asian economic development, Chinese philosophy, and ethics. He has published more than 100 academic articles and authorized 20 academic books in English by international publishing houses.
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Intellectual Capital and Knowledge Management in Collaborative Networks Carmen Agüero1 and Paloma Sánchez2 1 ITESM, Monterrey, Mexico 2 UAM, Madrid, Spain
[email protected] [email protected] Abstract: Nowadays there is an increasing trend for grouping Small and Medium-Sized Enterprises (SMEs) in Collaborative Networks (CN). This phenomenon results from the SMEs’ necessity of sharing capacities and knowledge to face the world-wide market in order to create new products or services and to open new markets. This paper regards CN as entities generating innovations. Therefore, it focuses on their inherent need to measure their intellectual capital and to manage their knowledge flow. The main contribution of this work focuses in the following aspects: i) to explain the existing relationship between the Collaborative Networks, Knowledge Management and Intellectual Capital concepts, and to propose two new concepts supporting them: Collaborative Knowledge Management (CKM) and Collaborative Intellectual Capital Management (CICM). These new proposed concepts arose from the following foundations: the Intellectual Capital Management (ICM), considered at the highest business strategy level and focused in the creation and extraction of value (Wiig, 1997a and Edinsson, 1997 mentioned in Zhou, 2003) and Knowledge Management (KM), which is focused on developing tactical and operative activities related to knowledge, in order to facilitate its creation, capture, transformation and use (Wiig, 1997a mentioned in Zhou, 2003); ii) the necessity to sensibilize about the Measurement and Management of the Intellectual Capital in Collaborative Networks concept, for being one of the recent years’ organizational growing trends, due to the increasing need to unify capacities in order to compete in the global market. This paper shows a case study of the IBEROEKA Network (Science and Technology Manager Organism of 21 Ibero-American countries) describing its present situation, and showing how the members of this network can improve their competitive position through the intellectual capital information and KM in order to manage it as an inducer for innovation, creating an additional sub-level in the network comprised by SMEs, universities and scientific institutions of each involved country in this network. This proposed improvement can even provide new intangible and more valuable assets towards the creation of a Network of Virtual Collaboration IBEROEKA for Innovation, whose functionality would be supported by an information technologies’ application system within a collaborative work. The proposed improvement also considers the development of automated processes of Knowledge Management and Measurement of the Intellectual Capital. Keywords: Collaborative Networks, Intellectual Capital, Knowledge Management
1. Introduction The core of innovation is the knowledge. Knowledge, together with capital and labor, is quickly taking place as the key elements of developed economies (Egbu, 2005). The intellectual assets are becoming the keys for the companies’ performance; aspects such as networking, cooperation and knowledge flowing inwards and throughout the companies are reaching a remarkable importance. In this changing atmosphere, innovation has become the nearest connection between the scientific progress and the economy (OECD, 2007). The construction of knowledge does not rely on machines; it is only possible as a human task, because every human is embedded in their own abstract information structure. The knowledge is less and less a personal and individual experience, since it is constituted by networks in a collective experience in which the learning and teaching processes are changing positions and these positions are randomly distributed. The collaborative construction of the knowledge is more and more the normal way to acquire knowledge; the human communities are learning entities and the utopian “Knowledge Society” is getting closer (Reyes, 2004). In the global economy the companies are trying to re-invent their businesses and to maintain a competitive advantage through collaboration. This collaboration, expressed in terms of supply chains, extended value chains, companies, virtual companies and clusters are getting to be common terms. Nevertheless, collaboration just for the sake of collaborating is not enough, since a business must focus to maintain its competitive advantage and to maintain its operation. Collaboration allows the development of new ideas as a team, allowing the creation and sharing of knowledge in a high confidence atmosphere. The Collaborative Networks (CN) are similar to the models of knowledge generation, since each one of the models require social interactions of committed people and arranged within certain collaborative spaces. Collaboration environments favor the cooperation between fellow workers and
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Carmen Agüero and Paloma Sánchez therefore the interchange of ideas, which is the base for constructing knowledge again. According to Camarinha-Matos, et al (2005), progress in CN continues showing a growing number of manifestations including virtual organizations, virtual enterprises, dynamic supply chains, professional virtual communities, collaborative virtual laboratories, etc. The practice of these collaboration forms represent variations of a general paradigm and has lead to the consolidation of CNs as a new scientific paradigm This trend generates the need to manage a CN with focus on measuring its Intellectual Capital (IC). As a result, some questions will need to be solved:
How networks must be managed in order to know their value?
Why do we need to determine the value of networks and to whom does this value belong? ;
The answers can come from different points:
To determine its value in order to know how solid the network is and to assess whatever has been increased proportionally to the value of each company.
To determine the improvements and development of new activities for its fortification.
To establish new communication and cooperation mechanisms in order to improve the individual and group indicators.
The present paper shows the relationship between fundamental concepts such as KM (Knowledge Management), ICM (Intellectual Capital Management) and their application in CNs. Two new concepts are proposed to support these issues. A case study is presented mapping these concepts and reorganizing a real CN named IBEROEKA.
2. KM and ICM Knowledge Management (KM) is the fundamental activity for obtaining, increasing and maintaining the Intellectual Capital (IC) in organizations. Thus, success of IC is widely related to the organizational processes of KM and therefore it implies the successful use of KM ensuring the acquisition and growth of the IC (Marr, 2003). The source of IC is the knowledge obtained from the people in the organizations. There is a necessity of creating value through the interactions between the human capital, the financial capital, intangible and the latent capacities and competences inside and outside of the company (Edvinsson, 1997). Figure 1 shows the relation between the concepts of KM and ICM where the knowledge is the common denominator; nevertheless in the literature KM is not very often considered within the management of the ICM as a necessary intangible activity for the measurement of the intangible assets.
ICM
Intangible Resources
Intangible Activities
KM Knowledge Tacit
Implicit
Figure 1: Relation between concepts KM and ICM Starting off in the MERITUM model of ICM it is possible to determine in which part of this process the intangible activity of KM is located, because this is the stock (knowledge) conforming the intangible activities. These also represent different “actions” such as: a) to acquire or to develop new intangible internal resources; b) to increase the value of the existing resources and; c) to evaluate and to control the results of types of previous activities, (MERITUM, 2002).
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Carmen Agüero and Paloma Sánchez According to Rasmus (2003) and mentioned in Kevin O´Sulivan (2008, P. 119) certain KM practices are not a technological concept, therefore it will be necessary to consider architectures for a KM System being independent from technology and based on components which can be implemented using a variety of technologies and products. KM is an intangible activity which helps to create new intangible resources. Figure 2 shows an adaptation to the model proposed by MERITUM. This adaptation delimits its scopes and establishes the existing communication between the ICM and KM. ICM is considered at a high level of business strategy and is focused in the creation and extraction of value (Wiig, 1997ª and Edvinsson, 1997 mentioned in Zhou, 2003). Knowledge Management (KM) is focused towards the development of tactical and operative activities related to knowledge in order to facilitate its creation, capture, transformation and use (Wiig, 1997ª mentioned in Zhou, 2003). Both ICM and KM are generally interpreted as similar things or equals, in this case the authors of this work consider they are independent activities, but intimately related by sharing the same factor which is “the knowledge”. The KM process could be framed within the scheme of ICM proposed by MERITUM as one of the most important activities for the creation of new intangible ones. Value Creation
Vision of The company
Strategic Goals
Critic Intangibles
Intangible Resources
Resources and Activities
Intangible Activities
Existence Knowledge
Human Capital Structural Capital
Existence Knowledge
Relational Capital
Existence Knowledge
System of Indicators
Knowledge Management (KM)
Indicators
Figure 2: Relationship between the ICM and KM (on the basis of the MERITUM, 2002 model)
3. KM and ICM in Collaborative Networks The relation between CN (Collaborative Networks) and KM (Knowledge Management) appears in a minimum percentage of published material and, in a tangential way, both are topics being worked together, as in the case of Camarinha-Matos & Afsarmanesh (2006b). This work is based strictly on using ICM incorporating the KM (as an intangible activity) to apply both process in the measurement of the Intellectual Capital in CN. The authors of this work remark the necessity to highlight the fact that if ICM is not developed in a CN it will not either be possible to measure and/or to manage the intellectual capital of the collaboration networks. Knowledge must be managed through certain policies in the organization, being these focused on aspects such as: leadership, structure, prizes and incentives and measurement. An innovative and entrepreneurial culture has the vision to focus in the learning process, the value of knowledge; it also generates confidence and communication, and tolerates questioning and errors. A scheme based on teamwork, in the organization of networks, will develop communication and collaboration. Leadership plays the most important role in the implementation of the cultural, organizational and technical changes required for the development of KM, whereas the aspects of the measurement imply to establish a series of directives to evaluate the knowledge resources. In CN, people interact and therefore a flow of intellectual capital in major or minor degree exists, following a network-type (of supply chain, professionals, development of products, etc.). Also, there is an interchange and
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Carmen Agüero and Paloma Sánchez production of knowledge due to the interactions taking place within the network. The questions needing to be solved are: is it really necessary to manage the knowledge in Collaborative Networks? , How seriously it is necessary to know the real value of these networks? How are they contributing in the creation and generation of new knowledge for the industry and society? According to Wiig (1997a), the ICM concentrates in the construction and the management of the intellectual assets of strategists and in the perspective of the company’s management with a certain approach in tactics. Its function is to take total control of the intellectual capital of the company. In the other hand, KM has an operational and a tactical perspective. Its vision is more detailed and focused in the facilitation and the handling of knowledge activities, such as creation, capture, transformation and use. Its function is to plan, to execute, to handle functions and to supervise all the activities and knowledge required programs for making the ICM an effective one. Wiig (1997a, P. 399) also mentioned that “the two initiatives complement each other besides having important overlaps. From a wider perspective, the ICM and KM are fundamental for the construction of a management model for the 21st century”. The IC has intangible assets and, as a part of KM it is the intangible element being tried to maximize and to distribute. ICM is considered at strategic and higher levels of the administration and in the creation and extraction of the value. The objective of the ICM is to create and to raise the level of active intangible resources to improve the value of the companies creating competencies from a strategic perspective (Edvinsson & Malone, 1997; Wiig, 1997a). When talking about CN there is the necessity to apply both concepts (ICM and KM) in their functionality and for this reason, it is necessary to introduce two new concepts to establish the mechanisms of its management, by means of the application of the models reviewed in literature, these are Collaborative Knowledge Management (CKM) and Collaborative Intellectual Capital Management (CICM). An efficient atmosphere of collaboration is the key to connect competent employees and competent communities for networks. Within this competition of networks, the use of flowing knowledge, combined with improved knowledge sharing mechanisms allows a better process of decision making. As it has been indicated in the state of the art review, the meaning of collaboration (to work together) and starting off from the fact that collaboration can be seen like a process of shared creation, the authors of this work consider there exists knowledge in the network which flows and knowledge which emerges, as a product of the members’ interaction in the network. In order to determine CN´s efficiency level and to know how their intangible factors make them better; according with the networks’ researched tendencies, it becomes necessary to think about a knowledge management method in networks, which in the present study is denominated: Collaborative Knowledge Management (CKM). The CKM is the process for gathering, storing, analyzing and distributing the knowledge produced in the interaction between the members of a CN (companies and individuals) considering their different cultures and their geographic dispersion. As shown in Figure 3, a CN without an implemented CKM will not be able to define its CICM process as knowledge is the main element of the CICM; therefore it’s essential to consider KM either with low or high levels of technology. Then from the result in the CKM we will identify the increase or decrement of the generated intangible assets. The successful administration of the intellectual capital is widely related to the organizational processes of the KM and therefore it implies a successful use of the knowledge management ensuring the acquisition and growth of the intellectual capital (Marr, 2003). The Networking Intellectual Capital Report (NICR) is the communication tool complementary with actual management organizations and will help the CN to create a suitable behavior to the generation of the value creation. It will be necessary to make an indicator selection and classify them as show in Table 1 of the case study.
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Figure 3: CKM and CICM in CN A CN is a group of independent entities (companies, institutions, and individuals) voluntarily join by agreements to reach objectives and goals (open new markets, product innovations, supply chains and others) that will not be possible to reach in an individual way. Each entity of the CN will have as well to register its participants: two elements were counted, organizations (companies) and individual organizations (people) being part of the CN. With this information the CKM can be designed in the first instance, keeping in mind that the main interest is to learn from the others and to share process, designs, abilities, etc.) in the form of knowledge. Starting from the theoretical foundations of KM we must establish the mechanisms of capture of the tacit and explicit knowledge of the individual organizations, under a registry scheme on line which can be distributed to all the members of the network (bearing in mind any confidentiality requirements) in agreement with their needs. Because this activity is voluntary the members will have to establish the mechanisms to share knowledge. The CICM is the process to identify the intangible assets generated in a collective way in order to determine the capacity of the CN to face and answer the exigencies coming from the market, allowing establishing the value produced by this collective work. The companies, and in particular the SMEs, are paying more attention to these concepts, mainly to those related to knowledge, abilities and competences, since these are going to be decisive in the development of their competitiveness in this knowledge economy. That is why the companies must evolve towards organizations facilitating the learning process of their personnel and must be immersed in a continuous change, looking for maintaining and improving their competitiveness. The SMEs by themselves generate an IC but, grouped together, they generate a greater IC which affects positively their individual indicators. These indicators must be determined and properly managed because the grouped SMEs will have another dimension and any attempt to measure their individual IC will be even more complex, and this complexity would flow in the CNs. The value of an intangible resource does not reside in its possession but in its use. Only working resources benefit the company’s strategy (Agüero and Vazquez, 2009). The CICM process will have to manage: Resources of Human Capital, this will be the network with many organizations interacting as organizational entities (companies) and individual organizations (people). The human capital is harnessed almost tacitly because there are hidden competitions and capacities in each organizational entity for being shared and to put them at the network’s service will increase their value. A flow arises from new knowledge being generated and shared; the motivation from the members is also part of this human capital, a new culture of network generated by the interactions emerges from the interaction of the organizations and all this must be identified in order to sum it up to the value being generated in the process for the new organization which is part of the network.
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Carmen Agüero and Paloma Sánchez On the other hand CNs also own resources of Structural Capital so that they work on processes, routines, information systems, images, data bases, etc., which contribute with the network to generate collective and individual wealth by means of their activity. Here a distinction arises, for example, patents can be a generated product of the collaborative work between organizational and/or individual organizations and will belong to the network. The network will have its own indicators of intellectual capital which will belong to the whole group, if this network disintegrates the capital is lost, although explicit knowledge by means of processes of the CKM could be profiteers. The Relational Capital, without a doubt, is the one that clearly can be observed in a CN, since it rests in the value of the good relations that the company maintains with the outer world: their clients, suppliers or with their partners in general terms, etc. Understanding the internal relations of a network will modify the perception that the external world has towards the CN. In order to secure the application of CKM it will be necessary to design an organizational structure to manage the activities of the CKM in the network and to guarantee its continuity. As shown in Figure 4, the organizational structure will be as simple as possible, in which the existing representative of each leader of a manager organism would be a leader to manage the process supported by a Technology Information and Communication (TIC) tool.
Figure 4: Organizational structure for CKM
4. Case study: virtual collaborative IBEROEKA network The IBEROEKA Innovation Projects, launched in 1991, is a tool designed to promote cooperation in technological research and development between industrial sector companies of Ibero-america. The particularity of the IBEROEKA Network is the fact that it foments and facilitates the industrial, technological and scientific cooperation between the participants with the specific aim to develop new products, processes and services directed to a potential market. IBEROEKA is run by the IBERO-American Network of IBEROEKA Management Organisms (IMOs). These are appointed in each country taking part in the program. According to Agüero (2007), it was possible to observe two key aspects which could help to turn IBEROEKA into a CN, these are: i) The members of IBEROEKA Network are the IMOs and the network is limited only by 21 organizations; the companies (SMEs) and the organisms of investigation are not visible in the network, since they only appear in the process when presenting an innovation project idea. The present scheme does not facilitate the interaction with others nor establishes a
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Carmen Agüero and Paloma Sánchez process of permanent collaboration in which they can share knowledge, resources and responsibilities to implement the program activities in order to reach a common goal to generate an increase in the innovation project ideas; ii) IBEROEKA lacks of an ITC technological platform providing a sense of collaboration to the network, allowing it to manage in an efficient way the innovation projects ideas. As mentioned by Sanchez M. (2008, p.576) “what is really important is the creation of value of the organizations and, mainly, to improve its innovating capacity, is to manage these resources suitably, to which he is the same to invest in the activities necessary to acquire them, to improve them, to evaluate them or to control them”. As it showed in Figure 5, the proposal consists of the creation of three levels of members in the network to give the sense of collaboration in order to generate the bases for the automated application of the processes of: CKM and CICM.
The innovation projects ideas of IBEROEKA CN can be developed jointly between companies and public organisms of R&D of 21 member countries of the program. The primary target is to increase the productivity and competitiveness of the companies in such a way that harnesses a sustainable development of the Ibero American Region. Each participant country has designated a manager organism responsible called IMO, to which organisms the companies must go in first instance to present an innovating idea to materialize it in an innovation project with other countries of Ibero-america. This present situation we will call the first level of collaboration.
The second level of collaboration goes beyond IBEROEKA CN which performs under the IMO. It must integrate the SMEs Official Institutions of Support (SOIS) which have a great data base of SMEs by sectors. Other members considered in the second level are: individual companies, Universities and Other Scientific of Research Institutions. The second level network can be gradually extended to the third level of collaboration, as we can observe in Figure 5, there are small networks in the member countries that can be integrated into the IBEROEKA Network.
The main processes to develop the generation of the proposed technological platform are: a) Process of creation of an IBEROEKA CN data bank; b) Process of registry and distribution of the projects’ ideas; c) Process of automatic search of partners; d) Process of negotiation, agreements and consolidation of partnerships; e) Process of Collaborative Knowledge Management (CKM); f) Process of Collaborative Intellectual Capital Management (CICM); g) Information Exploitation. Only process e) and f) will be described because they are the core of this research.
Process e): Collaborative Knowledge Management (CKM) CKM will allow the pursuit of the execution of IBEROEKA projects by means of a model that identifies and operates the knowledge generated in these interactions between the members of the network, for example: thematic of discussion, resolution of problems or sharing good practices; and through one or several tools of TICs facilitates the distribution and operation of that knowledge to all the members of the network (Figure 5). The intention is to take that intangible resource that is the knowledge through an equally intangible activity that is KM so that the members of the network, as a result of their enrichment of knowledge use, improve their innovations or produce new innovation of products. The CKM requires an operational center that manages and maintains the platform of CKM, creating new Forums thematic of interest, causing the interchange of capacities and knowledge between the members of the network thus each member organization will be able to propose subjects of discussion according to its interest sector. Process f): Collaborative Intellectual Capital Management (CICM) The CICM must be supported in TICs tools that extract the information corresponding to the intangible resources that are generated or that are used in the network. Indicators by economic sectors will have to be part of the statistical information of a Virtual Collaborative IBEROEKA Network (VCIN) that will have to be used for the measurement of the intellectual capital of the network. The application of the CKM would make possible the generation of the information necessary to identify the knowledge that flows in the network, the system will have to provide statistical information to facilitate the determination of the intangible indicators in the measurement and informs into the Intellectual Capital of Network IBEROEKA. Although, some indicators can be obtained with facility with a revision of the intangible activities others will have to be content in data bases. Our proposal consists of the emission of an automatic report by means of a scheme of data collection. Before doing this, it is only necessary to determine: data banks of the intangibles of the network of where the knowledge will be retrieved.
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1st. Level: IBEROEKA ORGANISMS MANAGER (IMOs)
Sector A
CYTED
Sector B
3rd. Level: Associations, Clusters and Networking Companies from different economic sectors.
2nd. Level: Companies, Universities and Cientific Centers.
Figure 5: IBEROEKA CN in three levels of collaboration In order to obtain the NICR of IBEROEKA CN it will be necessary to select the indicators classified as show in Table 1 according with European Commission (2006). Table 1: NICR based on Meritum and European Commission Investment Goods Efects
Human Capital Training Network Skills; abilities of network experts R&D partners
Structural Capital ICT procedures; R&D of the networking; Net-Data bases Number of Patents
Relational Capital Meetings of the Network and participants
Percent incomes of new products
Members satisfaction
Network Image
So, the question could be: What is the difference between the present situation and a collaboration network? The difference is that the possessors of ideas of innovation projects will be able to interchange information with the application of the CKM by means of a technological platform and the network will have indicator of IC with the CICM.
5. Conclusions The application of the CICM is possible and the authors of this work believe it will be even more effective to determine the indicators in a CN of SMEs than separately because these are very weak before the exigencies of the market, and their individual value isn’t really appreciated. Nevertheless, when determining the value in a CN indirectly it will be possible to determine also the indicators that each organization separately contributes in the collaboration by means of an automated application of the CICM. The KM is an intangible activity that must be circumscribed in the CICM because this will facilitate the determination of indicators of Human Capital with greater exactitude. The consumptions (existing knowledge) that enter to KM as well as the products that result (new knowledge) will be considered as indicators of intellectual capital for the measurement of their intangibles. The ICM in a company that does not have a mechanism of a suitable KM will lose their character of intangible capital due to the forgetfulness of acquired knowledge and therefore their indicators will be damaged. In the same way in the case that a suitable KM exists, the intangible capital will be seen, not only increased, but also better reflected approaching its real value to the market. The CN are entities in which the application of a process of CKM is better adapted because the capacities are shared and an interchange of knowledge which flows is made in a natural way. Nevertheless, practicing is not simple because we don’t only look for support in technologies of
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Carmen Agüero and Paloma Sánchez information but also in policies, norms and procedures, in a culture of knowledge and a high degree of confidence in which each one of the actors of a CN can share their knowledge freely, taking into account that knowledge is the only capital that grows when it’s shared.
Acknowledgements The authors acknowledge the support of CYTED Program (IBERO American Science and Technology Development) and CDTI of Spain (Technological and Industrial Development Center) for their support in the case study research. We are also grateful for the comments and suggestions given by Fiorella Barbagelata from UPCH University, PhD (c). Miguel Ramírez and MSc Luis Gaxiola, Professorsresearchers from Monterrey Institute of Technology (ITESM).
References Agüero C. (2007), El Capital Intelectual y la Gestión del Conocimiento en las Redes de Colaboración: Bases para la Innovación Tecnológica, PhD. Advanced Studies Certification (DEA) by Complutense University of Madrid, Spain. Agüero C. and Vásquez-Parraga A. (2009), Strategic Planning of Intangible Resources, Proceedings of the European Conference on Intellectual Capital (EICC), 28-29 April, INHoland University of Applied Sciences, Haarlem, The Netherlands, ISBN 978-1-906638-30-6 Camarinha-Matos, L.M., Afsarmanesh, H. and Ortiz, A. (2005), “Foreword: Collaborative networks – towards consolidation”, in Collaborative Networks and Their Breeding Environments, Camarinha-Matos, L.M., Afsarmanesh, H. and Ortiz, A. (Eds.), Springer Science Publisher, 2005, pp. Foreword, ISBN: 0-387-282599. Camarinha-Matos, L.M. and Afsarmanesh, H. (2006b), “Collaborative Networks – Value Creation in a Knowledge Society”, in Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing and Management, Wang, K., Kovacs, G., Wozny, M. and Fang, M. Eds.), Springer Science Publisher, 2006b, pp. 26-40, ISBN: 0-387-34402-0. European Commission (2006), “Reporting Intellectual Capital to Augment Research, Development and Innovation in SMEs”, Belgium, ISBN 92-79-02149-4 Edvinsson, L and Malone, M.S. (1997), Intellectual Capital, Realizing you company´s Thrue Value by finding Its Hidden Brainpower, Judy Piaktus (Publishers) Ldt, London Egbu Ch.(2005), Knowledge Management as Driver for Innovation, En Ch Anumba, Ch. Egbu y P. Carrillo (121131), Blacwell Publishing Ld. Oxford Gann, D.M. (2000), Building Innovation: Complex Constructs in a Changing World, Thomas Telford, London Marr Bernard et al.. Intellectual capital and knowledge management effectiveness.Management Decision. Vol. 41, No. 8; 771 - 781. Año 2003. MERITUM (2002). Cañibano, L.; Sánchez, P.; García-Ayuso, M.; y Chaminade, C.; (Eds.), Guidelines for management and diffusion of information on intangibles, Vodafone, Madrid. OECD (2007), Creating Value from Intellectual Assets, prepared for the 2006 Meeting of the OECD Council Ministerial Level Rae Jeneanne, A Ripe Time for Open Innovation . Recuperado el 19 de marzo de 2008, de http://www.businessweek.com/innovate/content/mar2008/id20080319_656312.htm Reyes, M (2004), VIII Congreso de Educación a Distancia CREAD MERCOSUR/SUL 2004, Córdova Argentina Rumizen, Melissie (1998), Report on the Second Comparative study of Knowledge creation conference. Journal of Knowledge Management. Vol. 2, No. 1; 77 - 81. Sanchez M. Paloma (2008, Julio-Agosto), Papel de los Intangibles y el Capital Intelectual en la creación y difusión del Conocimiento en las Organizaciones, ARBOR Ciencia y Pensamiento y Cultura Wiig, K. (1997a), “Integrating intellectual capital and knowledge management”, Long Range Planning, Vol. 30 No. 3, pp. 399-405. Zhou Albert Z., Fink Dieter (2003), “The intellectual capital Web a systematic linking of intellectual capital and knowledge management”, Journal of Intellectual Capital Vol. 4 No.1
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Intellectual Capital and Value Creation in the Machinery and Equipment Industry João Francisco de Aguiar, Leonardo Fernando Cruz Basso and Herbert Kimura Mackenzie Presbyterian University, São Paulo, Brasil
[email protected] [email protected] [email protected] Abstract: Economists have concluded that the intangible assets growth have exceeded expectations in the last years. Although there is no general agreement on its conceptualization the intangible assets´s effects in the company’s statements have been widely recognized, raising interest from researchers and economic agents on methods of appraisal. The purpose of this paper is to provide ideas for discussion by testing Pulic’s theory of Value Added Intellectual Capital – VAIC, for manufacturing industries operating in Brazil, in the period of 2000 to 2006. Pulic´s Theory was selected mainly because it is applicable based on public data gathered from company’s financial statements. The sample is comprised of Brazilian non listed and listed companies with more than 100 employees belonging to the Annual Industrial Research (Pesquisa Industrial Annual – PIA) annually undertaken by the Brazilian Institute of Geography and Statistics - IBGE). Research was undertaken in the field of quantitative methodology. Static and dynamic regression models were applied to a data panel. In general, the results achieved indicate that the VAIC model is relevant to explain value creation by the Brazilian companies. Keywords: intellectual Capital, value creation, panel data analysis, machinery and equipment This study was financed by MackPesquisa and CNPq
1. Introduction Law 11,638 dated December 28, 2007 amended Laws 6404/76 and 6385 dated December 7, 1976, and when stating intangibles established (the President’s Office, 2008): Article 179 – VI – intangibles: those rights in connection with intangible assets intended for the company’s maintenance or employed to this end, including goodwill acquired. Article 183 – VII – rights stated in intangible assets, at acquisition cost less the balance in the respective depreciation account. According to Delloite (2008), this law was intended mainly to update Brazilian corporate law, to enable the convergence of accounting practices adopted in Brazil with those found in international norms. According to Yokoi (2007), there is an international trend for the convergence of accounting standards to the rules contained in the do International Financial Reporting Standards – IFRS, already approved by the US International Accounting Standards Board – FASB. To book intangibles, it is necessary to explain the concept’s meaning as well as how it will be put in place and measured. There is no general agreement on conceptualization, and the purpose of this paper is to provide ideas for discussion by testing Pulic’s theory for manufacturing industries operating in Brazil. In addition to this introduction, the paper is composed of theoretical references whereby we explain the difficulties in conceptualizing and measuring intangible assets (focusing on intellectual capital), the criteria employed in choosing the industry, the econometric hypotheses and procedures used, and the analyses and conclusions. It is our aim to contribute to the discussion on the best manner to conceptualize, put in place variables (when opting for a construct), and measure intangibles, focusing on their essential component, intellectual capital.
2. Theoretical references Reilly and Schweihs (1999) contend that there are numerous definitions for intangible assets from the legal, accounting, and fiscal viewpoints, yet for an intangible asset to exist from an economic or appraisal viewpoint, it needs to have a number of attributes and characteristics:
To be identifiable and reasonably described;
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To be subject to legal protection and existence;
To be subject to private property, which may be legally transferred;
To be subject to some form of tangible demonstration of its existence, such as am agreement, a license, registration document, floppy disk, customer list, etc.;
To have been created at an identifiable time or as a result of an identified event; and
To be subject to destruction, or to termination at a point in identified time, or to have its existence discontinued as a result of an identified event.
These assets’ importance is suggested by Furrer et al. (2001, p.341) “The most important resources owned by a company are intangible assets, those which create sustainable revenues”. According to Furrer et al. (2001), a number of intangible assets have already been identified, as in the case of brands, registered trade marks, patents, copyrights, registered designs, contracts, commercial secrets, relationship networks, information bases, and company reputations. According to the authors, the presence of these intangibles have brought about a distortion with bookkeeping, And they are more present in manufacturing industries such as in pharmaceuticals where patents are critical, in consumer goods in which reputation brands are crucial, and in service provision where reputation is essential. If there is common agreement on relevance, this cannot be said about the meaning of concept, how it should be put in place and measured. Table 1 shows 25 models for valuing intangibles. Table 1: Methodologies for conceptualizing and measuring intangibles Methodology
Year of publication
Main authors
1.Balanced Scorecard 2.Calculated Intangible Value 3.Citation Weighted Patents 4.Economic Value Added 5.Holistic Value Approach 6. Human Resource Accounting 7. Intellectual Capital Audit 8. Intellectual Capital Index 9. Inclusive Value Methodology 10. Intangible Asset Monitor 11. Intangibles Scoreboard 12. Intellectual Capital Benchmarking System 13. Intellectual Capital Dynamic Value 14. Intellectual Capital Statement 15. iValuing Factor
1992 1997 2001 1994 1997 1989 1996 1997 2001b 1997 2001 1999/2001a 2002 2001a/2002 2001
Kaplan R. and Norton D. Stewart, T.A Hall, B.H. , Jaffe, A, Trajtemberg, M. Stern, Stewart Co. Roos, G. et al. .Sackman et al. Brooking Roos et al. McPherson, Pike Sveiby Lev, B. Viedma Bounfour Mouritsen et al. Standfield
16. Konrad Group 17. .Market to Book Ratio 18. Options Approach 19. Skandia Navigator 20. Sullivan’s Work
2003 1991/1997 1998a,b,c, 2000 1998 1918-2002 1996, 1999, 1994 2003 2001
Konrad Van der Berg Edvinsson, Malone Sullivan
21. Technology Factor 22. Tobin’s Q 23. Valuation Approaches 24. Value-Added Intellectual Coefficient 25. Value Chain Scoreboard
Khoury Tobin, J. Lee (1996); Reilly & Schweijs (1999), Parr (94) Pulic Lev, Baruch.
Source: Models from 1 to 25 (Andriessen, 2004). Among these methods, we selected Pulic’s model (2000, 2002a, and 2002b) because the data may be borrowed from financial statements, which allows us to test the theory. The industry’s choice was based on the typology prepared by Pavitt, who developed a sectoral rating for degrees of innovation. We were based on the hypothesis that the greater the degree of innovation, the greater the volume of
11
João Francisco de Aguiar et al. intellectual capital required to seek out discoveries that will be converted into new products (innovation) or processes. Pavitt (1984) emphasized the relevance of the systematic development of a pool of knowledge (gathering numerical information and developing a theory) which would imply the production of technology and would reflect on sectoral diversity. Based on an existing database in England from 1945 to 1979, the author examined 2000 significant innovations made by innovative companies, and proposed a sectoral rating with four classes, having as its base the innovative company as a unit of measurement.
Controlled by suppliers (agriculture, housing, private services, and traditional manufacturing);
Intensive scale production (volume, such as steel and glass) and assembly (durables);
Specialized suppliers of equipment (machinery and instruments), and
Based on electronic, electrical, and chemical sciences.
As a result of this rating, Pavitt (1984) was able to rate industries by identifying sectoral “technological change standards." Of the five industries selected in the Pavitt scale, three are controlled by suppliers, one is based on sciences, and one is a specialized supplier of equipment. Table 2: Rating of industries analyzed according to Pavitt (1984) CNAE 34 36 19 17 29
Industries selected from the PIA Manuf. and Assmb. of Motor Vehicles, Trailers, and Bodies Manuf. of Furniture and Sundry Industries Processing of Leather and Manuf. Of Leather Goods, Travel Items, and Footwear Manuf. of Machinery and Equipment
Pavitt’s rating Based on sciences Controlled by suppliers Controlled by suppliers Controlled by suppliers Specialized suppliers of equipment
Source: prepared by the author Pavitt (1984) conducted an investigation on production, the use of technology, and innovation at the sectoral level. Several indicators were selected for these industries, as found in Table 7. It will be seen there that: a) of the innovations produced, the major part involves processes, ranging from 26% to 42% in the five industries selected; b) the same table shows that a large part of the technologies in the sectors selected is produced away from them, with the exception of leather and footwear. Table 3 reflects indicators for the five industries selected according to Pavitt (1984), and among these, that companies in one industry do not usually produce a great deal of innovation outside of their industry. Table 3: Creation of innovation according to Pavitt (1984) Industry
Innovations produced Of those used, % Of those produced, produced in the % of product industry innovations 37.6 35.2 N/A N/A 60.0 26.5 16.2 32.9 60.7 42.3 Innovation In other industries by In the industry by companies with main companies with activity in the industry main activity in other industries 33.5 18.0 N/A N/A 50.0 26.5 24.7 36.3 16.0 32.1 34.3 31.4
Automotive Manuf. of Furniture and Sundry Industries Leather and Footwear Textiles Metal Manufacture Industry
Automotive Manuf. of Furniture and Sundry Industries Leather and Footwear Textiles Mechanical Engineering Metal Manufacture
Source: prepared by the author based on Pavitt (1984) Table 3 also shows that: a) the textile industry produces relatively little of the innovation employed as compared to the others; b) the leather and leather goods industry produces the greater part of the
12
João Francisco de Aguiar et al. innovation that it employs, and also contributes significantly to innovation in other industries. According to Pavitt (1984), research by authors performed in the 1970’s points to the fact that product innovation seems to be more relevant than process innovation, at least in two developed countries (the UK and the USA), which account for over 70% of all the innovations produced. Acccording to Dosi and Nelson (1994, p.1) “ There are signs that evolutionary analysis and models may be making a comeback in economics. The authors stated that (op.cit, p.165): The findings in Pavitt (1984) on size and principal activities of innovating firms suggest that significant groups of industrial sectors might not conform to the “life cycle” description, due for example to the specificity and tacitness of the knowledge that individual firms embody and to the absence of strong tendencies toward economies of scale (…) In theory each field of activities has its respective stock and flow of knowledge, therefore we can expect different intensity degrees of contribution for the Intellectual Capital formation of each field. .
3. Hypotheses The hypotheses were borrowed from two theoretical proposals that sought to explain value creation based on Intellectual Capital: a) from the “Value Added Intellectual Coefficient - VAIC" Theory by Pulic (2000, 2002a, and 2002b), and the Calculated Value Added – CIV according to Luthy (1998). The dependent variable or the companies’ Return on Total Assets obtained from their financial statements is represented by Gross Profit on Total Assets. The option for gross profit is due to the theoretical justification that intellectual capital is responsible not only for creating operating profit, but also for allocating the sum obtained by determining mark-ups on sales. The models shown included independent variables according to the referred theories, in addition to dummies for each year of research, as follows: a) Key variables: Borrowed from the VAIC Theory, applied jointly or separately according to the hypothesis made, with the feature of flows:
Added Value Intellectual Coefficient: VAIC (CEE + ICE) or (CEE+HCE+SCE)
Intellectual Capital Efficiency: ICE (HCE + SCE)
Human Capital Efficiency (HCE)
Structural Capital Efficiency (SCE) and
Employed Capital Efficiency (CEE)
b) The complementary variable, the Calculated Intangible Value - CIV, a control variable, to estimate the stock of companies’ Intellectual Capital, defined in Luthy (1998). The models were estimated by means of Minimum Mean Squares in a panel, in static and dynamic forms according to Asteriou and Hall (2007). The sample borrowed from the PIA formed the database for the survey, which is composed of the machinery and equipment industry of companies which operate in Brazil. Static and dynamic regression models were applied to a data panel in order to test the mentioned hypotheses. Owing to the excessive number of possible models to be worked on (possible static and dynamic), we opted to work on models closer to Pulic’s theory, as well as with models with individual regressors, in the light of Andriessen’s criticism to the concept of structural capital, which we deem to be relevant. The selected models were “run” in the static and dynamic option in the following order, for the stated hypotheses:
in which
13
João Francisco de Aguiar et al.
in which
e
in which
,
e
in which
in which
in which
in which
in which Each one of the models was “run” with five specifications, one of which in the static model; two in the dynamic models, and two in the dynamic models with a first difference to eliminate the fixed effect.
4. Population and sampling The population is composed of the Brazilian manufacturing industry, a universe based on which Instituto Brasileiro de Geografia e Estatística – IBGE registers companies based on certain requisites such as enrollment in the corporate taxpayers’ registry and enrollment at IBGE by means of the National Classification of Economic Activities – CNAE (IBGE, 2005). From this universe, IBGE performed an annual survey from 1968 to 1979, the Annual Industrial Survey – PIA, the criteria for which have been improved. The number of sample companies has grown over the period, starting with 99,999 companies and rising to 155,000 in 2006. A number of adjustments were required in the original PIA database (IBGE). The starting point was 81,185 companies in 22 industries and 281,615 notes found in CNAE 2, in the Processing Industry in the 2000 – 2006 PIA. Below follows general information on the sampling’s adjustment:
Although IBGE made available PIA data for an 11-year period, from 1995 to 2006, only the 2000 – 2006 period includes companies’ Total Assets, a key component to estimate ROA (Return on Assets), included s of 2000 in the PIA questionnaire.
The IBGE registry does not show names but CNAEs (National Classification of Economic Activities). The CNAE 3 included very small companies, and the CNAE 1, very large ones. The initial option was for a broader base composed of CNAE 2 and small companies with over 30 employees, but the Descriptive Analysis showed greater distortions (a high average standard deviation). The choice of companies with over 100 employees;
There was an important group of medium-sized companies (with less than 1000 employees) that entered and exited the survey in the middle of the period under analysis, which gave rise to lost information in the panel, one of the key reasons why it was not balanced.
14
João Francisco de Aguiar et al. Other limits were required to be inserted in order to remove errors by respondents in the sums of the models’ variables in the financial statements. The sampling’s adjustment required a number of exclusions, which ended up by forming a database with 4191 companies and 15,106 notes, of companies with over 100 employees located across the country. Please also note that:
The 4191 companies are more concentrated in some industries such as leather and leather goods (2301 notes), metallurgy (1353), and motor vehicles, trailers, and bodies (1192). Hence, there are three industries with over 1000 notes, 11 industries with 500 to 1000 notes, and eight with 99 to 500 notes.
There are industries with diversified technological intensity, such as “leather and leather goods, travel items, and footwear (2301 notes) and furniture (496), manufacture of chemicals (496), motor vehicles, trailers, and bodies (1192), manufacture of IT equipment, electronic and optical goods (847).
As described in item 2.17, static tests were performed on the five industries, as follows; Manufacture of textiles; Processing of leather and manufacture of leather goods; Manufacture of machinery and equipment; Manufacture of motor vehicles, trailers, and bodies; Manufacture of furniture, and Sundry Industries.
The study employed a multiple regression estimate by means of a Data Panel. This technique, according to Asteriou and Hall (2007), is often considered an efficient analytical method to deal with econometric data; the technique combines a complete series in time for each cross-section element, which permits a variety of estimation methods in addition to contributing with a larger number of notes.This study considered a non-balanced panel, owing to the above-described reasons and to the adjustments that were required in the data made available by the PIA, described in this paper’s topic 3.5. The econometric interest in Panel Data, in particular in micro-econometric applications, have been the outcome of at least two motivations (ARELANO, 2003, p.7): a) the desire to explore data lined up in a panel with the purpose of controlling unnoticed heterogeneous effects, invariable in time, in crosssection models, and b) use of the panel data technique with the aim of finding variance components and estimating the likelihood of a transition between states (scenarios, situations), and in a more generic manner, to study the dynamics of the cross-section models population. To Stock and Wilson (2004, p. 185), the Data Panel regression is: a method to control some kinds of omitted variables without actually seeing them. This is possible, according to the authors, when the analysis of changes in the dependent variable in the course of time makes it possible to eliminate the unnoticed effects of the omitted variables that differ from the entities, but that are constant in time. According to Asteriou and Hall (2006), this technique (the “first differences equation”) makes it possible to eliminate invariable features from the shorter time series, the case of models estimated under this model. The Data Panel Model was applied in two stages, as follows; a)The Static Effect Model and tests for selecting the best model
Common Constant Model (“Polled OLS Method”)
Fixed Effects Model
Random Effects Model
Residual Variance Robust Estimator
b) Dynamic Effects Model and tests for selecting the best model
GMM Estimator
Arelano and Bond Estimator
Residual Variance Robust Estimator
The information was gathered in the Annual Industrial Survey (PIA) by IBGE interviewers. The information does not include all the main balances in the Financial Statements’ accounts, and they are not organized as recommended by the Brazilian Corporate Law, for which reason a number of variables were estimated by means of formulas, such as in Gross Profit; for example, Third Parties’ Capital, Permanent Assets, Net Profit, among others, are not available. On the other hand, the PIA
15
João Francisco de Aguiar et al. has advantages such as greater details in the Income Statement – DRE, such as in Payroll and Social Charges, a critical variable in estimating a company’s Human Capital, one of the key variables in the tested model. Robinson (1995) lists metrics such as ROA, return on assets, return on equity (ROE), return on investment (ROI), and return on sales (ROS). As the PIA does not disclose the Net Worth of companies and Permanent Assets, the only possible ratio among those suggested was ROA. Several types of ROAs and ROS were tested, with the purpose of seeking the best model configuration at an early stage. Finally, ROA was selected as the most appropriate metrics in terms of compliance with models, with tests being performed on several kinds of ROAs to perform the ratings.
5. Analysis and results Table 4 shows the industry’s descriptive statistics; we eliminated from the sample profit margins in excess of 150% in the belief that these high margins are a result of a under assessment of the assets. Table 4: Descriptive statistics CNAE 29
Observation
Mean
ROA4 LnCIV CEE VAIC ICE HCE SCE
1192 513 1192 1192 1192 1192 1192
0.615853 16.35994 0.5769677 3.044662 2.467694 2.036207 0.4314876
Standard Error 0.2340492 1.615071 0.2297372 1.437842 1.405375 1.276788 0.1714946
95% Confidence Interval 0.0685668 10.64507 0.0524225 1.366807 1.011757 1.005896 0.0058614
1.427474 21.2706 1.709138 21.84272 21.17067 20.22013 0.9505444
Source: prepared by the author based on STATA SE 10 output. Table 5 shows a considerable degree of multicolinearity between some of the VAIC model variables, which reduces it’s significance degree. Table 5: Correlation between variables Variables CNAE 29 ROA4 ICE HCE SCE VAIC CEE LnCIV
RO4
ICE
HCE
SCE
VAIC
CEE
LnCIV
1.0000 0.0798 0.0815 0.0408 0.1722 0.7937 -0.0235
1.0000 0.9981 0.7484 0.9931 0.0532 0.4025
1.0000 0.7058 0.9918 0.0584 0.3818
1.0000 0.7366 -0.0168 0.5148
1.0000 0.1702 0.3916
1.0000 -0.0480
1.0000
Source: prepared by the author based on STATA SE 10 output Tables 6 and 7 show the tests for the static and dynamic models. Industry: in Table 6 below, the static model's results for industry, Manufacture of Machinery and Equipment., 29 sector according IBGE (2005) . Table 6: Static model, industry 29: ROA4 = f( LnCIV; VAIC; Dummy year 2001 to 2006) Variables and data OLS Pooled
Independent Variables LnCIV Variables and data
VAIC
Results and Significance tests Within Random Fixed Wt st Fixed Effect Effect Effect, with 1 difference Dependent Variable ROA 4 (1)
-0.015701**
0.0057032 0.0176664** 0.0138785** Results and Significance tests Within OLS Random Fixed Wt st Fixed Effect Pooled Effect Effect, with 1 difference Dependent Variable ROA 4 (1)
0.0244558*
0.0472513
16
0.0867533 *
0.079729*
Within Fixed Effect (2) Robust Variance
0.0138785** Within Fixed Effect (2) Robust Variance 0.079729*
João Francisco de Aguiar et al.
Dummy for 2001-2006 Constant Statistics / Tests FIV Factor Heteroscedasticity(8) Serial autocorrelation Notes Adjusted R2 / Within (3) F Test regression (4) Degrees of Liberty Test F (197.307) (5) Breush-Pagan chi2 (1)(6) Hausman (7)
Yes 0.830134*
* Yes 0.434812*
Yes 0.0579315*
Yes 0.2138823**
1.70 0.68 (0.4086) 16.858 (0.0001) 513 513 287 513 0.0455 0.0057032 0.3885 0.3231 3.00* 86.81* 26.96* 11.13* F(8.504) Waldhi2(8) F(7.279) F(8.307) Statistics p-value Result 9.14 0.0000 Fixed Effect 243.72 0.0000 Random Effect 71.13 0.0000 Fixed Effect
Yes 0.2138823**
513 0.3231 11.13* F(8.307) Significance 1% * 5% ** 10% ***
Source: prepared by the author based on results from Stata. SE/10 and PIA (IBGE). (1) ROA4 = Return (Gross Profit) on Total Assets (2) Pursuant to the Newey-West estimator, according to Yafee (2008) (3) R2 adjusted for Pooled OLS, R2 Within for others (4) F Test for joint significance of regression coefficients, the same for the Random Effect Wald test (5) Second F Test for decision between Fixed Effects and Pooled OLS Models: if significant, Fixed Effects will prevail (6) Breush-Pagan Test comparing Random Effects with Pooled OLS Effect, if significant, Random M will prevail (7) Hausmann Test comparing Random Effects with Fixed Effects, if significant, Fixed Effects will prevail The statistical tests indicated:
The 2.68 Variance Inflationary Factor (FIV) proved that there is a certain level of multi-colinearity in the model;
The heteroscedasticity test (Breusch-Pagan / Cook-Weisberg) did not reject the null hypothesis that the residual variances are constant;
Wooldridge’s residual autocorrelation test for panel data rejected the null hypothesis of the absence of first order residual autocorrelation at a 1% significance level, hence there is residual autocorrelation; and
The Hausmann Test pointed to the presence Fixed Effects at 1% of significance.
The F Test rejected the null hypothesis of the non-existence of robust variance regression at 1% of significance, and the t test rejected the null hypothesis at 1% of significance validating the VAIC and LnCIV coefficient in the White and Newey-West robust option (deals with autocorrelation and heteroscedasticity effects). The sign for both is positive for value creation. Table 7 shows the result of the dynamic models. Table 7: Dynamic model for industry 29: ROA4 = f( LnCIV; VAIC; Dummy year 2001 to 2006) ROA4 Dependent Variable
Alternative Dynamic Models, with robust variance for 1st to 2nd order lags
Independent Variables
Model 1 re
Model 2 re
Model 3 nre
Model 4 re
ROA4_1
-0.0673577
0.0793378
0.3785585**
0.2567828*
ROA4_2
0.2042632
0.0109414
n/a
n/a
Ln Civ
-0.0117891
-0.0129379
0.0129835
0.0215357**
17
João Francisco de Aguiar et al. ROA4 Dependent Variable
Alternative Dynamic Models, with robust variance for 1st to 2nd order lags
Ln Civ _1 Ln Civ_2
0.0295612*** 0.0050812
0.0222087*** n/a
0.0242165** n/a
n/a n/a
VAIC
0.0757848*
0.0761129*
0.0773593*
0.0950597*
VAIC_1
-0.0143342
-0.0186601
-0.0592097**
n/a
VAIC-2
-0.0434829**
n/a
n/a
n/a
Var. Dummy 2001-2006
Yes
Yes
Yes
Yes
Intercept
1424152
0.3338366
-0.3055088
-0.1888292
Dynamic Model Tests Notes
58
74
128
166
Wald 2 (chi2 com g.l. =... )
107.08* / gl=12
85.55*(gl=10)
147.09/ (gl=10)
105.45/ (gl=8)
Sargan Statistics: Est/p-val (1)
19.73161(0.072 3)
20.36647 (0.0605)
13.7506 (0.4685)
24.24132 (0.0428)
-1.1464
-2.0683
-3.2801
-3.1047
0.2516
0.0386
0.0010
0.0019
-1.80
-0.75957
0.51139
0.42534
0.0708
0.4475
0.6091
0.6706
VAIC + VAIC_1
(4)
3.28***
0.26
N/A
LnCIV + LnCIV_1
(4)
0.35
7.55*
N/A
Arelano-Bond ( 2) M1 (z) p.value ( Prob > z) M2 (z) p.value ( Prob > z) Wald Test: statistics/p-value
Source: prepared by the author based on results from Stata. SE/10 and PIA (IBGE). n/a: not applicable, i.e.: the variable was not considered in the model. (1) The statistics appears in first place and its respective p-value in brackets. (2) Arelano-Bond Tests for null autocorrelation in the first difference errors. (3) The models were described at the top of each column as nre (not rejected by the statistical tests) and re (rejected). (4) Model rejected by the tests. The test results for the dynamic model showed that:
The Sargan test validated the over-identified restrictions (the instrumental variables are valid) when it did not reject the null hypothesis in Model 3 only;
The first order lag in the dependent variable was significant; and
The Arelano and Bond tests confirmed the “absence of residual autocorrelation” with the instrumental variables. In the three models, 2, 3, and 4, the m1 statistics was negative and significant, and m2 was not significant. The null hypothesis was rejected.
Hence, dynamic model 3 was the only appropriate one, as in this model the LnCIV and VAIC variables were both significant, at 5% and 1% respectively, in addition to ROA 4 with a first order lag also at 1% significance, all positively explaining value creation through Intellectual Capital. The Wald Test did not reject the VAIC zero sum hypothesis with a first order lag, but rejected LnCIV with a first order lag. in model 3 resulted in a 0.03898 value for LnCIV, confirming its The long-term effect statistics positive effect in long-term value creation, but was not conclusive regarding the sign for VAIC in the dynamic model. In Table 8 we show the results for other hypothesis (interested parties may get in touch with the authors to gain access to the tests, not listed due to space limitations).
18
João Francisco de Aguiar et al. Table 8: Summary of results of the models applied to the industries Nr. 1
Model ROA4 = f( VAIC;LnCIV)
Variable tested VAIC creates value
(1-1)
ROA4 = f(VAIC)
VAIC creates value
(2-1)
ROA4 = f(LnCIV)
LNCIV creates value
3
ROA = f( LnCIV,;CEE;ICE)
ICE creates value
ROA4 = f(ICE)
ICE creates value
ROA4 = f(CEE)
CEE creates value
ROA = f ( Ln CIV; CEE;HCE;SCE)
HCE creates value
ROA4 = f(HCE)
HCE creates value
ROA = f ( Ln CIV; EE;HCE;SCE)
SCE creates value
ROA = f(SCE)
SCE creates value
4
5
Results of tests (1) Static: confirmed Dynamic: no Static: confirmed Dynamic: confirmed Static: confirmed Dynamic: confirmed Static: confirmed Dynamic: no Static: confirmed Dynamic: confirmed Static: confirmed Dynamic: confirmed Static: no Dynamic: no Static: confirmed Dynamic: confirmed Static: no Dynamic: no Static: confirmed Dynamic: confirmed
Source: The author, based on results of the models obtained with STATA SE/10 (1) Results based on the results reported. In general, the results achieved indicate that the VAIC model is relevant to explain value creation by companies, and the same applies to the representative variable in the Calculated Intangible Value (LnCIV). On the other hand at the VAIC component level, results do not have the same statistical significance, partially impaired by the multi-colinearity detected among three variables (Employed Capital Efficiency, Human Capital Efficiency, Structural Capital Efficiency), and between these and Calculated Intangible Value (LnCIV). The separate explanatory variables test was performed owing to Andriessen's criticisms on the concept and measurement of structural capital. As we deem these criticisms pertinent, we allowed models with alternative regressors should also be tested. The Study’s greater restriction involves the dynamic models, as they were not confirmed. We consider that this a research area still incipient in Brazil and dynamic models require a greater theoretical foundation. It is our intention to expand these tests in order to include other industries in the Brazilian economy, in addition to produce a comparative analysis among industries.
References Andriessen, D (2004). IC Valuation and Measurement: classifying the state of art. Journal of Intelectual Capital;5,5.p. 230. ABI/INFORM Global. Arellano, M.(2003).Panel Data Econometrics: advanced texts in econometrics. Oxford: Oxford University Press. Asterioux, D.; Hall, S. G (2006). Applied Econometrics. New York: Palgrave MacMillan. Carlton, Obert B.; Hofer, Charles W (2006). Measuring Organizational Performance: metrics for Entrepreneurship and Strategic Management Research. Edward Publishing Limited: Cheltenham,UK, Deloite.(2008). Leii 11.638: Mudanças nas práticas contábeis no Brasil. Deloitte Touche Tohmatsu,2008. Dosi, G.; Nelson, R.R. (1994). An Introduction to evolutionary theories in economics. J Evol Econ (1994) 4:153172. Furrer, O.; Sudharshn, D; Thomas, H. (2001). Organizatonal Structure I a Global Context: the Structure Intangible Asset Portfolio Link . In: CONTRACTOR, Farock J (org). Valuation of Intangible Assets in Lobal Operations.London: Quorum Books. IBGE - Instituto Brasileiro de Geografia Econômica e Estatística (2005). Pesquisa Industrial Anual. Rio de Janeiro.. Available at http://www.sidra.ibge.gov.br/bda/pesquisas/pia/default.asp?o=16&i=P, accessed August, 2008. Luthy, David H (1998). Intelectual Capital and Measurement. Utah State University, p.1-18. Available at http://www3.bus.osaka-cu.ac.jp/apira98/arquives/htmls/25.htm.Acesso em 22 de Julho de 2008. Pavitt, K (1984). Sectoral Pattens of Technical Change: Towards a Taxonomy and a Theory. Revista Brasileira de Inovação. P.343-373. North Holland: Elsevier Science Publishers.. Presidência da República (2007). Casa Civil. Subchefia de Assuntos Jurídicos. Lawi 11.638 of /December, 28th, 2007. Available at http://www.planalto.gov.br/ccvil_03/_ato2007-2010/2007/Lei/L11638.htm. Acessed August 2008.
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João Francisco de Aguiar et al. Pulic, A. (2000) VAIC TM: an accounting tool for IC management. International Journal of Technology Management, Vol.20, nº 5,6,7,8. 2000 ____________(2002a). MVA and VAIC : analysis of randomly selected companies from FTSE 150. Graz: April,. Available at http://www.vaic-on.net/download/ftse30.pdf. Acesso em Agosto de 2008. ___________. Do we know if we create or destroy value. Zagreb: 2002(b). Disponível em Http://www.emeraldinsight.com/.published/emeraldfulltextarticlepdf/2500086205_ref.html.2002.Acesso em 04 de julho de 2008. Reilly, R. F; Schweihs, Robert P (1999).Valuing Intangible Assets. New York: McGraw Hill. Robinson, K.C (1995). Measures of entrepreneurial value creation: an investigation of the impact of strategy and industry structure on the economic performance of independent new ventures .Unpublished doctoral dissertation .University of Georgia, Athens,Ga, 1995. Stock, J.; H; Watson, M. W. (2004) Econometria. P. 485. São Paulo: Pearson, Addison e Wesley. YokoyI, Y (2007). Mundo Novo. Revista Capital Aberto. São Paulo: Editora Simone Azevedo,
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Implementing Knowledge Management Through IT Opportunities: Definition of a Theoretical Model Based on Tools and Processes Classification Elena Alberghini1, Livio Cricelli2 and Michele Grimaldi2 1 University of Rome Tor Vergata, Rome, Italy 2 University of Cassino, Italy
[email protected] [email protected] [email protected] Abstract: Knowledge management has been recognised as a strategic competence through which organizations can address their need for supporting innovation, improving business performance and sustaining their long-term competitive advantage. Nevertheless, the implementation of effective and efficient KM is not an easy task and some organizations have failed in this purpose. Most of these failures have been related to the lack of a generally accepted framework and methodology. The abundance of KM tools, their fairly affordable purchase price, combined with short available time, lead in many cases to an unstructured implementation of KM. The recent advent of the Enterprise 2.0 phenomenon offers new opportunities to achieve the benefits of flexibility and utility of the web 2.0 technologies. These benefits, however, are not automatic and Enterprise 2.0 doesn’t elude the KM implementation issue. Even if in the literature there is still a debate about the relationship between Enterprise 2.0 and KM, from a practical point of view the need of an integrated architecture is emerging, in order to reduce expansive and recurring maintenance costs. The aim of this research is to provide a theoretical model for a quick and successful implementation of KM in organizations. This paper presents a three-dimensional model defined to identify opportunities in which IT can effectively facilitate KM application. The three dimensions, consisting in KM processes, KM tools and strategic purposes, are defined through a synthesis and a rationalization of KM literature. In particular, by analysing KM processes and their features, the model identifies and classifies knowledge application opportunities which can be implemented through an integrated and scalable architecture. Moreover, different categories of KM tools and enablers currently available on the market are analysed and associated with the KM life-cycle processes and the main strategic purposes. The study also presents an architecture schema as a base for KM implementation to coherently integrate the chosen tools from the theoretical model. The model aim is to provide organization management with a systematic approach in their challenge to implement KM and effectively reach KM strategic goals. The model also guides in the assessment of the current use of IT systems in respect to KM purposes and to diagnose possible needs to leverage the IT architecture and achieve higher benefits. Keywords: Knowledge Management, KM tool, KM processes, Web 2.0, theoretical model, classification
1. Introduction Knowledge exists within the people, products, and processes (Grant, 2007). Knowledge Management (KM) can be defined as a systematic discipline and set of approaches to enable information and knowledge to grow, flow and create value in an organization (Rao, 2005). KM is studied from several different perspectives, from organisational learning to information science and there are a variety of approaches to it. As of today no unique definition of KM has being coined. According with Gartner Group glossary: “Knowledge Management is a business process that formalizes the management and use of enterprise's intellectual assets. KM promotes a collaborative and integrative approach to the creation, capture, organization, access and use of information assets, including the tacit, uncaptured knowledge of people”. Inside an organization KM is increasingly seen as a guide to develop a more organic and holistic way of understanding and exploiting the role of knowledge in the processes of managing and doing work (Research Matters, 2008). According with Rasmus (2002), successful knowledge management organizations share some characteristics which range from technology infrastructure to a strong belief in the value of knowledge sharing and collaboration. Competitive advantage is critically dependent upon company’s ability to increase the effectiveness with which it acquires, shares and exploits information and know-how. Critical success factors can be categorized into people, process and technology oriented factors. People oriented factors include leadership, a vision actively promoted by top management, and culture, the combination of shared history, expectations, unwritten rules, and social customs that influence the perception of actions and communications of all employees. Process oriented factors include systematic processes, which help ensure consistent, efficient execution and adherence to policies and to consolidate the company culture and measurement, to provide clear
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Elena Alberghini et al. business benefits. Technological factors include collaborative and people oriented technologies and infrastructure.The paper is organised as follows. Section 2 provides information about the research methodology. Section 3 analyses the first two dimensions of the theoretical model which consists of strategic purposes and KM processes. Section 4 describes the third dimension of the model and proposes a classification which integrates the three dimensions. Sections 5 defines the theoretical model and describes in the detail the steps to effectively facilitate KM application and Sections 6 describes the architecture schema for KM deployment. Finally, last Section highlights and summarizes the strategic and organizational benefits deriving from the implementation of the theoretical methodology.
2. Research methodology The aim of this research is to provide a theoretical model for a quick and successful implementation of KM in organizations. To do this, the research is based on two main phases: the first phase regards the synthesis and rationalization of literature about KM and the analysis of the working experience; the second phase is related to the definition of a theoretical model. With regard to the first phase, as for Wild and Griggs (2008), this study recognizes the balance required between KM and information technology. Moreover, following Croteau and Bergeron’s methodology (2001), the literature review is organized into three basic constructs of the research: business strategy, technological deployment and organizational needs. As a consequence, they can be classified as follows:
Strategic Purposes. Company’s objectives: WHERE the company wants to go.
KM Processes. Knowledge needs identified in the KM life-cycle phase: WHAT companies need.
KM Tools and enablers. Means to reach the targets: HOW companies plan and manage information technology to benefit from its potential and effectiveness.
With regard to the definition of the theoretical model, the previous three KM aspects, which are all analyzed and classified, represent the three dimensions of the model. The main hypotheses of this research and, broadly, the interface between the literature review and the model is that the choice and application of technological systems should follow company’s needs basing on its strategic purposes. Moreover, once the KM strategy is decided, it is meaningful to identify the KM processes and their relative feature which better suite company’s needs. Finally, only by linking the KM Tools with the strategic purposes and KM Processes it is possible to easily identify the KM tools which better support KM implementation to effectively reach KM strategic goals. As a consequence, the theoretical model relates the three dimensions and provides a systematic approach to implement KM. In particular, the model aims to provide a clear vision at first sight and easiness of use by a simple and structured approach. The study also presents an architecture schema as a base for KM implementation to coherently integrate the chosen tools from the theoretical model. Data consistency, not redundant information, integrated and scalable architecture are the constraints of this schema.
3. Strategic purposes and KM processes In this section the first two dimensions of the theoretical model are analyzed.
3.1 Strategic purposes Success of KM depends strongly on the selection of initiatives that align with organizational strategy. According with Rasmus (2002), the best approach to targeting knowledge management initiatives identifies solutions that can be integrated into the general context to support strategic goals and objectives. To make business strategy part of knowledge management initiatives the first step of this model consists in creating a strategic purposes’ list congruent with wider corporate strategy. Once the general strategic purposes list is completed, it is important to establish a KM strategy, focusing on KM processes. As shown in the subsequent steps of this model, the strategic purposes list will be a sort of litmus test in choosing initiatives to develop. The following list describes the main strategic purposes identified from literature.
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Figure 1: Strategic purposes The major purpose of KM is to enhance value through innovation which will develop sustainable advantage for the future aligned with and supporting strategy. In this context technology has a fundamental role to support of KM, especially in large, distributed, multi-national organizations, because it can help collapse barriers of time and distance. To assist with the process of fitting knowledge and information into a business framework, it is useful to understand common knowledge initiatives used to support strategic imperatives. This study provides a classification of tools and associates them to strategic purposes and to KM processes, in order to provide with pragmatic understanding about integrating knowledge management strategy and technologies in business processes for successful performance. For each tool/initiative it is possible to associate the strategic purposes that can be reached.
3.2 KM lifecycle and processes In the current study five fundamental processes are selected: knowledge generation, knowledge organization, knowledge transfer, knowledge application and knowledge consolidation. Knowledge is never still and these processes follow each other in a cycle. The power of KM is in allowing organization to explicitly enable and enhance the productivity of these processes and to leverage their value for the group as well as for the individual (Ruggles, 1997). The following map is a synthesis of literary review (Alavi, 1997; Carayannis, 1999; Davenport and Prusak, 1998; Heisig, 2000; Malhotra, 1998; Nissen, 1999; Park and Kim, 2006; Van der Spek and Spijkervet, 1997; Wiig, 1993) and shows the list of the major KM processes and their related sub- processes, features and sub-activities. Knowledge Generation - Knowledge generation has been widely recognized to be strategically important for organizational learning and innovation. In this study knowledge creation and acquisition are considered as sub-processes of knowledge generation. Nonaka and Takeuchi’s (1995) focused on the importance of knowledge creation in the organization’s long term success and survival. Knowledge is created either as explicit or tacit knowledge. Explicit knowledge consists in anything that can be documented, archived and codified, often with the help of IT. Tacit knowledge resides within individuals and it is harder to capture, evaluate, share and leverage. Knowledge acquisition permits to identify and capture information from technology sources. It includes elicitation, collection, gathering, identification and search. It is important to underline that only the human being can convert information into knowledge. Human, organizational and cultural factors are the ultimate determinants of KM success.
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Figure 2: KM processes Knowledge Organization – For knowledge to be efficiently utilized within the organization, knowledge storage and organization are critical (Grant 2007). Knowledge structuring implies information interpretation, filtering, categorization and best practices. It involves the identification of meaningful knowledge, value finding processes and the creation of insights skills and relationships. Knowledge storage permits to find and compare information and includes information tracking and retrieval. Knowledge Transfer – It is the practical problem of transferring knowledge among the organization. It is more complex than a communication problem because knowledge resides in organizational members, tools, tasks, and their subnetworks (Argote & Ingram, 2000) and knowledge in organizations is mainly tacit or hard to articulate (Nonaka & Takeuchi, 1995). In this study sharing and distribution are considered as sub-processes of knowledge transfer. Knowledge sharing is marked out by making available what is already known, it makes use of collaboration and collaborative supports, may imply personalisation and provides timely information. Knowledge distribution is marked out by disseminating and making available new information, usually with formal communications.
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Elena Alberghini et al. Knowledge Application - This is the end goal of knowledge practice. It involves knowledge utilization and evolution. KM does not have any value if created knowledge cannot be applied and utilized to its potential. Knowledge utilization involves knowledge re-creation, producing and value adding processing. It implies decision making, contextualizing, projecting and compacting. Knowledge evolution involves review and replenishment of knowledge clusters continually in the organization and implies information review and re-interpretation. Knowledge Consolidation – This process involves applying the knowledge acquired to a range of other understandings and contexts. Knowledge disposition involves knowledge integration and targeting and decision making. It implies indexing, collaboration and application. Knowledge validation involves information and ideas and implies information codification and qualification.
4. KMS and KM evolution KM tools include anything that serves as a means for performing functions, processes, operations or tasks. In this paper KM tools are not only assumed to be something physical in nature but they include either intangible or abstract enablers, such taxonomies.Knowledge Management Systems (KMS) are systematic approaches to manage organizational knowledge typically utilizing information and communications technologies. KMS integrates an extensive range of tools depending on its capabilities. Besides Artificial Intelligence technologies, KSM also include intranets, document and content management systems, workflow management systems, business intelligence, visualization tools, groupware and e-learning systems. The purpose of KMS lies in enhancing the ability of employees to recreate value-added knowledge and increase their company’s intellectual asset by collaborating with knowledge workers and leveraging existing intellectual assets. In light of the SECI model (Nonaka & Takeuchi, 1995), the major objective of the system is to transform tacit knowledge to explicit knowledge and vice versa. ICT plays an important role in knowledge management systems since it facilitates many of the technology and people-based activities that are important to KM success. Moreover, with new technologies, the concept of KM is evolving towards a new vision more based on people participation. It is important underline that technology is a useful enabler rather than the solution of KM. Even if “KM activities are all over the map“(Malhotra, 2005), no KSM can work unless the participants fully understand the benefits and unless employees have formal and informal incentives to participate.
4.1 Enterprise 2.0 and KM evolution New technology environments are transforming the way knowledge is experienced. Internet, intranet and wireless media offer new ways to share knowledge and are developing a modern approach to knowledge management. Moreover the standards of today’s fast and hyperactive world are far from the ones in which the initial studies about KM were moving. According with David Pallard (2005) KM is evolving into a new generation. New technologies provide a simpler way to manage knowledge and offer new content format, including graphic and multimedia, which help and are able to incentive knowledge diffusion. Also communication styles and problem solving are evolving in a more collaborative approach, especially for the so called generation x and millennial people. Web 2.0 is the popular term for advanced internet technology and applications, including blogs, wikis, RSS and social bookmarking. It facilitates interactive information sharing, interoperability, usercentered design and collaboration on the world wide web. According with Gartner, web 2.0 is a collection of methodologies, technologies, and social and business models highlighted by openness, participation, the use of lightweight technologies, and decentralized, distributed processes. In this context a new concept of using these technologies inside an organization has emerged, the so called Enterprise 2.0 phenomenon. The term "Enterprise 2.0" was first coined in 2006 by Harvard Business School Associate Professor Andrew McAfee in an article for the MIT Sloan Management Review as the use of emergent social software platforms within companies, or between companies and their partners or customers. Even if there is still a debate about the relationship between Enterprise 2.0 and KM, applying web 2.0 technologies and emerging social behaviours can help to add greater interactivity into the KM environment. Social networking capabilities are providing vital information in a way that is adaptive
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Elena Alberghini et al. and user-driven. According with Davemport (2008), the most important difference between classical KM and E2.0 is the “emphasis on emergence of content structures in E2.0, rather than specifying them in advance, as early knowledge managers had to”. Another difference lies in the implementation approach. KM is characterized by a top down approach, while enterprise 2.0 means connecting and networks and it is marked by emergent autonomous behaviours. A good example is provided by blogs and wikis. Often installed as rogue software initially, they are inexpensive, easy to deploy and allow users to contribute content easily. The goal for a strategic E2.0 use is to offer easy publishing and management, while meeting enterprise realities for permission controls. Anyway, according with KM experts and consultants, web 2.0 is still very young and at the moment there are only vertical success cases of enterprise 2.0 applications. Collaboration technologies can lead to a tremendous proliferation of documents and it is necessary to find web dynamics adaptable to companies to find a way to create value from contents. If completely unregulated, the results could be chaotic and web 2.0 products need to have several key features which allow operating across the entire enterprise. Inside a company it is necessary to combine structure with flexibility and integrated architectures and semantic technologies are becoming fundamental to enhance the KM effectiveness.
4.2 Classification
Figure 3: KM tools classification Knowledge technologies are the methods and technologies behind KM-Tools (Mertins, Heisig, Vorbeck, 2003). In this study, the most common used set of tools and technologies available currently on the market are classified in order to enable a logical comprehension and easily associate them to the life-cycle KM processes, by making use of the literary review (Adamides and Karacapilidis, 2006; Huang et al., 2006; Rao, 2005; Reyes et al., 2002; Ruggles, 1997; Skyrme, 2000; Tiwana, 2000; Tyndale, 2002; Wensley, 2000). KMS integrates an extensive range of tools depending on its capabilities. The following list describes distinct categories of technologies which help and support KM
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Elena Alberghini et al. processes. KM functionalities are covered in different ways by the selected tools grouped in these categories.
Infrastructure – it satisfies the requirement that people should be able to connect into knowledge whenever and wherever they are.
Storage and Data Organizing – tools that manage and assure the accessibility, availability, and performance of stored information.
Gathering Discovering – tools that involve finding, selecting, and acquiring information.
Knowledge Organizing – tools that support knowledge organization, classification and mapping.
Collaboration - tools that support the need for people to work together and to be able to share knowledge.
Knowledge Worker Support – tools that support communicative and human centric processes and can be personal and customizable.
Application Specific – tools used in specific areas, like human resources or procurement.
Figure 4 and Figure 5 summarize knowledge management technologies as classified in this study. The table describes the types of technologies and subsystems that are recommended to implement KMS across the KM life cycle.
5. The theoretical model In choosing how to develop KM, methodology’s steps are the following ones:
Strategy and objectives definition.
As-is assessment and gap analysis.
Strategic purposes and process correlation.
Knowledge map creation.
Validation (quantitative analysis).
Architecture analysis.
Deployment.
Strategy and objectives definition In choosing how to develop KM, the first step is to make KM initiatives part of corporate strategy. Careful planning is essential before using the right tool. The choice should be oriented to what best suits the company’s culture and the business needs. KM is a wide subject and some companies need to mainly focus on some particular aspects and emphasise certain phases of the KM lifecycle, according with their corporate strategy. The three-dimensional model is defined to identify opportunities in which IT can effectively facilitate KM application and helps to analyse KM processes and to choose enablers that managers can use to implement KM. The first step of this model consists in creating a strategic purposes’ list congruent with wider corporate strategy. As-is assessment and gap analysis The model offers the opportunity to examine the current situation. For each tool it is possible to immediately verify the relationships with KM processes and strategic purposes. Managers can analyse KM processes and the enablers they can use to implement the particular process. It is also possible to analyse the gaps to fill in order to reach the desiderata situation Strategic purposes and process correlation A necessary condition of this study is that the choice of technological systems and applications should follow company’s needs basing on its strategic purposes. The strategic purposes list will be a sort of litmus test in choosing initiatives to develop.
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Figure 4: KM relations (part I)
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Figure 5: KM relations (part II)
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Elena Alberghini et al. Create a knowledge map Managers can start creating a knowledge map choosing, for each process, KM tools which better suit their needs. The model helps to analyse benefits and features of the tools highlighting the KM strategic purpose they can satisfy. The full table provides, for each tool, the definition of them and the description of features, benefits and applications. Validation (quantitative analysis) It is possible to validate the chosen set of tools by a quantitative analysis which can underline the KM processes best supported by the chosen tool categories. Architecture analysis Effective tools usage requires seamless interoperability and information fluidity, therefore an overall integrated architecture is necessary (Skyrme 2000). In this step the focus is on analysing the integration level of the chosen application frame to effectively support KM implementation. Deployment The last step consists in deploying the chosen tools. Application architecture requires continuous care and feeding to identify and solve problems, take advantage of new platforms, and handle new loads and new features.
6. The architecture schema A fundamental problem inside the organization is effectively managing the huge quantity of documents and transactions produced daily. Internet technologies are driving down the cost to retrieve and manage information, but the abundance of KM tools can create innumerable silos of information and risks to counteract the benefits of ease of use. Besides, boundaries of single tools tend to blur and, to support knowledge activities through ICT, an overall integrated and scalable architecture is needed (Skyrme, 2000).
Figure 6: The architecture schema KM environments should be able to lead users to reach relevant knowledge and should effectively promote participation. Following Mertins, Heisig and Vorbeck (2003) methodology, this study presents an architecture schema which aims to define tools and supporting processes needed at several levels. The base level consists of infrastructure tools and information and knowledge sources. The taxonomy level represents the interface between the data level and the information level. The next level provides mechanisms for threaded conversations and structured collaborative work. Knowledge services offer different possibilities for synchronous and asynchronous communications and tools for social networking and collaboration. The search level provides efficient contextual access to knowledge. A primary challenge of KM is to complete successful searches in a timely and inexpensive manner and the value of information grows every time it can be reused or integrated. Semantic technology extends traditional search tools with
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Elena Alberghini et al. improved data relevance and increased data coverage. Social tagging and other Web 2.0 trends expand the range of added perspectives that semantic search can reveal (West, 2009). The user interface is described as a portal that gives a single user access to the knowledge base. Moving up through each architectural layer tools are more characterized by human and process factors such as individuals' social interaction. A high-performance scalable architecture depends on continually optimizing application architecture so that different components can interoperate to support constant change and adding features.
7. Conclusions New technological environments are characterized by individuals' social interaction, therefore successful implementation depends on combining structure with flexibility. Solutions based on KM should give support to companies by ensuring the comprehensiveness of KM processes. This research provides a theoretical model defined to effectively facilitate KM implementation in organizations. In order to define the model, the literature review resulted in some classifications which, not only help to identify the KM processes and their relative feature which better suite company’s needs, but also integrate strategic purposes, KM Processes and KM Tools. The theoretical model is a guide to identify the KM tools which better support particular KM processes to effectively reach strategic purposes and to verify and validate the chosen frame of KM implementation. In the specific, the model allows organizations to assess the current situation (as is) and to understand where and how addressing the specific needs to effectively contribute to the practice of KM (to be). The study also presents an example of a structured architecture schema to coherently integrate the chosen tools from the theoretical model, as a base for KM implementation. Such a scalable and integrated architecture allows supporting constant change and handling new loads and new features. An application of this model may consist in exploring the technological frames of the organization and making a quantitative analysis to assess the current situation and to validate the desiderata one, highlighting the KM processes best supported by the KM tools. The quantitative analysis relates implementation of tools categories and KM processes from different perspectives for explaining and anticipating actions that usually are not easy to obtain.
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Intellectual Capital in Polish Corporate Groups Current Trends and Future Challenges Maria Aluchna and Beata Mierzejewska Warsaw School of Economics, Warsaw, Poland
[email protected] [email protected] Abstract: Corporate groups, their functioning, management systems and aspects of efficiency remain in the center of academic research and debate. Research searching for functioning patterns and performance of corporate groups attracted interest of both academics and practitioners also in Poland, particularly during privatization processes. Analyses show that the transition challenges, economic reforms as well as conditions of poor investor protection, inefficient stock market and weak legal system led to dynamic development of corporate groups in Poland. Besides the structural and transactional reasons for emergence of corporate groups the development of intellectual capital, close relations of founders and executives and access to unique know how and market experience play a crucial role. In many cases the corporate groups has been established to take advantage of the combined intellectual capital of the companies. Although, it is clear that the synergy effect in terms of intellectual capital is crucial for building corporate group, it is still challenging to assess and report its value. There are some attempts to set up the standards for reporting intellectual capital (ie. EFFAS initiative), but it seems that reporting intellectual capital of corporate groups is much more challenging than of individual companies. It is still discussed if the group intellectual capital should be analyzed separately for subsidiaries when group relations are mostly financial character. The paper is based on research conducted on the sample of selected Polish corporate groups and concentrates on their functioning paying major attention of the process of development of intellectual capital within their complex structure. The research delivers insights both of the importance of intellectual capital as a tool for building competitive advantage in rising economy and the challenges of assessing and reporting intellectual capital in corporate groups. Keywords: Intellectual Capital, corporate groups, reporting IC
1. Introduction Corporate groups, their functioning, management systems and aspects of efficiency remain in the center of academic research and debate. Corporate groups are one of the most popular forms of corporate activities that reveal many advantages as compared to single, individual companies. The topic of corporate groups has been widely researched and debated in 70s through 90s of 20th century whereas the examples of commonly applied business groups were found worldwide in Europe (Sweden, Germany, France, Belgium), North America (Canada), Asia (South Korea, India, Thailand, Japan) and Latin America (Brazil, Argentina, Mexico). The aim of the paper is to analyze corporate groups in Poland from the perspective of development and measurement of intellectual capital. Polish corporate groups reveal own specificity rooted in their origin as privatized former SOEs or newly established companies in transitioning economy after 1990. Beside the structural and transactional reasons for emergence of corporate groups the development of intellectual capital, close relations of founders and executives and access to unique know how and market experience play a crucial role. In many cases the corporate groups has been established to take advantage of the combined intellectual capital of the companies. Although, it is clear that the synergy effect in terms of intellectual capital is crucial for building corporate group, it is still challenging to assess and report its value. There are some attempts to set up the standards for reporting intellectual capital (ie. EFFAS initiative), but it seems that reporting intellectual capital of corporate groups is much more challenging than of individual companies. It is still discussed if the group intellectual capital should be analyzed separately for subsidiaries when group relations are mostly financial character. The paper is organized as follows. The first section presents the definition and main characteristics and types of corporate groups worldwide. The specificity of Polish corporate groups is outlined in the section second referring to the transition programs based mostly on restructuring and privatization of former SOEs that acted in the forms of large conglomerates. Section three is focused on IC issues of corporate groups in current and future environment with particular emphasis on Polish listed companies. The conclusions are presented in the last section.
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Maria Aluchna and Beata Mierzejewska
2. Functioning of corporate groups 2.1 Definition Corporate groups are a separate form of economic activity and reveal own specific characteristics (Zattoni, 1999). The literature delivers wide range of different terms used such as business groups, groups of companies, conglomerates, holdings as well as depicts richness of definitions. A corporate group is usually defined a set of legally separate and independent firms tied with stable relationships and operating in strategically unrelated activities and under common ownership control. However, some definitions refer to a wider more general character of ties connecting the company including informal or social relationships such as family ties between CEOs or interlocking directories of independent firms or administrative or financial control, interpersonal trust or related to ethnic or commercial background (Khanna and Yafeh, 2005; Cuervo-Cazurra, 2006). This wider definition of business groups relates mostly to Indian business houses or Japanese keiretsu. On the other hand the narrower definitions derives from economic aspects of business groups functioning point at relationships between separate firms initiated by a family that remains the controlling shareholder at the same time, strong ownership ties and business and financial interlocks (Ghemawat and Khanna, 1998; Fisman and Khanna, 2004). Korean chaebol, Latin American grupos, Thai family groups or continental European pyramids serve as examples for this narrower perspective. American conglomerates usually fit the economic definition however reveal different, usually heavily dispersed ownership structure with no or very small family involvement. The analysis of origin and organizational form of corporate groups refers to the debate on the boundaries of the firm, the scope of ‘make’ or ‘buy’ activities. Corporate groups are perceived as a network of companies, but their characteristics distinguished them from other types of network structures such as supplier or distribution (e.g. franchises) networks, strategic alliance, geographic associations. The place of business groups seen as diversified networks is presented in figure 1 below.
Markets
Hierarchies Firm networks Supplier networks (e.g. parts supplier, subcontractors)
Diversified (business groups) - widely held - family controlled - state controlled
Distribution networks (e.g. franchises)
networks
Strategic networks (e.g. tech alliances, research consortia) Geographic networks (e.g. Silicon Valley, Hollywood)
Figure 1: Business groups vs. other firm networks source: Cuervo-Cazzura (2006), p. 18. As shown in figure 1 business groups called that are example of diversified networks are divided between three main types according to their ownership structure. The widely held business groups
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Maria Aluchna and Beata Mierzejewska represented by Japanese keiretsu do not reveal any controlling shareholder and their ownership structure is based on cross shareholding between members of the groups. The second type refers to the family controlled corporate group which can be illustrated by Indian business houses, Korean chaebols, Japanese zaibatsu or Latin American grupos. These groups are centered around the controlling family that with the extensive use of multilayer pyramidal structures separating controlling and cash flow rights allow the family to exert the full control over the group. It should be added that pyramids are also a popular form supporting business groups in Sweden, Italy, Belgium and Canada. The last type of business group presented in figure 1 refers to the state controlled conglomerates and are best illustrated by Chinese business groups.
2.2 Pros and cons of corporate groups Corporate groups are build to achieve certain advantages related to the organizational form, specificity of the functioning and the synergy potential both in finance and management. These reasons become crucial at the beginning of 20th century in line with the processes of industry concentration and consolidation in various sectors led by the opportunity for additional profits generated from the economy of scale and constant cost reduction. Reasons for building corporate groups can be summarized as follows (Trocki, 2004):
economies of scale and scope – business activity within a group allows both for standardization and differentiation of products/ service. The opportunity for cooperation between affiliated firms provides the potential for synergy in production, marketing, sales, management, finance and HRM;
focus on core competences and possibility to lower the risk – corporate group gives the unique opportunity for risk diversification via implementation of related or unrelated diversification strategy allowing for the specialization in narrow areas represented by separate business units;
possibility to overall economic activity optimization and encouraging local entrepreneurship – corporate activity adopts both central management as well as provides independence and autonomy for separate business units.
The activity within corporate groups is however often criticized due to several possible shortcomings. First, the diversification strategy may cause problems referring to inefficient resource allocation and lack of adequate knowledge or managerial experience in selected sectors. Additionally, current complex and challenging environment may prefer focused strategy based on core competences and targeted market segment (Kumar Kakani, 2001). Business groups consisted of many companies may result in rigid and complicated structure what translates into lack of flexibility and high management and organizational costs. Tracking the profitability of a wide portfolio, HRM requirements towards development and compensation policy and problems in balancing interests of all shareholders and stakeholders as well as SBU managers in company’s corporate governance system (higher information asymmetry, increased moral hazard) may further lower the overall performance of the business group (Weiner, 2005).
3. Specificity of Polish corporate groups 3.1 Corporate groups built by former SOEs Research searching for functioning patterns and performance of corporate groups attracted interest of both academics and practitioners also in Poland, particularly during privatization processes (Trocki, 2000, 2004; Wiankowski et al., 2000). However, current analyses focusing on corporate groups founded after 1990 in truly market economy are definitely lacking. The research on corporate groups in Poland referred mostly to the former SOEs and the process of restructuring and privatization of large conglomerates adopting horizontal and vertical diversification specializing in heavy industry (steel, coal mining, power generation) or having the monopolistic position (telecom) that appeared to be a dominant organizational form of 45 years of centrally planned economy. SOEs proved to be inefficient and lacking synergy effects despite combining related diversification under one roof. The former SOEs that managed to survive the transition reforms and successfully emerged as business groups include three types: 1) companies privatized to strategic, usually foreign investors such as Polish Telecom (TPSA) privatized to France Telecom, bank PEKAO privatized to Unicredit, 2) companies privatized partially to minority shareholders with a remaining stake of State Treasury such as bank PKO BP, petroleum company PKN Orlen, energy conglomerate (PGE) or copper mining holding KGHM and 3) companies still fully or partially controlled by the State Treasury such as the
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Maria Aluchna and Beata Mierzejewska insurance company (PZU), railways (PKP) or a handful of companies acting in energy sector. These companies constitute large and powerful business groups operating in many sectors and note usually positive financial results although their overall performance is not fully researched. However, the latest analyses point at the improved results of parent company at the cost of subsidiaries.
3.2 Polish corporate groups founded after 1990 The analysis of public companies listed on Warsaw Stock Exchange reveals that vast majority of them (ca. 80%) operate in the form of a corporate group. This trend may also result from the dominating characteristics of substantial ownership concentration, rapid growth and expansion of Polish companies and relatively weak institutional order, particularly inefficient legal system. Among Polish groups listed on the stock exchange ca. 50% are corporate groups established after 1990. These groups represent the new generation of entrepreneurs and are not biased by socialistic heritage hence they do not suffer from characteristic former SOEs problems such as rigid structure, poor equipment and infrastructure, exaggerated diversification, excessive employment or socialistic mentality of employees. The development of Polish corporate groups both privatized and newly founded followed one of the four paths that include 1) development through mergers and acquisitions transactions, 2) development via outsourcing, 3) development based on organic growth and establishing new subsidiaries operating on new markets and 4) development via shareholders consolidation. Table 1 below presents examples of Polish corporate groups that adopted one of these four development paths. Table 1: Four development paths of Polish corporate groups Mergers & acquisitions Elektrim Agros Stalexport Ciech Rolimpex InterCars
Outsourcing
Organic growth – new subsidiaries
Shareholder consolidation
KGHM Huta Katowice PSE KS Wieliczka NG2
Optimus Integer.pl ITI
Nafta Polska Próchnik NFI Polski Cukier PGE
Source: own compilation based on corporate materials and Trocki M. (2001). „Struktury grup kapitałowych”, conference materials Grupy kapitałowe – wyzwania w obecnej sytuacji rynkowej, Institute for International Research, Warszawa.
3.3 Polish corporate groups – current stage of development and future challenges Several analyses shown that the transition challenges, economic reforms as well as conditions of poor investor protection, inefficient stock market and weak legal system led to dynamic development of corporate groups in Poland. Thus the number of corporate groups, founded after 1990 and experiencing currently a dynamic growth, is rising. Groups realizing their operation within the complex structure, usually associated with concentrated ownership and tight relations between subsidiaries and parent companies prove to be well suited to the developing environment of Polish economy. Understanding of the current stage of development and future challenges for Polish corporate groups requires reference to international experience and comparative analysis. The stage of development of corporate groups is often measured by the evolution of these structures from a single individual company through group, strategic holding to financial holding that depends on the complexity of the structure and the role of the parent company. The term of corporate group refers to the organization of the parent company and its subsidiaries. Strategic holding is based on a holding parent company that does not operate in any market but is solely dedicated to group management. The financial holding means that the financial parent company operates at the apex of the group and it does not engage in strategic group activity but is mostly concentrated on the management of controlled stakes. Figure 2 presents five stages of corporate group development. The comparative analysis reveals that 60% of corporate groups are represented by operational holdings, 30% management holdings whereas 10% are financial holdings in the case of developed economies. Polish corporate groups operate mostly in the form of organizational holding (80%), followed by strategic holding (15%) and financial holding (5%) what illustrates the initial stage of their
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Maria Aluchna and Beata Mierzejewska development. Polish groups acting in the form of operational holding are characterized by a significant role of the parent company with its 95-60% contribution to the overall sales of the whole group and lack of balance between other members of the group (Trocki, 1998). Table 2 presents examples of Polish corporate groups referring to their types. Shareholder
Shareholder
Shareholder
Shareholder
Shareholder
Company Company development Operational activity
Single company
Parent company Group development Operational activity
Holding parent company Group development
Financial holding company Stakes management
Financial holding company Stakes management
Holding parent company Group development
Subsidiary Company development Operational activity
Subsidiary Company development Operational activity
Subsidiary Company developmen t Operational
Operational holding
Strategic holding
Strategicfinancial holding
Subsidiary Company development Operational activity
Financial holding
Figure 2: Types of corporate groups Source: Trocki (1998). „Grupy kapitałowe w Polsce”, Zarządzanie, s. 28. Table 2: Examples of Polish corporate groups’ types Organizational holding (ok. 80%) KGHM Huta Katowice Mostostal Warszawa Ciech Metalexport TPSA
Strategic holding (ok. 15%) Burmar Waryński Mostostal Export Agros Optimus Port Gdynia Holding
Financial holding (ok. 5%) Kulczyk Holding Bartimpex NFI
Source: Trocki M. (2001). „Struktury grup kapitałowych” prezentacja z konferencji Grupy kapitałowe – wyzwania w obecnej sytuacji rynkowej, Institute for International Research, Warszawa. The identified shortcomings of Polish corporate groups refer to inadequate groups structure, poor integration of member companies, poor group management, limited funds for group development, poor access to external financing and limited possibilities for group restructuring. The reasons for these problems depend on the origin and type of a given corporate groups but are mostly rooted in the policy of the Treasury, economic slowdown, lack of know how on group management, weak corporate governance. Additionally, Polish corporate groups adopt exaggerated diversification strategy, build costly complicated organizational structures and suffer from poor transparency due to intensive internal group shareholdings and controlling shareholder realizing his/her private benefits and abusing minority shareholders rights. On average higher effects of synergy, improved performance and better know how on group management is observed in companies privatized to
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Maria Aluchna and Beata Mierzejewska strategic investors (that are a subsidiary on a bigger global group) and to some extend in newly founded groups. Polish corporate groups undoubtedly face a era of significant changes. The reforms will be heavily based on the controlling shareholder changes towards the improvement of the quality of corporate governance practice. Additionally changes will refer diversification strategies in terms of targeted sectors and the type (vertical or horizontal) of integration approach. Polish corporate groups are expected to limit the unrelated diversification, adopt spin offs and divestitures to optimize their portfolios (Trocki, 2001) according to shareholders expectations towards firm value increase (Kononowicz, 2002; Chadam, 2002a). Moreover, Polish groups are on the quest for synergy effects in management (Falencikowski, 2008; Kreft, 2004; Chadam, 2002b; Chadam i Pastuszak, 2004). Therefore Polish groups are expected to develop from operational to strategic and financial holdings. The last trend assumes further integration of Polish subsidiaries within the global corporate groups that operate in Poland.
4. Intellectual Capital 4.1 Definition IC is associated with “human capital” or “knowledge” that adds value to organizations and their stakeholders. The terms “intangible assets”, “knowledge assets/capital” or “intellectual assets/capital” are often used as synonyms. The wealth-creating function of intellectual capital has been recognized years ago by Alvin Toffler or Peter Drucker. Currently, the literature delivers wide range of intellectual capital definitions. Some of them refers to the person as the owner of the intellectual capital or the company as the owner of the inputs that are converted into intellectual capital (Dobija 2002). The others mention the knowledge that generates value (Sulivan 1996), intangible assets that are formalized, captured and used for value creation (Stewart 1997) or intellectual property (MSR 38). Nevertheless, one of the best known notion of intellectual capital is the framework presented by Leif Edvinsson (Edvinsson 1997). According to Edvinsson market value of the company is determined by both its financial and intellectual capital. Intellectual capital is represented by human capital and structural capital (that consist of client capital and organizational capital) (Edvinsson 2001). The majority of definitions of IC (i.e. H. Saint-Onge, N.Bontis, A. Brooking, T. Stewart, L. Edvinsson) include the following taxonomy:
Relational capital: All relations a company entertains with external subjects, such as suppliers, partners, clients (brands), research centers, etc.;
Human capital: Knowledge and competences residing with the company's employees;
Organizational capital: Collective know-how, beyond the capabilities of individual employees. E.g.: information systems; policies; intellectual property (Wikipedia).
In case of the research project on assessing and reporting intellectual capital of the listed companies (MOKI project conducted by the Warsaw Stock Exchange) the following definition has been accepted: “intellectual capital represents company’s potential to succeed in the future”
4.2 Attempts and models towards assessing and reporting IC The traditional accounting focuses on the tangible assets, while does not appreciate the intangible assets. It results from the fact that the value of intangibles often can not be measured directly (e.g. using the historical cost of purchasing), and their value depends on the strategy adopted by the company and the environment the company operates in. The value of intangible assets is associated with business processes that transform the intangibles into real value. Moreover, intangible assets are not linear and additive by nature. Thus, in order to create value, intangibles must be combined. As a result, balance sheet, which is a report of a linear and static data, do not adequately reflect the value of resources held by the company. One of the biggest challenge that experts of IC are facing is the method for assessing and reporting intellectual capital they decide to use. There are many different methods worldwide depending on the needs of the stakeholders. Some of them are based on “hard data” (ie MV/BV, Q Tobin, VAIC etc), the others are focused more on strategic perspective (IC Rating, IAM, Skandia Navigator etc).The
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Maria Aluchna and Beata Mierzejewska classification suggested by K.E. Sveiby, that is presented in the table below, is based on the method of measuring intangible assets. Table 3: Methods of IC measurement Category Direct Intellectual Capital
Market Capitalization Return on Assets
Scorecard methods
Methods of IC measurement Technology Broker Citation-Weighted Patents Inclusive Valuation Methodology The Value Explorer™ Intellectual Asset Valuation Total Value Creation (TVC™) Accounting for the Future Q Tobin Investor Assigned Market Value Market-to-Book Value Economic Value Added (EVA™) Human Resources Costing & Accounting Calculated Intangible Value Knowledge Capital Earnings Value Added Intellectual Coefficient (VAIC™) Human Capital Intelligence Navigator Skandia Navigator Value Chain Scoreboard™ IC-Index™ Intangible Asset Monitor Balanced Scorecard (R. Kaplan, D. Norton) IC-Rating
Source: K. E. Sveiby, Methods for Measuring Intangible Assets, www.sveiby.com Analyzing these methods one can note that none of the methods has been widely accepted as a valid method of IC assessment. Methods such as Skandia Navigator or IC-Rating evaluate the different components of intellectual capital. They combine the quantitative and qualitative indicators, depending on the needs of customers. Therefore, they well reflect the characteristics of the business and could be used for creation of intellectual capital management systems. On the other hand, these methods are more subjective, which in turn can cause difficulties in comparing different companies. Methods such as VAIC ™ and Tobin's Q ratio, in turn, constitute an attempt to valuation of intellectual capital throughout the organization, without reference to its individual components. The purpose of these methods is to estimate the potential value created through IC, not so much the IC itself. It should also be noted that all methods of IC measurement are based, at least in part, on subjective opinions, either in development, data gathering or in the interpretation phase. Therefore, it is necessary to provide a dedicated method that will combine the advantages of both approaches, quantitative and qualitative. Although, there are several initiatives towards reporting intellectual assets (Ricardis Project by EU, DATI by Danish Agency for Development of Trade and Industry, WICI by OECD, IAbM by METI, Japan etc), there is still lack of common framework, widely accepted and adopted 1 . The frameworks ed for most of the intellectual capital reporting models have various similar characteristics, they are not fundamentally different, however, they do serve different purposes, or use different approaches. Some of them take managerial perspective (i.e. Wissensbilanz) and relate resources, internal activities and processes to company’s strategy and business goals in order to indicate the interdependencies in value creation processes. The others, developed in accordance with the balanced scorecard framework (Kaplan, Norton 1996) focus on the various aspects of intellectual capital management. The models give a broad picture of the various intellectual capital components which are related to each other, but which are not combined into a bottom line figure. The models do not incorporate the information on intellectual capital in the traditional accounting framework. They force narrative reporting as an additional information. The Warsaw Stock Exchange, together with Innovatika, has taken the challenge to develop such a method. Their ambition was to provide both capital market actors and companies’ boards with the tool that will help them to assess the firm’s potential to succeed in the future. The developed model 1
More initiatives on reporting intellectual capital are presented in the table 4 (by OECD):
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Maria Aluchna and Beata Mierzejewska attempts to combine managerial (internal issues) and capital market perspective (company valuation). Thus, the method consists of:
IC Business Report – the set of quantitative indicators based on “hard data” (quantitative, auditable);
self assessment – the framework for more in-depth assessment, based on qualitative data.
Table 4: More initiatives on reporting intellectual capital
Since the method is divided into four parts: Business model, Relational Capital, Structural Capital and Human Capital, the interdependencies between ICBR indicators and Self-assessment factors are clearly indicated. So, both sides interested in company’s IC are satisfied: capital market actors receive reliable, tangible indicators that are comparable between companies, and management board receive the tool that helps them know what they can do internally to increase these indicators in order to better perception of the company’s potential. As prototyping phase of the project shows, the method works great for individual companies, but in case of corporate groups it faces many challenges.
4.3 Challenges for assessing and reporting IC of a Polish listed corporate group Corporate groups usually represent complex systems, covering different sectors, different markets, different clients and – often – different business models. Obviously, intellectual capital of listed corporate groups can be calculated as the relation “market value to book value”, which is one very simple method of assessing IC. However, this method gives no insight into factors that constitute the group’s capability to succeed in the future. That simple number represents no information why investors should put their money in the given entity. Although there is some research on intellectual capital of companies listed on the Warsaw Stock Exchange (Sopińska 2008; Kunasz 2005), it is mostly based on the MV/BV approach, which depicts differences between companies, but does not show what factors (competences, structures, relations, etc) are responsible for different results. Thus, there is a need to have an insight into the company’s business recipe. Analyst and investors interviewed within the project emphasized non-financial information as a main factor differentiating companies. Still, they have limited access to such information, as the Polish market is not sufficiently transparent for that purpose. In addition, the more complex the company is, the more difficult its assessment tend to be. In terms of intellectual capital, the structural capital seems to be the most challenging for assessment. As interviewees noted, within their fundamental analysis exercises they seldom assess the target company’s internal processes, organizational procedures, innovation initiatives etc., as they have no sufficient data. Moreover, corporate groups – as the analysis target – have many
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Maria Aluchna and Beata Mierzejewska intraorganizational relations, and many functions are accomplished group-wide, so it seems to be almost impossible to allocate particular competencies or resources to individual entities within the group. Thus, assessing intellectual capital of the individual group member is truly challenging. However, only the parent company is usually listed on the stock exchange. The aim of the MOKI project (Model for Assessing and Reporting Intellectual Capital) is to prepare the method for assessing and reporting intellectual capital of the listed companies, in spite of their nature, size, the sector they operate in, etc. The initial results of the prototyping phase of the MOKI project show that even though most of listed companies are officially considered as corporate groups (i.e. they consolidate financial statements), they usually are not diversified broadly. Therefore, one would be able to assess their intellectual capital (as one can assess IC of an individual company), since they are in fact similar to individual companies. Still, there are several truly diversified corporate groups, i.e. Agora SA (that consolidates different media – press, outdoor, interactive ads, radio, etc. – under one roof), Asseco SA (that operates across different geographies and several sectors), PMPG SA (that has a number of complementary businesses), PGE SA (that spans almost the whole length of its supply chain). Assessing intellectual capital of such groups seems to be challenging due to following considerations:
A company could have both group members and „external” companies In the clients’ portfolio. It causes, therefore, difficulties in relationships assessment. One can hardy estimate the dynamics of relationships with no distinction between clients;
Operating on the various markets may require different skills and knowledge, and therefore the assessment of both human capital as well as structural and relationship capital sometimes varies depending on the market.
Employees shifting between group members determine intellectual capital transfer. Although employees work sometimes for different group members (i.e. being involved in any group-wide project) they are still on one payroll. This causes problems with fair assessment of human capital of the group. On the other hand, the more advanced the group (in terms of the stage of groups’ development), the less important reporting IC issues of the whole group seem to be. In the case of financial holdings, brand capital or structure capital (that includes i.e. internal processes) become less and less important. Many such groups have several brands under one roof, with no operational relations between one another – to mention just a NIF examples. Therefore, discussing common intellectual capital seems unjustified in that context. In addition to the extent of diversification, another obstacle for measurement and reporting IC of a corporate group seems to be its internal market. Initial research conducted among Polish listed holdings shows that while using the Model for Assessing and Reporting Intellectual Capital (MOKI), the more developed internal market of the group is, the more difficult the measurement can be. It is almost impossible to identify clients of the group precisely enough to calculate their retention rate. Moreover, usually internal transactions are based on intraorganizational conditions, so the assessment does not reflect the company’s real market position (its relation capital). Some of the shortcomings of Polish corporate groups become their advantages in terms of assessing intellectual capital. For example, poor integration of member companies simplifies the assessment process (as they can be analyzed as individual companies). There are not many interdependencies both in the structural and relational capital. Unfortunately, poor integration in case of mostly organizational holdings means that – on the other hand –company takes no advantage of the potential synergy effects and does not profit from combined intellectual capital.
5. Conclusion Researchers aim to find out, what information about IC companies disclose, how they classify IC, what techniques and mechanisms they use to communicate non-financial information, how such openness influence companies value and etc. To date no serious evidence has been published for intellectual capital reporting and information disclosure within Polish companies. The issue of practical intellectual capital management and reporting, including the amount, content, trends of IC information disclosure in Poland, has not been analyzed. The lack of evidence and attention to the issue of IC disclosure does not necessarily mean, that Polish companies don’t manage IC or don’t disclose such information to stakeholders. Nevertheless, overall assessment method comparable between companies has not been introduced so far. Assessment of intellectual capital is particular challenge in case of corporate groups due to their complexity, different characteristics depending on the stage of development and historical roots. Furthermore, lack of common method of IC assessment seems to be additional argument restraining Polish corporate groups the reporting IC. As majority of Polish
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Maria Aluchna and Beata Mierzejewska corporate groups operate as organizational holding one can expect that they should stress on developing their intellectual capital as a synergy effect. In the near future they would likely focus on assessing intangibles, as they realize the intangibles could be the source of their competitive advantage. Therefore there is a need for developing reliable, common method for assessing and reporting IC. Introducing MOKI by the Warsaw Stock Exchange should then be a driver for widely spread reporting movement.
References Chadam J. (2002a). „Finansowe aspekty zarządzania czynnikiem ludzkim w polskich grupach kapitałowych”, Przegląd Organizacji, nr 7-8, s. 51-55. Chadam J. (2002b). „Synergia w zarządzaniu organizacją holdingową”, Organizacja i Kierowanie, nr 1 (111), s. 43-58. Chadam J., Pastuszak Z. (2004). „Zarządzanie wiedzą a problemy synergii w organizacjach wielopodmiotowych”, Organizacja i Kierowanie, nr 4 (118), s. 54-70. Cuervo-Cazzura A.(2006). „Business groups and their types”, Asia Pacific Journal of Management, vol 23, s. 419-437. Dobija D. (2001). Metodyka szacowania wiedzy, [in:] Zarządzanie wiedzą w przedsiębiorstwie. Materiały konferencyjne. Polska Fundacja Promocji Kadr – Zarząd, Warszawa. Dobija M. (2002). Kapitał ludzki i intelektualny w aspekcie ekonomii i rachunkowości, [in:] Ekonomia nie tylko dla wtajemniczonych, M. G. Woźniak (red.), AE, Kraków. Edvinsson L., Malone M. (1997). Intellectual Capital: Realizing Your Company's True Value by Finding Its Hidden Brainpower, HarperBusiness. Edvinsson L., Malone M. (2001). Kapitał Intelektualny, PWN, Warszawa. Falencikowski T. (2008). Kształtowanie swobody decyzyjnej w zarządzaniu grupami kapitałowymi“, TNOiK, Toruń. Fazlagic A. (2006). Intellectual capital and benchmarking, Wydawnictwo Rys, Poznań. Fisman R., Khanna T. (2004). „Facilitaling devlopment: the role of business groups”, Word Development, vol. 32, no. 4, s. 609-628. Ghemawat P., Khanna T. (1998). „The nature of diversified business groups: A research design and two case studies”, Journal of Industrial Economics, vol. 46, s. 35-61. Kumar Kakani R. (2001). “Explaining diversified business groups failure and focuses business groups success in India”, XLRI Jamshedpur School of Business and Human Resources, Working Paper no. 2001-07, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=906455 Khanna T., Yafeh Y. (2005). „Business groups in emerging markets”, European Corporate Governance Institute, Working Paper no 92. Kononowicz Ł. (2002). „Wycena i podnoszenie wartości spółek grupy kapitałowej”, materiały szkoleniowe Institute for International Research, Warszawa. Kreft Z. (2004). Holding. Grupa kapitałowa, PWE, Warszawa. Kunasz M. (2005). Próba wyceny kapitału intelektualnego przedsiębiorstw notowanych na Giełdzie Papierów Wartościowych w Warszawie [in:] D. Kopycińska, Konkurencyjność rynku pracy i jego podmiotów, Szczecin. Lev B., Zarowin M. (1999), The Boundaries of Financial Reporting and How to Extend Them, Journal of Accounting Research 37. Sanzhar S. (2006). “Discounted buy not diversified. Organizational structure and conglomerate discount”, http://ssrn.com/abstract=387800 Sopińska A. (2008), Wiedza jako strategiczny zasób przedsiębiorstwa. Analiza i pomiar kapitału intelektualnego przedsiębiorstwa, SGH Monografie i Opracowania nr. 556, Warszawa. Edwards S. (1997). The brain gain. CA Magazine, April. Sullivan P., Edvinsson L. (1996). Developing a Model for Managing Intellectual Capital. European Management Journal, no 4. Trocki (1998). „Grupy kapitałowe w Polsce”, Zarządzanie, s. 28. Trocki M. (2000). „Zarządzanie grupą kapitałową” w Romanowska M., Trocki M., Wawrzyniak B. Grupy kapitałowe w Polsce, Diffin, Warszawa. Trocki M. (2001). „Struktury grup kapitałowych” prezentacja z konferencji Grupy kapitałowe – wyzwania w obecnej sytuacji rynkowej, Institute for International Research, Warszawa. Trocki M. (2004). Grupy kapitałowe. Tworzenie i funkcjonowanie, Wydawnictwo Naukowe PWN, Warszawa. Wiankowski S., Bogusławski Z., Borzęcki J. Karmańska A. (2000). Praktyka funkcjonowania grup kapitałowych, Instytut Organizacji i Zarządzania w Przemyśle ORGMASZ. Zattoni A. (1999). „The structure of corporate groups: The Italian case”, Corporate Governance, vol. 7, no 1, s. 38-48.
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Intellectual Capital Development by Means of Knowledge Conversions Eckhard Ammann Reutlingen University, Germany
[email protected] Abstract: Based on a new conception of knowledge and knowledge dynamics, an approach for intellectual capital development in an organisation is given, which raises the notion of knowledge conversions to the level of intellectual capital domains. Intellectual capital development can be modeled with this approach by means of general transformations between domains and between appropriate parts of these domains, which themselves are refined and modeled with general knowledge conversions. To attain this approach, a new conception of knowledge and knowledge dynamics is introduced. The knowledge conception is represented by a knowledge cube, a three-dimensional model of knowledge with types, kinds and qualities. The type dimension addresses the internal-external aspect of knowledge, seen from the perspective of the human being. The kind dimension distinguishes various knowledge kinds like propositional or procedural knowledge. Finally, in the quality dimension, several quality measures of knowledge are given. Built on this conception, knowledge dynamics is modeled with the help of general knowledge conversions between knowledge assets. A set of basic knowledge conversions is given in a way, such that more complex general conversions may be easily gained by building on this set. Through this conception we gain a sound basis for knowledge management and development in an enterprise. Raising this knowledge development approach to the more strategic and resource-oriented intellectual capital level in an organisation, general transformations between the three main intellectual capital domains (individual competence, internal and external structure) and between parts of them can be described. With their help a model for intellectual capital development is gained: In a top-down approach, general transformations of intellectual capital are broken down to the notion of general knowledge conversions. This leads to development of the intellectual capital, i.e. to value creation in a company. To indicate the applicability of our approach, an example for the development of customer relations capital is given. Keywords: Intellectual capital development, transformations of intellectual capital, intangible resources, value creation, conception of knowledge, knowledge conversions
1. Introduction Intellectual capital of a company is defined as all non-monetary and non-physical resources that are fully or partly controlled by the organisation and that contribute to the value creation of the organisation (Roos 2005). Three domains of intellectual capital can be distinguished: External structure is a family of intangible relationships with customers and suppliers, which partly may be converted into legal properties such as trademarks and brand names. The internal structure includes patents, concepts, models, IT systems and processes, which are created by employees and owned by the company. The third domain is the individual competence of the employees (see Sveiby 2001). This concept of intellectual capital helps to let intangible resources of a company be measured, communicated and interpreted. See (Andriessen 2004, Sveiby 2001 and Roos 2005) for more detail. Economic value creation of a company is based on intangible resources to a high and increasing degree. Development of intellectual capital of a company therefore is a key activity for value creation. Generalizing the transformation approach between intellectual capital domains given by Sveiby (Sveiby 2001), we introduce general transformations between whole domains and between intangible resources, which make up the intellectual capital domains. These transformations are drivers for intellectual capital development in a company on a strategic level. In a top-down approach, they can be refined to be modeled as general knowledge conversions. We introduce these general knowledge conversions based on a new conception of knowledge and knowledge dynamics. In a way our approach augments and complements the complex system approach to intellectual capital development, where in a hierarchical structure intellectual capital components, elements and variables together with their interconnections are identified. See (Bueno 2006) for an introduction into this kind of approach. It represents a static system view with only 1-to-1 interconnections, while our approach targets on dynamic n-to-m transformations. It is important to note however, and this a common core between the approaches, that our general transformations are taking course along interconnections, which can be identified with the complex system approach. Our new conception of knowledge and knowledge dynamics establishes a sound basis for knowledge management in a company. A number of knowledge management approaches exists, including the classic asset-oriented, the process-oriented approach, the knowledge-intensive process-oriented and
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Eckhard Ammann the community-oriented approach, see (Ammann 2008, Gronau/Fröming 2006, and Lehner 2008). While the management aspect of knowledge management seems to be understood to some extent, there is no common concept and understanding of knowledge and of knowledge development as basis for it. Existing approaches include the knowledge development model by Nonaka and Takeuchi (Nonaka/Takeuchi 1995), which is built on the distinction between tacit and explicit knowledge and on four fundamental knowledge conversions between those knowledge types (SECI-model), and the introduction of the type/quality dimensions of knowledge in (De Jong/Fergusson-Hessler 1996). Finally, important distinctions of implicit knowledge are given in (Hasler Rumois 2007). In this paper, we introduce a new conception of knowledge, which combines and resembles parts of existing approaches and extends them substantially. It is represented by a knowledge cube, a threedimensional model of knowledge with types, kinds and qualities. The type dimension addresses the internal-external aspect of knowledge, seen from the perspective of the human being. Here explicit knowledge is a kind of interface between those two types, which drives human interaction and knowledge externalisation. This type dimension is crucial for knowledge management, because knowledge conversions in the explicit direction make the knowledge of employees more available. As second knowledge dimension, the kind dimension distinguishes various knowledge kinds, namely propositional, procedural and strategic knowledge, and familiarity. Finally, in the quality dimension, several quality measures of knowledge are given. Using this conception we introduce general knowledge conversions between the various knowledge (and information) assets. First a basic set of such conversions is defined, which extends the set of the four conversions of the SECI-model. Building on this set, general knowledge conversions can be defined, which reflect knowledge transfers and development more realistically and do not suffer from the restrictions of the SECI-model. These conversions are the building blocks to model knowledge dynamics, i.e. all of acquisition, conversion, transfer, development and usage of knowledge. General transformations of intellectual capital can be refined to general knowledge conversions. Following this path, we end up in an approach to model those general transformations on the level of knowledge dynamics, with a model at hand which has been introduced in this paper. In total, we present an approach for intellectual capital development based on refinement to the deeper level of knowledge conversions. These general knowledge conversions have been built up in a bottom-up approach, based on a new conception of knowledge and on basic knowledge conversions. As an indication of the applicability of this approach, an example of the development of intellectual capital in the external structure domain in a company is given. This example aims at the development of the customer relations capital, more specifically at the introduction of an inquiry contact scheme for customers, developed under involvement of individual and organisational resources.
2. Intellectual capital domains and their transformations Intellectual capital of a company is defined as all non-monetary and non-physical resources that are fully or partly controlled by the organisation and that contribute to the value creation of the organisation (Roos 2005). Intangible assets, which are main contributors to intellectual capital, include legal assets like trade secrets, copyrights, patents and goodwill as well as competitive assets like knowledge, collaboration, leverage and structural activities. Legal intangible assets partly generate legal property rights, which are defensible in court of law, while competitive assets do not. The concept of intellectual capital helps to let intangible resources of a company be measured, communicated and interpreted. See (Andriessen 2004, Sveiby 2001 and (Roos 2005) for introductions into the discipline. Three domains of intellectual capital can be distinguished: External structure is a family of intangible relationships with customers, suppliers and other external stakeholders, which partly may be converted into legal properties such as trademarks and brand names. The internal structure includes patents, concepts, models, IT systems and processes, which are created by employees and owned by the company. The third domain is the individual competence of the employees (see Sveiby 2001). Transformations between intellectual capital domains and between their parts are the means of intellectual capital development. The following two subsections introduce the concepts of domain transformations and of general intellectual capital transformations, respectively.
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Eckhard Ammann As an ongoing example the development of an inquiry contact scheme for customers as part of the external structure of a company is presented. While in this section this development undertaking is modeled on the intellectual capital layer (see Figures 2 an 3), it will then be refined to the layer of general knowledge conversion in section 3.
2.1 Transformations between domains Intellectual capital development succeeds by means of the deployment and management of intellectual capital resources and their transformations (into other intellectual capital resources or into traditional economic resources) to enable and foster value creation in the organisation as seen by its stakeholders (changed from Roos 2005). Here transformation is understood as both conversion and transfer as will be clear in the following. The development of intellectual capital in a company is therefore modeled on an overall level by transformations between the three intellectual capital domains: external structure, internal structure and individual competence. Basic intellectual capital domain transformations are 1-to-1 transformations between the three domains. There exist nine basic domain transformation, which are depicted in Figure 1. For example, the third transformation from external structure to individual competence applies to learning effects of employees of a company from customer, supplier and community feedback such as new ideas, experiences or new technology (see Sveiby 2001).
Figure 1: Basic domain transformations (reworked after (Sveiby 2001)) In reality intellectual capital development often succeeds not in this 1-to-1 manner, but relates to several domains on both source and destination sides of transformations. Therefore we generalise this notion of basic domain transformations to general intellectual capital domain transformations, which are n-to-m transformation between the three domains. Figure 2 gives an example of a general 2-to-1 domain transformation. This is the high-level representation for our ongoing example; it models the development of the external structure under involvement of the individual competence and the internal structure domains.
Figure 2: Example of a general domain transformation
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Eckhard Ammann Here resources from the individual competence and internal structure domains are utilized to enhance the external structure. In total there exist 43 general intellectual capital domain transformations, among the 9 basic transformations described before. From an overall perspective, the notion of intellectual capital domains and of general domain transformations between them constitutes a meta-model for intellectual capital development. In the next sub-section, we instantiate the entities and relations of this meta-model and gain the notion of general transformations between the parts, i.e. the single resources, of the three domains.
2.2 General intellectual capital transformations Going down one layer of abstraction we now treat single intellectual capital resources instead of the whole domains. In analogy to the domain transformations in the previous sub-section, we are able to introduce general intellectual capital transformations, which are n-to-m transformations between single resources. Figure 3 gives an example, where in a general intellectual capital transformation the individual competences of several employees and contents of the internal structure are utilized in order to further develop the external structure of the company. Here the single resources are symbolically named together with the domain they are belonging to. An inquiry contact scheme and an information system for customers are to be developed.
Figure 3: General intellectual capital transformation As a method to identify the single arrow connections between source and destination assets in a general intellectual capital transformation of a company, the complex system approach (e.g. see Bueno 2006) can be used. Our approach augments and complements this approach to intellectual capital development, where in a hierarchical structure intellectual capital components, elements and variables together with their interconnections are identified in a more or less static way. Our dynamic n-to-m transformations would therefore utilize a set of possible interconnections identified with the complex system approach. In the next section, a new conception of knowledge and knowledge dynamics is introduced. The general knowledge conversions, which constitute knowledge dynamics, will be the means for refinement of general intellectual capital transformations. That means, that a general intellectual capital transformation can be broken down and modeled as a group of interrelated general knowledge conversions.
3. A Conception of knowledge and knowledge dynamics In this section, a new conception of knowledge and knowledge dynamics in a company is described. More details of this conception are given in (Ammann 2009c).
3.1 Knowledge conception We provide a conception of knowledge, and of knowledge types, kinds and qualities. As our base notion knowledge is understood as justified true belief (at least in the propositional kind), which is (normally) bound to the human being, with a dimension of purpose and intent, identifying patterns in its validity scope, brought to bear in action and with a generative capability of new information, see
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Eckhard Ammann (Hasler Rumois 2007, Lehner 2008). It is a perspective of “knowledge-in-use” (De Jong/FergussonHessler 1996) because of the importance for its utilisation in companies and for knowledge management. In contrast, information is understood as data in relation with a semantic dimension, but without the pragmatic and pattern-oriented dimension, which characterises knowledge. We distinguish three main dimensions of knowledge, namely types, kinds and qualities, and describe those in the following three sub-sections. The whole picture leads to the three-dimensional knowledge cube, which is introduced at the end of this section. 3.1.1 The type dimension of knowledge The type dimension is the most important for knowledge management in a company. It categorizes knowledge according to its presence and availability. Is it only available for the owning human being, or can it be communicated, applied or transferred to the outside, or is it externally available in the company’s organisational memory, detached from the individual human being? It is crucial for the purposes of the company, and hence a main goal of knowledge management activities, to make as much as possible knowledge available, i.e. let it be converted from internal to more external types. Our conception for the type dimension of knowledge follows a distinction between the internal and external knowledge types, seen from the perspective of the human being. As third and intermediary type, explicit knowledge is seen as an interface for human interaction and for the purpose of knowledge externalisation, the latter one ending up in external knowledge. Internal (or implicit) knowledge is bound to the human being. It is all that, what a person has “in its brain” due to experience, history, activities and learning. Explicit knowledge is “made explicit” to the outside world e.g. through spoken language, but is still bound to the human being. External knowledge finally is detached from the human being and may be kept in appropriate storage media as part of the organisational memory. Figure 4 depicts the different knowledge types.
Figure 4: Conception of knowledge types Internal knowledge can be further divided into tacit, latent and conscious knowledge, where those subtypes do partly overlap with each other, see (Hasler Rumois 2007). Conscious knowledge is conscious and intentional, is cognitively available and may be made explicit easily. Latent knowledge has been typically learning as a by-product and is not available consciously. It may be made explicit, for example in situations, which are similar to the original learning situation, however. Tacit knowledge is built up through experiences and (cultural) socialisation situations, is specific in its context and based on intuition and perception. Statements like “I don’t know, that I know it” and “I know more, than I am able to tell” (adapted from Polanyi 1966) characterise it. 3.1.2 Kind dimension of knowledge In the second dimension of knowledge, four kinds of knowledge are distinguished: propositional, procedural and strategic knowledge, and familiarity. It resembles to a certain degree the type dimension as described in (De Jong/Fergusson-Hessler 1996). Propositional knowledge is knowledge about content, facts in a domain, semantic interrelationship and theories. Experience, practical knowledge and the knowledge on “how-to-do” constitutes procedural knowledge. Strategic knowledge is meta-cognitive knowledge on optimal strategies for structuring a problem-solving approach. Finally,
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Eckhard Ammann familiarity is acquaintance with certain situations and environments, it also resembles aspects of situational knowledge, i.e. knowledge about situations, which typically appear in particular domains. 3.1.3 Quality dimension of knowledge The quality dimension introduces five characteristics of knowledge with an appropriate qualifying and is independent of the kind dimension, see (De Jong/Fergusson-Hessler 1996). The level characteristics aims at overview vs. deep knowledge, structure distinguishes isolated from structured knowledge. The automation characteristic of knowledge can be step-by-step-doing by a beginner in a domain of work or automated fast acting by an expert. All these qualities of knowledge measure along an axis and can be subject to knowledge conversions, see section 3. Modality as the fourth quality of knowledge asks for the representation of it, be it words versus pictures in situational knowledge kinds, or propositions versus pictures in procedural knowledge kinds. Finally, generality differentiates general versus domain-specific knowledge. Knowledge qualities apply to each knowledge asset. 3.1.4 The knowledge cube Bringing all three dimension of knowledge together, we gain an overall picture of our knowledge conception. It can be represented by the knowledge cube, as shown in Figure 5.
Figure 5: The knowledge cube Note, that the dimensions in the knowledge cube behave different. In the type and kind dimensions, the categories are mostly distinctive (with the mentioned exception in the sub-types), while in the quality dimension each of the given five characteristics are always present for each knowledge asset.
3.2 Knowledge dynamics In this section we give a conception of knowledge conversions. The transitions between the different knowledge types, kind and qualities are responsible to a high degree for knowledge development in an organisation. These general knowledge conversions are the building blocks to model knowledge dynamics, i.e. all of acquisition, conversion, transfer, development and usage of knowledge, in an enterprise. Most important for knowledge management purposes are conversions between the knowledge types and they will be the focus in the following. Among those especially those conversions, making individual and internal knowledge of employees usable for a company, are crucial for knowledge management. The explicitation and externalisation conversion described in this section achieve this. Implicitly, socialisations between tacit knowledge of different people also may contribute to this goal. 3.2.1 Basic knowledge conversions Five basic knowledge conversions in the type dimension are distinguished here: Socialisation, explicitation, externalisation, internalisation and combination. Basic conversion means, that exactly one source knowledge asset is converted into exactly one destination knowledge asset. Furthermore exactly one knowledge dimension is changed, i.e. the type dimension in this case. More complex conversions may be easily gained by building on this set as described later in the sub-section 3.2.2. They will consist of n-to-m-conversions and include information assets in addition. Socialisation converts tacit knowledge of a person into tacit knowledge of another person. For example, this
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Eckhard Ammann succeeds by exchange of experience or in a learning-by-doing situation. Explicitation is the internal process of a person, to make internal knowledge of the latent or conscious type explicit, e.g. by articulation and formulation (in the conscious knowledge type case) or by using metaphors, analogies and models (in the latent type case). Externalisation is a conversion from explicit knowledge to external knowledge or information and leads to detached knowledge as seen from the perspective of the human being, which can be kept in organisational memory systems. Internalisation converts either external or explicit knowledge into internal knowledge of the conscious or latent types. It leads to an integration of experiences and competences in your own mental model. Finally, combination combines existing explicit or external knowledge in new forms. These five basic knowledge conversions are shown in Figure 6.
Figure 6: Basic knowledge conversions in the type dimension The Nonaka/Takeuchi-model (Nonaka/Takeuchi 1995) uses four basic knowledge conversions in the sense defined above and interacts in a spiral of knowledge creation, which becomes larger in scale as it moves up the ontological dimension from the individual to groups and the whole organisation. This limiting linearity of its knowledge development spiral concept and the restriction to basic conversions in their approach have been criticised, besides the discussions on the meaning of explicit knowledge. Basic knowledge conversions in the kind dimension of knowledge are very seldom, normally the kind dimension of knowledge remains unchanged in a knowledge conversion changing the type dimension. Those in the quality dimension are mostly knowledge developments aiming at quality improvement and will not change the type and kind dimensions of the involved knowledge assets. In three out of the five quality measures, basic conversions can be identified, which are working gradually. Those are, firstly, a deepening conversion, which converts overview knowledge into a deeper form of this knowledge. Secondly, a structuring conversion leading to a change in the singularversus-structure scale of the structure measure. And finally, conscious and step-by-step-applicable knowledge may convert into automated knowledge in an automation conversion, which describes a process from beginner to expert in a certain domain. The remaining two quality measures of knowledge, namely modality and generality, do not lend themselves to knowledge conversions. They just describe unchangeable knowledge qualities. 3.2.2 General knowledge conversions Our conception allows the generalisation of the basic five knowledge conversions described above. General knowledge conversions are modeled converting several source assets (possibly of different types, kinds and quality) to several destination assets (also possibly different in their knowledge dimensions). In addition, information assets are considered as possible contributing or generated parts of general knowledge conversions. Note, that a general knowledge conversion may change any knowledge dimension of the involved knowledge assets. For example, in a supervised learning-bydoing situation seen as a complex knowledge conversion, a new employee may extend his tacit and conscious knowledge by working on and extending an external knowledge asset in a general conversion, using and being assisted by the tacit and conscious knowledge of an experienced colleague. A piece of relevant information on the topic may also be available on the source side of the conversion. See Figure 7 for a visual representation of this general knowledge conversion in the BPMN-KEC2 notation for knowledge-intensive business processes (Ammann 2009a). Here the greyshading of knowledge objects is decreasing as the corresponding knowledge asset is becoming more externalized.
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Figure 7: Supervised learning-by doing situation The additional tagging of knowledge assets with their specific knowledge type and their knowledge kind has been omitted in Figure 7 for the sake of clarity
4. Implementation of intellectual capital transformations Section 2 described the path down from the three intellectual capital domains, to basic and general domain transformations and finally to general transformations between parts of the three domains. Section 3 laid the foundation in form of a knowledge conception and of basic and general knowledge conversions. Implementation of intellectual capital development activities now succeeds by sticking the two parts together. The important thing to this end is the refinement of general intellectual capital transformations as composition of general knowledge conversions. This means to refine a more strategic view of intangible resources and their transformations to a more operative view of knowledge and knowledge conversions. Important to note is, that human-to-human interactions, which are a substantial part in intellectual capital development activities, can also be modeled with the help of our knowledge dynamics approach (see Ammann 2009b). From an overall perspective, the top-down approach from intellectual capital domains and their transformations meets the bottom-up approach from knowledge and knowledge conversions, see Figure 8. Hence our approach to intellectual capital development can be seen as appropriate combination of the two single approaches.
Figure 8: Layers of modelling
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Figure 9: Propose customer inquiry scheme (refinement of Figure 3) In the ongoing example, the development of a inquiry contact scheme for customers is described, which would allow customers to selectively contact appropriate employees of the company and/or use a customer information system. To this end Figure 2 in section 2 gave the general 2-to-1 domain transformation, involving individual competence and internal structure domains on the source side and external structure on the destination side. Figure 3 broke this down into a general (n+1)-to-2 intellectual capital transformation, having several employees with their individual competences and organisational regulations on the source side and two external structure resources (customer information system, customer inquiry scheme) on the destination side. Here we concentrate on the inquiry scheme introduction and restrict n to 4. Figure 9 shows a sequence of general knowledge conversions, which together refine the general intellectual capital transformation (at least the first part of this activity until a documented proposal of a customer inquiry scheme). Two sales persons, a sales manger and a developer bring in their competences, a security person seen as part of the internal structure guarantees the compliance of the inquiry scheme to security regulations in the organisation. Note, that in Figure 9 the knowledge objects are tagged. The internal knowledge objects of the sales person 1 and the developer are of subtype conscious as defined in section 3. The three explicit and external knowledge objects all have the propositional knowledge kind.
5. Summary and conclusion An approach for intellectual capital development has been given, which is based on general transformations between whole domains and between parts of the three intellectual capital domains. These transformations can be refined to general knowledge conversions. In order to attain these knowledge conversions, a new conception of knowledge has been described. Built on it, knowledge dynamics in a company can be described with the help of general knowledge conversions. In the end, a comprehensive and overall approach for intellectual capital development has been gained. From the strategic domain level it refines overall development undertakings to more operative knowledge conversions, while from the knowledge management perspective it builds up from a new knowledge conception. Unlike existing approaches, it not only identifies and describes one-to-one interrelationships between intellectual capital domains and between parts of them, but general manyto-many transformations, which are further refined to the knowledge development level. To indicate the applicability of this approach, an example of the development of intellectual capital in the external structure domain in a company has been given. Specifically the given example targets at the development of the customer relations capital of a company by introducing an inquiry scheme for the company’s customers.
References Ammann, E. (2008) “A Meta-Model for Knowledge Management”, in: Proc. of the 5th International Conference on Intellectual Capital and Knowledge Management (ICICKM), New York, USA, pp 37-44. Ammann, E. (2009a) “BPMN–KEC2 – An Extension of BPMN for Knowledge-Related Business Process Modeling”, Internal Scientific Report, Reutlingen University, Germany. Ammann, E. (2009b) “Modeling of Knowledge-Intensive Business Processes with Human Interactions”, in: Proc. of the 4th Int. Conf. on Internet, Web Applications and Services, Venice, Italy, pp.608-613.
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Eckhard Ammann Ammann, E. (2009c) “The Knowledge Cube and Knowledge Conversions”, in: Proceedings of the World Congress of Engineering, International Conference on Data Mining and Knowledge Engineering (ICDMKE), London, UK, pp 319-324. Andriessen, D. (2004) Making Sense of Intellectual Capital, Elsevier. Bueno, E., Salmador, M., Rodriguez, O., De Castro, G.M. (2006) “Internal Logic of Intellectual Capital: A Biological Approach”, Journal of Intellectual Capital, Vol.7, No.3, pp.394-405. De Jong, T., Fergusson-Hessler, M.G.M. (1996) “Types and Qualities of Knowledge”, Educational Psychologist, 31(2), pp.105-113. Gronau, N.,Fröming, J. (2006) „KMDL® - Eine semiformale Beschreibungssprache zur Modellierung von Wissenskonversionen“ (in German), Wirtschaftsinformatik, Vol. 48, No. 5, pp. 349-360. Hasler Rumois, U. (2007) Studienbuch Wissensmanagement (in German), UTB orell fuessli, Zürich. nd Lehner, F. (2008) Wissensmanagement (in German), 2 ed., Hanser, München. Nonaka, I., Takeuchi, H. (1995) The Knowledge-Creating Company – How Japanese Companies Foster Creativity and Innovation for Competitive Advantage , Oxford University Press, London. Polanyi, M. (1966) The Tacit Dimension, Routledge and Keegan, London. Roos, G., Pike, St., Fernström, L. (2005) Managing Intellectual Capital in Practice, Elsevier. Sveiby, K.-E. (2001) “A Knowledge-Based Theory of the Firm to guide Strategy Formulation”, Journal of Intellectual Capital, Vol.2, No.4, pp.344-358.
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Transformational Leadership as a Tool of Knowledge Dynamics Sorin Anagnoste, Simona Agoston and Ramona Puia The Academy of Economic Studies, Bucharest, Romania
[email protected] [email protected] [email protected] Abstract: The purpose of this paper is to analyze the transformational leadership from the perspective of emotional knowledge, which affects the knowledge dynamics. The invisible force of the internal drive activates the most important thing in the world, which is the emotion, the very force of life. Effective leaders have the ability to consistently stimulate themselves and the others to act because they understand the “invisible forces” that shape us. Emotional knowledge may be transformed into cognitive knowledge in two ways: the science of achievement and the art of fulfillment. When people fail to achieve something, the reasons they usually mention are the lack of: time, money, knowledge, experience, contacts, technology, and management. Such resources may be accurate, but they are not the defining factor. Resources are not the defining factor, but resourcefulness: creativity, determination, passion, resolution, curiosity, coaching are the defining factor; this is where transformational leaders step into. The actions made by such leaders make the difference in people’s lives and emotion helps the emotional knowledge to be transformed into cognitive knowledge. Therefore, the paper is based on a theoretical research with practical examples of change within knowledge dynamics from the perspective of transformation of emotional knowledge into cognitive knowledge as a result of transformational leadership. This knowledge shapes our destiny in three clear-cut ways, which the article will try to address: What am I going to focus on? What does it mean? What am I going to do? Transformational leadership starts with the development of a vision, a view of the future that will excite and convert potential followers and by definition, seek to transform. By doing so, such leaders do not only communicate their vision in an emotional way, but they also accomplish the transformation of emotional knowledge into cognitive knowledge. As a result, we can create value, which is the core of every business organization and any leader’s mission. Keywords: Transformational leadership, emotional knowledge, cognitive knowledge, intellectual capital, motivation
1. Approaches about transformational leadership as a change driver One of the most important approaches which have been developed by researchers in the last year is the transformational leadership approach, and as its name implies, transformational leadership is process that changes and transforms people. The transformation happens through emotions, values, ethics, standards and long-term goals and includes assessing followers’ motives, satisfying their needs, and treating them as full human beings. Another leadership style is transactional leadership, but this style of leadership differs from transformational leadership in that the transactional leader does not individualize the needs of subordinates or focus on their development. Transactional leaders are influential because it is in the best interest of subordinates to do what leader wants. (Kuhnert & Lewis, 1987). Transformational leadership was first discovered at political leaders, and according to Burns (1978), this model of leadership develops a process in which leaders and their followers succeed due to their high level of morale and motivation. Differences between leadership and management can be seen at behavioral level. Transformational leadership creates significant changes in the lives of people and organization; they rediscover the values and perceptions, changing expectations and aspirations of employees. Intellectual capital is composed of human capital, structural capital and relational capital (Andriessen, 2004). Due to the fact that these three components are not independent from the point of view of their content (Bratianu & Andriessen, 2008), this is a static model, which may lead to errors in the process of evaluation of intellectual capital in an organization. The knowledge organization is composed of explicit and tacit knowledge and the organizational intelligence is composed of cognitive and emotional knowledge. (Bratianu, 2008a). The Western civilization has always considered that the learning process is exclusively rational while the Eastern civilization has considered that the learning process has to be based on practical experience and to be rational. The emotional knowledge, such as the cognitive emotions, is becoming
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Sorin Anagnoste et al. more and more important. For example, in the state of Illinois the scholar curricula contain information about emotional and social intelligence and children learn how to recognize emotions and the way emotions determine them to act. Starting with high school, they practice lessons and learn how to settle disputes and they are supported in a manner that will end with a “win-win” situation (Goleman, 2007b). As we have already mentioned in the abstract, emotions are the force of life, but when it comes to transformational leadership they are the core of the change. Great leaders have great minds, and they can rationalize anything, they can make anything happen. It is said that we work for our self interest, but we do not work for our self interest all the time because emotions change our way of thinking. Transformational leaders use two clear cut ways: first, in order for their followers to contribute more; and second to be aware of the followers and not just to understand them in a way we can appreciate the others more and create the kinds of connections that can stop some of the challenges we face in our society. What makes a difference in people’s lives? The two master lessons are represented by the science of achievement whereby every leader is a master. It is about how you make the invisible and then make it visible, no matter what it represents: your business, money, your family, contribution to society etc; the other lesson is the art of fulfillment, which is about appreciation and contribution. Everyone has failed to achieve a goal at a moment in his or her life, but as we have mentioned in the abstract, the reasons people mention for failing to achieve a goal are related to resources like time, technology, money, knowledge, experience, which could be accurate, but they are not the defining factor; instead resourcefulness is the defining factor: creativity, determination, passion, resolution, curiosity, coaching. Followers can experience emotions from leaders at a very profound level; if leaders communicate that emotion the result will be positive and the transformation will occur. So, there are emotions that can block people’s abilities and capacities to achieve a goal and we may say that decisions shape the destiny. There are three decisions we always make in our lives: What am I going to focus on: past, present or future?, What does it mean? and What are you going to do?. Right now we are deciding what to focus on, consciously or unconsciously. The minute you decide to focus on something you give it a meaning and whatever that meaning is, it produces emotions that influence our decisions and shape our destiny. For example, when it first started, Google decided to sell its technologies. How would the world be with that decision versus the decisions of their leaders who supported the idea of building their own culture? The history of our world is made up of such decisions, for example: the decision of a woman who refused to sit in the back of the bus shaped the American culture; or someone standing in front of a tank in Tiananmen Square in 1989, or being in a position like Lance Armstrong who got testicular cancer, but his decision regarding on what to focus on was different from that of most of the people: for him, this is not the end, it is just the beginning and so, he goes and wins for seven times in a row the most important competition in cycling: Tour of France. He had not won any of Tour of France championships before the cancer because he had had no emotional fitness, nor psychological strength. So, what shapes Lance Armstrong and what shapes people are two invisible forces or primary patterns like our physical or emotional “state”. On the long-term, it is the model of the world that shapes us and it is also the filter that makes us decide whatever we decide.
2. Influencing behavior through transformational leadership When we want to influence somebody we should know what it is that already influences that person, and the answer is made up of three parts:
What is your target? The target is not your desire, but the needs you have. There are six human needs: Certainty – everyone need certainly to avoid pain, or at least to be comfortable. However, we cannot have 100% certainly, but this prevents us from getting bored; Uncertainty – in order for a leader to transform the people, he needs to exhibit variety before the followers; Significance – we all need to be special, unique; Connection and love.
Everyone can find a way to meet these four human needs. They are called the “needs of personality”, and the last two needs are the “needs of the spirit”. This is where the transformation of emotional knowledge into cognitive knowledge takes place. If you do not have fulfillment, you cannot succeed. Grow – if your business is now growing or if your relationship is not growing you fail. The reason we grow is because we have something to give. The last need is represented by
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Sorin Anagnoste et al. the need to contribute beyond ourselves - because our culture is not about “me”, it is about “us” and this is how leaders transform their businesses and their followers so they can contribute more in a “win-win” situation. This is true only if they have the chance to experience and not just talk about it.
We all need these six human needs (Robbins, 2009). Whatever your leadership system is, it takes you in a certain direction and as you are moving in a direction it means you have a destination and a destiny.
Once you know where your target is leading you and what the truth is, you then find your map and the system of beliefs telling you how to satisfy those needs in order to transform the emotional knowledge into cognitive knowledge. Through these needs some people want to destroy the world, some people use them to build something, create something or love someone;
The last part is represented by the “fuel” you pick;
3. Emotions and transformational leadership There are 6000 emotions for which there are words in the English language. A group was tested over a period of one week (Robbins, 1994). The test consisted in giving the members of the group everything they needed. The test revealed that they experienced less than 12 emotions and half of them did not feel good. Transformation can occur, but people have to explore their web, needs, beliefs and the emotions that are controlling them for two reasons: because there is more of you to give and in order to appreciate what is driving other people. This is how the world can change. The best leaders are different from the others because they can understand the crucial role played by emotions at the workplace. Such emotions do not only refer to tangible things, but also to intangible elements like self-esteem, motivation and devotion. Not all emotions can be spread in the same way. A study made by Yale University - School of Management revealed that buoyancy and enthusiasm propagate the most; irritability is less contagious, while depression does not propagate at all (Barsade & Gibson, 1998). The high rate of spreading of positive emotions has direct effects on the results of every business, and leaders know that. The study also revealed the fact that the way people feel influences their efficiency at work and that the optimist mood influences and stimulates cooperation, honesty and performance in business. When the job is becoming more and more important, the degree of emotional implication increases proportionally and at the same time the leader has to encourage and support the followers. If a leader succeeds to transfer his feelings onto the followers, they will respond positively and in this way the emotional knowledge will be easily transformed into cognitive knowledge. This can be seen in the positive results of the organization. We are not the first to mention that the secret of a strong team or organization stands in the sharing of emotions. Transformational leaders work on building motivation while mentioning positive things to the followers in a way that makes them follow one dream, the dream of the company. There is a strong argument for the fact that emotions have this extraordinary capacity (LeDoux, 1996). Emotions are crucial for survival and represent the way the brain alerts us when there is an emergency, by providing us at the same time a plan of action to run, fight or not move. Another strategy adopted by leaders in the transformational process of the company and his followers is represented by their humor. In a research (Ciampa & Watkins, 1999) conducted on the US and European leaders about the ups and downs of their career it was discovered that the transformational leaders tend to say three more times amusing comments regarding their career as compared to the rest of the leaders interviewed. So, as a second conclusion we may say that humor plays a vital role in a leader’s portfolio of strengths, because it encourages the followers to transform their emotional knowledge into cognitive knowledge. It was also noticed that people who usually do not encounter obstacles in transforming their emotional knowledge into cognitive knowledge also are driven by the desire to obtain good results, by the ability to take the initiative, by their talent to collaborate with the others and by their capacity to lead their own team. The success of organizations and institutions over time is not granted by the charisma of a leader, but by the way of leading the followers. This is how
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Sorin Anagnoste et al. “built-to-last” organizations are formed. Only after the leaders have got to know their followers very well can they start to build connections that will help to provide their mission. As a third conclusion, the trust of the followers in the leaders helps the process of transformation of the emotional knowledge into cognitive knowledge. It has been said that the leader’s commitment to transformation can be replaced by trainings, but it was proven that the results of the training are not built to last and this is not good news for organizations as over 60 billion dollars are spent on trainings only in the USA (Chernis & Adler, 2000). If people do not want to learn or acquire information any process proves to be useless. Even the masterpiece of business, the so-called MBA (Master of Business and Administration) proves its limits when it comes to time and the information learned by the students over a year or two. In the research called “The differential effects of experiential learning activities and traditional lecture classes in accounting” made by Specht & Sandlin (2001) it was discovered that the knowledge learned during an MBA program will last no more than six weeks, so instead of spending thousands of Euros or Dollars, the students should focus more on coaching and mentoring. This represents the fourth conclusion on our transformational process. Most of the people who can easily transform emotional knowledge into cognitive knowledge are optimist, according to a research (Seligman, 1991), and their chances to succeed are above the average. Due to their optimism, they recharge themselves with enthusiasm and energy for all the challenges they face. David Kolb developed a set of rules for MIT (1984), which apply to all types of learning people. Today, after more than twenty years, this model still stands. Kolb discovered that people usually learn by using one of the following methods: practical experience, reflection of past experiences, building a model which applies everything you have learned so far and, last but not least, learning by trying. Usually, leaders use two or three methods of learning and they combine these methods in a creative way that makes learning a pleasure. By doing so, leaders develop a set of rules that transform their emotional knowledge into cognitive knowledge (as mentioned in the examples above) and then they focus on passing the vision and mission onto the followers through the connections they have made. Positive groups help the followers and the leaders to make positive changes, especially if the relations among the followers are sincere, trustful and optimist (Nam, Price & Vinokur, 1999). Moreover, emotions are contagious, meaning that followers usually get contaminated with optimism from the leader, whose role is to transform the organization and empower the followers to make decisions and take initiative. These are all embodiments of the transformation of emotional knowledge into cognitive knowledge. Loehr and Schwartz (2000) demonstrated that most of the athletes practice a lot: in their mind for a long time and into application only in a small part of their time. In a hurry to reach their objectives, leaders miss the stage of learning how to improve their way of leading. In order to transform the emotional knowledge into cognitive knowledge, followers have to comply with a set of unwritten rules, but the more involved the leader is, the shorter the road of transformation is.
4. Conclusions The present paper is the result of reviewing a series of theoretical researches in the field of knowledge dynamics and leadership. Thus, the authors aim to demonstrating the importance of leadership in the process of increasing the organizational intellectual capital. The relation between this type of leadership and the business results will be empirically analyzed and evaluated in further studies conducted by our research team. The method of research used was, in principal, literature searches which involve reviewing all readily available materials. These materials can include internal company information, relevant trade publications, newspapers, magazines, annual reports, company literature, on-line data bases, and any other published materials. It is a very inexpensive method of gathering information, although it often does not yield timely information. I focused also on the autobibliographys of the main characters mentioned in this research and especially on psychologist reports on their writings in the books. Doing so, I discovered that emotions play a crucial role in the organizational development. They can be created, directed and sustained by remarkable leaders who have the capacity to successfully use
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Sorin Anagnoste et al. the tools of transformational leadership. These people listen to themselves, rather to listen to what others are saying. These people do not come along very often, but when they do they remind us that once you set up a path, even though critics may doubt you, you should follow and stick to it. Leaders are made, not borne, and we as becoming leaders, have to realize that transformation can occur. We must explore our web for two reasons: because there is more of you to give and in order to appreciate what is driving other people. That is the only way the world can change.
Acknowledgement This article is a result of the project „Doctoral Program and PhD Students in the education research and innovation triangle”. This project is co funded by European Social Fund through The Sectorial Operational Programme for Human Resources Development 2007-2013, coordinated by The Bucharest Academy of Economic Studies
References Andriessen, D. (2004) Making sense of intellectual capital. Design a method for valuation of intangibles. Amsterdam: Elsevier. Andriessen, D. (2008a) Knowledge as love. How metaphors direct our efforts to manage knowledge in organizations. Knowledge Management Research & Practice Barsade, S., Gibson. D. (1998), Group emotion: A view from the top and bottom. JAI Press, Greenwitch, CT Bratianu, C., (2008a) A dynamic structure of the organizational intellectual capital. In: Naaranoja, M. (ed) Knowledge management in organizations, pp. 233-243. Vaasa: Vaasan Yliopisto Bratianu, C., Andriessen, D. (2008) Knowledge as energy. Proceedings of 9th European Conference on Knowledge management, Southampton Solent University, UK, 4-5 September 2008, pp. 75-82. Reading: Academic Publishing Limited Ciampa, D. and Watkins, M. (1999), Right from the Start, Harvard Business School Press, Boston, MA. Chernis, C. & Adler, M. (2000), Promoting emotional intelligence in organizations: make training in emotional intelligence effective. American Society for Training and Development. Goleman, D. (2007b). Emotional intelligence. Bucharest. Curtea Veche Kolb, D.A. (1984), Experiential learning: Experience as the source of learning and development. Prentice-Hall, Englewoods Cliffs, NJ Kuhnert, K.W., & Lewis, P. (1987). Transactional and transformational leadership: a constructive /developmental analysis. Academy of Management Review, 12(4), 648-657 Loehr, J & Schwartz, T. (2000). The making of the Corporate Athlete . Harvard Business Review, presented at Weatherhead School of Managament, 17 November 2000. Nam,J.C, Price, R., Vinokur, A. (1999). How context works in groups: the influence of group processes on individual coping outcomes. University of Michigan, Institute for Social research. (Not published yet). Robbins, A. (2009). Inner Strength: Harnessing the Power of Your Six Primal Needs. New York: Free Press Robbins, A. (1994). Giant Steps. New York: Fireside. pp. 416 pages Seligman, M.P. (1991), Learned optimism: How to change your mind and your life. Alfred Knopf, New York, 1991. Spech, L & Sandlin, P. (2001) The differential effects of experiential learning activities and traditional lecture classes in accounting, Simulations and Gaming, 22, nr2 / 1991, pp. 196-210.
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The Aptness of Knowledge Related Metaphors: a Research Agenda Daniel Andriessen INHolland University of Applied Sciences, Hoofddorp, The Netherlands
[email protected] Abstract: Metaphors are common phenomena intellectual capital and knowledge management theories and practice. An important question to ask is: what are the ‘best’ metaphors we can use in our theorizing on intellectual capital and knowledge management? This paper addresses the question of the aptness of knowledge related metaphors. It concludes that the aptness of metaphorical expressions depends on three factors: the richness of the semantic field of the source domain, the validity of the mapping, and the ideological implications of the mapping. This conclusion results in a research agenda on the aptness of metaphor in knowledge management and intellectual capital theory and practice. Keywords: Knowledge; metaphor; knowledge management; intellectual capital; aptness
1. Introduction Since Berger and Luckmann’s (1966) treatise on the social construction of reality organization scholars have begun to view organizations as linguistically created worlds (Tatchenkery 2001). The language we use in organizations plays a crucial role in the way organizations function. Language structures what we see in organizations and how we act in them. With the attention for language came the debate about the role of metaphor in organizations and organizational theory. More recently the interest into the role of metaphor in organizational theorizing has reached the intellectual capital (IC) and knowledge management (KM) community . Hey (2004) was one of the first in the KM field to do a metaphor analysis of the key concepts in KM theory: data, information and knowledge. He concluded that knowledge is either conceptualized as a solid or a fluid. Andriessen (2006) identified not two but 22 different metaphors that are used in relation with knowledge in a systematic metaphor analysis of three seminal KM texts. It seems that metaphors are important meaning making devices our IC and KM domain. If that is the case, then an important question is whether it is possible to distinguish between ‘good’ and ‘bad’ metaphors in our theorizing and practice. What are the ‘best’ metaphors we can use in our sense making about IC and KM? In metaphor theory this question is known as the question about the “aptness” of a metaphor (Chiappe, Kennedy, & Chiappe 2003). In this paper we will explore the question of the aptness of knowledge related metaphors. Not by giving a definite answer but by investigating the question and its facets. This will result in an overview of research questions about the aptness of knowledge related metaphors that we believe should be answered through empirical research. We hope this research agenda will help guide IC and KM scholars in their search for the role of metaphor in theory and practice and their quest for finding better metaphors. The paper is structured as follows. First, we will briefly summarize the literature on the debate on how metaphor works in language and thought. metaphors in IC and KM. We will continue our literature review discussing metaphors in IC and KM. Next we will explore the dominant definition of aptness which states that aptness is “the extent to which a comparison captures important features of the topic” (Chiappe, Kennedy, & Chiappe 2003, p.52). This leads to a selection of three criteria relevant for the discussion on the aptness of knowledge related metaphors. Based on these three criteria we suggest a research agenda on determining aptness for metaphors in KM and IC theory and practice.
2. Literature review 2.1 Literature on how metaphor works In this paragraph we will position ourselves within the ongoing debate about how metaphor works. For an extensive overview of the debate see (Steen 2007). Ortony (1993) summarizes the discussion as a debate between the “constructivist” and the “nonconstructivist” position. The nonconstructivist position treats metaphors as rather unimportant, deviant, and parasitic on ‘normal usage’. In the constructivist view metaphors play a vital role in both language and thought. In this paper we adopt the constructivist view. We believe that many metaphorical expressions are more than just a specific use
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Daniel Andriessen of language. Many of them somehow structure and direct our thinking. We often think about one domain using the characteristics of another domain. A variety of theories have been developed on how metaphor works. These differ in their view on how the process of transferring meaning from one domain/space/category 1 to another takes place. Tsoukas (1991) suggests the transformational model, Oswick et al. (2002) the comparison model, Black (1993) and Cornelissen (2005) the interaction model. Steen (2007) lists four additional models: the two domain approach as advocated by Lakoff and Johnson (1980; 1999), the many-space approach promoted by Fauconnier, the class-inclusion approach by Glucksberg and the career of metaphor approach from Gentner. Describing the ins-and-outs of these theories is beyond the scope of this paper. However, the following three questions that rise from this academic debate are important for the purpose of this paper: 1) Do metaphors represent pre-existing similarities between source and target or do they create new ones? 2) To what extent do metaphors influence our thinking before they are expressed in language? And 3) to what extent are metaphors embodied in our brain and body? 2.1.1 To what extent represent metaphors pre-existing similarities? This debate centers on the question whether metaphor is simply a matter of correspondence, highlighting the analogies in a source and target domain of the metaphor, or whether metaphor does more than that. Oswick and Jones (2006) favor the correspondence theory, which states that individuals pick a source domain that fits the characteristics of the target domain they want to highlight. For example, when we want to express that knowledge needs to be accessible to all members of an organization we may want to compare it to a fluid and say that knowledge needs to flow. Cornelissen (2005; 2006) presents the domains-interaction model as an alternative. According to this model, the process that makes metaphor work is a two-way process in which the target and the source concepts are aligned, and correspondence is constructed and created, rather than deciphered. In this model the structure of the source helps to structure the target. In the example above, the very expression that knowledge needs to be accessible is based on the idea that knowledge is like some sort of physical substance to which members of an organization can have access. Knowledge as an abstract concept has on its own not much characteristics; it gets these through the use of metaphor (Lakoff & Johnson 1999). In this paper we adopt the view that metaphor is capable of producing new meanings beyond preconceived similarities. When used to conceptualize the abstract concept of knowledge, the structure 2 of the source helps to structure the target. For example, from the KNOWLEDGE AS A COMMODITY metaphor it follows that knowledge can be sold, stored and distributed. 2.1.2 To what extent do metaphors influence our thinking before they are expressed in language? In the transformational and comparison models as well as the interaction model, the use of metaphor is seen as a deliberate. Scholars decide what metaphor to use to create a certain effect. In this view, authors have the option to use either literal or metaphorical language. For example, Tsoukas portrays metaphorical and scientific languages as the two ends of the same continuum (Tsoukas 1993). So although metaphors help structure reality, in his view authors have the option to use them or not. In contrast, Lakoff and Johnson (1999) have shown that in many cases individuals unconsciously use metaphor to conceptualise and structure the target domain. Especially abstract concepts like time, knowledge, and relationships get their structure from metaphor. It is impossible to think or talk about any of these concepts without using some type of metaphor. Lakoff and Johnson claim that we do not first decide what characteristic of a phenomenon to highlight and then pick our metaphor, but that the metaphor allows us to bracket (Weick 1995), certain characteristics that would not be possible without metaphor.
1
Each metaphor theory adopts a different vocabulary, whether it is source and target domains, vehicle and tenor, vehicle and topic, spaces, or categories. It is interesting to note that each of these in itself is a metaphor. In this paper we will use the vocabulary of source and target domain. 2 Following Lakoff and Johnson (1999) we will write metaphors in capital letters.
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Daniel Andriessen In this paper we adopt the view that metaphor plays an important role in unconscious framings and conceptualizations (Marshak 2003). Metaphors go beyond reasoning. Many metaphorical expressions are not used deliberate (Steen 2008). They are used nondeliberate by the speaker and understood by the hearer because they play a role in the unconscious processes of the brain. This position has two consequences. First, it indicates that metaphors have considerable influence on our thinking of which we may not be aware. Second, it implies that we do not always have a choice to use metaphorical or literal language. Especially when we use abstract concepts like we do in the field of IC and KM, we don’t have a choice but to use metaphorical expressions. 2.1.3 To what extent are metaphors embodied in our brain and body? Lakoff and Johnson are proponents of the strong conceptual metaphor view (Cameron 2007a) that assumes that conceptual metaphors are embedded in our body and that linguistic expressions are the result of connecting two of those conceptual domains. According to Lakoff and Jonhson we use so called conceptual metaphors that are based on the sensor and motor functions of our body and that are ‘hardwired’ in our brain. A weak conceptual metaphor view excepts the idea that not all metaphorical expressions are reflections of conceptual metaphor. Not all metaphors are hardwired in our brain and we have a choice to use alternative metaphors. In this paper we adopt the weak conceptual metaphor view. We follow Cameron (2007a; 2007b) in her idea that people’s use of metaphor is contingent, shifting and variable. The use of metaphor is as much the result of conceptual metaphorical structures in our brain as it is of the social contexts of our discourses. This view opens the way for research into the way dominant metaphors in cultures, public discourses and scientific domains influence our thinking. And it implies that we are not predetermined to use only a limited number of metaphors that are closely related to the functioning of our body. We have a choice to use a variety of metaphorical imagery.
2.2 Literature on metaphors in intellectual capital and knowledge management theory and practice From the overview above it follows that in this paper we see metaphors as thinking devices that determine how we think and talk about the concept of knowledge in intellectual capital and knowledge management theory. They determine what characteristics of knowledge we highlight and what characteristics remain hidden. This process of making sense of knowledge to a large extent takes place at an unconscious level of our thinking. However, we do have the option to make the influence of these metaphors explicit and to adopt alternative metaphors. In this paragraph we will briefly explore the literature that deals with knowledge related metaphors. Already in 1980 Lakoff and Johnson (1980) identified several conceptual metaphors that are used in relation to knowledge and that are widely used in the English language. For example: THEORIES ARE BUILDINGS (Is that the foundation of your theory? The theory needs some more support. The argument is shaky.) IDEAS ARE PRODUCTS (We have generated a lot of ideas this week. He produces new ideas at an astounding rate. His intellectual productivity has decreased in recent years.) And IDEAS ARE COMMODITIES (It’s important that you package your idea. That idea just won’t sell. There is always a market for good ideas.) Reddy (1993) showed that much of the language that we use to talk about language is based on the CONDUIT metaphor. We conceptualize ideas, concepts, thoughts, meaning, feelings and sense as objects; words and sentences as containers; and communication as an act of sending and receiving these containers through a conduit (Try to get your thoughts across better. You still haven’t given me any idea of what you mean) . Related to this is the MIND AS CONTAINER metaphor (It is at the back of my mind. You have a mind like a sieve) and UNDERSTANDING IS GRASPING (I get what you mean. That went over my head) (Lakoff & Johnson 1999). Bereiter (2002) has shown that these conduit metaphors have a strong impact on how we reason about education, and not always for the good. Hey (2004) was one of the first in the KM field to do a metaphor analysis of the key concepts in KM theory: data, information and knowledge. He concluded that knowledge is either conceptualized as a solid or a fluid. Andriessen (2006) identified not two but 22 different metaphors that are used in relation with knowledge in a systematic metaphor analysis of three seminal KM texts. Both Hey (2004) and Andriessen (2006) are examples of research within the IC and KM field that is aimed at finding
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Daniel Andriessen metaphors in existing IC and KM texts. This field of empirical research studies the impact of metaphors on theorizing and practice. The aim is to discover how our thinking about KM and IC is limited by conceptual metaphors and to reveal their ‘hidden’ ideology. More recently two further strands of metaphor research have developed in this field. One strand tries to find alternative, novel metaphors for KM theorizing (Andriessen & Van den Boom 2007;Bratianu & Andriessen 2008). This research is based on the idea that all metaphors highlight certain characteristics of the topic and hide others. The idea is that we need new metaphors to highlight previously ignored characteristics of the complex notion of knowledge. This field of creative research aims to come up with novel metaphors that might expand our thinking about KM and IC. The challenge is to come up with novel metaphors that stick. Goatly (2007) warns us that this is a difficult task: “Original metaphors perhaps have the merit of undoing ready-made linguistic and cultural categories and the ontologies and ideologies which they manifest (…). However, because they are original, they are, by definition, one-off attempts to do this. Conventional metaphors, on the other hand, do not unsettle our modes of perception or action at all, since they have achieved currency as an acceptable way of constructing, conceptualising and interacting with reality. (…) there is an ideological or hegemonic struggle to get one’s metaphors accepted as the conventional ones” (pp.28-29). Another strand of research is looking for ways to use metaphors in KM interventions in organizations (Andriessen 2008;Moser 2004). Here the idea is that the aim of KM and IC research is to improve organizational reality. Metaphors may be a useful new tool in this endeavor. Introducing specific metaphors into an organizational discourse on KM may help improve the quality of the conversation and thereby the quality of the KM intervention. In the chapter 4 we will argue that for all three strands of metaphor research in the field of IC and KM the question of the aptness of metaphors is an important question. First we will suggest three criteria that may be used in all three strands to determine the aptness of knowledge related metaphors.
3. The aptness of metaphor This chapter deals with the aptness of metaphors. Our aim is to identify relevant criteria by which the aptness of metaphors in KM and IC discourse can be judged. A good starting point is the definition for aptness as proposed by Chiappe et al. (2003). They define aptness as “the extent to which a comparison captures important features of the topic” (p.52). To illustrate this they compare two metaphors for life. The first is ‘‘life is a valuable gift’’. “This metaphor captures some important features of life, such as the fact that it is precious and that we are lucky to have it. However, perhaps it does not capture as many important features of life as does ‘‘life is a journey’’ (Lakoff and Johnson, 1980). According to the ‘‘conventional metaphor’’ view, the latter reflects a longstanding conceptual mapping between the domains of ‘‘life’’ and ‘‘journeys’’ that can be elaborated in many different ways. As a result, it captures many important features associated with life—that it often has a goal and destination, that it can be long and arduous, that one can lose one’s way, that it can be undertaken with fellow travelers, and so on. Understood in this fashion, the comparison between life and journeys may be more apt than that between life and a valuable gift” (p.52). This definition includes two important criteria for aptness. The first is a quantitative one and relates to the potential of the source domain to transfer characteristics to the target domain. The potential of LIFE IS A JOURNEY is bigger than LIFE IS A VALUABLE GIFT. A journey has more elements related to it than a gift and therefore the mapping from source to target is potentially bigger. This criterion is phrased more precise by Tourangeau and Sternberg (1982) as “within-domains similarity” which they define as “the degree to which we succeed in constructing a system of beliefs within the domain of the tenor parallel to our beliefs about the vehicle” (p.225). Put differently, a source domain that is rich in features has a bigger potential in providing useful mappings then a source domain that is less rich. Each source domain refers to a semantic field. Semantic fields are “a set of lexemes which cover a certain conceptual domain and bear certain specifiable relations to each other” (Kittay & Lehrer 1981, p.32). These specifiable relations are what Tourangeau and Sternberg refer to as a ‘system of beliefs’. For example, the semantic field of “resources” includes lexemes (words) like “use”, “produce”,
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Daniel Andriessen “run out of”, “waste”, “use up”, “useful”, etc. (Lakoff & Johnson 1980, p.48). The more lexemes a semantic field of the source domain covers, the higher the potential of the metaphor. In our discussion on the aptness of knowledge related metaphors the quantitative criterion is useful. Metaphors whose source domain refer to a rich semantic field have a bigger potential for being apt than metaphors whose semantic field is poor. For example, the KNOWLEDGE AS COMMODITY metaphor underlies metaphorical expressions like: to buy knowledge, to sell knowledge, and to store knowledge. The source domain of commodity refers to a rich semantic field in which commodities can, among other things, be bought, exported, imported, peddled, promoted, recycled, retailed or traded (Goatly 2007). The second criteria that is included in the aptness definition is a qualitative one and is related to the importance of the features of the topic that are captured by the comparison. Chiappe et al. (2003) state in their “life” example that journeys not only capture more features but also that these features are important features of life. A good metaphor is capable of capturing important features. This qualitative criterion is a bit problematic. What does it mean that a feature is important? And important to whom? On what basis can we decide that a feature a comparison captures is important? For the purpose of assessing the aptness of knowledge related metaphors we need to expand this qualitative criterion. In our view two factors need to be taken into consideration: a) the validity of the metaphorical mapping and b) its ideological implications. a) In many cases not all characteristics of the source domain can be mapped onto the target domain. For example, in our KNOWLEDGE AS COMMODITY example the source domain of commodities has the characteristic that commodities tend to get used up when they are utilized. This is not the case with knowledge. The platitude that “knowledge is the only resource that increases through use” refers to this invalid mapping. When judging the aptness of metaphorical expressions the validity of the mapping needs to be taken into consideration. However, with abstract concepts like knowledge this is not a straightforward thing to do. As knowledge has no physical referent in the real world there are hardly any objective criteria to judge the validity of the mapping. Much depends on one’s epistemological point of view. Epistemology is the philosophy of knowledge and over the history of philosophy many different theories have been developed. Each theory will have a view on the validity of metaphorical mapping. For example, if knowledge is seen as something that cannot exist outside a person then the idea of storing knowledge, based on a metaphorical mapping of KNOWLEDGE AS A RESEOURCE will not be valid. b) In assessing aptness the ideological implications of the mapping also needs to be taken into consideration. Following Goatly (2007), we define ideology as the set of beliefs by which a group or society orders reality so as to render it intelligible. The ideological implications of the features captured by a metaphor depends on the effects the highlighted features have on the discourse in which they are used and on the actions that result from this discourse. “Language is not some transparent medium though which we think, but that shapes our thoughts and practices” (Goatly 2007, p.4). Because metaphors shape our practices, their aptness depends on whether they help shape our practices in the right direction. To judge the aptness of metaphor we have to look at the consequences the highlighting of certain features has for action. In doing so, we need to take three elements into consideration: 1) the context in which the discourse takes place, 2) the position of the person using the metaphor, and 3) the overall values with which to judge the rightness of the action. 1) The rightness of the actions that result from the discourse in which the metaphors are used is highly contextual. For example, in some situations it is very effective to conceptualize knowledge as a commodity (think about the $1 billion in licensing fees IBM receives each year by selling knowledge). In other situations the same conceptualization can lead to dehumanization of organizations because the knowledge of the employees is seen as a commodity that can be taken out of their heads, making the people obsolete. 2) The people using the metaphors may hold a specific position within the organization. Certain metaphors may support their interests and position and help to exploit other people (Tinker 1986). Andriessen (2008) reports an experiment in which managers in an organization preferred the KNOWLEDGE AS WATER metaphor above a KNOWLEDGE AS LOVE metaphor because the water metaphor allowed for better control of knowledge. Employees on the other hand preferred the love
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Daniel Andriessen metaphor because it helped them in expressing their needs for improved working conditions. So in judging the aptness of metaphors, the effect of metaphors on power relations should be included. This is in line with a strand of research called “critical metaphor analysis” (Charteris-Black 2005). Critical metaphor analysis “(…) demonstrates the importance of metaphorical patterns in vocabulary and grammar of English for representing and shaping ideologies and social practices” (Goatly 2007, p.2). 3) Whether our practices go in the right direction depends upon our values. So a discussion on the aptness of metaphors cannot do without values. Yet, this is not always acknowledged. When Cornelissen and Kafouros (2008) discuss the impact of metaphors on organization theory they leave out the question what the impact of metaphors through the theory is on practice. Instead they seem to assume organization theory is value free. However, this is not the case. For example, one of the metaphors that Cornelissen and Kafouros discuss is the ORGANIZATIONAL IMPROVISATION AS JAZZ metaphor. This metaphor promotes certain values. It was proposed by Frank Barret (Barret 1998) with the specific purpose to counter “the mechanistic, bureaucratic model for organizing – in which people do routine, repetitive tasks, in which rules and procedures are devised to handle contingencies, and in which managers are responsible for planning, monitoring and creating command and control systems to guarantee compliance (…)” (p.620). To summarize, the aptness of metaphors depends on three factors. The first one is the aptness potential. This is a quantitative factor that can be assessed by looking at the richness of the semantic field the source domain of the metaphor refers to. However, this is a necessary but not sufficient factor for aptness. The second factor is the validity of the mapping. Not all characteristics of the source domain can be mapped onto the target domain. The third factor is the ideological implications of the mapping. This is a qualitative factor that can be assessed by looking at the discourse in which the metaphor is used and at the consequences the highlighting of certain features has for action. In this qualitative assessment of aptness the context, the power relations within that context, and explicit values need to be taken into account. The importance of this qualitative assessment is expressed by Goatly (2007, p.27): “For the influence of language upon our thought and perception of reality is most powerful when we are unaware of it, when it expresses hidden or, technically speaking, latent ideology”.
4. Applying the criteria for aptness to research on knowledge related metaphors As we have seen, three types of metaphor research are developing in the KM and IC arena: 1) analyzing the role of metaphors in IC and KM theorizing and practice, 2) finding alternative, novel metaphors to be used in KM and IC theorizing, and 3) using metaphors in KM interventions in organizations. In each of these strands of research aptness needs to be considered. For all three strands the question of aptness is important. Because no research has yet been done in this field we will suggest a number of important research questions. This research agenda is summarized in table 1. When analyzing the role of metaphors in IC and KM theorizing and practice we should look at the aptness of the dominant metaphorical expressions. What are the dominant metaphors and what is the size of their semantic fields? How are the metaphors that authors and practitioners use related to their epistemological point of view? Are these two congruent? And what are the ideological implications of the metaphors used? To what extent highlight and hide the metaphors certain characteristics of knowledge and to whose favor? When finding alternative, novel metaphors aptness should be our main guiding principle. We should look for novel metaphors that have a big aptness potential because they cover a rich semantic field. We should look for metaphors whose mappings are valid given our epistemological point of view. And we should try to identify new metaphors that can help highlight characteristics of knowledge that are underrepresented, so they can be used as an aid to influence power structures and humanize organizations. When using metaphors in KM interventions in organizations again aptness should be the leading criterion. Here research can be aimed at finding metaphors with a big aptness potential and a high validity and that can be used in interventions. And we should look for practical ways these metaphors
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Daniel Andriessen be used in interventions in organizations to help shape company strategies, influence power structures and humanize organizations.
5. Conclusion Research on knowledge related metaphors is an exciting new field of research within the IC and KM field that has serious practical and societal consequences. In this new field the question of the aptness of knowledge related metaphors is pivotal. The aptness of metaphorical expressions depends on three factors: the richness of the semantic field of the source domain, the validity of the mapping, and the ideological implications. No empirical research has yet been done on the aptness of knowledge related metaphors. Therefore we proposed a research agenda as summarized in table 1. Addressing these nine research questions requires a mixture of disciplines including linguistics, psycholinguistics, psychology, sociolinguistics, sociology, and organizational science (change management, KM and IC theory). The IC and KM field has a rich tradition of multidisciplinary research and the topic of knowledge related metaphors provides the opportunity to expand upon this tradition in a new and exciting way. Table 1: Research agenda on the aptness of knowledge related metaphors Analyzing the role of metaphors in IC and KM theorizing and practice
Aptness potential What is the size of the semantic fields of the dominant knowledge related metaphors in IC and KM?
Finding alternative, novel metaphors to be used in KM and IC theorizing and practice
What alterative knowledge related metaphors have big semantic fields?
Validity of the mapping What is the relationship between an authors’ knowledge related metaphors, his or her formal definition of knowledge and his or her epistemological point of view? What mappings from alternative, novel knowledge related metaphors are valid?
Using metaphors in KM interventions in organizations
What knowledge related metaphors that can be used in interventions have a big aptness potential?
What is the validity of the mapping of knowledge related metaphors that can be used in interventions?
Ideological implications To what extent highlight and hide knowledge related metaphors certain characteristics of knowledge and what are the ideological implications? What novel metaphorical mappings can help to address important underrepresented characteristics of knowledge? How can these metaphors be used to help shape company strategies, influence power structures and humanize organizations?
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Daniel Andriessen Cornelissen, J. P. 2006, "Dialogue", Academy of Management Review, vol. 31, no. 2, pp. 485-488. Cornelissen, J. P. & Kafouros, M. 2008, "Metaphors and theory building in organization theory: What determines the impact of a metaphor on theory?", British Journal of Management, vol. 19, pp. 365-379. Goatly, A. 2007, Washing the brain; Metaphor and hidden ideology John Benjamins, Amsterdam. Hey, J. The data, information, knowledge, wisdom chain: The metaphorical link. 2004. Ref Type: Unpublished Work Kittay, E. F. & Lehrer, A. 1981, "Semantic fields and the structure of metaphor", Studies in Language, vol. 5, no. 1, pp. 31-63. Lakoff, G. & Johnson, M. 1980, Metaphors we live by The University of Chicago Press, Chicago. Lakoff, G. & Johnson, M. 1999, Philosophy in the flesh Basic Books, New York. Marshak, R. J. 2003, "Metaphor and analogical reasoning in organization theory: Further extensions", Academy of Management Review, vol. 28, no. 2, pp. 9-12. Moser, K. S. 2004, "The role of metaphors in acquiring and transmitting knowledge," in European perspectives on learning at work: the acquisition of work process knowledge, M. Fischer, N. Boreham, & B. Nyhan, eds., Office for Official Publications of the European Communities, Luxembourg, pp. 148-163. Ortony, A. 1993, "Metaphor, language, and thought," in Metaphor and thought, 2nd edn, A. Ortony, ed., Cambridge University press, Cambridge, pp. 1-16. Oswick, C. & Jones, P. 2006, "Dialogue", Academy of Management Review, vol. 31, no. 2, pp. 483-485. Oswick, C., Keenoy, T., & Grant, D. 2002, "Metaphorical and analogical reasoning in organization theory: beyond orthodoxy", Academy of Management Review, vol. 27, no. 2, pp. 294-303. Reddy, M. J. 1993, "The conduit metaphor: A case of frame conflict in our language about language," in Metaphor and thought, 2nd edition edn, A. Ortony, ed., Cambridge university press, Cambridge, pp. 164201. Steen, G. J. 2007, Finding metaphor in grammar and usage John Benjamins Publishing Company, Amsterdam. Steen, G. J. 2008, "The Paradox of Metaphor: Why We Need a Three-Dimensional Model of Metaphor", Metaphor and Symbol, vol. 23, pp. 213-241. Tatchenkery, T. J. 2001, "Mining for meaning; Reading organizations using hermeneutic philosophy," in The language of organization, R. I. Westwood & S. h. Linstead, eds., Sage, London. Tinker, T. 1986, "Metaphor and reification: Are radical humanists really libertarian anarchists?", Journal of Management Studies, vol. 25, no. 4, pp. 363-384. Tourangeau, R. & Sternberg, R. J. 1982, "Understanding and appreciating metaphors", Cognition: and international journal of cognitive psychology, vol. 11, pp. 203-244. Tsoukas, H. 1991, "The missing link: A transformational view of metaphors in organizational science", Academy of Management Review, vol. 16, no. 3, pp. 566-585. Tsoukas, H. 1993, "Analogical Reasoning and Knowledge Generation in Organization Theory", Organization Studies (Walter de Gruyter GmbH & Co.KG.), vol. 14, no. 3, pp. 323-346. Weick, K. E. 1995, Sensemaking in organizations Sage, London.
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Using e-Portfolios to Evaluate Intellectual Capital of Online Learners Bob Barrett American Public University, Charles Town, USA
[email protected] Abstract: This paper will focus on how one university has changed their focus in evaluating student learning in a strategic approach to incorporate a new type of “capstone” course for all business undergraduate students. As Knowles (1987) noted that “everyone [learners] should be able to participate and control their own learning process.” As a result, there are still students leaving educational institutions not fully prepared as to how to approach the job hunting process. Thus, these graduates tend to wonder why they have not been fully prepared for the next step after college. In consideration of the student’s need for career development and assessment, this paper will demonstrate how both can be accomplished with the creation and implementation of an electronic portfolio (e-Portfolio) program in the online learning environment. Instead of focusing solely on a teachercentered approach, this course encourages students to personalize or customize their final project work in the context of an e-Portfolio, as well as focusing on the use of such a strategic tool for future career endeavors. The author will discuss potential applications of e-Portfolios to include academic works and achievements, which best represents the student’s ability to demonstrate what they have learned and what they can offer to potential employers. Further, this paper will discuss how the e-Portfolio was created and developed in this university program in terms of assessing the intellectual capital of their senior undergraduate students, as well as using this approach to help them prepare for their future career paths. While this program was just implemented in Spring 2008, it has been perceived and evaluated as a valuable final course offering to help evaluate the overall knowledge of the senior undergraduates in the context of learning objectives of the core business administration courses. Each e-Portfolio is a work in progress throughout the term as the instructor works with the student in the creation of each phase of their e-Portfolio development. From the students’ perspective, as well as the faculty member’s comments, it appears that the implementation of the e-Portfolio element in the final course has proven to be a valuable strategic tool for evaluation and reflection. This particular academic tool has been helpful for the instructor to evaluate the students’ abilities and skills to apply content knowledge gained from the core business administration programs. Finally, this paper will help to demonstrate, from an appreciative inquiry perspective, the positive benefits of incorporating an e-Portfolio into their curriculum. Also, it will help to illustrate how the students learn from their peers as to how they are achieving similar or comparable results with their approaches to e-Portfolio work. This particular paper is suitable for people interested in measuring the intellectual capital of students in the online learning environment. Also, this paper will help provide an overview of how one university has changed its final capstone course to incorporate the use of an electronic portfolio (E-Portfolio). Keywords: e-Portfolios, online learning, virtual intellectual capital, assessment, learning communities, needs assessment, social capital
1. Introduction As of the end of 2006, 38 states in the United States have established state-led online learning programs, policies regulating online learning, or both. Also, 25 states have state-led online learning programs, and 18 states are home to a total of 147 virtual charter schools serving over 65,000 students (http://www.nacol.org). In 2001, 56% of traditional learning institutions offered distance learning programs. An additional 12% of schools stated they planned on adding distance learning programs to their curriculum within the next three years (National Center for Education Statistics, 2003). Thus, more secondary- and post-secondary level teachers will need to seek additional education in order to obtain and master quality online teaching skills and strategies. As a result, more universities are offering online education courses and teacher training in order to help recruit and hire more online instructors. Thus, there is a growing need to increase the number of online instructors to teach at many educational institutions. Neal and Miller (2006) defined distance education as “education that takes place independent of location, in contrast to education delivered solely in the classroom, and that may be independent of time as well (para. 4). ASTD, an education/training & development professional organization, noted that “distance education can be characterized as an educational situation in which the instructor and students are separated by time, location, or both. Education or training courses can be delivered to remote locations via synchronous or asynchronous means of instruction (Neal & Miller, 2006, para. 5). Just prior to this definition, the U.S. Government and Department of Education started to view the value of education and technology in a different light. They realized that technology was the tool to
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Bob Barrett help facilitate learning, as well as prepare students for the workplace. As a result, in the early and mid-1990s, the U.S. Secretary of Education released the nation’s first educational technology plan in 1996. The Plan was entitled, “Getting America’s Students Ready for the 21st Century: Meeting the Technology Literacy Challenge. While this plan was focused on elementary and secondary schools, it did provide a framework for educators on the post-secondary level to consider as they prepare future educators for work on the elementary and secondary school levels. The goals were:
Goal 1: All students and teachers will have access to information technology in their classrooms, schools, communities, and homes.
Goal 2: All teachers will use technology effective to help students achieve high academic standards.
Goal 3: All students will have technology and information literacy skills.
Goal 4: Research and evaluation will improve the next generation of technology applications for teaching and learning.
Goal 5: Digital content and networked applications will transform teaching and learning. (www.edu.gov, 2008, para. 7).
2. Online learning and its growing population While traditional teachers have seen the impact of economic crises, on a global scale, many students have started to move away from traveling to and from the “physical” on-ground classroom and opting for online courses. Also, schools have seen an increase in their student populations, and they are faced with the growing dilemma of lack of physical classroom space. Thus, the need for additional online courses has risen as a result of these ever-increasing student enrollments. Further, many traditional teachers have been considering or reconsidering the option of transitioning over to the online learning environment, as opposed to limiting their career possibilities solely to the on-ground teaching experiences. Finally, many current and potential instructors are seeking training in order to obtain proper online instruction to prepare them for online teaching. As more technology has become available in many parts of the globe, a new type of student population has emerged. The traditional student image of higher learning has been somewhat limited in many countries, but given the impact of the Internet, this traditional “student body” has changed to online communities. In the field of business and management, educators have recognized the demographical changes of the student population. In a virtual environment, the student is not the same traditional student seen in classrooms in previous years, but rather one that reflects a vast array of cultural differences and needs that require educators to help build “new learning paths” towards the creation of virtual learning communities. The number of students taking at least one online course continues to expand at a rate far in excess of the growth of overall higher education enrollments. The most recent estimate, for fall 2007, places this number at 3.94 million online students, an increase of 12.9 percent over fall 2006. The number of online students has more than doubled in the five years since the first Sloan survey on online learning. The growth from 1.6 million students taking at least one online course in fall 2002 to the 3.94 million for fall 2007 represents a compound annual growth rate of 19.7 percent. The overall higher education student body has grown at an annual rate of around 1.6 percent during this same period (from 16.6 million in Fall 2002 to 18.0 million for Fall 2007 - Projections of Education Statistics to 2017, National Center for Education Statistics). As the following table illustrates, over one-fifth of all higher education students are now taking at least one online course. Who are these 3.9 million students? The overwhelmingly majority (over 80 percent) are studying at the undergraduate level with only 14 percent taking graduate level courses and the remainder in some other for-credit course. Using survey results and figures from the most recent federal data (Digest of Education Statistics: 2007, National Center for Education Statistics) to compare enrollment patterns shows only slight variations in the proportions of students by education type. The proportion of undergraduates in online education (83.9 percent) is slightly below that of the total population of higher education students (85.6 percent). (Allen & Seaman, 2008) While technology has provided a powerful infrastructure, the emerging technologies have allowed educational institutions, educators, and students to provide education on a much higher playing field – in a virtual learning environment. Consequently, the business world has also worked with technology
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Bob Barrett in this venture to help assist educational institutions in becoming more modern and adaptive for change. As a result, these changes only signify to the educational and learning communities that technology recognizes a need for change – but do we, as educators, recognize the need for change completely? Finally, many educational institutions, as well as the business world, are focusing on the learning process and its impact on their organizational structure and employees. As more people consider online learning to be a new academic endeavor, one must also consider the next step for college graduates – the world of work. In this regard, the corporate environment may differ from the academic setting; however, the need to learn and grow is equally important in the world of business and academia. While online learning has been focused more on the academic setting, many companies are learning that online learning has many benefits in the corporate/business environments. Thus, many companies and organizations are focusing on the “learning” element of their employees’ daily work lives, as well as helping to build their knowledge management with a concentration on helping the organization learn from previous and current experiences. Thompson (1995) stated that “organizational learning involves the acquisition of new information and the ability to analyze that information creatively, learn from it, and apply that learning in useful ways.” (p. 95). As organizations realize that there is a need to “capture” and “utilize” this type of learning, they also realize the need for the organization, as a whole, to concentrate on building up this learning element to include everyone in their respective organization, as well as setting up informal and formal learning center or circles. While organizations and communities view the impact of online learning in the U.S., we also have to look at how type of learning is impacting the world and our global capital.
3. The global impact on virtual learning In the business world, when “a brand expands it reach around the globe, it achieves favored perceptions that are greater than the sum of its national parts” (Holt, Quelch, and Taylor, 2004, p. 191). This branding, known as global branding, relies on the input of the various stakeholders and how their cultural differences can enhance the quality and acceptance of such a brand. In the field of education, one needs to understand how these new virtual learning communities have created a new type of global branding of education in terms of linking various stakeholders throughout the world into a stronger and more diversified learning environment. Contextually, one can see a new, global branding of course management systems, which affects and supplements the needs of growing, virtual learning communities. As a result, educators need to reflect on the historical changes in distance learning in order to better understand how technology can enhance their virtual teaching – as well as helping them to adapt from their traditional “framework of teaching” into a more modern and culturally diversified way of teaching. Thus, this leads us to the next part of this paper’s focus, the impact of social and human capital in the context of online learning applications.
4. Social capital According to Daniel, Schwier and McCalla (2003), “Social capital is an imprecise social construct that has emerged from a rather murky swamp of terminology, but it is still useful for exploring culture, society and social networks. The notion of social capital originated from studies of conventional or temporal communities. Social capital highlights the central importance of networks of strong personal relationships that develop over a period of time.” (p. 2) As organizations continue to grow and invest in technology, sometimes workers feel a disconnect between them and their own network of family, friends, and colleagues. As they start to interface more with technology, they tend to question their worth and value in their respective places of employment. In this regard, they may feel a need to network more with internal and external parties. As educational institutions help to prepare students for their future career endeavors, they have been searching for ways to achieve both assessment, but yet also to help provide meaning to a student’s academic endeavors. One way that they have been able to achieve both goals is through the use of electronic portfolios. Acosta and Liu (2006) noted that “ePortfolios can contribute to the development of social capital by: (1) building trust relationships between parties to promote social interactions and engagement; (2) sharing information, personal experiences, and knowledge to contribute to social capital inventory; (3) improving students’ sense of responsibility, accountability, and commitment; and (4) increasing the efficient use of resources worldwide. ePortfolios are becoming more and more important to the development of intellectual resource management” (p. 21). Thus, this leads us to the next question. How do e-Portfolios benefit students in terms of intellectual grow and contribute to intellectual capital? According to Tosh (2004), students can gain the benefit of this tool by accessing “their records, digital repository, reflections to achieve a greater understand of their individual growth” (para. 4). The key
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Bob Barrett element here is value – what is valuable to the student, educational institution, and others? One particular factor that has caught the interest of academicians and practitioners is the value, or the value associated with intellectual capital and education in particular.
5. Intellectual capital According to Answers.com, “Intellectual capital collectively refers to all resources that determine the value and the competitiveness of an enterprise. As such, it includes as subsets the attributes that concur to building all financial statements as well as the balance sheet” (para. 1). While intellectual capital can be examined and discussed in terms of employees, organizational capabilities or customers, the main focus of this paper will focus on the area of human capital. Stewart (1997) classified Intellectual Capital into three identified areas in the following:
Human Capital: The capabilities of the company’s employees necessary to provide solutions to customers, to innovate and to renew. In addition to individual capabilities, human capital includes the dynamics of an intelligent (learning) organization in a changing competitive environment, its creativity, and innovativeness.
Structural Capital: The infrastructure of human capital, including the organizational capabilities to meet market requirements. Infrastructure includes the quality and reach of information technology systems, company images, databases, organizational concept and documentation.
Customer Capital: The relationships with people with whom a company does business. Although this usually means clients and customers, it can also mean suppliers. It has also been referred to as relationship capital. (cited in http://www.cpavision.org/vision/wpaper05b.cfm
In order to manage Human Capital, management needs to measure various factors to determine whether employees are benefitting the organization and to what degree. How can they be measure? Here are some items that management can evaluate in this area.
Training programs
Credentials
Experience
Competence
Recruitment
Mentoring
Learning programs
Individual potential
Personality (cited in http://www.cpavision.org/vision/wpaper05b.cfm)
While many companies may promote the importance of training and education, many entities are rethinking and re-evaluating the use of their funds to help support their employees. On the other hand, there are still many companies who offer tuition reimbursement to encourage employee development and/or career development opportunities. While many companies still offer tuition reimbursement, they also require employees only to take courses directly related to their given jobs. Consequently, this does narrow the educational and career development opportunities for these employees. Educational institutions, like other businesses and organizations, are taking note of their various resources that determine the value and competitiveness of their industry. While they examine what works and does not function, they are starting to look more at their assessment instruments to determine if there is something else they should be examining from within their educational facilities. As a result, they have been focusing on the concept of portfolios, in particular, electronic portfolios (ePortfolios). Barrett (2001) believes “an electronic portfolio is really a living history of lifelong learning” (para. 8). It is this living history that draws upon the individual’s collection, selection, and reflection of one’s work in order to provide this living history, or rather the contents of a portfolio. However, the strength of any e-Portfolio lies in the commitment of the individual and organization (university) in the creation of such a project from start to finish. Barrett (2005) commented that if organizations want to make such portfolios be effective, they need to develop and nurture a culture of evidence. As some organizations may develop and design some portfolios in a few courses or programs, there is a growing need to obtain the buy in from all stakeholders. In order to help develop and nurture these portfolio programs, this culture of evidence helps to reinforce and strengthen the use of these
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Bob Barrett portfolios within the organization or university. Therefore, it is important that educational institutions be committed to the use of e-portfolio programs in their culture in order to sustain them and to gain from their value in terms of assessment and practical applications. Further, it helps to add to their intellectual capital to show value and their competitiveness in their particular industry. In the following section, this paper will address how the American Public University helped to evaluate the human capital (students) in terms of their academic and professional knowledge, skills, and abilities. As noted in the section above, they reexamined how they evaluated their final capstone course and redesigned it to serve in a two-fold process. First, they incorporated the concept of an electronic portfolio (E-Portfolio) into this final senior seminar (capstone). Second, they linked all course objectives to measure the core courses offered in their Bachelors of Business Administration program. Third, this change helped to form a final evaluative instrument in order to measure the academic achievements, as well as the “individual potential” of the students in terms of future career endeavors. Finally, this approach to “measure intellectual capital” in terms of human capital helped them to reassess their academic curriculum, as well as adjusting this final course to help better prepare undergraduate seniors in the area of career development. The next section will give an overview of the E-Portfolio concept and then lead into how the American Public University approached this topic in terms of redesigning their senior seminar to help measure and evaluate the human capital, and build upon their intellectual capital, in terms of their various stakeholders.
6. e-Portfolios: from concept to practice According to the University of Berkeley (2004): “An e-Portfolio functions like a file cabinet with file drawers and file folders. Students store personal, educational, career, skill assessment, nonacademic/work experience, certification, and rewards information in their portfolios. The information placed in an ePorfolio is referred to as an artifact.” (http:/bearlink.berkeley.edu/ePortfolio/page5.html) Goldsby and Fazal (2001) noted that student created portfolios are commonly “used in teacher preparation programs to demonstrate teaching skills and expertise. This practice was introduced as test scores alone lack the comprehensive scope needed for effective assessment and evaluation, portfolios can be implemented to interpret/make decisions regarding learning of teaching competences” (pp. 607-608). Further, Constantino and De Lorenzo (2006) noted: “The process of developing a portfolio requires that teacher candidates become more responsible for integrating and documenting the knowledge, dispositions, and skills learned through their courses and experiences. The process of reflecting and documenting what they learned is highly empowering and contributes to their self-confidence as novice teachers” (p. 5). However, Acosta and Liu (2006) noted the difference in electronic portfolios as they “emphasize analysis and reflection, and the process, not the product” (p. 17). Further, Ravet and Layte (2004) stated that e-portfolios are “the expression of learning as a social activity” (para. 7). This leads us to the creation and application of e-portfolios in the learning environment.
7. e-Portfolio applications According to Greenberg (2004), “the e-portfolio is not simply a personal home page with links to examples of work . . . it is a network application that provides the author with administrative functions for managing and organizing work (files) created with different applications for controlling who can see the work and who can discuss the work (access) . . . (pp. 28-29). The application of e-portfolios in the academic environment has been increasing over the decades. However, with the onset of the technological evolution, the use of computers in the academic setting has enabled many instructors, administrators, and staff members to create and implement a variety of educational applications. Rather than focusing on only one single final course project, the e-portfolio serves as a replacement for the final course project, as well as enhancement of the learning experience. The e-portfolio serves as a showcase of a collection of selected “created” academic achievements to demonstrate a student’s writing and researching skills. Robinson and Udall (2004) wrote that “learners are best engaged with curriculum when they are able to record their own progress, selfassess against learning outcomes and reflect critically upon their development over time” (Pelliccione & Dixon, 2008, pp. 751-752). Further, Pelliccione Dixon (2008) “an ePortfolio approach that spans a course of study and beyond to a professional setting allows participants to originate and maintain ‘conversations’ about their learning and by doing so they become active in formative assessment rather than passive receivers of graded results” (p. 752). As with any type of assessment, the process by which it is created, implemented, and evaluated is equally as important. In particular, the process by which the faculty member is involved in does require a certain degree of commitment and
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Bob Barrett attentiveness. In addition, many American colleges and universities are including the use of ePortfolios into their various courses, programs, and curricula in order to assess student work, as well as compete with others in their industry. While there are strong arguments for the use of portfolios in coursework, there are some limitations. There are three major reasons portfolios are not appropriate for higher education assessment programs: They are (a) not standardized, (b) not feasible for large-scale assessment due to administration and scoring problems, and (c) potentially biased. Indeed, course grades, aggregated across an academic major or program, provide more reliable and better evidence of student learning than do portfolios. (Shavelson, Klein & Benjamin, 2009, para. 3) However, as more American universities and colleges are moving away from teacher-centered learning, they are now focusing more on student-centered learning. In any event, the instructor is still involved in the learning, and he or she works as a facilitator of learning. In terms of e-portfolio creation, the instructor/facilitator helps guide the process, but yet allows the learner a certain degree of creativity in the formation and completion of his or her e-portfolio project. The e-portfolio is a process by which the faculty member helps to guide, facilitate, mentor, and evaluate the student’s ability to critically think, write, and research. With the passage of key education legislation, such as the No Child Left Behind, ADA, etc., there is a growing need for documentation/evidence of academic achievement. Further, with the movement of transforming today’s classrooms from a teacher-centered approach to one of a learner-centered approach – the instructors helps to facilitate and guide the student through the e-portfolio process; however, it is the student that ultimately selects their best work and begins his or her journal to develop his or her own e-portfolio. One technique that educators use in the implement of e-portfolios in the classroom is by the use of dialogue, or rather in the planned discussions to help facilitate and being the process of eportfolio creation. The American Association for Higher Education (2003) has proposed a “Taxonomy for Electronic Portfolios” to facilitate discussions about the various kinds of eportfolio implementations. This taxonomy has three main discriminators: context, author, and purpose. Understanding these dimensions for your specific need is important to select the “right” portfolio system or tool. The context describes the setting in which the portfolio was developed. Contexts include courses, programs, institution, inter-institutional, and independent portfolios (para. 7). Thus, leads us to the next segment of the paper on the construction of e-portfolios.
7.1 e-Portfolio construction In this paper, we will address the creation of E-Portfolio program, or course component, in terms of four phases: 1) Needs Assessment; 2) Design and Development; 3) Implementation; and 4) Evaluation. In the following sections, each of these phases will focus on important questions that all instructors, course designers, and administrators need to consider as they move forward in the creation and implementation of E-Portfolios in their courses and programs.
8. Needs assessment Prior to the design and development of E-Portfolio, a needs assessment should be conducted. Some of the questions to be considered during the assessment phase may consist of: 1) What are the current educational versus career needs of your student population in terms of an e-Portfolio? 2) Are the necessary projects available now – or do new projects and courses need to be created? 3) Do you have the “buy-in” from the Administration to create and implement an e-Portfolio program? and 4) How soon can you implement this program? Following the research phase of the needs assessment, it is important to focus on the intended components of the E-Portfolio to be used in the proposed course.
8.1 Elements of an e-portfolio at APUS In the GM498 – Senior Seminar, students will construct their final E-Portfolio with the following elements:
Resume (standard or virtual resume)
Cover Letter
Web page (creation of a potential business idea)
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Brochure (business)
Business Plan (planning a potential business venture)
Reflective Essay
Design and Development
The above noted assignments were created as E-Portfolio components based on the course objectives derived from the undergraduate Business Administration core courses (to be later discussed in this paper). While the author and others were considering applications of their potential E-Portfolio programs, they considered the following types of portfolios.
Academic Portfolios
Career Portfolios
Special Projects Portfolios
Practicum Portfolios
Group Projects Portfolios
The final decision was made to combine elements of an academic portfolio with a career portfolio for a two-fold purpose. First, the elements of an academic portfolio would enable the students to display elements of their academic skills. Second, the use of a career portfolio would help the students to create a final undergraduate project that could be used also for future career development endeavors. As noted earlier in this paper, the assignments contained in the E-Portfolio project were based on the course description and learning objectives of the GM498 Senior Seminar. Further information about this course and learning objectives will be discussed in the next section.
8.2 The GM498 senior seminar The following section outlines the course description and learning objectives created for the GM498 Senior Seminar, which is the undergraduate Capstone course. GM498 – Senior Seminar Course Description. The Capstone course is a senior level course designed to allow the student to review, analyze and integrate the work the student has completed toward a degree in Business Administration. The student will complete an approved academic electronic portfolio that demonstrates mastery of their program of study in a meaningful culmination of their learning and to assess their level of mastery of the stated outcomes of their degree requirements. GM498 – Learning Objectives. The successful student will fulfill the following learning objectives:
Identify essential elements for a successful business plan, as well as design and develop a functional business plan.
Design and develop a marketable business brochure for an organization.
Create a webpage for advertising possible services.
Design and develop a functional or chronological resume, along with an appropriate cover letter to be sent to potential employers
Reflect and discuss key issues and topics in a reflective essay on the overall process of capstone experience.
These learning objectives were constructed with a coding feature called Learning Objective Coding (LOC). Each core course in the undergraduate Business Administration program was given a LOC code. Each of the learning objectives were matched up with each of the LOCs associated in the core courses. GM498 – Senior Seminar – Coding & Objectives. Courses/Learning Objective Coding:
Here is a listing of the Core Requirement
GM229 - Accounting (LOC1)
GM 306 - Business Theory (LOC2)
GM317 – Law & Ethics in the Business Environment (LOC3)
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GM328 – Global and Competitive Strategy (LOC4)
GM401 – Operations Research (LOC5)
GM402 – Principles of Financial Management (LOC6)
MC300 – Principles and Theory of Management (LOC7)
MC302 – Management of Information Systems (LOC8)
MC306 – Applied Statistics – (LOC9)
MK300 – Principles and Theory of Marketing (LOC10)
SS101 – Microeconomics (LOC11)
SS102 – Macroeconomics (LOC12)
After these preliminary developmental tasks were completed, the author moved directly into the design and development phase. Information gathered from the previous research was used in the preparation of this phase in order to capture some of the best practices used in the area of EPortfolios.
8.3 Design and development phase During the Design and Development Process, the course designer and instructor focused on the development of an 8-week online course to help prepare senior (undergraduate) students to create their own e-Portfolio. The decision was made to use the current educational online software program, Educator, to create the E-Portfolio elements of the course. Discussion boards were created to help encourage students to discuss the development of each assignment, as well as to help guide them through the portfolio process. Instead of going into a detailed discussion of this particular phase, the author has limited the discussion here to give a brief overview of the process. After the course was developed, a review of the e-Portfolio course by the Academic Dean, School of Business Interim Dean, and Program Director was conducted. In the next section, the implementation phase will help to explain how the course was then implemented into the online course offerings.
8.4 Implementation phase During the Implementation Phase, there were some considerations that needed to be addressed prior to the launching of this course. First, who will be in charge of the implementation phase? The instructor, who was also the course designer, was appointed at the lead instructor for this course in charge of all course design and implementation issues. Second, it was decided that there would be a need for documentation of the course effectiveness. Third, the question of whether there should be monitoring or reporting device was considered important, but the final decision was to allow the University’s current course evaluation survey to service as the key reporting instrument. Meanwhile, the instructor would record observations and feedback gained by students during each term to be later discussed with the Dean. The implementation of the course was completed within a sixty-day period, and the course enrollment was slow at first. However, this was seen as positive to allow the instructor to take notes along the way and to focus closely with a smaller class size with the introduction of this new course offering.
8.5 Evaluation phase As noted before, it was decided that the current University’s course evaluation survey would serve as the primary evaluation tool. However, for course audits and reviews, the individual E-Portfolios would serve as the primary assessment tools. Also, many universities are using E-Portfolios for course assessment and for accreditation purposes. For purposes of this paper, discussion of the evaluation process was limited in scope.
9. Conclusion As technology has increased the possibility of more adult learners to participate in taking online courses, in light of various family, business, and personal constraints, changes still need to be done in terms of embracing diversity in education in terms of meeting the needs of our human capital, as well as evaluating and nurturing the overall intellectual capital. In particular, the educational administrators, faculty members, and students are all contributing stakeholders in the process of eportfolio creation. We can see the use of e-portfolios serving in a two-fold manner. First, it serves as
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Bob Barrett a vehicle for assessment and demonstrates to the academic community the educational talents, skills, and abilities of the student. Second, it helps to add value to educational institutions in demonstrating the value of education, displaying the student’s various works, as well as serving as an academic artifact (or record) for future assessment purposes. The use of e-portfolios is appearing quite steadily in the online learning environment as a matter of best practices. As such, this particular practice has become more apparent as more universities and colleges compete for students and enter into the online learning environment. As these changes are happening increasingly in both the online and face-to-face learning environments, educators need to be proactive in making appropriate changes in the curriculum to help strengthen course offerings, as well as incorporating teaching strategies and technique that meet the needs of the course objectives, as well as motivating and encouraging the adult learners to want to learn even more. We need to continue to nurture and guide our learners as they pursue academic and career developmental endeavors. Thus, this helps to maintain the value and competitiveness in the educational field. Finally, educators and administrators need to recognize that the demographics of our students are changing – and for the better. We need to embrace diversity and help nurture it along the way. Merryfield (2003) summed it up best in the following quote: “Online technologies provide opportunities for teachers to experience a more global community than is possible face to face” (p. 165). Further, the intellectual capital of today’s educational institutions is vastly different from that of previous decades. However, the key to education is value and what it adds to the field of education and world of work. As we strive to build stronger global and virtual learning communities, we need to remember that each member of these communities have a unique gift – their personal and cultural values. These values are important to a learning community – and we, as educators, can set the example by making changes today that will reflect upon our society tomorrow. E-Portfolios serve as another strategic tool to evaluate intellectual capital, as well as adding value to education and business.
References Acosta, T., Liu, Y. (2006). Chapter II ePortfolios: Beyond Assessment. Idea Group Allen, I.E., & Seaman, J. (2008). Staying the course: Online education in the United States, 2008. The Sloan Consortium. Retrieved July 2, 2009, from http:www.sloanconsortium.org/publications/survey/pdf/staying_the_course.pdf. AAHE. (2003). Towards a taxonomy of electronic portfolios. http://webcenter1.aahe.org/electronicportfolios/taxonomy.html#taxonomy Answers.com . Intellectual Capital. http://www.answers.com/topic/intellectual-capital#External_links. Retrieved Nov. 5, 2009. Barrett, H. C. (2001). Expert showcase: Dr. Helen Barrett on electronic portfolio development. Retrieved October 10, 2003, from http://ali.apple.com/ali_sites/ali/exhibits/1000156/ Barrett. H. (2005). Researching electronic portfolios: Learning, engagement, collaboration, through technology. In L. Pelliccione and L. Dixon (2008). ePortfolios: Beyond assessment to empowerment in the learning landscape. Ascilite Melbourne 2008 proceedings. Retrieved 2/1/10.Brinker, B. Intellectual Capital: Tomorrow’s Asset and Today’s Challenge. http://www.cpavision.org/vision/wpaper05b.cfm. Retrieved November 5, 2009. As cited in T.A. Stewart, (1997). Intellectual Capital. New York: Doubleday Currency. pp. 62-63. Costantino, P., De Lorenzo, M., & Kobrinski, E. (2006). Developed a professional teaching portfolio. USA: Pearson. Daniel, B., Schwier, R., & McCalla, G. (2003). Social capital in virtual learning communities and distributed communities of practice. Canadian Journal of Learning and Technology, 29(3), 113-139. Goldsby, Fazal (2001). In John DiMarco (2006). Web Portfolio Design and Applications. Idea Group Inc. pp. 607-608. (retrieved 6/1/2008) Greenberg (2004). In John DiMarco (2006). Web Portfolio Design and Applications. Idea Group Inc. pp. 607608. (retrieved 6/1/2008) Holt, D. B., Quelch, J. A., & Taylor, E. L. (2004). Managing the global brand: A typology of consumer perceptions. The global market: Developing a strategy to managing across borders. San Francisco: Jossey-Bass. LDP e-Portfolio Report http://bearlink.berkeley.edu/ePortfolio/page5.html (retrieved 6/1/2008). Merryfield, M. (2003). Like a veil: Cross-cultural experiential learning online. Contemporary Issues in Technology and Teacher Education, 3(2), 146-171. Neal, L. & Miller D. (2006). The basics of e-learning: An excerpt from Handbook of Human Factors in Web Design, para. 4-5. In R.W. Proctor & K.L. Vu (2004), Handbook of Human Factors in Web Design,
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Bob Barrett Lawrence Erlbaum Associates. Retrieved March 19, 2008 from http://www.elearnmag.org/subpage.cfm?section=tutorials&article=20-1. Pelliccione, L. & Dixon, L. (2008). ePortfolios: Beyond assessment to empowerment in the learning landscape. Ascilite Melbourne 2008 proceedings. Retrieved 2/1/10. Ravet, S. & Layte, M. (2004). E-portfolio: Revolutionizing e-learning. In T. Acosta and Y. Liu (2006). Chapter II ePortfolios: Beyond Assessment. Idea Group Robinson, A., & Udall, M. (2004). A framework for formative assessment: Initiating quality learning conversations. Learning and Teaching in High Education, 1, 112-115. Shavelson, R.J., Klein, S., and Benjamin, R. (Oct. 16, 2009). Inside Higher Ed. The limitations of portfolios. http://www.insidehighered.com/layout/set/print/views/2009/10/16/shavelson. Retrieved 12/15/2009. Thompson, J.W. (1995). The renaissance of learning in business. In S. Chawla and J. Renesch (1995). Learning organizations: Developing cultures for tomorrow’s workplace. Portland, Oregon: Productivity Press. Tosh, D. (2004). E-portfolios and Weblogs: One vision for e-portfolio development. In T. Acosta and Y. Liu (2006). Chapter II ePortfolios: Beyond Assessment. Idea Group U.S. Department of Education (2008). E-Learning: Putting a world-class education at the fingertips of all children. Retrieved on March 16, 2008 from http://www.ed.gov//about/offices/list/os/technology/reports/elearning.html
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Intellectual Capital and Value Creation in the Production and Assembly of Vehicles and Auto-Parts Sector in Brazil Leonardo Fernando Cruz Basso, Herbert Kimura and João Francisco de Aguiar Mackenzie Presbyterian University, São Paulo, Brasil
[email protected] [email protected] [email protected] Abstract: This work investigates the relationship between intellectual capital and value creation in the sector of Production and Assembly of Vehicles and Auto-parts in Brazil. Through the access of the database from the Annual Industrial Research conducted by the Brazilian Institute of Geography and Statistics, we gathered 865 observations, from 2000 to 2006, of public and private Brazilian companies with more than 100 employees. The database allows the estimate of relevant aggregated variables such as national accounts, gross domestic product, intermediate consumption, as well as propitiates a sectorial study of business strategies and performance, including value added by individual companies. In particular, in this study we use data on variables associated to intellectual capital. To achieve the goal of the study, we consider intellectual capital as defined by Pulic (2000, 2002), including human capital and structural capital. For the analysis of business performance, we used Pulic’s VAIC index as a measure of efficiency of the employed financial and intellectual capital. Regression models were run to verify the relationship among the efficiency in the use of intellectual capital and the profitability of Brazilian companies. The gross income, calculated as before selling, general and administrative expenses, depreciation expenses, amortization and interest expenses, was used as measure of the flows of value creation and the profitability was measured by the gross income to the total assets of the companies. Considering the constructs defined by Pulic (2000, 2002), we tested, for the Brazilian sector of Production and Assembly of Vehicles and Auto-parts, the following hypotheses: (i) there is a positive relationship between value creation and intellectual capital, (ii) there is a positive relationship between value creation and stock of intellectual capital, (iii) there is a positive relationship between value creation and efficiency of the employed capital , (iv) there is a positive relationship between value creation and efficiency of the human capital, (v) there is a positive relationship between value creation and efficiency of the structural capital. The results of the study, obtained through panel data analysis and through the use static and dynamic models, support the hypotheses that the intellectual capital of the companies, in its flow and stock dimensions, is positively and significantly related to value creation. Keywords: Intellectual Capital, value creation, panel data analysis, production and assembly of vehicles and auto-parts sector in Brazil This study was financed by MackPesquisa and CNPq
1. Introduction We performed this survey with the purpose of testing two methodologies (one for flow and the other for inventories) in order to conceptualize and measure intellectual capital of companies in the Motor Vehicle, Trailer, and Body Manufacturing and Assembly Industry in Brazil. The search for consistent methodologies for conceptualizing and measuring intellectual capital become ever more urgent in the light of the new requirements brought about by the enactment of law 11,638, approved by Congress on December 28, 2007; and among these changes two are relevant for our purposes:
The insertion of new account sub-groups in balance sheets: intangibles (of which intellectual capital is a part) in permanent assets and asset valuing adjustments in net worth.
The inclusion of value added in the financial statements, an essential variable in both theoretical references we will employ to calculate intellectual capital.
This paper is divided into sections; in the first one we will explain several constructos which attempt to describe intellectual capital and Pulic’s conception, which is being successfully tested overseas and in Brazil. We will then describe the procedures for selecting the sample, and the analyses and results that we obtained. This paper is a first step in a more ambitious project which aims at making available to corporate managers in companies operating in Brazil instruments intended to conceptualize and measure intellectual capital, the most important variable for economic value creation. Conceptualizing Intellectual Capital
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Leonardo Fernando Cruz Basso et al. On examining scientific literature on intellectual capital, we will find that three concepts are found in a major portion of these writings: the concept of human capital, the concept of relational capital, and the concept of structural capital. Unfortunately, there is no consensus on each of these concepts or on their relation with intellectual capital. We examined models by several authors to confirm this lack of consensus. Intellectual Capital Management Group A group founded by Edvinsson and Sullivan, with the purpose of specifying measurements for a company’s market value figure 1 summarizes Sullivan’s concept.
Figure 1: Sullivan’s view (source: Andriessen (2004, p.352)) It conceptualizes intellectual capital as knowledge that may be converted into profits, a broad enough definition that embraces human capital and intellectual assets; human capital consists in company employees, each one with skills, competencies, expertise, and know-how. Intellectual assets consist in assets which are encoded, tangible, or physical descriptions of specific knowledge, of which a company has property rights. Intellectual assets with legal protection against undue appropriation are named intellectual property. Sullivan makes a distinction between intangible assets that may be sold and structural intellectual assets. This last classification includes an organization’s structure as well as the organizational structure, customer capital, an organization’s operating methods and procedures (routines), administration methods, and even the manner of doing business; some things, such as the manner of doing business has a common sense appeal, yet we must admit that it is difficult to conceptualize and to measure. Sullivan’s concept regarding structural capital differs from that by other authors who deal with this subject, and we have to explain it To Sullivan, structural capital includes all of the company’s support infra-structure and encompasses all of its tangible assets. The combination of tangible and intangible assets in the concept of intellectual capital is what creates a synergy which causes the intangible portion (expertise by employees, for example) to produce results which it would not do so without the use of tangible assets. The Skandia Navigator Edvinsson is the creator of the methodology known as the Skandia Navigator.
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Leonardo Fernando Cruz Basso et al. Edvinsson and Malone visualize intellectual capital as the ownership of knowledge, acquired experience that may be applied to a company, organizational technology, customer relations, professional competencies and skills which provide a company with a competitive advantage. Their concept is depicted in figure 2.
Figure 2: The Skandia Navigator – vision by Edvinsson and Malone for Intellectual Capital (Source: Andriessen (2004, p.346)) The concept of intellectual capital which appears in the first book written with Malone (Edvinsson and Malone, 1997) is associated with the idea of invisibility; hence immaterial assets, invisible assets, and expertise compose the concept of intellectual capital. The latter is divided into two components: structural capital and human capital. Human capital consists in a combination of expertise, skills and competencies, innovativeness and skills by company employees to undertake the assignments allocated to them. Structural capital is composed of hardware, software, databases, organizational structure, patents, trade marks, and everything else that belongs to the competencies which provide support to the increase (or maintenance) of workers’ productivity. The relation between intellectual capital, human capital, and structural capital may be seen in the following equation: Equation: Human Capital + Structural Capital + Intellectual Capital. In a book published subsequently (Edvinsson, 2002a), the synergy between human capital and Structural capital is acknowledged in a new algebraic depiction: Algebraic depiction: Human Capital x Structural Capital = Intellectual Capital. Structural capital is separated into two components: customer capital and organizational capital, which in turn is divided into innovation capital and process capitals. Edvinsson and Malone emphasize (2997, p. 46) that company managers should be concerned in converting human capital into structural capital, because the latter may be a part of a company (as opposed to human capital) and may be traded, so that a value may be allocated to it. We should point out the difference concerning Sullivan’s concept, in which the tangible component is a part of structural capital. This
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Leonardo Fernando Cruz Basso et al. should refer us to a clear definition of the concept which we will select. Stewart (1997), who had one of his books published in Brazil describes intellectual capital as intellectual material, which concept arises from expertise, information, intellectual property, and experience made available of company in order to create wealth. From this viewpoint, the tangible component of wealth cannot be associated with intellectual capital. A lack of consensus on conceptualizing and measuring intellectual capital led Bontis (2001 to propose a research agenda whereby researchers should create models whereby objective measures liable to generalization could be tested. Researchers should also compare their models with those by other researchers, theoretically as well as empirically, in order to confirm whether some of the concepts on which there is a consensus may arise as part of an overall model. As we have not yet arrived at this stage, overlapping concepts will lead us to opt for a pragmatic position: we will adopt a definition by the authors whose theories we are about to propose: Pulic’s concept.
2. The concept and measuring of intellectual capital in Pulic’s view Pulic introduced a condensed methodology in the Value Added Intellectual Coefficient (VAICTM), intended to aid managers to leverage the potential to create value in their companies, a methodology based on existing corporate performance (current) (PULIC, 2000b). His concept of intellectual capital is depicted in Figure 3. VAIC ™ VAIC™ = VACA
+
VAHU + STVA
↓
↓
CAPITAL EMPLOYED CE ↓ ↓
INTELLECTUAL CAPITAL IC ↓ ↓
PHYSICAL CAPITAL PhC
FINANCIAL CAPITAL FC
HUMAN CAPITAL HC
VACA = (OUT – IN) : CE = VA : CE
VAHU = VA : HC
VALUE ADDED CAPITAL EMPLOYED
VALUE ADDED HUMAN CAPITAL
STRUCTURAL CAPITAL SC STVA = SC : VA
VALUE ADDED – HUMAN CAPITAL VALUE ADDED
Figure 3: value added intellectual coefficient Pulic (IBEC, 2003) emphasizes that two resources are responsible for value added creation in companies: Capital employed and intellectual capital. Capital employed is the tangible portion of capital and consists in the physical portion (raw materials and fixed assets) and the financial portion. Intellectual capital encompasses human and structural capital. As mentioned above, a lack of consensus as to intellectual capital led us to opt for the specific conceptualization by each author for the theory’s test. Five steps are required to calculate VAICTM. a) The value added (VA) calculation, an identical concept as employed by economists. VA = OUT – INP, in which the gross value of production (OUT), or gross sales, represents total revenues arising from all the goods and services sold in the Market. Inputs (IN), also known as intermediary consumables in the national accounts methodology, contain expenditures of everything that the company purchased and that was used in the productive process. Pulic is emphatic in the
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Leonardo Fernando Cruz Basso et al. distinct role which he attributes to labor, as he asserts that expenditures (expenses) with this category cannot be considered as costs. Value added expresses the new wealth created over a period. We will describe value added by means of the following expression: VA = GM – sgaExp + LEXP, in which VA = value added; GM = gross margin; sgaEXP = selling, general, and administrative expenses; LEXP = expenditures (expenses) with the labor force (labor expenses). According to Pulic’s concept human capital is: HC = LEXP b) It will be necessary to calculate how efficiently value added was created. The option for a value creation rate is clear to Pulic. As VA was created by financial, physical, and intellectual capital, it will be necessary to determine the contribution by each portion. Pulic emphasizes that the objective of any business is clear: To create as much value added as possible based on an initial sum of physical, financial, and intellectual capital. The second relation proposed is known as VACA and represents the ration between value added (VA) and capital employed (physical and financial). . Capital employed is equal to the book value of a company’s net assets (Firor and Williams, 2003) (note: when it is not possible to find data on operating liabilities, total assets should be employed as a Proxy for net assets). The Value Added Capital Coefficient (VACA) shows the amount of value added created by a unit of capital (physical and financial) employed. ) The third step is to calculate the efficiency of IC and of its two components: human and structural capital. Pulic sustained the issue that human capital is expressed by expenditures with employees, TM including social benefits such as social security. As VAIC uses balance sheet data for expenses with the labor force, payroll costs represent human capital costs. He is not alone in this position, as Leif Edvinsson and Karl Erick Sveiby share the same point of view. Pulic assembles the value added coefficient produced by human capital (Value Added Human Capital Coefficient – VAHU), which represents how much of value added was created by a unit of currency invested in employees: VAHU = VA HC The fourth step required to calculate the ratio is the share by structural capital in value added created. The inventory for structural capital is different. Pulic sustains that structural capital and human capital are reciprocal, the larger the share by human capital, the smaller the share by structural capital.
This result is opposed to the model which defines intellectual capital as the sum of human capital and structural capital. (Intellectual Capital = human capital + structural capital) In this view, intellectual capital may be increased by raising structural capital simultaneously. Pulic justifies the opposite relation by asserting that structural capital is rewarded if human capital is . Hence, for a given sum of value added, structural deducted from value added capital may be increased only through a reduction in human capital. Pulic states that structural capital is received (it seems to me that a more appropriate word would be compensated) if human capital is . To calculate the share by structural capital in value deducted from value added added, Pulic created the STVA coefficient, defined as:
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Leonardo Fernando Cruz Basso et al. . All the indicators are obtained from bookkeeping data. The fifth step is to explain how the three indicators participate in creating value added. This is done by adding the three coefficients:
Our option in this paper was to test Pulic’s theory as well as its explanatory components. This option was adopted in view of the criticisms by Andriessen (2000) to Pulic’s theory. These criticisms allow a valuation of the components as having a greater explanatory power from a theoretical viewpoint.
3. Andriessen’s criticism Andriessen (2000) made relevant criticisms to the method. Based on the definition by Lev (2000) which describes an asset as a claim for an expected benefit. Andriessen (2004) distinguishes it from an expense which does not provide benefits beyond the accounting period. Labor expenses may include expenses that provide future benefits (such as training expenses or the work allocated to research and development). However, portions representing labor expenses provide immediate benefits and should not be treated as an asset. Treating every labor expense as an asset implies over-stating future benefits. Treating them as assets implies having a depreciation rate for these assets. It would be found that expressive portions of capitalized expenditures should be amortized during the same accounting period as they do not provide benefits beyond the current accounting period. Depreciation would bring the expenses once again to the profit and loss statement. The method makes no distinction between flows and inventories (Andriessen, 2004). Value added is a flow indicator for return on assets, as is capital employed, human capital, and structural capital (which are not inventory indicators. Labor expenses are a flow indicator, but under the VAIC method labor expenses are treated as inventories. Even if labor expenses are accepted as producing future benefits, we should still treat them as a flow, an investment in human capital, not as a human capital value. An investment is a contribution to an asset that produces future benefits. Assets often accrue owing to a series of investment in the course of then years (think about R & D). The inventory value of human capital is the result of the accrual of annual labor expenses. This is the inventory of human capital value which should be related to value added in order to calculate an efficiency rate, not the value for any year in particular. Another confusion between flow and inventory arises in the treatment of structural capital. Structural capital is an inventory, but in the method proposed by Pulic it is calculated as being a residue from two flows: Value added and Human Capital. To be consistent, this is the return on structural capital, not the value of structural capital. This concept leads to distinctive results, such as the finding that if operating income is negative, structural capital becomes negative. Another criticism is still more striking: The VAIC method’s purpose is to calculate the efficiency of human capital, of structural capital, and of capital employed. According to Andriessen (2004), this ratio does not mean efficiency, as it provides no information on the contribution by human capital in value creation. According to Andriessen (2004), the three components (structural, human, and employed) contribute to value added, but simply calculating the ratios will not show how much each one contributes (produces). A theory is required to explain the causal relation among these three types of capital and value added. Only by measuring the causal relation will efficiency be determined. The assumption that structural capital has the opposite effect of human capital will lead to strange results. When re-writing the formulas to HCE and SCE, it becomes clear why HCE is often greater than SCE:
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The result is absurd should operating profit become negative, showing a negative value for structural capital. This occurs because profit is a flow, and cannot mean an inventory of structural capital. A confusion arises between flows and stocks. Confirmation of Pulic’s theory leads to conflicting results. Williams (2001) found no relation between VAIC and the amplitude of the disclosure of intellectual capital in the annual reports. Pulic (2000a) found a correlation with market value. Firer and Williams (2003) tested VAIC receding against profitability, productivity, and market value, and found small and negative correlations among VAIC, productivity, and market value, an unexpected result according to Andriessen (2004).
4. Research hypotheses The test of Pulic’s theory and the reflections by Andriessen (2004) led us to create hypotheses to test the theory and also its components.
There is explanatory relevance in Value Creation by means of the Value Added Intellectual in the Motor Vehicle, Trailer, and Body Manufacturing and Assembly Coefficient ( Industry;
There is explanatory relevance in Value Creation by means of the Calculated Intangible Value (CIV);
There is explanatory relevance in Value Creation by means of Intellectual Capital Efficiency (ICE);
There is explanatory relevance in Value Creation by means of Capital Employed Efficiency (CEE);
There is explanatory relevance in Value Creation by means of Human Capital Efficiency (HCE);
There is explanatory relevance in Value Creation by means of Structural Capital Efficiency (SCE);
5. Analysis and results The automotive industry was selected through the use of three criteria: it has an expressive share in Brazilian gross domestic product, has a large overseas investment (which is associated by a number of authors – Queiroz e Carvalho,2005) with a high degree of innovation), and displays a high degree of innovation in the rating by the Índice Brasileiro de Inovação; hence, an expressive participation by human capital was to be expected in this industry with a potential to create an expressive volume of value added, and consequently of profitability. A number of adjustments were required in the original PIA database (IBGE). The starting point was 81,185 companies in 22 industries and 281,615 observations found in CNAE 2, in the Processing Industry in the 2000 – 2006 PIA, including the Motor Vehicles, Trailers, and Bodies investigated in this research, herein investigated. Below follows general information on the sampling’s adjustment for the Motor Vehicles, Trailers, and Auto-parts population:
a) Although IBGE made available PIA data for an 11-year period, from 1995 to 2006, only the 2000 – 2006 period includes companies’ Total Assets, a key component to estimate ROA (Return on Assets).
b) The IBGE registry does not show names but CNAEs (National Classification of Economic Activities). The CNAE 3 included very small companies, and the CNAE 1, very large ones. The initial option was for a broader base composed of CNAE 2 and small companies with over 30 employees, but the Descriptive Analysis showed greater distortions (a high average standard deviation). The choice was for companies with over 100 employees;
c) There was an important group of medium-sized companies (with less than 1000 employees) that entered and exited the survey in the middle of the period under analysis, which gave rise to lost information in the panel, one of the key reasons why it was not balanced.
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Leonardo Fernando Cruz Basso et al. The sampling’s adjustment required a number of exclusions, which ended up by forming a database of 144 entities and 865 observations for the motor vehicles, trailers, and auto-parts. The research is “non probabilistic” in light of the before mentioned adjustments, adapting the figures for panel data. Table 1 displays the descriptive statistics after adjusting the sample. As we found this to be unrealistic, every company with profitability above 150% was excluded; we believe that such high rates indicate an under-valuation of assets, as there would be no benefits arising from an overvaluation of profits. Table 1: Descriptive statistics Variables
Number of observations
Average
Standard Deviation
865 505 865 865 865 865 865
0.5801743 17.13552 0.5707403 3.219914 2.649174 2.168966 0.4802079
0.2120894 1.715694 0.2244401 1.178796 1.148334 1.020069 0.1516972
Motor vehicle industry ROA4 LnCIV CEE VAIC ICE HCE SCE
Reliability Interval
0.005117 8.773288 0.004479 1.289323 1.08226 1.041975 0.040283
1.416444 21.52949 1.370979 15.60434 15.02726 14.09819 0.9290689
Tables2 and 3 were condensed based on tests with models performed in IBGE facilities. Table 2: Static models, motor vehicle industry at: ROA4 = f( LnCIV; VAIC; Dummy year 2001 to 2006) Variables and data
Results and Significance tests Random Fixed Wt Fixed Effect Effect Effect, with Fixed Effect st 1 difference
OLS Pooled
Within Fixed Effect Robust Variance (2)
Dependent Variable ROA 4 (1) Independent Variables Ln CIV VAIC Dumm y for 20012006 Constant Statistics / Tests FIV Factor Heteroscedasticit y (8) Serial autocorrelation(8) Notes Adjusted R2 / Within (3) F Test regression (4) Degrees of Liberty Test F (175.321) (5) Breush-Pagan chi2 (1)(6) Hausman (7)
-0.022121* 0.0371378* Yes
0.0009509 0.064022* Yes
0.0327546* 0.0729665* Yes
0.0249368* 0.0729252* Yes
0.0249368* 0.0729252* Yes
0.8804112*
0.3919168*
-0.041779*
-0.0309638
-0.0309638
2.68 32.95* (0.0000) 4.554** (0.0358) 505 0.0493
505 0.2483
319 0.2900
505 0.2721
505 0.2721
4.26*
94.65*
19.56*
15.00*
8.09*
F(8.496)
Wald chi(8)
F(7.311)
F(8.321
F(8.321
Statistics 7.38
p-value 0.0000
Result Fixed Effect
274.78
0.0000
Random Effect
44.85
0.0000
Fixed Effect
Source: prepared by the author based on results from Stata. SE /10 and PIA (IBGE).
Note: (1) ROA4 = Return (Gross Profit) on Total Assets
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Significance 1% * 5% ** 10% ***
Leonardo Fernando Cruz Basso et al. (2) Pursuant to the Newey West estimator, according to Yafee (2008) (3) R2 adjusted for Pooled OLS, R2 Within for others (4) F Test for joint significance of regression coefficients, the same for the Random Effect Wald test (5) Second F Test for decision between Fixed Effects and Pooled OLS Models: if significant, Fixed Effects will prevail (6) Breush-Pagan Test comparing Random Effects with Pooled OLS Effect, if significant, Random M will prevail (7) Hausmann Test comparing Random Effects with Fixed Effects, if significant, Fixed Effects will prevail The statistical tests indicated: The 2.68 Variance Inflationary Factor (FIV) proved the existence of multi-colinearity in the model;
The heteroscedasticity test (Breusch-Pagan / Cook-Weisberg) rejected the null hypothesis that the residual variances are constant at the 1% significance level;
Wooldridge’s residual autocorrelation test for panel data rejected the null hypothesis of the absence of first order residual autocorrelation at a 5% significance level, hence there is residual autocorrelation;
The Hausman Test indicated the presence of Fixed Effects at 1% significance and the F test rejected the null hypothesis of the inexistence of robust variance regression at 1% significance; and
The t test rejected the null hypothesis at 1% significance validating the VAIC and LnCIV coefficient in the White and Newey-West robust option (deals with autocorrelation and heteroscedasticity effects). The sign for both is positive for value creation.
In Table 3 the results of the four dynamic model variations. In the four dynamic models, Models 1 and over 2 were treated by the first difference, with the aim of eliminating the fixed effect (from intercept regressors Hence, the most expressive result in this industry is model M2, with order 2 lagging variables, meeting all the statistical tests, and which is a more robust result. Table 3: Dynamic model for industry 34 ROA4 = f( LnCIV; VAIC; Dummy year 2001 to 2006) ROA4 Dependent Variable Independent Variables
Alternative Dynamic Models, with robust variance for 1st to 2nd order lags (3) Model 1re Model 2 nre Model 3 nre Model 4 nre
ROA4_1
0.4147911**
0.5224435**
0.3984015**
0.4188589*
ROA4_2
0.2212275
0.1723092***
n/a
n/a
Ln Civ
0.0007633
0.0195195
0.0262742
0.0365903*
Ln Civ _1
-0.0197263
-0.040423***
n/a
Ln Civ_2
0.0035201
n/a
-0.0262121 (0.119) n/a
n/a
VAIC
0.096416*
0.1027419*
0.0743491*
0.0841758*
VAIC_1
-0.0321786
-0.0297939
-0.0314227**
n/a
VAIC-2
-0.0098922
n/a
n/a
n/a
Var. Dummy 2001-2006
Yes
Yes
Yes
Yes
Intercept
0.2050819
0.2918305
0.2080426
-0.5502957*
Dynamic Model Tests Notes
90
109
174
202
Wald 2 / chi2 com g.l. = ...
67.35* / gl =12
61.85*/gl=10
50.92*/gl=10
130.08*/gl=8
Sargan Statistics: Est/p-val (1) Arelano-Bond ( 2)
8.4267 (0.7510)
4.9979 (0.9580)
(12.6079) 0.5576
9.0379 (0.8286)
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Leonardo Fernando Cruz Basso et al. ROA4 Dependent Variable m1 (z)
Alternative Dynamic Models, with robust variance for 1st to 2nd order lags (3) -1.374 -1.6629 -2.2788 -2.5726 0.1694
0.0963
0.0227
0.0101
-1.2021
-0.63814
0.67698
1.1301
p.value ( Prob > z) Wald Test (statistics/p.value) VAIC + VAIC_1=0
0.2293
0.5234
0.4984
0.2584
(4)
4.94**
3.20***
n/a
LnCIV + LnCIV_1=0
(4)
0.49
0.00
n/a
p.value ( Prob > z) m2 (z)
Source: prepared by the author based on results from Stata. SE/10 and PIA (IBGE). Note: n/a - not applicable. (1) The statistics appears in first place and its respective p-value in brackets. (2) Arelano-Bond Tests for null autocorrelation in the first difference errors. (3) The models were described at the top of each column as nre (not rejected by the statistical tests) and re (rejected). (4) Wald not reported because it was not approved by the Arelano and Bond test. The tests confirmed the validity of the three dynamic models with robust variance:
The Sargan test validated the over-identified restrictions;
The lags in the dependent variable were significant in all the models, in varying degrees;
The Arelano and Bond test confirmed the “lack of residual autocorrelation” with the introduction instrumental variables for models 2, 3, and 4. This test requires negative and significant statistics in first order errors, but non-significant in second order errors.
The Wald test showed that the sums of the independents and their lags are significantly different from zero (long-term statistics 2.19) in M2 and M3 for VAIC only, owing to the rejection in the null hypothesis. It was not possible to determine the sign’s direction for LnCIV.
In the long run the statistic is obtained from partial derivatives (formula 2.19) for VAIC is always positive in M2 and M3, “pushing” the dependent variable. This result indicated that it is the most reliable in view of the laxity of the statistical hypotheses. In the case of model M2 the regressors were pre-specified.
In model M4, the sole lagged item is RO4_1, and the two independent variables were significant. 0.063 for LnCIV and 0.145 for VAIC, In this case the long-term statistics display values of both positive, and VAIC “pushes” the dependent variable with greater intensity.
The main conclusion is that we confirm the existence of fixed effects and in three of the four dynamic models the results were satisfactory; it is not our intention to discuss which of the dynamic models would be the best. Table 4 displays a summary of the hypotheses and the results observed. We list the results for the hypotheses separately because, as we pointed out in Andriessen’s criticism regarding Pulic’s model, there are queries likely to lead to alternative models with greater explanatory power in theoretical terms. The results indicate that the hypotheses were confirmed for almost all of the models, with the exception of those in which we attempted to explain value creation with the unaligned variables originating from the Pulic concept, and the model which places structural capital individually, which may have had problems owing to Andriessen’s criticism (2004). The results obtained allowed to infer that the Value Added Intellectual Coefficient is significant to measure the efficiency of companies in
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Leonardo Fernando Cruz Basso et al. Brazilian Industry of Motor Vehicles, Trailers, and Auto-parts, according to both the static and dynamic estimation methods. Table 4: Summary of results of the models applied to the Industries Hypothesi s
Model
1
ROA4 = f( VAIC;LnCIV)
2
ROA4 = f(VAIC)
3
ROA4 = f(LnCIV)
4
ROA = f( LnCIV,;CEE;ICE)
5.
ROA4 = f(ICE)
The tested variable is significant and creates value VAIC and LnCIV VAIC LnCIV VAIC VAIC LnCIV LnCIV ICE ICE CEE CEE ICE creates value
ROA4 = f(CEE)
CEE creates value
4
ROA = f ( Ln CIV; CEE;HCE;SCE)
5
ROA4 = f(HCE)
5
ROA = f(SCE)
HCE HCE SCE SCE HCE HCE SCE SCE
Results for models C - confirmed NC – not confirmed Static: C Dynamic: C Dynamic: C Static: C Dynamic: C Static: C Dynamic: C Static: NC Dynamic: NC Static: C Dynamic: C Static: C Dynamic: C Static: C Dynamic: C Static: C Dynamic: NC Static: NC Dynamic: NC Static: C Dynamic: C Static: C Dynamic: C
Source: the authors, based on results of the models tested. The VAIC parameter obtained with robust estimation attended to all the required tests and indicates a positive relation with Gross return on assets in the long term, indicating that Intellectual Capital has made a positive contribution, an additional gain, to the results obtained by the industry in the period of 2000 to 2006; also, a similar, but lower contribution (almost 50% less) in magnitude has been generated by the Calculated Intangible Value (LnCIV).
6. Final considerations Brazilian law now requires that the major listed companies should state their intangibles. Intellectual capital is one of the most relevant intangibles for a company, and the conceptualization, measurement, and the relation with value creation is still an incipient research area in Brazil. One of this paper’s purposes is to contribute to the discussions, testing the theory proposed by economist Ante Pulic in the motor vehicle manufacturing and assembly industry in Brazil. We proved the theory in its aggregate form as well as in the variables that form VAIC. The option for the unaligned components test was due to Andriessen’s criticism regarding the structural capital concept proposed by Pulic. For those who consider the criticism relevant, it is encouraging that the individual components display a significant statistical relation with value creation. The model showed robust result attending Arelano & Bond estimation method and statistical tests considering lagged dependent and independent variables. The significative results obtained with the dynamic models opened a window for additional researches in order to investigate the time effect of Intellectual capital (Value Added Intellectual Coefficient and the Calculated Intangible Value) on value creation. On the other hand the weaker results of Human Capital and Structural Capital, VAIC components, create opportunity for the minor adjustments maintaining the original ideas proposed by professor Ante Pullic. Our next goal will be to expand the tests to other processing industries, and as far as possible adopt other models in order to compare results.
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References Andriessen, D.. Making sense of Intelectual Capital: designing a method for the Valuation of Intangibles. Elsevier: Oxford,2004. Arelano, M..Panel Data Econometrics: advanced texts in econometrics. Oxford: Oxford University Press,2003. Bontis, Nick. Assessing Knowledge Assets:a review of the models used to measure Intellectual Capital Intern. Journal of Management Reviews. Vol. 3, Issue 1, 2001, pp.41-60. Edvinsson, L. and Malone, M. S. (1997) Intellectual capital: realizing your company’s true value by finding its bidden brainpower. New York: Harper Business. Firer Steven; WILLIANS, Mitchell. Intelectual Capital and Traditional Measures of Corporate Performance. Journal of Intelectual Capital; 4,3; ABI/INFORM GLOBAL p. 348-360, 2003. IBGE - Instituto Brasileiro de Geografia Econômica e Estatística. Pesquisa Industrial Anual. Rio de Janeiro, 2005(a). Disponível e http://www.sidra.ibge.gov.br/bda/pesquisas/pia/default.asp?o=16&i=P, acesso em 16/08/2008 Instituto Brasileiro de Geografia Econômica e Estatística. Pesquisa Industrial Empresa e PIA – Produto 2006(b). Disponível em http://www.ibge.com.br/home/presidencia/noticias/noticia_visualiza.php?idnoticia=894&id_pagina=1 omunicação Social. Acesso em 16/08/2008. Lev, Baruch. Intangibles: management, measurement and reporting. Washington: Brookings Institution Press,2001. Pulic, A. (2000a) MVA and VAIC™ analysis of randomly selected companies from FTSE 250. Available at: www.vaic-on.net Pulic, A. (2002) Do we Know if we create or destroy value? Available at: www.vaic-on.net Pullic, Ante. (2000b) VAIC TM: an accounting tool for IC management. International Journal of Technology Management, Vol.20, nº 5,6,7,8. 2000. MVA and VAIC : analysis of randomly selected companies from FTSE 150. Graz: April, 2002 (a). Disponível em http://www.vaic-on.net/download/ftse30.pdf. Acesso em Agosto de 2008. Do we know if we create or destroy value. Zagreb: 2002(b). Disponível em Http://www.emeraldinsight.com/.published/emeraldfulltextarticlepdf/2500086205_ref.html..Acesso em 04 de julho de 2008. Queiroz, Sérgio; CARVALHO, Ruy de Quadros. Empresas Multinacionais e Inovação Tecnológica no Brasil. São Paulo em Perspectiva. Vol. 19, nº 2. São Paulo: Abril/Junho , 2005. Stewart. T. A. (1997) Intellectual capital: the new wealth of organizations. New York: Doubleday/Currency Sullivan, H. Value-Diven Intellectual Capital: how to convert Intangible Corporate Assets into Market Value. New York: John Wiley &Sons, Inc.,2000. Williams, S. M. (2001) Are companies’ Intellectual Capital performance and Intellectual Capital disclosure practices related? Presented at the 4th WorldCongress on Intellectual Capital, McMaster University, Hamilton, Ontario, Canada.
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Managing Intellectual Capital in Hungarian Universites – the Case of Corvinus University of Budapest Viktória Bodnár, Tamás Harangozó, Tamás Tirnitz, Éva Révész and Gergely Kováts Corvinus University of Budapest, Hungary
[email protected] [email protected] [email protected] [email protected] [email protected] Abstract: The changes of higher education market draw the attention of Hungarian higher education institution to the increasing role of knowledge resources. Responding on the recent changes, several European universities have began to measure systematically their intellectual capital, but well-developed models – except Austrian case – are still not available. Aiming to identify the main characteristics of measuring and reporting the intellectual capital, we are carrying out a research in Corvinus University of Budapest (CUB) based on document analysis, survey and executive interviews. Our paper raises three research aims: 1) First, by examining and systematizing the practice of external and internal reporting system at CUB, we give an overview of the content and method of intellectual capital management of a higher education institution. 2) Second, we identify the existing information on the different element of intellectual capital found in the reporting. 3) Third, we summarize the opinion of the university academic staff on the intellectual capital awareness of the university management. Keywords: Higher education, intellectual capital, reporting
1. Introduction Recent reforms in Hungarian higher education, in accordance with the so-called Bologna directives, aim at stimulating competition among institutions, reshaping the governance of universities and colleges and strengthening performance and management accountability. As consequence of these and other changes of the higher education market (e.g.: more heterogeneous audience, diversified program-portfolio, appearance of for-profit research centre as competitors etc.), the Hungarian institutions face higher competition. This emphasizes greater consciousness on performance and knowledge resources when performance measurement and management techniques are partly or wholly non- or misunderstood in Hungarian academia. Besides there are more general changes in the organizational life: many researches have discussed the increasing role of intangible assets in the value creation process – especially in knowledge-based institutes. Universities as really such organizations should be also aware that their performance and competitiveness depends even more on the management of its knowledge resources, so they need relevant information about the knowledge resources. Responding on the recent changes, several European universities have striven to measure systematically their intellectual capital and the performance of their lecturers and researchers, characteristics of their partnerships and formal structures. In the practice we can find a lot of methods focusing specially on monitoring the non-material resources in the higher education, but welldeveloped and tested models – except the Austrian universities and research centers – are still not available. Stemming from this requirement mentioned above, our analysis examines the situation of a Hungarian higher educational institution from the aspect of management and monitoring tools used for tracking of intellectual capital. Assuming that the Corvinus University of Budapest (CUB) – being one of the leading universities in Hungary that proceeds with both educational and researching activities – illustrates the aspects of the Hungarian and the Central- and Eastern-European comprehensive research universities well, we used case study methodology in our research. It was also an important factor of our choice that as associates of CUB we have had greater opportunity to understand the operations better and deeper. Moreover it has been assumed that our university as one of the leading regional higher educational institutes we can find state-of-the-art management practices – which are conductive to develop and maintain the new intangible assets based competitive advantage and management attitude in this sector as well – with higher probability.
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Viktória Bodnár et al. During the cognition and description of the intangible practices of the Corvinus University of Budapest we have done mainly document analysis and executive interviews. By using these analyzing methods we would have also given a critique of the university’s management, planning and reporting system, mainly based on criteria given among ‘universal’ recommendations but also taking specific factors of higher education into consideration. Based on the above-mentioned management problem the most important goals of our research can be summarized as follows:
Analyze and systematize the recent intellectual capital management practices on the Corvinus University of Budapest;
Identify the existing information on intellectual capital found in the reporting system – both external and internal reports;
Based on an actual survey, summarize the opinion of the university academic staff on the intellectual capital awareness of the university management.
2. Literature review: theoretical and methodological background of the analysis 2.1 Definition of intellectual capital In the literature there is no universal definition in the realm of management studies for the collective word of non-material resources. Thus, they are frequently referred as intellectual capital, although many different explanations and definitions exist. Since our goal is not to provide a detailed introduction of these explanations, hereby, we are highlighting the most important ones that bring us closer to our own approach of understanding intellectual capital:
Frequently, those resources are called intangible assets that have no physical-material or monetary shape or existence but still they bear value for the organizations (based on Kaufmann – Schneider [2004]; Arbeitskreis IWR [2001]).
Gu and Lev [2001] emphasize the role of context and declare that knowledge resources do not necessarily mean value, but they turn into values that support and integrate into the wealth and value producing processes. The authors look at R&D, marketing- and advertising, HR and IT practices as the most important sources of non-material assets.
In another definition the intellectual capital refers to the assets in the company that are based on knowledge. This approach emphasizes the (internal) attributes of employees’ knowledge and experience, organizational structure and processes or the informational systems of a company. Among the outside factors the brand value and customer loyalty and trust can be highlighted (Brennan – Connell [2000]).
Edvinsson and Sullivan give us a similar definition when they declare the intellectual capital as knowledge that can be converted to value (market results or company earnings) (Pfeil [2004]).
The RICARDIS research, supported by the European Union, is analyzing the role of intellectual capital among small- and medium size enterprises focusing on R&D. Here, the knowledge capital means the organization’s human capital, structural and network capital and the business operations related to these (RICARDIS [2005]. p.4).
The list of definitions could have been continued (see among others: Bontis et al. [1999]; Mouritsen et al. [2003]) but even reading the abovementioned ones it can be concluded that a all of them provide a rather global explanation and setting which makes their application less feasible and valuable in empirical researches. The latter one calls for a deep analysis of the subject matter, most researcher build a definition for intellectual capital by defining categories of its substantial elements. A well-known categorization can be found in the works of Sveiby, who divided the intangible wealth of organizations to the following three groups (figure 1). Beside Sveiby’s approach in the literature numerous other categorizations exist (e.g.: Stewart [1995]; 1 Bontis [1996]; Brooking [1996] ; Arbeitskreis IWR [2001], Edvinsson [2002]), which also do not provide a standard framework for understanding intellectual capital, but help us to get a hold on to the 1
See in: Roslender – Fincham, [2001]; Kannan – Aulbur, [2004].
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Viktória Bodnár et al. underlying characteristics of it, although the detailed explanation of these is not the purpose of this study.
Intellectual capital
Structural capital
Human capital
Relational capital
Figure 1: Categorization of intellectual capital (based on Sveiby [2001a] and [2001b]) In our research we define intellectual capital as the organizational and individual knowledge elements and the potential for creating value that derives from the internal and external relations and connections of the organization. In our specific analysis we are using Sveiby’s categorization, with regards to the fact that in the academic environment and among the Hungarian settings sometimes the implementation of new sub-categories or the use of different weighting of the elements may seem reasonable. We have to emphasize that the Austrian model of Wissenbilanz – which have been the basis of our research – also uses the same categories, only difference may lie in different weighting allocation of elements. Table 1: Our research-explanation of intellectual capital Elements of intangible assets Human capital
Content, meaning and feature of category Human capital includes the education, knowledge, ability, competency, motivation, commitment and willingness for growth and learning processes of the employee basis.
Structural capital
Structural capital consists of the attributes of internal business operations including the processes, internal cooperation and organization culture.
Relational capital
In the narrow sense it stands for the external relations of the organization – since the student related data shall not be considered here, but among the outputs – however, the student satisfaction, pass/fail ratio and even the quality and quantity of the students’ foreign study experiences can be included.
3. Case study: managing of intellectual capital at the Corvinus University of Budapest (CUB) 3.1 Introduction of the university The Karl Marx University, the predecessor of Corvinus University of Budapest, was established in 1948 as the first university of economic sciences in Hungary. At the end of socialist era the new name became Budapest University of Economic Sciences, which had three faculties: Faculty of Business Administration, Faculty of Economics, and Faculty of Social Sciences. In 2000, due to the integration process in Hungarian higher education, the university merged with a college, which became the Faculty of Public Administration, and offered a bachelor degree in this field. In 2004 three new faculties joined the university: Faculty of Horticultural Science, Faculty of Food Science, and Faculty of Landscape Architecture. The seven faculty university adopted the new name of ‘Corvinus University of Budapest’ (CUB) in 2004. The short term goal of the University is to maintain its leading position in Hungary and enhance its international reputation. The University is located in the capital of Hungary, the heart of the scientific and cultural life of the country, which makes the institution even more attractive. Most of the buildings are located on one of the three campuses in Budapest, although there are facilities outside the city (such as model gardens and botanical gardens). The infrastructure of the University improved immensely in 2007 when largescale investments were completed.
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Viktória Bodnár et al. Approximately 18,000 full-time and part-time students were enrolled for the courses of CUB in the academic year 2009/2010. In the recent years the ratio of full time/part time students has not changed much: the proportion of full time students has increased to some extent whereas the proportion of part time students has decreased (the ratio of full and part time students is about 60/40). The ratios of state-funded and fee-paying students have been nearly 50-50 %. In 2002 the university had 969 employees which grew to 1446 by 2007 (the size of the academic staff was 974). In 2007, 48 per cent of the employees were academic staff members and 52 per cent were non-academic employees. Revenues of the university in 2009 can be expected to total around 60 million euros (50% from state sources, and 50% from ‘market’ revenues). Table 2: Faculties and numbers of Corvinus University of Budapest (source: CUB [2008b]) No. of teachers, researchers (2007)
No. of students (2007) Faculties Faculty of Business Administration Faculty of Economics Faculty of Social Sciences Faculty of Public Administration Faculty of Food Science Faculty of Horticultural Science Faculty of Landscape Architecture CUB SUM
Statefunded 3421 1404 1404 839 549 1344 572 9533
Feepaying 2361 886 1209 2180 658 731 150 8175
Sum 5782 2290 2613 3019 1207 2075 722 17708
Part-time
Full-time
60 22 20 6 14 2 5 129
177 115 109 78 90 98 30 698
3.2 Available information on intellectual capital at the Corvinus University of Budapest 3.2.1 External reporting sent to authorities and other outer parties The Corvinus University of Budapest draws up neither Wissensbilanz nor any other report with similar content at present (it is not a legal requirement to prepare a report like that in Hungary). Nevertheless it has to submit several reports and statements to various external bodies (like to the ministry that provides the funding of the university), but none of these reports are equivalent with the Austrian model. Accordingly we could have found a lot of data on intangible resources that are available at the university and which are also made for the public in accordance. The base of information and measures of intellectual capital can be collected primarily from the latest Institute Development Plan (CUB [2007]) and from the self-evaluating documents submitted to the Hungarian Accreditation Committee in 2008 (CUB [2008a]). Besides the obligatory annual report that the university’s library has to submit to public authority also contains indicators which measure intangible resources. And finally, CUB has to collect and report data on immaterial resources to the Ministry of Education and Culture in the so called Research and Development Report and other documents (e.g.: compulsory record keeping of performance in research centres, reporting of incomes from corporate relations, CEEPUS Mobility Report etc.) which are drawn up based on the information supplied to the authority or other external parties by the university. Analysing the external reports, our university registers and makes public the following indicators of intangible assets (indicators are grouped into three categories according the Austrian Wissensbilanzmodel): At Corvinus University of Budapest PhD students are recognised as full member of academic staff in teaching and in research, consequently they are included in human capital completely. The swimming pool and other sport facilities, which are available for every student and university members, belong to the infrastructure of the university as well beside the lecturing theatres, seminar rooms and libraries. The indicators connected to this specific area are also involved in structural capital. Five indicators measure the assets in information technology, while a separate data is
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Viktória Bodnár et al. designed to measure the educational equipment (e.g. projectors, interactive whiteboards). In two faculties of the CUB biological researches are conducted. These bio-laboratories also supply data for structural capital. Finally, the number of research centres indicator implies also standardised information of the university’s research activities. Table 3: Indicators on intellectual capital in external reporting Indicators of intangibles (external reporting): A) Human capital: 1. Total number of employees in full time equivalent (FTE) 2. Academic staff’s’ FTE 3. The academic staff’s’ age distribution 4. The average age of academic staff according to different assessment criteria 5. Number of academic staff who teach languages (in FTE) 6. Number and ratio of academic staff with PhD or equivalent 7. Number of staff in research centres (total) 8. Number of PhD students 9. Fluctuation B) Structural capital: 1. Educational area per number of students 2. Educational area per number of academic staff 3. Area of sport facilities per number of full-time students 4. Number of computers in the university network 5. Number of computers provided for students 6. Number, area and capacity of computer laboratories and laboratories for other purposes 7. Library area per number of students 8. Weekly library opening hours 9. Number of seats in the library 10. Number of database in library (international and Hungarian) 11. Number and change in the number of library resources 12. Number of ordered journals (traditional and digital) 13. Cost of journals 14. Annual cost of research and development investments 15. Number and average age of educational equipment 16. Number of research centres C) Relational capital: 1. Number and sum of vocational education contribution (total and actual) 2. Number and income of innovation research (total and actual) 3. Chair-income 4. Number of memberships in scientific and professional organizations 5. Number of academic staff who are members in editorial boards 6. Number of library exchange programmes 7. Number of partner universities from abroad 8. Number of alumni-members
Some components of the relational capital possess specific Hungarian characteristics, because business organizations are supposed to pay a so-called vocational educational contribution and innovation contribution after reaching a certain size (it depends on the number of employees and the revenues). These obligations could also be fulfilled by supporting educational institutions or by making a contract with these institutions for innovation research. The amount of funding and the innovation researches’ proceeds could be charged as their legal financial obligations. Hence the indicators with the aim to measure the vocational education contributions and innovation researches are appropriate to denote the university’s relational capital (and also their utilisation). The professorial corporate scholarships (chairs) indicate a special type of relationship between university and business as well, in the case when a specific company gives funding for one of our colleagues’ educational or research activities for many years besides providing budget for material
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Viktória Bodnár et al. expenses too. These chairs are available for both senior researchers (for 5 year) and for PhD students (for 3 years). The memberships in different scientific and professional organisations imply another valuable relation networks to the university. Some of these are related to the CUB itself (for instance CEMS, EDAMBA), while in other cases one of the university’s staff member is involved in these bodies (for example in academic committees). The international and Hungarian exchange relations of the libraries are main sources of new scientific material and the university staff’s positions in journals’ editorial boards also have high value for CUB. Both are important components of relational capital. Besides CUB has alumni-system too, however it rather only implies even potential than real relation network for the present. But there are some improvements: a qualitative management system was set up, which also insisted on improving the alumni members’ registration. Furthermore, the university plans to rely on these relationships in organising internships for students, which is a requirement in the Bologna Process. But we can say it, that these relationships are not measured now (CUB [2008a] p.95). If we have a look on the available external intellectual capital measuring at the CUB, mainly we can find many sources and indicators connected to human, structural and relational capital. Nevertheless we found indicators which strive to denote effects and the results of intellectual capital management activities. This means financial and non-financial sources, which are available for measuring the effects of the teaching, research and other activities of the CUB. The main measures which are to quantifying the results of the result can be found in the following table: Table 4: Process-indicators on intellectual capital in external reporting D) Processes and results (output) – examples 1. Educational performance: number of students, proportion of students funded by the state, number of students participating in education programmes abroad, ratio of applications over the university quota, number of exchange students from abroad, average number of university years, number of awarded degrees; 2. Research (students): number of participants in the Scientific Student Conference, in the National Scientific Student Conference and the number of winners in the National Scientific Student Conference; 3. Research (academic staff) : number of publications (divided into categories as well); 4. Research (income): number of winner tenders (total and data from actual year), income of the tenders, income on tenders per one academic staff member, income per one FTE (total and per academic staff member); 5. Library: number of registered persons into the library, number of visitors (reading room, website, distance-visitors), number of lending, number of library trainings.
3.2.2 Internal reporting and organizational solutions used for managing of intangibles Thought the aim of this paper is to identify and analyse the indicators and available information for intellectual capital measurement and management it is also important to present the most important internal reports and organizational solutions related directly to intellectual capital categories. Some of the analysed organisational units have the dedicated goal to manage and report of the university’s intellectual capital. They are the following ones:
Innovations Centre, Inc. – its main tasks include the coordination of the research co-operations between the university and corporations, searching for research projects, supporting the search for their funding, the researches’ coordination and project management.
Corvinus Knowledge Centre – it is a three- year long research in food science. It aims to develop an international cluster for producers and distributors in food science research and innovation. It emphasises food processing, -preservations, -security and -quality.
Office of Corporate Relations – its aim is managing of the relationship with university’s primary external sponsors. Since it cannot cover the whole range of personal relations (e.g. private contacts of professors) it is difficult to obtain real information on CUB’s external relationships through the data supplied by this office.
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Office of International Relations – its main task is to manage university’s student exchange programs, to issue and coordinate the exchange opportunities and to support both the incoming and outgoing student and the lecturers.
Career Office – its mission is to establish relationship between highly qualified students and private or public sectors organizations, to support the students’ conscious career management, and in finding job that suits to their knowledge and interests mostly.
Office of Quality Affairs – it aims to coordinate and operate university-wide satisfaction-surveys. It also initiates and supervises the development of monitoring tools such as staff publication- and CV-database.
Central Office of Information and Education Affairs – its main task is to manage the general educational information system (NEPTUN) of the university. The Neptun-system is responsible for all course and exam registrations and for many financial transactions related to education.
The other units are supporting the regular or background functions of the university, thus, due to their characteristics they generate significant information concerning immaterial resources:
Head office for Financial and Technical Affairs – it is responsible for the central budget and financing of the university, as well as infrastructure-maintenance, tender-coordination, controlling and administration.
Information Service Centre – an internal service unit which operates and develops the university’s IT-infrastructure.
Central Library – it provides various management knowledge and information on lecturers’ publications, on database of professional literature and about the books and magazines that are available for the university citizens.
By analyzing the abovementioned organizational units we could identify internal management reports that help us get a hold of the organizational intellectual capital. We have to emphasize that the availability of these pieces of information is a huge development in the university’s life, however, we can’t really talk about a mature, regular and well-established integration or flow of information in management reporting yet, as a result of underlying motivational factors. Beside the abovementioned available information and internal reports (see: Table 5 and 6), the regular satisfaction surveys carried out at the university are other management tools of managing intellectual capital. The result of these surveys includes complementary (soft) information about the opinion and satisfaction level of students, employers, lecturers and researchers. The follow-up of the students’, academic staff’s and employers’ requirements is also related to the principle of the quality management model of the university (EFQM) and applies several indicators for the intellectual capital. The surveys take place every 2-3 years; the latest was in 2009. 3.2.3 The availability of information on the intellectual capital The Corvinus University of Budapest is supposed to report a part of the abovementioned information regularly. The obligation also applies to data on the library (see CUB [2006]). The number of employees should be submitted to the Social Insurance Office. The number of awarded teaching qualifications is to be submitted every half year to the Ministry of Education and Culture because it determines funding the university receives. Because of this latter aspect, reporting the number of teacher qualifications and student data is not only an obligation, but is also the interest of the university. The number of staff in research and development, the cost of research and development and the number of research centres must be presented in the annual research centres’ reports. Accordingly two-fifth of the discussed data-categories are to be measured and submitted to external parties regularly. Beside this regular reporting-obligation the university had to renew its accreditation in 2008. The Hungarian Accreditation Committee has specified and suggested on data that should or could be supplied, which also contains several indicators or components of intangible resources. Two third of the data in the external reporting table are included in the documents prepared for the accreditation, and they partly overlap with the obligatory data-supply mentioned before. Similarly, the last Institute Development Plan prepared in 2007 – as one-time report – contains intellectual capital information which also overlap with previously mentioned data considerably.
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Viktória Bodnár et al. Table 5: The main regular internal reports and other sources of information (human, structural and relational capital) Content of information A) Human capital: Report on Human resources (Breakdown in e.g. education, income, personnel expenses etc.)
Sender
Recipient
Frequency
Head office for Financial and Technical Affairs
Economic Council, Senate, Rector’s cabinet, deans
Quarterly
Central Office of Information and Education Affairs
Vice-rector of Educational Affairs
Semi-annualy
Technical-infrastructural expenditures (maintenance, refurbishment, trusteeship) in a faculty breakdown
Head office for Financial and Technical Affairs
Economic Council, Senate, Rector’s cabinet, deans
Quarterly
IT costs in faculty breakdown (IT-Controlling Report)
Information Service Center
Director
Annually
Library performance report: - Documents and services (including on-line) - Cost of document development - Library infrastructure C) Relational capital: Student preferences and satisfaction analysis (freshmen)
Central Library
Members of Senate, Annually rector, Vice-rectors, deans
Office of Quality Affairs (Cooperation with: Carrier Office)
Vice-rector of Educational Affairs
Annually / semiannually
Freshmen in a financial status breakdown
Central Office of Information and Education Affairs
Vice-rector of Educational Affairs, Senate
Annually
Cooperation among units in calls for tender
Office for Tenders
Available for lecturers through internal IP address
Monthly
Carrier Office performance report: - Number of advertising companies - Number of positions in diff. categories - Number of sent CVs and applications - Number of Job Fair participants
Carrier Office
Vice-rector of Educational Affairs, Also: rector, deans
Annually
Office of International Relations
Head of the office
Continuous monitoring and ad hoc reports
B) Structural capital: Course information, capacity usage (e.g.: educational infrastructure)
- Number of visits on jobsdatabase - Number and type of events Exchange students and lecturers in according to partner universities (e.g.: faculty or year breakdown etc.)
Central Library Library performance report: - Borrowing (documents) - International exchange (number of partners, documents)
Members of Senate, Annually rector, Vice-rectors, deans
Aid for vocational studies per company Office of Corporate and faculty Relations
Rector Semi-annually (on website as well)
Chair revenues
Rector
Office of Corporate Relations
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Annually
Viktória Bodnár et al. Table 6: The main regular internal reports and other sources of information (process and result dimension) Content of information D) Processes and results (output): Student information in a financial status breakdown
Sender
Recipient
Frequency
Head office for Financial and Technical Affairs
Economic Council, Senate, Rector’s cabinet, deans
Quarterly
Student satisfaction report (graduates) Office of Quality Affairs, Carrier Office)
Self
Annually / semiannually
Money circulation (material, personal and cumulated expenses in faculty breakdown)
Head office for Financial and Technical Affairs
Economic Council, Senate, Rector’s cabinet, deans
Quarterly
Tenders and sum won by units and partners
Office for tenders
Monthly
Number of external publications per lecturer
Central Library
Available for lecturers through internal IP address Director
Library performance report: - Number registered members - Member and file traffic (on-line as well) - Number of library events
Central Library
Continuous (Replaced by on-line publication system which is in progress.) Members of Senate, Annually rector, Vice-rectors, deans
The internal management reports contain also a large amount of information concerning the different aspects of intellectual capital. The sources of data are very heterogeneous there is no unified and integrated information system. Consequently, we can make the conclusion that at present:
The CUB does not report externally most of the intangible resources data constantly, only the basic headcounts, some indicators on research and development and data from the library are calculated regularly;
The content of information from the internal reporting system is many times overlapping with the external reports, but these data draw up more frequently. The internal management reporting system is under development, it does not mean an integrated management framework.
The other components of the educational and research infrastructure and the university’s relation network are explored only occasionally.
4. Discussion and further research directions The analysis of intangible resources management practices at CUB revealed that the university has not developed a standardised framework, yet, like the Wissensbilanz-model. Nonetheless there are some indicators, which strive to measure the intangible resources, but mainly occasionally. Moreover, there are some organisational units and management tools at CUB which are responsible for the improvement of education and for supporting academic staff in their development, for reinforcing innovation and research activities, for management of university’s relation networks, and for ensuring quality of operation. Data-collection and reporting on intellectual capital have evolved in response to external legal requirements and to the information needs of different external stakeholders such as Ministry for Education and Culture or Central Statistical Office. It is also a problem that these needs are not coordinated with each other so the university has to provide data slightly differently to each stakeholder. Some of the data needs are occasional or the needs change frequently therefore it is not easy to develop stable framework.
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Viktória Bodnár et al. The measurement and management of intellectual capital at CUB have the following specific characteristics as a result of the irregular and changing obligations in data supply:
The external reports get priority due to the legal requirements and the compulsory data supply to the Ministry. The fact that the reports must be submitted to various external parities, divert the attention from the importance of inner data supply.
It is the ministries’ changing requirements and not the strategic plans that determine the content, structure and frequency in measurement of the indicators presented in the external reports. Consequently the aim of their measurement is to satisfy external obligations rather than pulling down and monitoring the strategic plans, fulfilling other information requirements and supporting decision making in the university.
The external and internal requirements are irregular, occasional and the various parties expects different- often overlapping-information to provide from the university. The requirements’ irregularity and overlapping content result in parallelisms in the reports and in sub-optimal resource utilisation.
The content of internal reports are highly determined by the requirements of external reporting. They contain mainly financial data and some measures in kind, but we can not find e.g. comparisons or per unit indicators. The real using of management information in management decisions can be also next step of research.
Till 2009, the practices that CUB applied for reviewing and managing its intellectual capital were isolated from each other, their integration into a unified system was not common practice. Recently, a project was initiated by the CFO of the university to implement a data warehouse system to integrate the management information that is collected and used by the management of CUB. The DW project aims at providing a solution to integrate the data background of the management processes of the university and also developing the currently used reports and indicators into a comprehensive intellectual capital reporting system. The first phase of the project aimed at exploring all of the information demand of the individual members of the management of CUB and structuring it into a comprehensive framework. Anyhow, the results were ambiguous, because the expectations were very various and inconsistent, and the management did not have the willingness to come to a consensus in it. Nevertheless, the intention of using this project as an opportunity to create a system that can serve the information need of the different decision makers at the university in a consistent way is still present. Besides making serious steps towards an intellectual resource focussed planning, performance evaluation and reporting system at the university, the recent survey shows a very disappointing picture about the commitment and opinion of the academic staff on them. The first employee survey was made at 2007, targeting the academic staff. Among the questions there were some that focussed directly on the opinion of the staff on the intellectual resource cognition of the university management. The results of the 2007 survey showed the following:
The majority of the staff was not informed about the strategic preferences and strategies of CUB.
They did not experienced a clear performance focus, and did not face clear expectations from their super-ordinates.
They thought that the individual performance measurement and evaluation system was useless and – sometimes – incorrect.
The majority of the staff felt that the academic and service quality is not at all a driver of their future perspectives.
The reason behind those disappointed results could be the immaturity of the management systems and processes that we mentioned before (e.g. strategic planning, key performance indicator system, individual performance evaluation system, quality assurance system, carrier management system etc.). Our research team accepted this reasoning that is why it was rather disappointing to see, that the 2009 survey covering the same issues showed even less satisfaction and more frustration at the academic staff level. In the next phase of our longitudinal research we would like to focus on this discrepancy between the effort of the management to develop the performance and intellectual resource awareness in their managerial practice – supported by sophisticated formal systems – and the perception of the academic staff about them.
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References Arbeitskreis IWR (2001): Kategorisierung und Bilanzielle Erfassung immatieller Werte. Arbeitskreis „Immaterielle Werte im Rechnungswesen” der Schmalenbach-Gesellschaft für Betriebswirtschaft e. V., Der Bertieb, Heft 19, 11. Mai, p. 989-995. Bontis, N. – Dragonetti, N. C. – Jacobsen, K. – Roos, G. (1999): The knowledge toolbox: a review of the tools available to measure and manage intangible resources. European Management Journal, Vol. 17., No. 4., p. 391- 401. Brennan, N. – Connell, B. (2000): Intellectual Capital: current issues and policy implications. Journal of Intellectual Capital, Vol. 1., No. 3., p. 206-240. CUB (2006): Éves minisztériumi jelentés a könyvtári tevékenységről (Annual library performance report), Corvinus University of Budapest (available in Hungarian). CUB (2007): Intézményfejlesztési terv (Institute development plan.), Corvinus University of Budapest (available in Hungarian). CUB (2008a): Intézményi önértékelés: 2007-2008. Készült a Magyar Akkreditációs Bizottság számára. (Selfevaluation report, 2007-2008. Made for the Hungarian Accreditation Committee, Institutional Evaluation Process.), Corvinus University of Budapest (available in Hungarian). CUB (2008b): Corvinus University of Budapest: Self-evaluation report, 2007-2008, European University Association, Institutional Evaluation Programme. Corvinus University of Budapest. Edvinsson, L. (2002): Corporate Longitude. What you need to navigate the knowledge economy. Financial Times – Prentice Hall, London (etc.). FH-BFI-Wien (2006): Wissensbilanz 2006 der Fachhochschule de BFI Wien. http://www.fhvie.ac.at/files/Wissensbilanz%202006%20Web.pdf Gu, F. – Lev, B. (2001): Intangible assets – measurement, drivers, usefulness. Boston Universitiy and New York University. http://pages.stern.nyu.edu/~blev/intangible-assets.doc Kannan, G. – Aulbur, W. G. (2004): Intellectual capital: Measurement effectiveness. Journal of Intellectual Capital, Vol. 5., No. 3., p. 389-413. Kaufmann, L. – Schneider, Y. (2004): Intangibles – A synthesis of current research. Journal of Intellectual Capital, Vol. 5, No. 3., p. 52-63. Mouritsen, J. – Bukh, P. N. – Flagstad, K. – Thorbjornsen, S. – Rosenkrands Johansen, M. – Kotnis, S. Thorsgaard Larsen, H. – Nielsen, C. – Kjaergaard, I. – Krag, L. – Jeppesen, G. – Haisler, J. – Stakemann, B. (2003): Intellectual Capital Statements – The new guideline. Danish Ministry of Science, Technology and Innovation, February. OM (2008): Research and development, tenders and programs in the higher education. Report of Ministry of Education and Culture about the year 2006. January, 2008. Pfeil, O. P. (2004): Earnings from Intellectual Capital as a Driver of Shareholder Value. Haupt Verlag AG, Bern. RICARDIS (2005): Reporting Intellectual Capital to Augment Research, Development and Innovation in SMEs. End-Report, Introduction & Part 1, Version July 15 th, 2005. Roslender, R. – Fincham, R. [2001]: Thinking critically about intellectual capital accounting. Accounting, Auditing & Accountability Journal, Vol. 14., No. 4., p. 383-398. Sveiby, K. E. (2001a): A knowledge-based theory of the firm to guide in strategy formulation. Journal of Intellectual Capital, Vol. 2., No. 4., p. 344-358. Sveiby, K. E. (2001b): The New Organizational Wealth: Managing and Measuring of Konowledge-Based Assets. Hungarian translation, KJK-Kerszöv, Budapest. Universitätsgesetz (2002): Bundesgesetz über die Organisation der Universitäten und ihre Studien (Universitätsgesetz 2002). BGBl. I Nr. 120/2002. http://www.bmwf.gv.at/uploads/media/0oehs_ug02.pdf Wissensbilanz-Verordnung (2006): Verordnung der Bundesministerin für Bildung, Wissenschaft und Kultur über die Wissensbilanz (Wissensbilanz-Verordnung – WBV). BGBl. II Nr. 63/2006. http://www.bmwf.gv.at/uploads/media/wbv.pdf
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Distributed Artificial Intelligence in Organisational Management Stefan Adrian Boronea, Mihai Horia Zaharia and Gabriela Atanasiu “Gheorghe Asachi” Techincal University, Iasi, Romania
[email protected] [email protected] [email protected] Abstract: Organisational management has maintained a rapid development rate over the last decade, mostly because of the continuous value added to the organisational know-how and the constant increase in the size of organisations. In order to increase the accuracy of management evaluations for individual members and teams’ efficiency over time and take rapid decisions during key company restructuring processes, we propose a new approach that combines techniques from multiple research domains such as artificial intelligence, fuzzy systems, data clustering and multi-agent systems (MAS). The system performs a social network analysis within the organisation using Fuzzy System Modelling (FSM) and Dempster-Shafer belief and reasoning models, providing the organisation’s management with a more realistic view over the actual interactions between different individual members and teams as opposed to the somewhat fixed team structure. A system design is proposed, along with the main components and interactions: knowledge database, social networking, decisional MAS and organisational workflow management software. The impact of social interactions on individual and group performance is studied to determine actual stakeholders (super-stakeholders) and influence patterns within the teams. We therefore introduce two distinct notions for individuals and group cohesion: real influence spheres which resemble actual team structure and virtual influence which are determined through interactions between individuals, independent of the team structure. These spheres will be included in the decisional MAS as agents which monitor the progress of each member within their reach and help with management decisions, providing them with constant feedback. We then study how virtual influence spheres interact with the real influence spheres and how we can determine the influence of multiple super-stakeholders over the same group of people. The results of these analyses, along with the skill set and performance ratings which are given by the organisation’s management are used to determine how changes in team structure could improve an individual’s efficiency over time. Conclusions are drawn over the possible uses of this system in actual organisations, along with a discussion over possible directions towards extending the proposed multi-agent system. Keywords: MAS, FSM, agent, fuzzy, organisational management
1. Introduction The rapid growth of the organisational management phenomenon requires new methods and an increased speed for the organisation’s stakeholder redeployment according to new needs and development workflows. First we must find a method of determining the key members (superstakeholders) in the involved teams in order to allow this process to take place. Also to achieve an optimal dynamic of the organisation, some important basic factors such as the involvement of team members in their work, the organisational social network and the skills for each member have to be taken into account. Thus, our study proposes to combine two apparently incompatible types of information in order to gather the actual development process: structured performance data about the organisation and its members and unstructured data about the interactions within the previously mentioned groups (relatively difficult to formalize). In order to allow the rapid increase in organisational size and dynamics, Information Technology (IT) systems were introduced, which allow a vast series of operations to be made for organisation team members, indifferent of geographic location or team. These systems are already being used in project management, human resources and other departments in order to track the evolution and grade the performance of team members. Agent technology has become present in many domains and has found many applications, especially due to its simple concepts and the similarities between agents and human behaviour. The current state of the art solutions in MAS introduce and combine different methods in order to achieve the desired results. Thus, artificial intelligence algorithms have been used in this field. Fuzzy systems modelling (FSM) and the Dempster-Shafer theory have been introduced in (Yager 2008) as a solution to solving the uncertainty in modelling social behaviours and trends. Although some solutions and research exist on this matter (Ahmad 2008), we have found no solution which combines all of these ideas into a viable integrated system which would allow organisations to
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Stefan Adrian Boronea et al. rapidly change members from one team to another or determine key members or disposable members from their current structure in correspondence with the information and adaptability of each member. This system also provides the means for decision makers within the organisation to see how a change in the organisation structure would affect the teams as a whole and the team members in particular. A profit analysis is provided based on the knowledge gathered in previous projects for each team member, where the previously mentioned factors are taken into account.
2. Structures in organisational management Some patterns have emerged throughout the existing organisations in order to solve the previously discussed issues. Therefore, we have selected for the purpose of our research four of the most important systems which build up the organisational cyberspace (see Figure 1).
Figure 1: An overview of the systems involved in the organisational management
Knowledge database – any organisation must be able to provide quantitative data about its members, its processes and its evolution in time in terms of size, profits, efficiency, team composition, team members’ skills etc. In order to achieve the required expert system, data mining algorithms and tools need to be integrated into the organisational workflow.
Internal and external social networking – the social networking within an organisation must be closely followed in order to allow the collection of data (which will eventually be stored in the knowledge database for the Multi-agent System to use). Depending on the organisational policies, social networking may be discouraged during working hours, which might affect the stability and evolution of the considered team.
Organisational workflow management software – the actual processes involved in the organisational workflow, specific to each group.
Decisional Multi-agent System (MAS) – the base for our research. This component allows an organisation to redeploy its members according to the information within the knowledge database and the social networking patterns within the organisation. Thus, we consider each team member as a node, and the teams as influence areas (agents).
3. Knowledge management in organisations The basics of knowledge learning in organisations have been set by (Fudenberg and Levine 1998), where a technical approach was considered rather than the previously determined sociological models (Lepenies and Hollingdale 1988). The human resource is considered the most important in managing an organisation, based primarily on the idea that each new member of the organisation comes with a set of skills which are useful to the group and serve the organisation’s interests. Our study is based on the presumption that the data referring to these skill sets is already stored in a standardised manner within a database or centralised system. A simple example is shown in Table 1.
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Stefan Adrian Boronea et al. Table 1: A basic skill set for an employee. Employee Johnson, M. Johnson, M. Johnson, M.
Category IT IT Social
Skill LINUX servers management Solaris servers management Patience
Status Acquired Transmissible Required
This structure presents three basic states for a certain skill within the skill set: acquired (skills which have already been acquired by the individual), required (skills which are needed by the individual in order to achieve the level imposed by the management) and transmissible (skills which the individual may transmit to others who come in contact with him for a considerable amount of time).
4. Multi-agent Systems (MAS) Multi-agent systems have been developed to accommodate the need for fast, mobile, easy, reliable intelligent systems which rapidly adapt to changes in the environment. The multi-agent paradigm allows straightforward modelling of human-like behaviour, where an agent is aware of the changes which occur and communicates with other agents in order to solve the tasks which it was designed to do. In our study we propose an agent structure which facilitates the flow of information from the existing systems within the organisation and the new informational structures involved in evaluating and optimising the teams’ structure. Also, since the decisional process involves human behavioural study, in 4.2 we propose a fuzzy approach for the analysis of communication patterns and possible solutions for optimal team response.
4.1 Agent types The system we propose is based on the following types of agents:
Team member agent – each team member is represented as an agent in our system, having the following characteristics: individual profit, efficiency (based on the marks given by the management – available through the Knowledge database), social interactions (with whom, how frequent, for how long) and skills set.
Real influence sphere monitoring agent – the actual teams, as specified in the organisational structure. Each influence area has a centre point around which different members of the team revolve.
Virtual influence sphere monitoring agent - the virtual communities consisting of people who interact through social networking during working hours.
Social networking proxy – retrieves the social networking information from various sources (e.g. system logs, SMTP logs, phone use statistics – a good example of behavioural study from interaction data collected from mobile phones being that of Eagle in (Eagle 2006) and (Eagle et al. 2007))
Knowledge retrieval agent – the manager which keeps the information regarding agent skills updated.
Proactive team designer agent – has the tasks of redesigning the teams after each iteration and presenting the new organisational structure to the management level.
4.2 Fuzzy Systems Modelling (FSM) for MAS As Yager has shown in (Yager 2008), human behavioural modelling requires the ability to represent and manipulate imprecise cognitive concepts. It also needs to include the uncertainty and unpredictability of human action. A fuzzy system modelling (FSM) approach (Pedrycz 2007) is most effective when it comes to representing and modelling human cognitive concepts. We believe that such an approach would also be efficient in the modelling of human behaviours and interactions within an organisation, thus fulfilling the previously discussed tasks which would allow the MAS system to quantify human interactions. The FSM technology and its role in human behavioural modelling have been described in (Yager 2008) as a technology for the development of semantically rich rule based representations that can model complex, nonlinear multiple input output relationships or functions or systems. This methodology provides a framework for building models which can include both the complex cognitive concepts and unpredictability needed to model human behaviour. The basis for the FSM models are rules expressed in the following semantic form:
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Stefan Adrian Boronea et al. When U1 is Ai1 and U 2 is Ai 2 , . . . and U r is Air then V is Di . We can see that the variables U1 ,...,U r represent the input (antecedent) variables, while V is the output (consequent) variable. Each Aij is a term corresponding to a value of its associated variable, represented as a fuzzy subset over the domain X j of the associated variable U j . The output Di is also defined as a fuzzy subset of the domain Y of V. For the defining rules the antecedent specifies a condition that if met allows us to infer that the possible value for the variable V lies in the consequent subset Di . For each rule the antecedent defines a fuzzy region of the input space, X 1 X 2 ... X m , such that if the input lies in this region the consequent holds. Taken as a collection the antecedents of all the rules form a fuzzy partition of the input space. In order to determine the actual output value (or values) for the current system model, we will apply the Mamdani-Zadeh paradigm (Yager 1994), through a process called “reasoning”. The most interesting property of this method is that the output of a FSM can be a distinct value from the output of the system. Assume the input to a FSM consists of the values U j x j . In the following we shall use the notation
Aij to indicate the membership of the element x j in the fuzzy subset Aij . This can be seen as the degree of truth of the proposition U j is Aij given that U j x j . The procedure for reasoning used in the Mamdani-Zadeh method consists of the following steps:
For each rule calculate its firing level i Min j Aij x j
Calculate the output of each rule as a fuzzy subset Fi of Y where Fi Min i , Di y
Aggregate the individual rule outputs to get a fuzzy subset F and Y where F y Maxi Fi y .
A possible alternative is to compress the fuzzy set F into some precise value from the output space Y (defuzzification). The preferred approach in this case is taking a kind of weighted average using the membership grades in F to provide the weights. Using this method we calculate the defuzzification value as y
yi F ( yi ) i
F ( yi )
.
i
These fuzzy sets may also be expressed in a linguistic form as mentioned in (Yager 2004), so that this method will have an output which is easier to comprehend by less technical decision-making personnel.
5. System design and features Figure 2 shows a possible system configuration, with 4 teams of people (agents) and the corresponding virtual spheres (communication patterns) only for agents from distinct real influence spheres (no inner-sphere virtual spheres are shown). The following paragraphs describe the methods for determining critical variations within the organisation’s knowledge flows and possible scenarios which the management may consider for a more efficient cooperation and possibly more profitable business.
5.1 Detection of the virtual influence spheres The first task that our system needs to solve is determining the actual team structure in the system (through a process called community detection). Newman (Newman 2004) describes and exemplifies the possible approaches to this problem: bottom-up “sociological” or top-down “computer science”. The latter involves analysing communication patterns within the organisation in different forms (email, phone conversations and other internal communication logs).
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Figure 2: The overview of an organisation from the MAS perspective. Note that the virtual influence spheres within the real influence spheres were not displayed for clarity Several powerful community detection algorithms have been discussed and compared in (Steinhaeuser 2008), reaching the conclusion that a threshold method with edge weights based on node attributes is optimal in identifying the community structure in a large system (containing millions of nodes and edges). The Node Attribute Similarity (NAS) algorithm – as its name suggests – bases its analysis on node attributes (which is different from other similar algorithms) when determining the community structure (in our case, the virtual spheres). NAS starts with a previously determined edges weights, which in our case will be considered the communication index cI (i, j ) between the two individuals (nodes i and j ):
wna (i, j ) cI (i, j ) pij initial
k
dk d max
where: pij is the percentage of interactions which individual i had with j , d k is the duration of the discussion (or length for text messages) and d max is the longest duration (or length). The basic formula in NAS for calculating an edge’s weight between two nodes i and j is:
if i.ac j.ac , wna (i, j ) wna (i, j ) 1 But in order to speed up the process of knowledge exchange, we need to search for complementary attributes instead of equal attributes, in particular the status attribute pairs (required, transmissible) and (transmissible, required). Therefore, the previous equation becomes:
if (i.ac , j.ac ) (required , transmissible),(transmissible, required ) , wna (i, j ) wna (i, j ) 1 This way we allow the process described in the next paragraph to start with a more accurate system structure, closer to the ideal one in which every individual requiring a skill has a teacher for that skill within one of his virtual spheres (provided he exists in the organisation). The weights of the edges are then normalised for each attribute an using a normalising constant α:
wna (i, j ) (1 i.an j.an ) We may then apply a threshold value t for the edges’ weights in order to achieve maximum performance. Therefore, for two nodes i and j we may conclude that:
(i, j ) in the same virtual sphere, w(i, j ) t (i, j ) in different virtual spheres, w(i, j ) t 5.2 Agent-virtual sphere interactions Let us consider the case in Figure 3. Here we can see that agent A1 influences through the virtual influence sphere VS1 the agent A4. We intend on determining the following information in this situation:
What is the influence of A1 on A4 (in terms of profit, efficiency and/or skill)?
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Figure 3: An organisation structure containing two teams (real influence spheres) Using the method described in 4.2, we may assess the possible ranges for these variables by taking into account the involved agents’ skills, individual yearly profits to the organisation (k$) for the current iteration – PA ,i , the normalised efficiency score which he received from the management – E A ,i and k
k
the weights calculated in subsection 5.1. Using these values as input variables for the fuzzy system, by considering the steps in 4.2, we may determine a fuzzy set for each directly connected nodes in our graph. For the situation in Figure 3 we will have to quantify the variables previously specified (e.g. high, normal, low). By building input-output rules (e.g. when A1’s profit is high and A1’s efficiency is low and A1 has a skill which may be transmitted to A4 then A4’s profit increases by 5%, A4’s efficiency increases by 10% and A4 has a 30% chance of mastering the skill from A1 in the next cycle), we may determine the possible fuzzy sets for the next cycle. For each input-output rule we must compute (for every pair of connected nodes) the relevance (the matching situation) of the output value as described in 4.2. The result will give us a view of what are the most probable events to take place in the current situation: the expected profit and efficiency modifications on A4 and how has this interaction influenced A4’s skills set. This method could also help determine which of the node’s neighbours has the most effect on the individual’s performance and knowledge, which we named super-stakeholders. What is the influence of A1 on A4’s real influence sphere (i.e. its members)?
Although the previous paragraph described an ideal situation, in which a single agent interacts with a virtual sphere, real-life situations tend to have an individual interacting with more virtual spheres (apparently unconnected through the real influence spheres) at the same time and others where more individuals interact with the same sphere at the same time. Here is where the Dempster-Shafer theory (Shafer 1976) proves useful – the ability to aggregate multiple belief structures on the same variable. First, we must introduce a few concepts used in the Dempster-Shafer theory. Yager (Yager 2008) describes a Dempster-Shafer belief structure m as a collection of non-null subsets Ai of
X called focal elements and a set of associated weights m( Ai ) so that m( Ai ) 0,1 and
m( Ai ) 1 .
For every subset B of X we may calculate the probabilistic boundaries for the
i
occurrence of this situation: plausibility and belief. Plausibility of B , Pl ( B ) is defined as
Pl ( B )
m( Ai ) and is the lowest probability of occurrence. Belief of B , Bel ( B ) is defined as
m( Ai ) and is the highest probability of occurrence. We may simply determine that for
Ai | Ai B
Bel ( B )
Ai | B Ai
all subsets B of X Bel ( B) Pr ob( B ) Pl ( B) . An important property of same variable Dempster-Shafer belief structures is their ability to aggregate. Thus, if we wish to find the associated belief structure for two independent structures (such as is our case, where more individuals usually influence a single person). The resulting belief structure
m m1 m2 describes the conjunction of belief structures m1 , with focal elements Ai , i 1, n1 and m2 , with focal elements B j , j 1, n2 . The weights for this conjunction structure are given by the
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Stefan Adrian Boronea et al. equation m( Fk )
1 m1 Ai m2 Bj , where T m1 Ai m2 B j 1T A B
i
. By applying this
j
method recursively at every iteration we may determine how a single individual affects a real influence sphere or a virtual influence sphere. Determining this may provide the management with information about how the transfer of that individual to another team would affect both teams. After this process, another task would be to determine what the best team configuration for the given organisation is. A particle swarm social model (PASS) approach for group learning was presented in (Cui 2008), where a self-organising groups approach is taken in order to allow an optimal social learning process. This could easily be integrated within the system so that the requested optimisation (in terms of profit and/or efficiency) is achieved.
6. Conclusions The current study has shown how multi-agent systems, algorithms and paradigms specific to the artificial intelligence domain such as fuzzy system modelling and Dempster-Shafer theory may be used in determining optimal organisational structure and knowledge workflow. Future improvements for the system will primarily have to make a more thorough analysis of the communication content and patterns between the individuals. As shown in (von Neuforn 2008), in-depth text analysis could provide useful information in terms of relevance and impact on the knowledge workflow. Here probabilistic methods are applied to text messages in order to include them in predefined categories, thus eliminating irrelevant or day-to-day communication (e.g. FYI messages). Our work is focused primarily on the design of the MAS and integration with existing information flows within organisations. The currently proposed system does not deter management’s role in determining key individuals and the need for constant training, but it provides the means for better communication and knowledge diffusion among team members, acting as an augmentation tool and a general organisational overview tool. A notable disadvantage to this model is the lack of social and relational background information and factors which would prevent the knowledge flow. Nevertheless, constant management supervision could determine possible unwanted outcomes and take immediate action to solve the emerging situation. Also, an automatic XML-based scripting would efficiently allow managers to add sets of rules specific to each organisation’s needs, thus improving the performance and adaptability to custom individual situations.
References Ahmad, M. A. and Srivastava, J. (2008) “An Ant Colony Optimization Approach to Expert Identification in Social Networks”, Social Computing, Behavioral Modeling, and Prediction, pp. 120-128, Springer, New York. Cui, X. et. al. (2008) “Particle Swarm Social Model for Group Social Learning in Adaptive Environment”, Social Computing, Behavioral Modeling, and Prediction, pp. 141-150, Springer, New York. Eagle, N. and Pentland, A. (2006) “Reality Mining: Sensing Complex Social Systems”, Personal and Ubiquitous Computing, Vol 10, No 4, pp 255-268. Eagle, N. et al. (2007) “Inferring Social Network Structure using Mobile Phone Data”, PNAS. Fudenberg, D. and Levine, D. K. (1998) The Folk Theorem in Repeated Games with Discounting or with Incomplete Information, Econometrica Vol 54, pp 53-54. Lepenies, W. and Hollingdale, R. J. (1988) Between Literature and Science: The Rise of Sociology, Cambridge University Press, Cambridge. Newman, M.E. (2004) “Finding and Evaluating Community Structure in Networks”, Phys. Rev. Nguyêñ, H. T. and Sugeno, M. (1998) Fuzzy systems: modeling and control, Springer, New York. Pedrycz, W. and Gomide, F. (2007) Fuzzy Systems Engineering: Toward Human-Centric Computing, John Wiley & Sons, New York. Shafer, G. (1976) A Mathematical Theory of Evidence, Princeton University Press, Princeton, New Jersey. Steinhaeuser, K. and Chawla, N. (2008) “Community Detection in Large Real-World Social Network”, Springer Science+Business Media LLC, New York. Von Neuforn, D. S. and Franke, K. (2008) “Reading Between the Lines: Human-centred Classification of Communication Patterns and Intentions”, Social Computing, Behavioral Modeling, and Prediction, pp 218228, Springer, New York. Yager, R. R. and Filev, D. P. (1994) Essentials of Fuzzy Modeling and Control, John Wiley & Sons, New York. Yager, R. R. (2004), "On the retranslation process in Zadeh's paradigm of computing with words", IEEE Transactions on Systems, Man and Cybernetics, Vol 34, pp 1184-1195. Yager, R. R. (2008) Human Behavioral Modeling Using Fuzzy and Dempster-Shafer Theory, Springer Science and Business Media LLC, New York.
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Tacit Knowledge Sharing in Organizational Knowledge Dynamics Constantin Bratianu and Ivona Orzea Academy of Economic Studies, Bucharest, Romania
[email protected] [email protected] Abstract: The purpose of this paper is to analyze knowledge sharing as a core component of the organizational knowledge dynamics, and to present some specific organizational barriers in the Romanian business environment. Tacit knowledge represents the direct result of the interaction between the individual and the external environment. It is the knowledge acquired through direct experience, comprising also feelings, intuitions, beliefs and cultural values. Tacit knowledge sharing contributes directly to enhancing knowledge creation and to obtaining a competitive advantage in the business environment. However, tacit knowledge sharing may have many organizational barriers, especially in those new economies developed in the former socialist countries. We based our research on the Romanian companies, since our country suffered from a dictatorial socialism regime. Our research demonstrates that the organizational culture developed during the socialism regime has been based on fear, individual control, mistrust and a dictatorial managerial style. Having in mind the background of the Romanian’s culture and the requirements of a successful process of knowledge sharing our research aims at identifying the barriers that employees are faced with in sharing their knowledge and propose solutions to overcome these and improve the process of knowledge sharing within the Romanian business environment. Keywords: Knowledge dynamics, knowledge sharing, organizational barriers, tacit knowledge, trust
1. Introduction Defining knowledge is not an easy job because it is one of those concepts that don’t have a universal definition; different authors approach the concept in different perspectives and from different angles, resulting in multiple definitions. It can be looked upon as the processing of information with the main purpose of gaining understanding of the events occurring in the surrounding environment. It is a concept deeply influenced by the personality of the holder, its beliefs, attitudes and culture. Knowledge consists of information, technology, know-how and skills. Value and sustainability are created from the integration of these resources better than competitors (Endres, Endres, Chowdhury, Alam, 2007). Knowledge cannot be substituted or imitated, hence the key strategic asset resource character. Managing knowledge means to create an environment within the organization to facilitate the creation, transfer and sharing of knowledge (Bratianu, Vasilache, 2009). The most discussed activity in the process of knowledge management nowadays is knowledge sharing (Al-Alawi, AlMarzooqi, Mohammend, 2007; Duguid, 2005; Foos, Schum, Rothenberg, 2006; Lubit, 2001). Knowledge abounds in organizations, but its existence does not guarantee its use. And thus knowledge sharing leads to faster knowledge connection with portions of the organization that can greatly benefit from this new knowledge (Davenport, Prusak, 2000). This made Nonaka and Takeuchi (1995) recognize that sharing tacit knowledge among multiple individuals with different backgrounds, perspectives and motivations is a critical step for the organizational knowledge creation to take place. There are many studies (Riege, 2005) showing that knowledge sharing activities have not accomplished their objectives to manage companies’ knowledge assets and skills due to a large diversity of potential sharing barriers. The purpose of this paper is to analyze knowledge sharing as a core component of the organizational knowledge dynamics, and to present some specific organizational barriers in the Romanian business environment.
2. Why the focus on sharing tacit knowledge? Among the first studies of knowledge was the one of Michael Polanyi (1983), admitting that “we can know more that we can tell”. With the help of experiments he was able to demonstrate the existence of more types of knowledge that the human possesses - the tacit knowledge and the explicit knowledge. There is the explicit knowledge, that can be described in formal language (manuals, expressions, procedures, repositories), the “know-what”; and there is the tacit knowledge, the knowledge that cannot be easily transmitted and expressed. Tacit knowledge represents the direct result of the interaction between the individual and the external environment. It is the knowledge acquired through direct experience, comprising also feelings, intuitions, beliefs and cultural values. An explicit integration cannot replace its tacit counterpart. For example, the skill of a driver cannot be replaced by thorough schooling in theory. Tacit knowledge forms part of all knowledge (Polanyi, 1983,
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Constantin Bratianu and Ivona Orzea p. 20). Hence, if explicit knowledge can easily circulate within the organization, the limited access to tacit knowledge raised the interest of the organization to develop strategies for employees to bring their tacit knowledge into the equation. The principle equation is: better and purposeful sharing of useful knowledge translates into accelerated individual and organizational learning and innovation through the development of better products that are brought faster to a target market, thus enhancing market performance. For more than forty years Romania formed part form the Communist bloc. During this period the mentality of Romanian citizens has undergone multiple changes. The economy, the value system and the beliefs were also affected by the changes. The rights and people’s freedom were severely affected. Control over society became stricter and stricter. The very idea of being controlled induced a permanent tension in people and created an organizational culture based on fear (Bratianu, Vasilache, 2009). Changing the political regime faced Romania with another wave of changes, and once again the country was not prepared to make the switch from socialism to capitalism. The Government took economical and political decisions based on their historical and cultural traditions. Unfortunately these changes were faced by powerful inertia forces. When changes take place in the society the formal rules change but the informal constraints are still present for a long period of time. The same happens with the cultural values of people and the inertial thinking pattern, which cannot be changed overnight (Bratianu, Vasilache, 2009). There is a widespread agreement that knowledge assets are difficult to replicate and that they are fundamental sources of competitive advantage in open economies. The advantage of companies seems to be increasingly predicated on the ability of identifying and sharing knowledge so that the company can exploit it (Teece, Pisano, Shuen, 1997). Research in the field of knowledge sharing and transfer (Szulanski, 1995, Szulanski, 1996, Jensen, Szulanski, 2004) indicates that the process of sharing and transferring knowledge is a very difficult, sticky one. Szulanski (1995) introduced the concept of stickiness in knowledge transfer in order to underline the difficulty of transferring knowledge. Stickiness is seen as an important determinant of the degree of diffusion and utilization of superior knowledge and more broadly the ability of a company to grow and prosper by replicating existing assets and capabilities (Szulanski, 1995). Therefore the importance of exploring the factors of increasing or decreasing the stickiness when sharing knowledge, within an organization. With the help of a study undergone on major companies operating worldwide, Szulanski (1995, 1996) proved the intrinsic and the extrinsic origin of stickiness when talking about knowledge sharing and transfer. Knowledge sharing is thought to be influenced by factors both at the individual and at the organizational level. At the individual level some of the factors that could enhance knowledge sharing are the trust level in co-workers, whether or not the negative prior experiences with knowledge sharing have influenced the willingness of the employee to share his or her knowledge and last but not least the intrinsic motivation of the employee. Most people are unlikely to share their knowledge and experience without a feeling of trust in the person in front of them, they need to trust that the people will not misuse their knowledge, and to trust that the information that one receives is accurate and credible due to the information source. The level of trust that exists between the organization, its subunits, and its employees greatly influences the amount of knowledge that flows both between individuals and from individuals into the firm’s databases, best practices achieves and other records (De Long , Fahey, 2000). Knowledge is power and can lead to inequalities in status. Sharing one’s knowledge can lean to a perceived lack of job security. People can regard sharing their knowledge and experience as weakening their corporate position, their power within the company. There often is present in a working environment the fear among people that sharing their knowledge reduces their job security because they are uncertain about the intent of the people to whom they share their knowledge to. In a company can also be present employees that intentionally take ownership of their knowledge and experience so that they receive recognition from colleagues and peers. The main objective of the research is to see the impact of the factors enhancing knowledge sharing (both at the organizational and at the individual level) on an inertial organizational culture. Can they be considered barriers to knowledge sharing in an inertial organizational culture? Is there still inertia of the Romanian companies in terms of knowledge sharing and cooperating?
3. Research hypothesis and methodology In order to answer all the questions raised above the following hypothesis were formulated:
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H1: There is a positive impact of factors at the individual level (trust, prior experience to knowledge sharing and intrinsic motivation) in relation to knowledge sharing in a given organizational culture.
H2: There is a positive impact of factors at the organizational level (rewarding systems, communication process and the willingness of the company to invest in people) in relation to knowledge sharing in a given organizational culture.
In order to test the research hypothesis the quantitative research was approached. This research was undergone with the help of a survey. The questions were designed to assess knowledge sharing according to each respondent’s opinion and perception regarding the six independent variables (trust level, personal experience, intrinsic motivation, rewarding system, organizational communication, employers willingness to invest in its employees) in close relation to the dependent variable (knowledge sharing in their organizational culture). In order to assess the accuracy of the measurement the variables were extracted solely from the literature, from the previous research of authors such as Nonaka and Takeuchi (1995), Davenport and Prusak (2000), Reige (2005). The first part of the survey’s questions was dedicated to identifying the respondent’s details (age, gender, educational level, position within the company) that might be influencing factors for the dependent variable. Independent variables were measured by obtaining the respondents level of agreement with the existence of the indicators in their work environment. In order to measure their level of agreement a Likert scale was used, ranging from 5=totally agree to 1=totally disagree. A total of 330 questionnaires were distributed to both public and private companies. In order to obtain as much as possible an objective answer to the inertia of the organizational culture in terms of the selected factors the respondents were youngsters, not educated in the old socialist regime, but that work in an organizational culture which is believed to still have a certain degree of inertia. The rate of response was 76.3 percent (229 questionnaire received back). The results were analyzed with the help of Statistical Package for Social Sciences (SPSS) version 17.
4. Analysis results The first step of the analysis was to use factor analysis (with principal components extraction) in order to investigate whether these thirty statements represent identifiable factors. The measure was initiated with the assumption that all variables are correlated to some extent. Therefore, those variables that share similar underlying dimensions should be highly correlated, and those variables that measure dissimilar dimensions should yield low correlations. Table 1: KMO and Bartlett's test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity
.815
Approx. Chi-Square
1999.438
df
435
Sig.
.000
The Bartlett’s test of sphericity can be used to test for the adequacy of the correlation matrix. The null hypothesis in case of Bartlett’s test is that the correlation matrix is an identity matrix, meaning that all the diagonal terms are 1 and all off-diagonal terms are 0. In the present analysis the Bartlett’s test of sphericity yielded a value of 1999.438 and an associated level of significance smaller than 0.001. Thus, the hypothesis that the correlation matrix is an identity matrix is rejected; the correlation matrix has significant correlations among at least some of the variables. Examining the number of common factors extracted, and their associated eigenvalues, the percentage of variance accounted for by each factor and using the criterion of retaining only factors with eigenvalues of 1 or greater, nine factors were retained for rotation. Table 2 presents the percentage of each factor accounted in total variance. Table 2: Total variance explained Component
Initial Eigenvalues Total
% of Variance
Cumulative %
1
6.999
23.331
23.331
2
2.622
8.740
32.071
3
2.207
7.356
39.427
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Constantin Bratianu and Ivona Orzea Component
Initial Eigenvalues Total
% of Variance
Cumulative %
4
1.554
5.178
44.606
5
1.320
4.401
49.007
6
1.197
3.989
52.996
7
1.119
3.729
56.725
8
1.052
3.505
60.230
9
1.001
3.336
63.566
The screen plot (Figure 1) suggests also a nine-factor solution.
Figure 1: Screen plot Analyzing the component matrix after varimax rotation led to the identification of the nine factors. In order to do that it would be necessary to consider what items are loaded on each of these factors. Factor 1 contains eight items. An inspection of these items shows that the majority of these items reflect prior experience with knowledge sharing (e.g. sharing knowledge at work was never used against me; I feel safe to share my knowledge with the colleagues because I consider them wellintentioned; experience taught me that it is a good thing to promote total transparency of my thoughts and knowledge). Factor 2 contains five items that clearly reflect the importance of willingness of the company to invest in employees (e.g. the company treats with respect ideas, opinions and experiences diversity; the company invests in the continuous development of the employees). Factor 5 can be introduced in the same category as Factor 2, that of the importance of employee development. Factor 3, 4 and 7 denote the importance of organizational motivational systems (e.g. knowledge sharing rewarding system is efficient in employee’s motivation; knowledge sharing is a criterion in performance evaluation process). Factor 6 signals the importance of communication within the organization (e.g. face to face interaction among employees is frequent; the company has special places where employees can meet and have open, informal conversations). Factor 8 is closely related to intrinsic motivation (e.g. I feel useful when I share my knowledge with my colleagues; keeping knowledge to myself does not provide me with any special advantage up against my colleagues). And the last factor, Factor 9, reveals the importance of trusting the other party when sharing knowledge (e.g. I trust my colleagues that they will not use the knowledge that I share in their favor; I trust the knowledge and pieces of advice that come from my colleagues). The combination of factors is purely a subjective decision, aimed at reducing the number of extracted factors to a smaller, more manageable, and ultimately more meaningful set of factors. Given that the present factor structure appears to be represented by six dimensions of knowledge sharing motives (Trust, Prior Experience, Intrinsic Motivation, Organizational Motivational systems, Organizational Communication system and Personal Development), it was decided to rerun Factor Analysis, stipulating the extraction of only six
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Constantin Bratianu and Ivona Orzea factors. This six-factor model represents the combination of the nine original factors, and appears to reflect adequately the underlying factor structure of the 30-item knowledge sharing inventory. Descriptive analysis provides a description of the data gathered. The variables Gender, Age, Educational level, Industry in which the company is active, Position within the company and Number of years worked for the company are nominal (categorical) variables, and, as such, their mean, median, and standard deviation statistics are not meaningful, but still can provide the viewer with a description of the general background of the respondents. The remaining variables are measured at the ordinal level and, as such, their mean, median, and standard deviation statistics can be interpreted. Out of the total number of respondents the dominant majority were women (62.4 percent). The results of the analysis show that the 229 respondents in the survey have their mean age included in the 2024 years interval, consistent with the target of the research. Concerning the educational level of the respondents 62.4 percent of them has graduated a university, while 34.5 percent have finished or are currently undergoing masters programs. From the companies’ analysis point of view 59.8 percent of the companies studied have as object of activity rendering of services. The first independent variable analyzed in terms of descriptive statistics is trust level among coworkers. This variable was analyzed in terms of the answers given to the 5 statements related to trust from the survey. The overall mean concerning trust level among coworkers was 3.38, meaning that most of the employees are somehow indifferent with a tendency to agreeing that trust is important when it comes to sharing one’s knowledge and experience. Table 3: Trust variable components descriptive statistics Trust my colleagues
Keeping the knowledge to myself
Company interest towards me
Jeopardize position within company
Not letting to live saying
Valid
227
228
225
227
228
Missing
2
1
4
2
1
N Mean
3.15
3.51
3.17
3.88
3.20
Median
3.00
4.00
3.00
4.00
3.00
Mode
3
4
3
4
3
Std. Deviation
1.070
1.200
1.023
.897
.991
The prior experience had with knowledge sharing was also analyzed in terms of the statements in the survey designed to give us an understanding of this variable. Again as in the case of trust level among coworkers the overall mean of this variable states certain level of indifference with higher level of tendency towards agreeing (the overall mean = 3.41). If in cases of the two variables studied above there was a slight tendency towards agreeing with the importance of the variable in knowledge sharing availability in case of intrinsic motivation the tendency towards agreeing with the statements is highest so far from the variable designed to study the individual availability to share knowledge and experience at the work place (intrinsic motivation has a mean of 3.61). Table 4: Intrinsic motivation components descriptive statistics I feel useful
N
Valid Missing
229
Good deeds Sharing only if saying colleagues do 226
228
Colleagues appreciation
Knowledge sharing generosity
228
228
0
3
1
1
1
Mean
4.27
3.59
2.99
3.54
2.71
Median
4.00
4.00
3.00
4.00
3.00
Mode
4
4
3
4
3
Std. Deviation
.802
1.072
1.037
.841
.936
Analyzing the situation in terms of organizational factors influencing knowledge sharing, the first factor is organizational motivational system. The overall mean in terms of organizational motivational system is 3.31 showing the influence of this factor in the knowledge sharing process. For the statements asked to be ranked by the survey respondents with regard to organizational motivational systems 86.7 percent answered that sharing one’s knowledge leads to an increase in the overall knowledge of
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Constantin Bratianu and Ivona Orzea the company. Moreover, the respondents ranked negatively the efficiency of the rewarding system within their companies, this being closely connected to the also negative ranking of the existence of a rewarding system for knowledge sharing. The Romanian managers appreciate more the employees that share their knowledge according to the rankings of the respondents (mean = 3.43 with an average deviation from the mean of 0.87). The importance of communication within the organization has also been acknowledged by the respondents (with an overall mean of 3.574). Most of the communication is realized through internet, a resource available to all the employees within the company (mean = 4.18 with an average deviation from the mean of 1.13 and a mode of 5.00, the maximum level). Employees seem not to be so concerned about databases where solutions for most frequent problems can be found due to the proximity of answers to a mean of 3, equivalent of indifference. On the other hand, face to face interactions among the employees seem to happen frequently (mean=3.932). Another factor analyzed was the willingness of the company to invest in the employees and its impact on knowledge sharing process. According to the results the respondents appear to be indifferent to this factor with concern to knowledge sharing (mean = 3.01). The result can be in direct connection with the fact that the respondents scored very low the active participation of the employees in the decisional process. When asked to rank if the organization promotes aggressive competition based on individualism and opportunism the respondents disagreed (57.9 percent), which could lead to the decrease in the overall mean. Table 5: HR development variable components descriptive statistics Knowledge diversity
Company promotes trust values
HR development
Active participation
Aggressive competition
Valid
229
225
227
227
228
Missing
0
4
2
2
1
Mean
3.58
3.51
3.12
2.80
2.36
Median
4.00
4.00
3.00
3.00
2.00
N
Mode
4
4
3
3
1
Std. Deviation
1.008
1.053
1.060
1.048
1.181
The above analysis confirms the existence of factors that affect the process of knowledge sharing within a company. Among the factors identified within the analysis are: the willingness of the company to develop and invest in its employees, communication process within the organization, motivational systems, and also, intrinsic motivation, prior experience and trust among co-workers. With all the values obtained for these variables above 3, representing the level of indifference towards the statement, denote that they have an impact on the process of knowledge sharing within an organization, and consequently on the organizational culture of the company. This leads to the conclusion that the initial hypotheses are confirmed.
5. Overcoming inertial organizational cultures Changes within the political, economical, and cultural aspects of the Romanians’ life lead to a change of mentalities too. If twenty years ago people were always controlled and kept under close observation by the state and with a constant tension and fear of expressing ones beliefs, now a part of those fears and constraints on oneself have been left aside. The transformation that Romania undertook in the past twenty years, at political, economical, educational level (change of political regime, an open policy towards the external affairs, privatization of state owned companies, foreign investments, EU accession, the subscription to the Bologna process) lead to a change in the mentality of youngsters and people in general. The change from a centralized economy to a free functional economy and the contact of businesses to the new conditions of the market increased the openness towards novelty and change. Efforts have been made in changing and adapting to a more challenging business environment and overcoming the heritage of an old socialist regime organizational culture, but those efforts need to continue and in order to progress they need to be sustained by practical solutions in enhancing knowledge sharing and investing in factors that increase employees willingness to share their knowledge.
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Constantin Bratianu and Ivona Orzea Most of the people are unlikely to share their knowledge and experience without trust. It is important to trust your colleagues not to misuse the knowledge shared. Sharing of knowledge can also be regarded as weakening one’s position within the organization, leading to a sentiment of fear amongst employees that sharing reduces job security because people are uncertain about themselves and about the worthiness of their colleagues. Generally when an average person trusts his or her colleagues feels free to express the experience and knowledge. To create a feeling of trust among employees it is managers’ responsibility to create an organizational culture where they are encouraged to express freely their feelings and opinions, thus the interconnection with the importance of organizational culture. With an environment where the focus is on regulations and hierarchies employees perform accordingly to this rules and this could lead to decreases in the willingness to share one’s knowledge. Informal environments enhance employees’ opportunities to share their knowledge and capture new knowledge (Riege, 2005). Also to enhance the trust among co-workers social events, team-building sessions are recommended. In a company and in life in general, people perceive rewards as measures for a behavior appreciated by the management, or by other people around them. In order to acknowledge the sharing of knowledge a person has to be rewarded for that behavior. It is not sufficient to rely on the willingness of employees to share their knowledge, to increase the degree of shared knowledge within a company a specific behavior has to be educated and rewarded. For rewards to be successful to motivate employees to share their knowledge and experience they must be properly designed in order to fit employee’s needs. The customization of rewards is very important due to the fact that people react differently to stimuli. Rewarding employees does not have to limit to financial aspect only. Encouragement, incentives and stimulation are very adequate for environments oriented to knowledge sharing; they can help increase the intrinsic motivation to share knowledge. As seen in the survey people feel very useful when their knowledge can help a colleague in need (mean = 4.27), and if encouraged this behavior increases the openness of people and leads to a knowledge sharing behavior. Another way to recognize contributions to knowledge sharing is to introduce it as a criterion in performance evaluation. As demonstrated above, there is a positive relation between communication and knowledge sharing. A majority of the respondents acknowledged the existence of frequent face to face interaction with colleagues but a high percentage of the respondents (62.4 percent) rated negatively the free circulation of information within the company. Hence the impetuous need for managers to invest in improving the communication system within the company. This can be realized through a redesign of the office arrangement. Offices and departments have a tendency to be arranged in accordance with the hierarchies within the company, and disregard the need to work together and exchange ideas, experience, and knowledge. An increase of participation in decision making and reducing boundaries between different organizational levels could lead to an easier information flow within the company.
6. Conclusion The purpose of this paper was to analyze knowledge sharing as a core component of the organizational knowledge dynamics, and to present some specific organizational barriers in the Romanian business environment. The main barriers identified were divided on two levels, on one side the barriers present at the level of the individual comprising: trust between coworkers, prior experience to knowledge sharing, intrinsic motivation, and on the other side the barriers present at the organizational level (rewarding systems, communication within the organization and last but not least company’s willingness to invest in its employees). With a background of more than forty years of dictatorial socialism regime and an organizational culture based on fear, individual control, mistrust and a dictatorial management style, after twenty years of transition to a capitalist regime people seem to have left behind their past and made progress in terms of overcoming barriers to knowledge sharing. But this is a continuous process and further steps and practical solutions have to be adopted.
References Al-Alawi, A.I., Al-Marzooqi, N.Y., Mohammed, Y.F. (2007) Organizational culture and knowledge sharing: critical success factors, Journal of Knowledge Management, 11(2), pp. 22-42 Bratianu, C., Andriessen, D. (2008) Knowledge as energy: a metaphorical analysis. Proceedings of the 9th European Conference on Knowledge Management, Southampton Solent University, 4-5 September 2008, pp.75-82. Reading: Academic Publishing
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Constantin Bratianu and Ivona Orzea Bratianu, C., Vasilache, S. (2009) Implementing innovation and knowledge management in the Romanian economy, Proceedings of the Fourth International KMO Conference, Taipei, Taiwan, June 23-24 Davenport, T., Prusak, L. (2000) Working Knowledge - How Organizations Manage What They Know, Boston: Harvard Business School Press De Long, D.W., Fahey, L. (2000) Diagnosing cultural barriers to knowledge management, Academy of Management Executive, 14(4), pp. 113-127 Duguid, P. (2005) “The art of knowing”: Social and tacit dimensions of knowledge and the limits of the community of practice, The Information Society, 21, pp. 109-118 Endres, M.L., Endres, S.P., Chowdhury, S.K., Alam, I. (2007) Tacit knowledge sharing, self-efficacy theory, and application to the Open Source community, Journal of Knowledge Management, 11(3), pp. 92-103 Foos, T., Schum, G., Rothenberg, S. (2006) Tacit knowledge transfer and the knowledge disconnect, Journal of Knowledge Management, 10(1), pp. 10-18 Jensen, R., Szulanski, G. (2004) Stickiness and the adaptation of organizational practices in cross-border knowledge transfers, Journal of International Business Studies, 35, pp. 508-523 Lubit, R. (2001) Tacit Knowledge and Knowledge Management: The Keys to Sustainable Competitive Advantage, Organizational Dynamics, 29(4), pp. 164-178 Nonaka, I., Takeuchi, H. (1995) The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation. New York: Oxford University Press O’Dell, C., Grayson, C.J. (1998) If only we knew what we know: identification and transfer of internal best practices, California Management Review, 40(3), pp. 154-174 Polanyi, M. (1983) The tacit dimension. Gloucester: Peter Smith Riege, A. (2005) Three-dozen knowledge sharing barriers managers must consider, Journal of Knowledge Management, 9(3), pp. 18-35 Rivera-Vazquez, J.C., Ortiz-Fournier, L.V., Flores F.R. (2009) Overcoming cultural barriers for innovation and knowledge sharing, Journal of Knowledge Management, 13(5), pp. 257-270 Seidler-de Alwis, R., Hartmann, E. (2008) The use of tacit knowledge within innovative companies: knowledge management in innovative enterprises, Journal of Knowledge Management, 12(1), pp. 133-147 Szulanski, G. (1995) Unpacking stickiness: An empirical investigation of the barriers to transfer best practices inside the firm, Academy of Management Journal, pp. 437-441 Szulanski, G. (1996) Exploring internal stickiness: impediments to the transfer of best practice within the firm, Strategic Management Journal, 17(winter special issue), pp. 27-43 Teece, D., Pisano, G., Shuen, A. (1997) Dynamic capabilities and strategic management, Strategic Management Journal, 18(7), pp. 509-533
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A Critical Analysis of Nonaka’s Model of Knowledge Dynamics Constantin Bratianu Academy of Economic Studies, Bucharest, Romania
[email protected] Abstract: The purpose of this paper is to present a critical analysis of the well known knowledge dynamics model elaborated by Ikujiro Nonaka and his co-workers. The essence of this model consists of three layers of the knowledge-creation process: (a) the process of knowledge creation through socialization-externalizationcombination-internalization (SECI), the knowledge conversion process between tacit and explicit knowledge; (b) Ba – the platform for knowledge creation; (c) knowledge assets. The success and popularity of this model created premises and temptations for using it beyond the conceptual limits initially defined, generating this way a superficial interpretation of the complex organizational knowledge dynamics. Our critical analysis aims at the investigation of the operational power of Nonaka’s model of knowledge dynamics within the framework of organizational knowledge. In the same time, we would like to apply the entropy law to SECI model and to see how the conversion processes conceived by Nonaka satisfy this law. Actually, although Nonaka considers socialization, externalization, combination and internalization as being conversion processes, only externalization and internalization are truly conversions. They consist in transforming tacit knowledge into explicit knowledge, and explicit knowledge into tacit knowledge, respectively. Socialization and combination are only processes of knowledge transfer, i.e. tacit knowledge to tacit knowledge, and explicit knowledge to explicit knowledge. Also, the evolving spiral is possible with inputs from the Ba platforms for knowledge creation and not with knowledge generation from within. The same evolving spiral of knowledge creation passes sequentially through individual processes and organizational processes in a deterministic way, although knowledge dynamics is not a physical process based on deterministic laws. Keywords: Explicit knowledge, knowledge conversion, knowledge creation, knowledge dynamics, tacit knowledge
1. Introduction Ikujiro Nonaka and his co-workers created a consistent body of theory concerning knowledge creation in organizations based on four main ideas: a) knowledge creation at individual level is a direct result of the continuous dialogue between tacit and explicit knowledge; b) there are four basic knowledge conversion processes: socialization, externalization, combination and internalization; c) knowledge creation at the organizational level is based on these four conversion processes and a spiral driving force; d) there is a shared space Ba for knowledge creation (Nonaka, 1991, 1994; Nonaka et al., 1994; Nonaka & Takeuchi, 1995; Nonaka & Konno, 1998; Nonaka, Toyoma & Byosiere, 2001; Nonaka & Toyoma, 2007). The novelty of these ideas, and the correlation between them and Japanese companies success on the global market made of Nonaka one of the most prominent thinkers in knowledge management, and his model of knowledge creation became a new paradigm for organizational knowledge dynamics. Although we are going for simplicity of expression to refer to the Nonaka’s model of organizational knowledge dynamics we recognize implicitly all the other contributions coming from his co-workers, in different stages of model development. Powerful concepts and paradigms have been always extended beyond their initial semantic boundaries until new ideas will integrated them into a new knowledge creating paradigm. Although such a new comprehensive paradigm has not been yet conceived, there are some new contributions showing the limits of the Nonaka’s model, and there are some new ideas trying to build up a new perspective on knowledge creation and organizational knowledge dynamics (Agourram, 2009; Bereiter, 2002; Bratianu, 2008, 2009; Bratianu & Andriessen, 2008; Gourlay, 2006; Harsh, 2009; Hill, 2008; Styhre, 2006). The purpose of this paper is to critically analyze the conceptual and operational limits of the Nonaka’s model of organizational knowledge dynamics, and to show the new perspective of this complex process. The next section of this paper will present briefly the fundamental elements of the Nonaka’s model, and then we shall show its limitations and possible new directions of development.
2. The Nonaka’s model of knowledge dynamics In one of his seminal papers on the dynamic theory of organizational knowledge creation, Nonaka showed that previously the theory of organization has been dominated for a long time by the paradigm that conceptualizes a generic organization as a system designed for information processing and problem solving. Centrally to this paradigm is the efficiency of information processing in a static and deterministic environment. However, in his view “Any organization that deals with a changing
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Constantin Bratianu environment ought not only to process information efficiently, but also create information and knowledge” (Nonaka, 1994, p.14). Thinking of the Japanese companies interested in innovation, he considers that a paradigm based solely on information processing is not able to explain the innovation phenomenon. Having this shortcoming in mind he develops a new perspective based on a two-phase knowledge field, four basic conversion processes of knowledge, and a spiral driving force. Nonaka defines knowledge as being “justified true belief”, and consider knowledge as “a dynamic human process of justifying personal beliefs as part of an aspiration for the truth” (Nonaka, 1994, p.15). Thus, knowledge becomes a relative concept as personal belief, a view which limits very much its status of objectivity and its role in science. Based on the seminal work of Polanyi (1983), Nonaka considers knowledge composed of tacit knowledge and explicit knowledge. In his view, “Tacit knowledge is highly personal and hard to formalize, making it difficult to communicate or to share with others. Subjective insights, intuitions, and hunches fall into this category of knowledge. Furthermore, tacit knowledge is deeply rooted in an individual’s action and experience, as well as in the ideals, values, or emotions he or she embraces” (Nonaka & Takeuchi, 1995, p.8). Tacit knowledge contains two types of ingredients. One type refers to the skills and fingertips experience in mastering a certain domain of practical activity. The other one refers to the mental models, beliefs and perceptions so ingrained that we take them for granted. This second dimension is cognitive in its nature and generates our image of surrounding reality. The most important characteristic of the tacit knowledge is that it is hard to articulate it in words and communicate it using language. It is there in our brain and body but we do not know how to explain it. In a very suggestive expression Polanyi (1983, p.4) underlined this aspect: “I shall reconsider human knowledge by starting from the fact that we can know more than we can tell”. In contrast to this tacit knowledge which is very subjective and hard to express in words and numbers, explicit knowledge represents the rational part of our knowledge which can be express and explained easily in words and numbers. It can be communicated to other individuals and it can be processed. One of the most important ideas about these two forms of knowledge comes from their dynamics, as explained by Nonaka and Takeuchi (1995, p.9):”For tacit knowledge to be communicated and shared within the organization, it has to be converted into words or numbers that anyone can understand. It is precisely during this time this conversion takes place – from tacit to explicit, and, as we shall see, back again into tacit – that organizational knowledge is created”. Nonaka considers that developing and valuing explicit knowledge is characteristic mainly for the Western culture, while developing and using successfully tacit knowledge is a characteristic of the Eastern culture (Nonaka, 1994; Nonaka & Takeuchi, 1995). This kind of arguments may be found as well in the works of Andriessen (2006, 2008), Andriessen and Boom (2007). Nonaka (1994) considers two dimensions for knowledge creation: epistemological dimension and ontological dimension. The first dimension is related to the conversion of knowledge from tacit level to explicit level, and from explicit level to the tacit level. The second dimension is related to the conversion of knowledge from individuals to groups and further to organization. Combining these two motions Nonaka gets a spiral model for knowledge creation and processing. Also, he makes a fundamental assumption which is the core of the SECI model: ”The assumption that knowledge is created through conversion between tacit and explicit knowledge allows us to postulate four different ‘modes’ of knowledge conversion: (1) from tacit knowledge to tacit knowledge, (2) from explicit knowledge to explicit knowledge, (3) from tacit knowledge to explicit knowledge, and (4) from explicit knowledge to tacit knowledge” (Nonaka, 1994, p.19). The first process, of creating tacit knowledge through shared experience has been called socialization. Tacit knowledge is hard to formalize and to express using language. It is context related. It is the way apprentices learn their craft through observation and imitation from their masters. The second process is a result of social interaction through language. This process of creating explicit knowledge from explicit knowledge has been called combination. The third and forth processes are different from the previous ones since they involve both types of knowledge. These transformation processes are based on idea that tacit and explicit knowledge are two complementary forms of knowledge in a continuous interaction. The third process of transforming tacit knowledge into explicit knowledge has been called externalization. The success of this process depends on sequential use of metaphors, analogies and models (Nonaka, Toyama & Byosiere, 2001). The fourth process is dealing with transformation of explicit knowledge into tacit knowledge, and it has been called internalization. This is a process of embodying explicit knowledge as tacit knowledge. It is closely to learning by doing. The first three processes are related in Nonaka’s view to organizational learning, while the last one is related to individual learning. Based on these above ideas, Nonaka concludes that organizations create knowledge continuously by restructuring the existing knowledge basis through the synergy of the four fundamental processes of
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Constantin Bratianu knowledge transformation: “That is to say, knowledge creation centers on the building of both tacit and explicit knowledge and, more importantly, on the interchange between these two aspects of knowledge through internalization and externalization” (Nonaka, 1994, p. 20). The foundation of these four basic processes is Ba, a rather fuzzy concept proposed by the Japanese philosopher Kitaro Nishida, and further developed by Shimizu. Ba is defined “as a context in which knowledge is shared, created, and utilized, in recognition of the fact that knowledge needs a context in order to exist” (Nonaka, Toyama & Byosiere, 2001, p.499). This context can be tangible, intangible or any combination of tangible and intangible elements. In this perspective, the concept of knowledge is strongly related to a given material and cultural context, beyond the fact that it is has been considered a personal belief. Knowledge belonging to given person may be shared, recreated or amplified when that person is an active actor in Ba. To make things even more confused, Nonaka, Toyama and Byosiere (2001, p.499) consider that “Ba as an interaction means that Ba itself is knowledge rather than a physical space containing knowledge or individuals who have knowledge”.
3. Functionality of the Nonaka’s model and its limits The main assumptions of this model constitute in the same time the degree of freedom and the limits of its functionality. One such assumption is the relative consistency of knowledge as a justified true belief. That means that knowledge creation can be described with respect to a given cultural framework, which is at a microscale the cultural horizon of individual, and at macroscale the cultural horizon of a country. The Nonaka’s model of knowledge dynamics in organizations can be very well understood and used in the context of Japanese culture, but it is unlikely to produce successful results in other cultures. The basic cornerstone is the concept of Ba which hardly can be understood in a culture where the Cartesian dualism produced such a gap between rational and non-rational worlds. Also, this concept is related to the Japanese specific interpretation of no-thing-ness: “Nothing-ness is not to be understood as a ‘thing’ because it then would be based on a conception of something, which would be no-thing… If you understand what exists then you can understand that which does not exists. This means that although it is impossible to know that which does not exists, it is possible to know that if “anything is anything, then everything is everything’… The spirit of no-thingness means that there is no such thing as relying upon anything at all outside of your individual mind” (Kaufman, 1994, pp.104-105). Postulating the four basic processes of knowledge dynamics, i.e. socialization, externalization, combination and internalization, and integrating them into a pattern of knowledge conversion Nonaka is blurring the lines between individuals and groups. Knowledge conversion from tacit to explicit and from explicit to tacit, according to the epistemological dimension (Nonaka, 1994; Nonaka & Takeuchi, 1995), is clearly a process developed at the individual level. There is no meaning for such a process to be developed between the tacit knowledge of a given person and the explicit knowledge of another person. However, the knowledge conversion from tacit to tacit, and from explicit to explicit develops between different individuals. If the whole spiral of knowledge creation would be considered for only two individuals, at the limit, it could be understood. But, if we would consider a group of people, it is hardly difficult to explain and demonstrate how the knowledge conversion works because of the sequential interplay between strictly individual processes and group processes. As a metaphor, the spiral of knowledge creation (Nonaka &Takeuchi, 1995, Fig.3-3) is an excellent solution. However, for any attempt of practical analysis and evaluation this spiral knowledge creation represents an almost impossible task. Although Nonaka and his co-workers consider all four basic processes to be designed for knowledge conversion, actually only two of them satisfy the condition of transforming one form of knowledge into another form of knowledge. They are: externalization and internalization. Socialization and combination are processes designed for exchange of knowledge from one person to another, and not for knowledge transformation. Thus, Nonaka’s model is not actually a cycle of knowledge conversion processes, as claimed by authors. The spiral of organizational knowledge creation considered with respect to the ontological dimension (Nonaka & Takeuchi, 1995, Fig. 3-5) originates in the middle management and evolves upward and downward. This might be the specific of Japanese management, but it is hardly efficiently in the Western management, where the decision making process is always a top-down process. The Nonaka’s model for organizational dynamics is based on creation and flow of knowledge. The analogy is made with the flow of water, but we know from fluid dynamics that any flow is generated by a pressure difference. Looking into this knowledge dynamics model we see no such thing as a pressure
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Constantin Bratianu field and no pressure difference able to generate the flow of knowledge. Once again, the metaphor is beautiful but the practical application is rather difficult. Harsh (2009) reiterates that Nonaka does not consider the fact that a significant part of the initial knowledge is flowing through the cycle many times, which actually means that there is a kind of reusable knowledge. “It is a surprise that in spite of great attention to knowledge creation and sharing theories and issues, the reusable knowledge has not been discussed explicitly during knowledge transformation in the Nonaka model” (Harsh, 2009, p.2). Also, Harsh reminds us that any conversion or transfer of knowledge consumes time, which does not appear as a variable in the Nonaka’s knowledge dynamics model. The organizational knowledge changes with time, and the effective knowledge of a generic organization can be increased through the reuse of knowledge. Thus, reusable knowledge is a fact of organizational life and it must be included in the modelling of knowledge dynamics. Since the SECI model is basically a two dimensional construct, Harsh introduces a third dimension, proposing a three dimensional knowledge management and explicit knowledge reuse (Harsh, 2009).
4. Knowledge dynamics and thermodynamics Bratianu and Andriessen (2008) analyzing the metaphor knowledge as energy showed new opportunities for understanding knowledge dynamics. Knowledge can be considered as a field, a continuous nonuniform and nonhomogeneous distribution of meanings and feelings in a certain organizational design and physical space. Time variations and space nonuniformities generate forces trying to decrease field nonuniformity. This new perspective may help in explaining the generic forces able to determine the flow of knowledge in organization. If there is a concentration of the knowledge field in the middle level management with respect to the top management or the executive line management, then and only then the flow of knowledge will have the direction and motion described by the Nonaka’s spiral knowledge dynamics. Bratianu and Andriessen (2008) made an analogy between potential energy and tacit knowledge on one hand, and kinetic energy and explicit knowledge on another hand. Having in mind the transformation process of the potential energy into kinetic energy and mechanical work, the authors postulate the same possible process for transforming tacit knowledge into explicit knowledge. That means that externalization should be used actually for generating cognitive work through explicit knowledge. Cognitive work means any rational process done in decision making. In the reverse way, kinetic energy can be transformed into potential energy by consuming mechanical work, which means that explicit knowledge cannot transform itself into tacit knowledge without some work to be done. It is necessary to consume cognitive work in order to realize the internalization process. Thus, knowledge conversion processes postulated by Nonaka and his co-workers cannot be realized by themselves without any production or consumption cognitive work. In conclusion, with all their limitations, Nonaka and his co-workers developed the dyad of tacit knowledge – explicit knowledge, and all their effort is to describe the dynamics between these two forms of knowledge. However, considering knowledge as a field of meanings and feelings already we may promote a new dyad: cognitive knowledge – emotional knowledge. Emotional knowledge is generated by emotions, which may be considered as states of our body and mind. Emotions are characterized by the following generic constituents (Hill, 2008, p.78):
A feeling component – physical sensations, including chemical changes in the brain.
A thinking component – conscious or intuitive ‘thought’ appraisal.
An action component – expressive reactions (like smiles), as well as coping behaviours (think fight or flight).
A sensory component – sights, sounds, etc., which intrude and serve to trigger the emotional response.
According to Hill (2008, p.79): “Emotionality is distinguished from rationality because the latter only involves one of these four components: thinking. Unlike an emotion, thinking may, but is less likely to, have a sensory component”. However, emotionality does not contain rationality. Rational thought involves conscious, deliberate, evaluative assessments. Emotions, on the other hand, are existential states of body and mind generated by feelings. Due to their direct short-cuts to the mind, emotions are always faster than thoughts in the decision making process, and thus they are able to mobilize the body in case of emergency. Emotions work very well with the adaptive unconscious, and they are
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Constantin Bratianu able to yield a snap judgement based on so called “thin-slicing”. This mechanism refers to the power of our slices of experience (Gladwell, 2005). Emotional knowledge has two dimensions: time of existence, and intensity of manifestation. The first dimension is a quantitative one and it can be measured easily in a psychology laboratory. The second dimension is qualitative in nature and it can be measured more difficult. By contrast, cognitive knowledge has only one dimension which is closely related to a metrics. Thus, the quantity of cognitive knowledge should be evaluated in a different way than the quantity of emotional knowledge. However, at this moment knowledge evaluation is in its early trial and error phases, without workable method and metrics. Knowledge as energy is a challenging metaphor since we may use the fundamental concepts of thermodynamics. As a science, thermodynamics is concerned with the generation, transport, and dissipation of heat as a form of energy. That means also the transformation process of mechanical work into heat, and of variation of heat into mechanical work in complex systems. In a similar way we can postulate that the variation of total knowledge at a certain level is a result of cognitive work and emotional heat involved in the transformational process. By cognitive work we may refer to any knowledge processing event which is capable of generating action at individual or organizational level. In the field theory, any non-uniform distribution in time or space generates forces, and any variation of these forces generates fluxes which tend to produce uniformity. This is true for the knowledge field as well, and we may coin the concept of cognitive work as a result of variation of cognitive fluxes at the individual level or organizational level. A cognitive work is actually any flux which may generate, or which can be generated by a knowledge field variation. By emotional heat we may consider the emotional flux which has been induced or produced as a result of a knowledge field variation. Considering all of these new aspects of knowledge creation and transformation, we should be re-thinking the Nonaka’s model of knowledge dynamics. The second law of thermodynamics has many formulations and interpretations. However, the kernel of this law is that heat can flow by its nature from a body with a higher temperature, toward a body with a lower temperature. These two bodies can be in direct contact, or not. The reverse process can be done only by performing mechanical work. Using our metaphor, we may say that in the target domain knowledge can be transferred only from a person having a higher knowing level toward a person with a lower knowing level. The reverse process can be done only by performing some intellectual work. This idea can be further developed by using similarities between the Carnot cycle used in thermodynamics and the SECI cycle used in knowledge management.
5. Conclusions The purpose of this paper is to present a critical analysis of the knowledge dynamics model elaborated by Ikujiro Nonaka and his co-workers. The essence of this model consists of three layers of the knowledge-creation process, including Ba platforms for knowledge creation, and SECI (socialization-externalization-combination-internalization) evolving spiral for knowledge conversion. Our critical analysis aims at the investigation of the operational power of Nonaka’s model of knowledge dynamics within the framework of organizational knowledge. One of our first conclusions is that the whole knowledge dynamics model is embedded in the Japanese culture and the Japanese companies’ organizational behaviour. Thus, limitations come from the working assumptions made by these above authors. Then, considering the whole cycle we may postulate the fact that o good part of the flowing knowledge passes several times through the spiral channels, which raises the question of reusable knowledge. Introducing this reusable knowledge into the model means to expand the two dimensional knowledge dynamic model into a three dimensional one. The emergence of a new knowledge dyad composed of cognitive and emotional knowledge suggests a new dynamics: transforming cognitive knowledge into emotional knowledge, and of emotional knowledge into cognitive knowledge. However, there are some new aspects related to the dimensionality of each form of knowledge. Cognitive knowledge has only an extensive dimension, while the emotional knowledge has an extensive dimension, and an intensive dimension. By similarity to the thermal energy we may use the concept of temperature for this intensive dimension of emotional knowledge. Finally, the metaphorical analysis of knowledge as energy shows that we may consider the entropy law to suggest that knowledge can be transferred only from a higher level of knowing toward the lower level of knowing.
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References Andriessen, D. (2006) “On the metaphorical nature of intellectual capital: a textual analysis”, Journal of Intellectual Capital, Vol.7, No.1, pp.93-110. Andriessen, D. (2008) “Knowledge as love. How metaphors direct our efforts to manage knowledge in organisations”, Knowledge Management Research & Practice, No.6, pp.5-12. Andriessen, D. & Van den Boom, M. (2007) East is East and West is West and (n)ever its intellectual capital shall meet. Journal of Intellectual Capital, Vol.8, No.4. Agourram, H. (2009) “The quest for the effectiveness of knowledge creation”, Journal of Knowledge Management Practice, Vol.10, No.2, June, pp.1-7. Bereiter, C. (2002) Education and mind in the knowledge age, Lawrence Erlbaum Associates, Mahwah, NJ and London. Bratianu, C. (2008) “Knowledge dynamics”, Review of Management and Economical Engineering, Vol.7, Special Issue, pp.103-107. Bratianu, C. (2009) “Challenges for knowledge management research”, in: Bratianu, C., Lixandroiu, D., Pop, N. (eds.) Business excellence, Vol.1, pp.52-56, Infomarket, Brasov. Bratianu, C., Andriessen, D. (2008) “Knowledge as energy: a metaphorical analysis”, Proceedings of the 9th European Conference on Knowledge Management, Southampton Solent University, 4-5 September 2008, pp.75-82, Academic Publishing, Reading. Gladwell, M. (2005) Blink. The power of thinking without thinking. New York: Back Bay Books. Gourlay, S. (2006) “Conceptualizing knowledge creation: a critique of Nonaka’s theory”, Journal of Management Studies, Vol. 43, No.7, November, pp.1415-1436. Harsh, O.K. (2009) “Three dimensional knowledge management and explicit knowledge reuse”, Journal of Knowledge Management Practice, Vol.10, No.2, June, pp.1-10. Hill, D. (2008) Emotionomics. Leveraging emotions for business success, Revised Edition, kogan Page, London. Kaufman, S.F. (1994) The martial artist’s book of five rings. The definitive interpretation of Miyamoto Musashi’s classic book of strategy, Tuttle Publishing, Boston. Nonaka, I. (1991) “The knowledge-creating company”, Harvard Business Review, Vol.69, No.6, pp.96-104. Nonaka, I. (1994) “A dynamic theory of organizational knowledge creation”, Organization Science, Vol.5, No.1, February, p. 14. Nonaka, I., Byosiere, P., Borucki, P.C., Konno, N. (1994) “ Organizational knowledge creation theory: a first comprehensive test”, International Business Review, Vol.3, No.4, pp.337-351. Nonaka, I., Takeuchi, H. (1995) The knowledge-creating company. How Japanese companies create the dynamics of innovation, Oxford University Press, Oxford. Nonaka, I., Konno, N. (1998) “The concept of ‘Ba’: building a foundation for knowledge creation”, California Management Review, Vol.40, No.3, Spring, pp.40-54. Nonaka, I., Toyama, R., Byosiere, Ph. (2001) “A theory of organizational knowledge creation: understanding the dynamic process of creating knowledge”, in: Dierkes, M., Antal, A.B., Child, J., Nonaka, I. (eds.) Handbook of organizational learning and knowledge, pp.487-491, Oxford University Press, Oxford. Nonaka, I., Toyoma, R. (2007) “Why do firms differ? The theory of knowledge-creating firm”, in: Ichijo, K., Nonaka, I. (eds.) Knowledge creation and management. New challenges for managers, pp.13-32, Oxford University Press, Oxford. Polanyi, M. (1983) The tacit dimension, Peter Smith, Gloucester, Massachusetts. Styhre, A. (2004) “Rethinking knowledge: a Bergsonian critique of the notion of tacit knowledge”, British Journal of Management, Vol.15, pp.177-188.
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Knowledge Dynamics - a Metaphorical Approach Stefan Bratosin1 and Mihaela Alexandra Ionescu2 1 Paul Sabatier University of Toulouse 3, France 2 National School of Political Studies and Public Administration, Bucharest, Romania
[email protected] [email protected] Abstract: The present proposal focuses on the metaphors analysis in the emerging field of knowledge dynamics. The article aims to describe two perspectives of defining knowledge and organizational learning, perspectives obtained through metaphorical mediation: knowledge as a resource and knowledge as product. The main premise of the proposal is that the metaphor is a linguistic tool used for a referential purpose to understand, describe and establish a world/ a domain and communicate about and in it. Metaphors allow us to understand one domain of experience in terms of another. The world/ domain can be a disciplinary field called, in our case, knowledge dynamics. The methodological approach is founded on metaphorical mediation perspective. Metaphorical mediation is understood as a key assumption for intelligibility through which we can access the theoretical accumulation of a disciplinary field. In this key of comprehensibility of the metaphorical mediation raise the question that will answer our proposal: how the main metaphorical mediations work? Theoretical and methodological scaffolding according to which we answer to the question is articulated and configured on: a) the theoretical approach we have developed starting of Paul Ricoeur's (1984) theory about metaphor, b) possible openings toward cognitive-pragmatic power of metaphor offered in the studies of George Lakoff and Mark Johnson (2003), which have the merit to provide additional directions, very useful for the study of knowledge. Keywords: Knowledge dynamics, knowledge as a resource, knowledge as product, metaphorical mediation, communication
1. Introduction The proposed approach focuses on the following interrogations: on one hand what is the role of metaphor in the knowledge, explanation and establishment of a field and, on the other hand, how the field of knowledge dynamics makes use of the metaphors/metaphorical mediation to define and describe the phenomena of knowledge and organizational learning? Approaching the metaphor in its natural logic, our analysis will start from Aristotle and his attempt to define metaphor as "epiphora" of the name, in two of his works – Rhetoric and Poetics (Aristotle, 1998, 2004). We will, then, articulate the significant elements of the hermeneutic and phenomenological tradition in the analysis metaphor, with emphasis on the theoretical and methodological developments of Paul Ricoeur (1984) and the alternate approaches of Lakoff and Johnson for a pragmatic metaphorization (Lakoff and Johnson, 2003). From these perspectives, we will explain the methodological construct of metaphorical mediation as a key assumption for intelligibility through which we can access the theoretical accumulation of a disciplinary field in order to identify and describe recurring metaphorical mediation of the discourse of the emerging fields of knowledge dynamics. In other words, we clarify the analysis model of metaphorical mediation based on which we propose a debate on the metaphorizations involved in the texture of the conceptual discourse about knowledge. Basically, our approach will focus on understanding the ways in which metaphors come to confer cognitive legitimacy to the discourse in this area.
2. Defining metaphor: from metaphor as aesthetic value to metaphor as primary source of conceptual language The Greek philosopher Aristotle was the first to provide a systematical approach on the understanding of metaphor that guided the Western scholars in one direction, for nearly two millennia: "Metaphor is the application of a strange term either transferred from the genus and applied to the species or from the species and applied to the genus, or from one species to another, or else by analogy" (Aristotle, 1998, 1457 b). In fact, Aristotle's concern for metaphor was quite marginal and primarily related to the study of rhetoric and poetics, a definitive proof being the definition of metaphor as the epiphora of name, used in both treaties. As such, the metaphor was not raised to a level of cognitive dignity, deserving to be studied only as a simple technique of persuasion or as a figure of language, unable to produce a true knowledge.
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Stefan Bratosin and Mihaela Alexandra Ionescu Until the 19th Century, the interest for metaphor was driven by Aristotle’s perspective, focus passing, then, slowly from the problem of defining the metaphor to the issue of classifying tropes. Fr. Nietzsche, the modern philosopher who has reopened the file on metaphor, remains within the limits set by Aristotle, without attempting to analyze it from a different perspective (Haaz, 2006). “What then is truth? A mobile army of metaphors, metonyms, and anthropomorphisms: in short, a sum of human relations which have been poetically and rhetorically intensified, transferred, and embellished, and which, after long usage, seem to a people to be fixed, canonical, and binding. Truths are illusions which we have forgotten are illusions – they are metaphors that have become worn out and have been drained of sensuous force” (Nietzsche, 1998, p. 198). th During the 20 Century, on the route opened by Nietzsche and Heidegger (Heidegger, 2005), Gadamer (Gadamer, 1976) and Derrida (Derrida, 1972), the metaphor “comes to power". The fact that Nietzsche sees the concept as a dead metaphor, blunted by the use of language and thrust deep, into the texture of traditional philosophical discourse represents the turning point that marks the beginning of a phase of resuscitation and review of the role of metaphor in relation to conceptual thinking. Following these traces, Heidegger (2005) uses hermeneutically the themes of metaphorization and of poetic discourse that structure thought and language, and he gets to claim that "language is the house of being" as the being expresses itself through language. This source of Heideggerian thinking quickly becomes a huge river of successful metaphorical mediation, a mediation that has crashed and attenuated the accumulated intensified tensions between the major universes of discourse of European thought in the interwar period. Gadamer (1976) goes even further by identifying thinking itself with the language then Derrida retries to highlight the diminished dignity of concept and, reiterating the Hegelian aesthetics in a hermeneutic approach, speaks of the conceptmetaphor dialectics, a tension where the metaphor "fertilizes" the concept.
3. Reviewing the problem of metaphor: Ricoeur's theory on metaphor Starting from the semantics of metaphor’s death and life, Ricoeur reviews the problem of metaphor and makes a monumental synthesis, offering the solution to understanding metaphor as originating not in words, neither in phrases, nor in speech but in what he called "tension within the verb to be", and tension between “is not” "and “is like" (Ricoeur, 1984). But analyzing Ricoeur’s imperative of moving from semiotics to hermeneutics in going from the reference of phrase to that of speech, we do not support his position, reassessing the role and meaning of discourse, within the common meaning, in relation to the metaphor seen as metaphorical mediation. Ricoeur rather focuses on the triad text/author/lecturer in place of the speech/speaker/auditor (used here as a standard and natural form of speech). This approach has an influence of form on the understanding of metaphor in the sense that it places it in the old aesthetic assumption. An assumption that seems to be a pre-understanding of the fact that metaphor is part of the skilled speaking/writing and that its production takes place in the mind’s workshop of the poet or writer. In other words, the problem of metaphor is treated each time, even by Ricoeur, starting from an aesthetic pre-understanding without noticing that, in the reality of language use, metaphor is essentially a product of metaphorisation, a strategy of language that we call metaphorical mediation. More specifically, the metaphor is an "artifact" of language produced by a speaker to a goal eminently pragmatic and not aesthetics. This aestethic assumption does not allow an analysis of the referential of the text/discourse in a truly generic sense and it influences the analysis of metaphor as a mediation process, both for phrase and for text. The need for a hermeneutic perspective inevitably brings him to a moderate hermeneutic solution, having the merit of offering an answer, but also the disadvantage of being too vague and narrow-minded to produce reasonable explanation about the specific processes of metaphorization and their origin (Ionescu, 2009). Therefore, the identification of this presupposition of aesthetic discourse on metaphor and the return in phenomenological terms to the real situations of use of the metaphor is the horizon within which we treat the construction of the metaphoric mediation.
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4. The metaphorical mediation as a grid of intelligibility for the conceptual thinking This re-positioning in relation to Ricoeur helped us fundament our approach on metaphorical mediation, understood as a specific process through which a community of speakers (we refer here to any community, including the scientific one) establish, describe, explain one world/ knowledge field and communicates within it (Ionescu, 2009). More precisley, metaphorical mediation is a presupposition for a grid of intelligibility through which we can understand, describe even explain conceptual scaffolding of the various scientific domains. According to this key intelligibility and logic of the metaphorical mediation, we formulate a question which attempts to answer this part of our proposition: how works the metaphorical mediation in shaping the conceptual thinking, in influencing perceptions and behavior, or in orienting the actions of social actors? We will formulate the answer starting from the cognitive-pragmatic approach of George Lakoff and Mark Johnson. Their contributions to the study of metaphor highlight the understanding of metaphor/metaphorical mediation as part of everyday language, of our experience, as component of our conceptual system (Lakoff and Johnson, 2003). "Metaphors are, for most people, devices of the imagination, poetry and also rhetorical ornaments" (Lakoff and Johnson, 2003, p. 3) or even simple features of the language. In common sense, a metaphor is a matter of words and less of thought and action. Lakoff and Johnson's research has repositioned metaphor, addressing it as "part of everyday life, not just in language but in thought or action" (Lakoff and Johnson, 2003, p. 4). The way we think, the way we experience, what we do in a daily routine is a matter of metaphor: most of our common conceptual system is metaphoric in nature. The two researchers started from the more general question "How people define and understand their language and experience?". This perspective is very important because it reflects the reframing of the discussion about metaphors in an approach that aims to introduce the metaphor "through the front door”. Thus, the metaphor appears not only as a matter of language/speech, but also as a subject of thought and action. All these queries form the background of the analysis of the two Americans. Their main thesis is that concepts which we live with, in terms of which we think and act are of metaphorical nature (Lakoff and Johnson, 2003). Our ways of thinking and acting do not go from concept to metaphor, from concept to action, but from metaphor to concept, from metaphor to action. The study of how our conceptual system works, the study of language, the activities and experiences that surround its use, reveals that the trajectories of our thinking, and the translation of cognition into language starts from the same source – the metaphor, although we are almost never aware of it. "But our conceptual system is not something we are normally aware of. In most of the little things we do every day, we simply think and act more or less automatically along certain lines. Just what these lines are is by no means obvious. One way to find out is by looking at language. Since communication is based on the same conceptual system that use in thinking and in acting, language is an important source of evidence for what system is like" (Lakoff and Johnson 2003, p. 3). According to Lakoff and Johnson, human thought processes are metaphorical, and metaphors as linguistic expressions are possible because the individual's conceptual system, or the conceptual 1 system of a community of speakers is metaphorically structured (mediated ). The concept produces agreement through a metaphorical mediation, which in turn can be mediated so that, through continuous association and sliding effect, it creates and continuously re-creates a new significance, restores and reorganizes semantic and discourse encyclopedias, namely the world or the fields of 1
Lakoff and Johnson see metaphorization as structuring; we built it as mediation. Their reason is that they position themselves in relation to metaphorization in a gestaltist-experientialist light. As far as we are concerned, we do not talk about structuring, but about mediation, which has as an effect structuring, seen as specific semantic organization. This way, our proposal is complementary to that of Lakoff and Johnson, and has emerged from theoretical and methodological, and not empirical, reasons of legitimacy. Therefore, our approach is inter-translatable with the one of the American researchers, because it can be found at the level of theoretical and practical consequences. We will use this for highlighting the points of convergence that will help us state more coherently the pragmatic valences of metaphorical mediation.
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Stefan Bratosin and Mihaela Alexandra Ionescu knowledge. In this sense the concept is metaphorical because it is the result of metaphorization and the metaphor is conceptual as it mediates understanding, cognition, and, therefore, the concept. “(...) human thought processes are largely metaphorical. This is what we mean when we say that the human conceptual system is metaphorically structured and defined. Metaphors as linguistic expression are possible precisely because there are metaphors in a person's conceptual system. Therefore, whenever in this book we speak of metaphors, such as ARGUMENT IS WAR, it should be understood that metaphors means metaphorical concept “ (ibid., p. 6). The metaphorical concept is systematic, and the way we talk about this aspect of the concept is, in turn, systematically (idem). In other words, when we argue something, there are some rules that prescribe certain interpretations, in a certain way, giving us permission as to what we are and we are not allowed to do. The perspective of our approach - metaphorical mediation - allows us to integrate the theoretical intuition of Lakoff and Johnson, by understanding the collision of discourses, which 2 prescribe their own reading instructions . Therefore, metaphorical mediation (or metaphorical concept) becomes a matrix of intelligibility through which we can gain access to the theoretical accumulations of the universe of humanities and social sciences, just as to a living world, which re-generates and is generated continuously. Metaphorical mediation is a cognitive, pragmatic and communicational approach by which a community of speakers understand, introduce, describe, modify and interact in a world. A community of speakers (a scientific community, for exemple) is that community which has a common dictionary, an encyclopedia of common meanings and a common collection of referents and in which metaphorical mediation makes possible knowledge and communication. Taking into account these theoretical and methodological delimitations, we will go further and identify and analyze the metaphors of knowledge as a resource and knowledge as product, metaphors that are structuring (mediating) two paradigms defining knowledge in the field of knowledge dynamics.
5. Perspectives on knowledge dynamics as a result of metaphorical mediations: discussions The field of knowledge dynamics offers two defining paradigms of knowledge: knowledge as a [basic economic] resource (Drucker, 1993; Spender, 1996) and knowledge as product (Skyrme, 1998). These two perspectives on knowledge describe and explain the concept on the basis of transfers made to different universes of discourse, by analogy and association, that is, through metaphors (Andriessen, 2008; Bratianu and Andriessen, 2008). Knowledge as a resource and knowledge as product are two metaphorical concepts that enlighten the understanding of knowledge in an imaginative-intuitive way (Lakoff and Johnson, 2003). These concepts provide a metaphorical understanding of knowledge in terms of similarity and analogy with the resource and product discourses. These two discourses that collide in a metaphorical mediation are actually prescribing indications for the definition, explanation, description, and interpretation of the term knowledge. Metaphor of knowledge as a [basic economic] resource (in line with the cognitive approach according to which knowledge is a mobile resource) is the result of the collision of multiple universes of discourse. Knowledge is mapped as a source with an important potential of valorization in a context, and can be a natural, social, individual, organic, inorganic, etc. source/resource. This semantic and conceptual encyclopedia is made possible by the collision of universes of discourse of different patterns of reality: the natural, psychosocial, economic, etc. Basically, knowledge exists in the state in terms of resource potential in any area of reality and, once translated into action, it can be spread in any domain of the reality. Defining knowledge as a resource is not based on our standard understanding of what defining means, that is, the objects defined have got inherent properties which people understand only in 2
To be consistent in relation to the example "argument is war" used by Lakoff and Johnson, we can say that one of the indications prescribed for the reading by one of the two discourses, say that of "war", is "you can not go to fight in order to just talk!". Simultaneously, there is an indication of reading the discourse "argument" which says that "in talks, people do not kill each other". Understanding the metaphor means to simultaneously respect both rules that the two speeches prescribe to any lecturer.
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Stefan Bratosin and Mihaela Alexandra Ionescu terms of those properties. The understanding knowledge is produced through metaphors, through translation of meaning and use of the images from which intuitive cognition is derived - in our case the image of the resource. Te properties of knowledge are isolated by resemblances, similarities, analogies with the resource, which has become an image so worn that we produce the illusion that it is a conceptualization, and not a concept intuitively obtained by the use of metaphors (Lakoff and Johnson, 2003). The metaphor of knowledge as a resource structures and describes the classifications of knowledge resources. We are talking about tangible resources of knowledge such as infrastructure, human capital, procedural manuals, training, etc. (Roos and Roos, 1997), and about intangible resources such as individual skills and organizational know-how, processes, relationships, etc. At their turn, intangible resources are tacit (e.g. mental models), explicit (communicated knowledge), individual, social, procedural, relational, pragmatic, etc. Moreover, existing forms of knowledge are part of the discourse universes that collide in the metaphor of knowledge as a resource; and then we talk about embodied knowledge similar to individual tacit knowledge, represented knowledge similar to explicit knowledge (procedures, documents, manuals) and embedded knowledge (processes, rules, relationships) (Gamble and Blackwell, 2001). The metaphor of knowledge as a resource ends up becoming a concept, it is the privileged means of classifications and descriptions, of the cognitive contents of knowledge. In the same time, the metaphor is not just a matter of language, of conceptualization, but of thought and action. The metaphor of knowledge as a resource clarifies and makes coherent certain aspects of experience, of the individual, organizational, social, economic, etc. experience. It is not only a way of understanding and explaining knowledge, of cognitive mediation, but also a way to organize coherently aspects such as: organizational learning, the direction of the organizational action, the flows and routes of knowledge, the knowledge transfer between individuals, organizations, institutions, social networks, etc. The metaphor of knowledge as product launched by Birkinshaw and Sheehan (2002) has strong descriptive-explanatory and intuitive valences as a consequence of the marked polysemous horizon as the metaphor of knowledge as a resource. This metaphor mediates through the discursive openings of quantity and quality the establishment of the metaphorical concept of knowledge as a product and of its conceptual descriptors. If the first metaphor of knowledge can be read as a permanent unity between internal / external, tacit / explicit, between the individual / the global, between potential / current, the metaphor of knowledge as product suggests a lecture rather focused on the descriptors of the explicit, group / global, current, external. In this case, as well, we talk about the same collision between the universes of discourse that cover different fields (material, social, economic, etc.). In the same time, the suggestion of external focus is more pregnant because the product is a result that allows a different imaginary semantic approach: that of a process, of a benefit, of efficiency, etc. In fact, knowledge as a resource allows for an unlimited potential, innovation per se, while knowledge as a product requires innovation with a purpose, it is a process of extraction that can be operationalized in phases, assessed and, finally, exploited (Van der Henst, 1999); and the cycle can be resumed.
6. Conclusions The analysis undertaken in this work is not exhaustive and its limits have been partly set by the very theme assumed. We have not applied the metaphorical mediation in knowledge dynamics to the detriment of conceptualization, but our intention is to lay the foundation, together with other authors, of an approach that analyzes how the metaphor mediates explicatively and cognitively the discourse about knowledge, to identify and describe some examples of metaphoric mediation which resulted in a type of cognitive relation within the phenomenon of knowledge and in the premises of bridging up the way for future studies and research. As argued and reinforced many times throughout this proposal, we started this labor under the pressure of valorizing the potential of an approach. This way, our analysis of the field of knowledge dynamics has moved towards the disenchantment of conceptualization, especially since the metaphor mediates, and does not replace the concept and theory. We are more concerned with a construction project, rather than with a deconstructing one, a project able to explain the understanding and articulation of realities in knowledge management in intellectual capital and knowledge of dynamics,
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Stefan Bratosin and Mihaela Alexandra Ionescu which are all very closely related, and in the dialectical process between theory and practice, between practice and theory.
References Andriessen, D. (2008) “Knowledge as love. How metaphors direct our efforts to manage knowledge in organizations”, Knowledge Management Research & Practice, No.6, pp. 5-12. Aristotel, (1998) Poetica (Poetics). Bucharest: IRI. Aristotel, (2004), Retorica (Rethorics). Bucharest: IRI. Birkinshaw, J. & Sheehan, T. (2002) “Managing the Knowledge Life Cycle”, MIT Sloan Management Review, Fall, pp. 75-83. Blackwell, J. & Gamble, P. (2001) Knowledge Management. UK: Kogan Page Ltd. Bratianu, C. & Andriessen, D. (2008) “Knowledge as energy: a metaphorical analysis”, in: Proceedings of the 9th European Conference on Knowledge Management, Southampton Solent University, UK, 4-5 September 2008, pp. 75-82. Derrida, J. (1972), “La mythologie blanche”, in Marges de la philosophie, Paris, Minuit. Drucker P. F. (1993) Post-capitalist society. Butterworth-Heinemann: Oxford. Gadamer, H.G. (1976) Vérité et méthode, les grandes lignes d'une herméneuthique philosophique. Paris: Seuil. Haaz, I. (2006) Nietzsche et la métaphore cognitive. Paris: L'Harmattan. Heiddeger, M. (2005) Being and Time. UK: Blackwell Publishing. Ionescu, M.A. (2009) Médiations métaphoriques dans les discours des sciences de l'information et de la communication. Toulouse: Université Le Mirail de Toulouse 2. Lakoff, G. & Johnson, M. (2003) Metaphors we live by. Chicago: The Chicago University Press. Nietzsche, Fr. (1998) “A doua consideratie inactuala” (“Second innoportune consideration”, Opere complete (Papers Collected), vol. II, Timisoara: Hestia. Ricoeur, P. (1984) Metafora vie (The rule of metaphor). Bucharest: Univers. Roos, J., Roos, G., Dragonetti, N.C. & Edvinsson, L. (1997) Intellectual Capital: Navigating the New Business Landscape. London: Macmillan Press. Skyrme, D.J. (1998) Developing a Knowledge Strategy, [online] available at: http://www.skyrme.com/pubs/knwstrat.htm, accessed May 2008. Van der Henst, J.B. (1999) “The mental model theory of spatial reasoning re-examined: The role of relevance in premise order”, British Journal of Psychology, No. 90, pp. 73–84. Spender, J. C., Grant, R. M. (1996) “Knowledge and the firm: Overview”. Strategic Management Journal, vol. 17, no. winter, pp. 175–179.
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Knowledge Management in Local Government Sector: the Role of the Quality Certification Elisabeth Brito1, Leonor Cardoso2 and Catarina Ramalho2 1 University of Aveiro, Portugal 2 University of Coimbra, Portugal
[email protected] [email protected] [email protected] Abstract: The present study is an integrant part of a wide research focused on local and public management competitiveness (represented by the clients’ satisfaction perspective), considering knowledge management (KM) and quality management (QM) as its main influence vectors. We present the results of an empirical research focused on KM processes and developed in 81 municipalities, which belong to all Portuguese regions (north, centre and south). Thus, we have two main groups: 41 municipalities with quality certified services (specifically, urbanism and/or customer services) and municipalities without any quality certified services. The main purpose of our paper is to identify and characterize organizational processes related to KM that are developed within those local government organizations. To gather the data under analysis, it was applied the GC questionnaire, originally developed by Cardoso (2003). This questionnaire was also validated and adapted by Brito (2003) to the local government sector. Considering a large sample made up of 1.353 (one thousand and three hundred and three) participants, corresponding to the wide research previously referred, we proceed to a random selection of 800 participants that developed their professional activity within the municipalities services under study (400 participants belonging to municipalities with certified services and 400 participants belonging to municipalities with non-certified services). In this way, we are proposing to describe the results obtained in those two contexts. Results obtained through exploratory factorial analysis (EFA) showed the existence of differences in KM processes, thereby expressing the importance of the certification in their occurrence. Keywords: Knowledge Management; knowledge management processes; quality; quality certification; local government sector
1. Introduction Knowledge became the most valuable benefit of nowadays, being a major concept of interest to local government organizations. The emergence of knowledge supremacy over other organizational resources, as pointed by several authors (Grant 1996, Nonaka and Takeuchi 1995, Drucker 1993, Quinn 1992, Stewart 1988, Sveiby 1997), has contributed significantly to a change of organizational values within municipalities, where intangible goods, as also workers competencies, relationship with clients and suppliers, and technical knowledge have more value than tangible goods. This paper is conceived under the theoretical approach of KM and aims to present the results of an empirical study focused on local and public management competitiveness (represented by the clients’ satisfaction perspective), considering KM and QM as its main influence vectors. This study was developed in 81 municipalities, which belong to all Portuguese regions (north, centre and south). For data gathering, we used the GC Questionnaire, developed by Cardoso (2003). This instrument was based on a conceptual model formulated by the author, which integrates contributions from several approaches concerned with organizational means of generating and managing knowledge resources (Davenport, De Long and Beers 1999, Davenport and Prusak 1998, De Long 1997, Edvinsson and Malone 1997, Huseman and Goodman 1999, Kaplan and Norton 1992, 1997, Nonaka 1990, 1991, 1994, 1997, 1998, Nonaka and Konno 1999, Nonaka and Takeuchi 1995, Nonaka, Toyama and Byosière 2001, Nonaka, Toyama and Konno 2001, Stewart 1997, Sveiby 1997, Sveiby and Mellander 1994, Zuboff 1998). Thus, after a brief explanation of this theoretical perspective, we will present the methodology used in this study, exploring the sample, procedure and statistical analysis. We will end by presenting the main achieved results, pursuing its discussion and meaning attribution, regarding knowledge management conceptual framework.
2. Background KM can be defined as the creation and development of internal organizational conditions that catalyze the whole processes related with knowledge (creation/acquisition, sharing/diffusion, storage, recuperation, use, etc.) (Davenport and Prusak 1999, Nonaka and Takeuchi 1995), allowing and
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Elisabeth Brito et al. facilitating the achievement of organizational objectives (Cardoso 2003: 186). Trough its intervention scope, KM highlights the active and constructive role performed by the human element in the context of organizational knowledge management. From other side, we consider that quality may be defined as “meeting certain set standards and requirements, being suitable for use, or the degree of customer/client satisfaction” (Ruzevicius 2006: 31). Additionally, there is a generalized acceptance that QM has a positive impact on organizational performance (Kaynak 2003, Douglas and Judge 2001, Hendricks and Singhal 1996, 1997, 2001, Flynn et al 1995, Easton and Jarrell 1998). Therefore, quality is considered a strategic competency and a competitiveness key that cannot be ignored by any organization (Eldridge et al 2006). To implement a quality certification system, we believe that an organization needs a wide methodological system that enables an adjustable management of work processes, aligned with the organizational vision and strategy, enclosing a permanent interaction with clients and a constant concern with clients’ feedback. In the same way, we assume KM as a detailed and systematic methodology focused on the facilitation and management of activities related with organizational knowledge (creation/acquisition, sharing/diffusion, storage, recovery and use), whose main functions are to plan, implement and develop actions and programs related with organizational knowledge, which are determinant to the effectiveness of the intellectual capital management within organizations. From the literature review, regarding the relationship between those two systems (KM and QM), we realized that there are two emphasized perspectives. From one side, there are some authors that perceive KM and QM as two processes that can create synergies through the establishment of a continuous and sequential process (Wilson and Asay 1999, Velasco and Garcia 2005, Férnandez et al 2006). Some others acknowledge the creation of synergies that would come from the joint accomplishment and operation of those two processes (Hsu and Shen 2005, Jaime et al 2006, Lim et al 1999, Molina et al 2004, 2007). It can be easily understandable that the advantages and benefits obtained through the identification and management of organizational knowledge constitute an extension of Total Quality Management (TQM), in the sense that it encourages and promotes a progress on quality improvement services or products, reducing meaningfully time, energy and costs because KM makes available the knowledge with added value to those who need it and when they need it (Wilson and Asay 1999). Thus, decisions can be taken quickly and at different hierarchical levels, allowing workers to perform with less supervision and intervention, enabling cooperative work among teams. Therefore, we can perceive that there is a sustained linkage between TQM and KM. With the same theoretical approach, Velasco and Garcia (2005) systematized organizational phases and systems of support to KM processes. According to those authors, the implementation of KM1 basic phases requires not only an adequate organizational structure, but also the use of suitable technologies to manage tacit and explicit knowledge and the development of a human resources management structure and organizational culture that promote knowledge creation, transfer and absorption. To facilitate the implementation of this proposed management model within organizations, the authors point out an integration of KM with TQM postulates, considering that the latter constitutes a structured discipline which creates synergies to KM implementation. Additionally, Férnandez et al (2006) consider that the KM focus can be based on TQM. From a conceptual and operational perspective, the developed and implemented efforts prosecuted by organizations in the establishment of a TQM system can be a useful point of departure to develop a KM system. Taking into account the model of spiral construction of knowledge from Nonaka and Takeuchi (1995), the conceptual model proposed by Férnandez et al (2006) considers the formal system of TQM and its conception and implementation programs (documentation, quality circles, suggestions programs, training programs, etc.) as a basis factor for the success of a KM system. Therefore, a continuous synergetic, cooperative and innovative process of organizational improvement can be constructed and developed through the conciliation between of TQM operational politics and knowledge spiral perspective. Thus, the authors believe that we can identify a specific relationship in both approaches, because TQM programs are assumed as activators of the interaction 1
The basic phases of KM are: identification and measurement, creation, capture, storage, access and transfer and application and integration of knowledge.
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Elisabeth Brito et al. and exchange processes that occur between tacit and explicit knowledge, as proposed by Nonaka and Takeuchi (1995). Despites the fact of being comprehensible a continuous process between TQM and KM that contributes to the improvement of organizational effectiveness, these two systems can coexist in a coherent and harmonious way, in the sense that both processes share a set of similarities: orientation toward results, management based on people, team work, support to leadership, client focus, etc. (Hsu and Shen 2005). The synergies creation and the theorized continuous relationship between KM and QM processes is also pointed by Jaime et al (2006), which state the joint establishment of methods from these two systems. Lim et al. (1999) conceptualize KM as a QM strategy, in as much as they identify several benefits coming from its use, such as: reduction of loss regarding the intellectual capital with turnover, reduction of costs regarding the development of a new product/service, increasing of workers productivity, putting knowledge more accessible to all workers and improving their satisfaction. Accordingly, the empirical results obtained by Molina et al (2004) confirmed the complementary relationship among TQM, the use of ISO 9000 Certification and KM. Thus, the effects are synergic because these models related with QM have an influence in different aspects of knowledge transfer. From one side, the normative patterns of ISO 9000 help the transfer of knowledge due to the impact they have on it. From other side, TQM also has its contributions because it is focused on issues related with human resources management and organizational culture. Authors concluded that both ISO 9000 and TQM can constitute two complementary techniques to KM because they improve cooperatively knowledge transfers that take place inside organizations, between workers and between workers, clients and suppliers. The linkage between those two systems of organizational performance improvement is also stated in the results presented in another research developed by Molina et al (2007), which indicates the importance of organizational cooperation with both suppliers and clients, since it was evident that its maintenance leads to knowledge transfers improvement inside the organization. Those knowledge transfers constitute latent variables, not directly managed, that are strongly influenced by QM aspects. In conclusion, once QM has a positive influence on knowledge transfers, having an impact on the resources and organizational abilities, as also on its competitive advantage, the results obtained by those authors provide a support to the theoretical and operational relationship between KM, QM and organizational performance.
3. Empirical study 3.1 Objectives Our main purpose is to identify and characterize organizational processes related to KM in two different contexts: municipalities with certified services and municipalities with non-certified services. For data collection, we used the survey method, opting for the technique of self-administered questionnaire (Ghiglione and Matalon 1997), aiming to gather information related to KM process in those contexts.
4. Methodology 4.1 Type of study This study presents a correlational design, adopting the survey research and the technique of selfadministered questionnaire.
4.2 Sample Both samples are composed of 400 subjects each, from 81 municipalities. Table 1 presents the distribution of respondents in both, municipalities with certified services and municipalities with noncertified services, regarding: work length, post held, functional area, age, gender and qualifications.
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Elisabeth Brito et al. Table 1: Distribution of respondents Municipalities with certified services Work length Less than 1 year From 1 to 5 years From 5 to 10 years More than 10 years Unanswered Post held Administrative Adviser Supervisor Operator Qualified Operator Professional Technician Superior Technician Unanswered Functional area Administrative and financial Advice Social Urbanism Office Service Works and Infrastructure Other areas not specified Unanswered Age From 18 to 24 years From 25 to 34 years From 35 to 49 years From 50 to 64 years More than 65 years Unanswered Gender Female Male Unanswered Qualifications From 1 to 4 years From 5 to 6 years From 7 to 9 years Secondary Bachelor Undergraduation Master PhD Unanswered Total
Municipalities with non-certified services
Frequency
%
Frequency
%
16 63 101 214 6
4.0 15.8 25.3 53.4 1.5
14 66 98 218 4
3.5 16.5 24.5 54.5 1
181 2 29 6 4 64 102 12
45.2 .5 7.3 1.5 1 16 25.5 3
174 2 33 3 4 71 107 6
43.4 .5 8.3 .8 1 17.8 26.7 1.5
28 2 181 112 29 32 16
7 .5 45.2 28 7.3 8 4
34 4 5 224 59 32 21 21
8.4 1 1.3 56 14.7 8 5.3 5.3
10 118 210 53 2 7
2.5 29.5 52.5 13.3 .5 1.7
5 130 207 52 1 5
1.3 32.4 51.7 13 .3 1.3
246 143 11
61.5 35.8 2.7
234 157 9
58.5 39.2 2.3
6 6 38 197 11 128 5 1 8 400
1.5 1.5 9.5 49.2 2.7 32.0 1.3 .3 2 100.0
1 32 222 17 110 11 1 6 400
.3 8 55.4 4.3 27.4 2.8 .3 1.5 100.0
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4.3 Instruments GC was originally constructed by Cardoso (2003) and it was applied in industrial organizations and validated by Brito (2003) for the local government sector with four dimensions: “knowledge oriented culture”, “knowledge management practices”, “social and discursive knowledge management” “strategic knowledge management”, respectively. GC aims to assess and diagnose organizational KM processes through the perception of organizational actors. The questionnaire is composed by 56 items with five optional answers: 1 – Almost never applies; 2 – Applies little; 3 – Applies moderately; 4 – Applies a lot, and 5 – Applies almost totally.
4.4 Statistical procedures We used SPSS, version 17.0. The main technique applied for data treatment was Exploratory Factorial Analysis (EFA) applying Principal Components method.
5. Results 5.1 KM processes in municipalities with certified services We started with an analysis of responses tendency to each 56 items of GC. We noticed that the responses followed a general normal distribution, with the exception of items 8 and 20 that presented a concentration of responses superior to 50% in one option (in both cases, option 3 - applies moderately). Thus, we decided to eliminate them, guaranteeing an adequate variability of answers. GC dimensionality study started with an EFA. After an initial solution, four factors were emerging as indicated from a statistical point of view based on Parallel Analysis (Lautenschlager 1989), Scree Test of Cattell, the Criteria of Kaiser, explained variability and also from a theoretical perspective. Analyzing the rotation, at this point composed by 54 items, we noticed that 28 items presented loading scores to low (< to .50) or loading scores similar in more than one factor (differences < to 1). In this sense, we decided to eliminate them and repeat EFA - items were removed one by one and all tests and analysis were repeated after each removal. Repeating the statistical procedure with 26 items in the same sample, eigenvalues from random data exceed the eigenvalues from research data after the fourth factor in parallel analysis, there were no items with high loadings on more than one factor (i.e., cross-loadings), neither with low loadings. The final solution is presented in Table 2. Since we removed several items, we re-run the final EFA in a random independent sample and the results were replicated. Factors are well defined (more than 3 indicators that strongly relate to the factor), ensuring high factorial stability across replications. Analyzing the Kaiser-Meyer-Olkin (KMO) and the Bartlett’s Sphericity Test, the data allowed the prosecution of the EFA (Hair, Anderson, Tatham and Black 2005, Stevens 1986). KMO measure (.89) revealed a good sampling adequacy. The second indicator demonstrated that the correlation matrix of the 26 items is significantly different from an identity matrix [2(325) = 3267.83 p < .001]. After extracting four factors, we realized that the total explained variance was 47.68%. The first factor have an eigenvalue of 7.13, explaining 27.44% of the variance, the second factor assumes an eigenvalue of 1.94 explaining 7.44% of the variance, the third factor presents an eigenvalue of 1.85 and a explained variance of 7.11% and the fourth factor presents an eigenvalue of 1.56 and an explained variance of 5.98%. In order to sift the factors making up the KM construct, we seek items that prevented the factors from reaching a proper level of reliability using Cronbach’s alpha measurements. It yielded a Cronbach’s alpha of .84 for Factor 1, .81 for Factor 2, .75 for Factor 3, and .69 for Factor 4. All values surpassed the established threshold of .6 for an exploratory study (Hair et al 2005) and all variables had a correlation item-scale above the established value of .3 for such effects (Nurosis 1993). Table 2: Factor analysis of GC for municipalities with certified services Items 33. We seek information that can improve the quality of what we do 17. What we know is seen in the way we work 29. Each one of us has a task to fulfil 32. We act according to the way we are organized
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Factor 1 .698 .684 .633 .625
Factor 2 .313 .059 .112 .206
Factor 3 .072 -.023 .130 .131
Factor 4 .020 .168 .050 -.072
Elisabeth Brito et al. Items 31. We reflect about the way we solved our problems in the past (through our successes and failures) 44. We all are responsible for what we should know to work with quality 18. Sometimes, it is in the way we perform tasks that we find solutions to new problems 21. We act according to certain kind of principles 16. We seek to understand the important things that are happening in this Municipality 13. We are encouraged to take initiative 14. Those who share what they know are rewarded 56. We attend training courses or we have training on the job 34. We attend to seminars/conferences, we read publications or we hire experts 37. We get together to solve some of our problems 12. We collaborate with other organizations to gather more information 36. We exchange information in work meetings 11. Our superiors call our attention to what is important to know 26. We talk about our work when we casually meet each other (for example taking a coffee) 43. We talk about our Municipality 48. We tell to each other funny stories that happened in our work 03. We talk about our work in leisure moments 54. We talk about our tasks 23. We are attentive to what other institutions are doing (we adopt the best tricks) 22. What we know is a “weapon” to surpass other Municipalities 19. We know that other Municipalities have information about us 06. What we know is seen in what we do better than other Municipalities
Factor 1
Factor 2
Factor 3
Factor 4
.624
.142
.250
.008
.606
.274
.222
.068
.584
.022
-.045
.305
.544
.187
.145
.162
.506
.167
.128
.236
.181 -.074 .176
.657 .619 .611
.081 -.061 .094
.295 .263 -.042
.139
.608
.061
-.036
.293 .153 .314 .323
.598 .585 .580 .560
.183 .141 .229 .033
.010 .319 .081 .207
-.010
.021
.770
.087
.231 .206 .021 .283
.176 .087 .030 .221
.699 .656 .617 .601
.127 .043 .196 .057
.108
.102
.190
.757
.168 -.021 .247
.084 .208 .097
.095 .162 .042
.754 .574 .554
5.2 KM processes in municipalities with non-certified services We started with an analysis of responses tendency to each 56 items of GC. We noticed that the responses followed a general normal distribution with the exception of the item 20 that presented a concentration of responses superior to 50% in one option (namely, option 3 - applies moderately). For guaranteeing an adequate variability of answers, we decided to eliminate it. GC dimensionality study started with an EFA. After exploring an initial solution, we realized that there were 4 factors emerging. From a statistical point of view, we used three common factor selection procedures based on eigenvalues: Parallel Analysis (Lautenschlager 1989), Scree Test of Cattell and the Criteria of Kaiser. Additionally, we used explained variability and a theoretical perspective. Analyzing the rotation, at this point composed by 55 items, we noticed that 30 items presented loading scores to low (< to .50) or loading scores similar in more than one factor (differences < to 1). Thus, we decided to eliminate them and repeat EFA – once again items were removed one by one and all tests and analysis were repeated after each removal. Repeating the statistical procedure with 25 items in the same sample, eigenvalues from random data exceed the eigenvalues from research data after the fourth factor in parallel analysis, there were no cross-loadings, neither items with low loadings. The final solution is presented in Table 3. We re-run the final structure in a random independent sample and the results were replicated. All factors have more than 3 indicators that strongly relate to the factor, ensuring high factorial stability across replications. Analysing Kaiser-Meyer-Olkin (KMO) and Bartlett’s Sphericity Test, the data allowed the prosecution of the EFA (Hair et al 2006, Stevens 1986). KMO measure (.88) indicated a good sampling adequacy. The second indicator revealed that the correlation matrix of the 25 items is significantly different from an identity matrix [2(300) = 3090.73 p < .001]. After extracting 4 factors, we realized that the total explained variance was 48.22%. The first factor have an eigenvalue of 6.68, explaining 26.74% of the variance, the second factor assumes an eigenvalue of 2.14 explaining 8.56% % of the variance, the third factor presents an eigenvalue of 1.85 and a explained variance of 7.39% and the fourth factor presents an eigenvalue of 1.38 and an
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Elisabeth Brito et al. explained variance of 5.52%. The entire loadings of items presented in one factor were > to .50 (Pestana and Gageiro 2005, Tabachnick and Fidell 2001). The Cronbach’s alpha indices surpassed in all cases the established threshold of .6 (Hair et al., 2005). It yielded a Cronbach’s alpha of .85 for the Factor 1, .80 for Factor 2, .71 for Factor 3, and .65 for Factor 4 and all variables had a correlation item-scale above .3 (Nurosis 1993). Table 3: Factor analysis of GC for municipalities with certified services Items 44. We all are responsible for what we should know to work with quality 33. We seek information that can improve the quality of what we do 31. We reflect about the way we solved our problems in the past (through our successes and failures) 17. What we know is seen in the way we work 29. Each one of us has a task to fulfil 18. Sometimes, it is in the way we perform tasks that we find solutions to new problems 21. We act according to certain kind of principles 07. We know what is expected from each one and from the Municipality 32. We act according to the way we are organized 08. We know that our clients /councils have an idea about us 02. We know the ideas by which Municipalities exist 34. We attend to seminars/conferences, we read publications or we hire experts 56. We attend training courses or we have training on the job 36. We exchange information in work meetings 37. We get together to solve some of our problems 47. We share information among us (through reports, internal journal, email for example) 48. We tell to each other funny stories that happened in our work 43. We talk about our Municipality 49. We discuss subjects that we do not totally understand 42. We ask our colleagues how have they solve some problems 54. We talk about our tasks 22. What we know is a “weapon” to surpass other Municipalities 23. We are attentive to what other institutions are doing (we adopt the best tricks) 19. We know that other Municipalities have information about us 06. What we know is seen in what we do better than other Municipalities
Factor 1 .667 .659 .628
Factor 2 .201 .305 .160
Factor 3 .124 .023 .205
Factor 4 -.032 .063 .177
.624 .614 .612
.054 .127 .075
.072 .088 -.034
.173 -.106 .204
.608 .600 .574 .518 .505 .170
.012 .279 .098 -.250 .235 .770
.054 -.019 .134 .230 .193 .031
.228 .216 -.110 .111 .260 .217
.055 .277 .215 .121
.709 .663 .663 .621
.043 .155 .248 .259
.094 .139 .190 .066
.050 -.031 .133 .305 .132 .203 .028
-.010 .095 .241 .119 .133 .158 .232
.759 .668 .647 .584 .566 -.006 .076
-.025 .171 .045 .109 .029 .752 .727
.054 .323
.035 .206
.226 -.010
.590 .500
6. Discussion In the Municipalities with certified services as with non-certified services 4 dimensions emerged from EFA and the positioning of the factors is similar. We named this factors as: “knowledge oriented culture” (first factor), “knowledge management practices” (second factor), “social and discursive knowledge management” (third factor) and “strategic knowledge management” (fourth factor). The “knowledge oriented culture” factor points aspects framed in a cultural perspective, because it depicts common, known, assumed and shared values, which guide workers in terms of their way of being, act and work. It states a set of values that conform with the culture and enable the organization in persecuting better results through good practices (quality orientation), enabling an individual perspective (people orientated) that promotes KM, supporting the organization in its changing values and developing processes oriented to the creation of unified individual perceptions, to move for a sustainable development and to achieve competitive advantages. The second one (“knowledge management practices”) reflects a concern with knowledge creation and acquisition through a set of knowledge sharing practices provided by training, by attending to conferences, or in solving problems in groups, as by the use of several circles of knowledge transfers. Thus, it emerges the enrichment practices of explicit nature, as the formal organizational procedures and processes in the sense of promoting new knowledge creation/acquisition, its conservation, sharing and use.
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Elisabeth Brito et al. The “social and discursive knowledge management” factor translates the importance that a discursive practice assumes in the knowledge creation and sharing. It represents the informal organization and the whole interactions network that characterizes and facilitates a social construction of knowledge and the creation of a common language and meaning. It refers to knowledge from a tacit nature, that is not easily interpretable or shared and, in order to be understandable, it requires a collective and shared reality that distinguishes what is relevant within the organization. The fourth (“strategic knowledge management”) is related to the organizational environment as to the oriented behaviour focused on organizational external context, highlighting the importance that clients and competitors roles have for organizations that assume an orientation towards competitiveness. This orientation aims an organizational response more and more effective to the stakeholders, the adaptation to the external environment and the promotion of competitive advantage and sustainability. The four factors structure was already found in previous studies from the first author of this article in municipalities with quality awards (Brito 2003). Despite the small qualitative differences, the great distinction between the two structures is related to the number of items in each dimension, to the Cronbach’s alpha indices and to the indicator-factor loadings. In municipalities with non-certified services emerged more items in the “knowledge oriented culture” and in municipalities with certified services emerged more items in the “knowledge management practices”. The Cronbach’s alpha indices are higher in municipalities with certified services for “social and discursive knowledge management” and “strategic knowledge management” factors, being similar in the other dimensions. The appointed differences make sense to these authors since KM processes involved in both cases are in some sense distinct. Firstly, in municipalities with non-certified services, cultural values are clearly identified and shared, and promote the pursuit of better results through best practices. Their workers are focused on the set of cultural values and responsibilities to work with quality. Those municipalities are also more orientated towards knowledge acquisition. Additionally, the more prevalent intra-organizational perception about “strategic knowledge management” practices may be due to an underdeveloped incorporation of daily practices regarding its collaboration with external environment, stakeholders and citizens. In municipalities with certified services, “social and discursive knowledge management” construct is better defined, clearly promoting the tacit knowledge construction and sharing through informal network interactions. Moreover, they are more concerned with knowledge creation and acquisition through a set of knowledge sharing practices that can incorporate cooperation with other organizations. Additionally, its workers are mainly seeking information to improve quality. Finally, they have a more defined “strategic knowledge management” practices, which means that they have an oriented behaviour focused on organizational external context, highlighting the importance of clients and competitors’ practices and knowledge and assuming an orientation towards competitiveness and quality.
7. Conclusion This study highlights that in both samples, there is a similar structure in KM processes, since the differences in terms of the items content are small, and the number of factors is equal. The highlighted differences apparently indicates the importance that the municipalities with certified service attribute to the quality processes and to the creation of internal processes leading to a more accurate KM dimensions. These practices seem to represent the whole potential of differentiation between the analyzed organizations and, because of that, pointing paths to be followed by local government organizations towards a service with quality that incorporates organizational knowledge.
References Beijerse, R.P. (1999) “Questions in Knowledge Management. Defining and Conceptualizing a Phenomenon”, Journal of Knowledge Management, Vol 1, No. 3, pp 94. Brito, E. (2003) Estudo Empírico sobre a Gestão do Conhecimento em Câmaras Municipais que Adoptaram a Gestão pela Qualidade, Tese de Mestrado em Psicologia do Trabalho e das Organizações não publicada, Faculdade de Psicologia e Ciências da Educação da Universidade de Coimbra.
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Elisabeth Brito et al. Cardoso, L. (2003) Gerir Conhecimento e Gerar Competitividade, Estudo Empírico sobre a Gestão do Conhecimento e seu Impacto no Desempenho Organizacional, Dissertação de Doutoramento em Psicologia, Especialização em Psicologia do Trabalho e das Organizações, Faculdade de Psicologia e Ciências da Educação da Universidade de Coimbra. Davenport, T.H., De Long; D.W. and Beers, M.C. (1999) “Successful Knowledge Management Projects, In J. W. Cortada and J. A. Woods (Eds), The Knowledge Management Yearbook 1999-2000 (pp.89-107), Butterworth-Heinemann, Boston. Davenport, T. H., & Prusak, L. (1998) Working Knowledge: How Organizations Manage What They Know, Harvard Business School Press, Boston, Massachusetts. De Long, D. (1997) Building the Knowledge-Based Organization: How Culture Drives Knowledge Behaviors, Ernst & Young LLP, Center for Business Innovation, Boston. Douglas, T.J. and Judge, W.Q. 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(1995) “The Impact of Quality Management Practices on Performance and Competitive Advantage”, Decision Sciences, Vol 23, No. 5, pp 659–691. Ghiglione, R. and Matalon, B. (1997) Les Enquêtes Sociologiques: Théories et Pratique, Armand Colin, Paris. Grant, R. M. (1996) “Prospering in Dynamically Competitive Environments: Organizational Capacity as Knowledge Integration”, Organizational Science, Vol 7, No. 4, pp 375–388. Hair, J., Anderson, R, Ronald, T., and Black, W. (2005) “Análise Multivariada de Dados” (5th ed.), Bookman, São Paulo. Hendricks, K.B. and Singhal, V.R. (1996) “Quality Awards and the Market Value of the Firm: An Empirical Investigation”, Management Science, Vol 42, No. 3, pp 415–436. Hendricks, K.B. and Singhal, V.R. (1997) “Does Implementing an Effective TQM Program Actually Improve Operating Performance? Empirical Evidence From Firms That Have Won Quality Awards”, Management Science, Vol 43, No. 9, pp 1258–1274. Hendricks, K.B. and Singhal, V.R. 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(1989) “A comparison of Alternatives to Conducting Monte Carlo Analysis for Determining Parallel Analysis Criteria”, Multivariate Behavioural Research, 24, 365-395. Lim, K.K., Ahmed, P.K. and Zairi, M. (1999) “Managing for Quality through Knowledge Management”, Total Quality Management, Vol 10, No. 4-5, pp 615-621. Molina, L.M., Montes, J.L. and Fuentes, M.D.M. (2004) “TQM and ISO 9000: Effects on Knowledge Transferability and Knowledge Transfers”, Total Quality Management, Vol 15, No. 7, pp 1001–1015. Molina, L.M., Montes, J.L. and Moreno, A.R. (2007) “Relationship between Quality Management Practices and Knowledge Transfer”, Journal of Operations Management, Vol 25, No. 3, pp 682-701. Nonaka I. (1990) Chishiki-Souzou Mo Keiei (A Theory of Organizational Knowledge Creation), Nihon Keizai Shimbun-Sha, Tokyo. Nonaka I. (1991) “The Knowledge Creating Company”, Harvard Business Review, Vol 69, No. 6, pp 96-104. Nonaka I. 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Elisabeth Brito et al. Nonaka, I., Toyama, R. and Byosière, P. (2001) “A Theory of Organizational Knowledge Creation: Understanding the Dynamic Process of Creating Knowledge”, In M. Dierkes, A. Berthoin Antal, J. Child, and I. Nonaka (Eds.), Handbook of Organizational Learning and Knowledge (pp. 491-517), Oxford University Press, Oxford. Nonaka, I., Toyama, R. and Konno, N. (2001) “SECI, Ba and Leadership: A Unified Model of Dynamic Knowledge Creation”, In I. Nonaka and D. Teece (Eds.), Managing Industrial Knowledge: Creation, Transfer and Utilization (pp.13-43), Sage Publications, London. Nurosis, M.J. (1993) “SPSS Statistical Data Analysis”, IL., Chicago. Pestana, M. H. and Gageiro, J. (2005) Análise de Dados para Ciências Sociais: A Complementaridade do SPSS (4ª ed.), Sílabo, Lisboa. Quinn, J.B. (1992) Intelligence Entreprise: A New Paradigm For a New Era: How Knowledge and Service Based Systems Are Revolutionizing the Economy, All Industry Structures, and the Very Nature of Strategy and Organization, Free Press, New York. Ruževičius, J. (2006) “Integration or Total Quality Management and Knowledge Management” [online], Information Sciences (Informacijos mokslai), Vol 37, pp 30-38, www.ceeol.com. Stevens, J. (1986) Applied Multivariate Statistics for the Social Sciences, Hillsdale, Erlbaum Associates, Lawrence, NJ. Stewart, T.A. (1997) Intellectual Capital: The New Wealth of Organizations, Doubleday Currency, New York. Stewart, T.A. (1998) Capital Intellectual, Campus, Rio de Janeiro. Sveiby, K.E. (1997) The New Organizational Wealth: Managing and Measuring Knowledge Based Assets, BerretKoehler Publishers, Inc., San Francisco. Sveiby, K.E. and Mellander, K. (1994) Tango: Business From Knowledge Learning Guide, Malmö, Celemi. Tabachnick, B. and Fidell, L. (2001) Using Multivariate Statistics (4th edition), Allyn and Bacon, Boston. Velasco, C.A.B. and Garcia, C.Q. (2005) “Proceso y Sistemas Organizativos Para La Gestión Del Conocimiento – El Papel De La Calidad Total”, Boletin Economico de ICE, March 14-20 (2838), pp 37-52. Wilson, L.T. and Asay, D. (1999) “Putting Quality in Knowledge Management”, Quality Progress, January, Vol 1, pp 25. Zuboff, S. (1998) In The Age of The Smart Machine: The Future of Work and Power, Basic Books, New York.
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On the Importance of Managing Intangible Assets as Part of Corporate Strategy Annie Brooking Anglia Ruskin University, Cambridge, UK
[email protected] Abstract: Given that a high number of companies return value to investors via acquisition rather than a public offering the development of intangible assets is the bait that sets up the acquisition. This paper discusses how companies can fast track to high valuation by strategic growth of certain intangible assets such as customer tribes, brands, and intellectual property, comparing those strategies to larger companies. It further describes a strategic planning methodology using four asset categories (Market, Infrastructure, Human Centred and Intellectual property) to describe the enterprise as it would be if it had achieved its strategic goals. This state is referred to as the “Dream”. The Dream is characterised by a set of affirmations describing the “health” of the enterprise’s assets. This is called a “Dream Ticket”. Keywords: Intellectual Capital, SMEs, IC methodology, strategic business planning
1. Introduction Strategic planning can be described as the process of determining what a business should become and how it should go about achieving its goals whilst capitalising on its opportunities and addressing its challenges. For management teams this can be a real challenge, especially if the strategic goal is couched solely in terms of revenue. For SMEs this is likely to be the case as the number one priority may well be just to stay afloat in very difficult times. For small companies defining corporate strategy can be a challenge, especially where they have limited access to business mentors or consultants that can assist in facilitating the process, so the CEO typically sets the scene by stating where the company needs to be in one or more years time by way of a mission statement that encapsulates values, vision and purpose. As such it’s a “what we will do” type statement that may mean a lot to the management team that brainstormed it but hard for employees who did not participate in the process to grasp or believe in. In the same way as a book title may not convey the story to the reader until after the story has been read, a mission statement may not convey the strategy until the story can been shared. The Dream Ticket method enables management to share the story with employees. Entrepreneurs start companies by bootstrapping or raising angel or venture capital. The high technology sector typically takes both angel and venture capital as there is usually a time to market issue when the company will be in research and development mode and will not be generating revenue. Given most VC funds seek to realise a return on investment that requires an exit in less than ten years it doesn’t leave the entrepreneur much time to create an enterprise of value. This situation is exacerbated when there are several years of development ahead before product is ready for market. Ironically the more revolutionary or disruptive the technology, the longer the time to mass market share shortening the time to grab it. Further, given exit is a requirement for investors, the challenge facing the management team and Board is how to get the highest valuation at exit in the time available. As high technology companies tend to manifest value by way of intangible assets it makes sense to develop a corporate strategy that grows and develops them. The challenge is to figure out what assets to develop and how to do it for maximum value. The Dream Ticket method acts as the mechanism to do this.
2. The Dream Ticket methodology There are four sets of assets that are used to build a Dream Ticket:
1. Market Assets, these are assets which belong to the company and give it power in the marketplace. They include brands, positioning, customer base, company name, backlog, distribution channels, collaborations, franchise agreements, licensing agreements, favorable contracts and so on.
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2. Infrastructure Assets, these are assets which include management philosophy, corporate culture, management and business processes, compliance to standards such as FDA, financial relations, methodologies and IT systems which enable the organization to function and communicate with its customers. Examples include methodologies for assessing risk, methods of managing a sales force, financial structure, databases of information on the market or customers, communication systems such as e-mail and teleconferencing systems. Also included is the financial status of the business, whether it is stable or at risk, wealthy or constantly seeking funding. Basically infrastructure assets are the elements which make up the way the organization works. These assets belong to the company, and make the company operationally strong.
3. Intellectual Property Assets are assets resulting from the mind that belong to the company and are protectable in law. These include patents, copyright, design rights, trade secrets, and trademarks.
4. Human Centred Assets include the collective expertise, creative, problem solving capability, leadership, entrepreneurial and managerial skills embodied by the employees in the organization. Key is knowledge of aspects of the business to include market knowledge and management expertise. Human Centered Assets also include psychometric indicators on how individuals may perform in given situations, such as in a team or under stress. These assets do not belong to the company but are contracted to the company by way of employee contracts, unless they can be made explicit, thus becoming Infrastructure assets (Brooking, A. 1998)
Thus market, infrastructure and intellectual property assets can be bought and sold, human centred assets can’t. These four categories of assets are used to develop a Dream Ticket that describes the business as it would be if it had already achieved its strategic goal. For example if the goal of the business was to double its revenue to Euros 10 million this may require a more favourable relationship with its target customers than it had today.
3. Case study: company X Take as an example a 30 person company (X) that manufactured facial scanning systems that assessed the photo damage in a woman’s skin and estimated her skin age. If she was a sun worshipper or had excessively used sun beds and tanning salons a 30 year old woman’s skin age might be calculated as 35 and a woman of the same age who had always used sun block might be assessed as having the skin age of 25. The target market is spas and salons who charge the client £50 for a skin age consultation. For company X a desirable set of market assets that may enable them to double revenues could be:
M1. Every customer who buys from us recommends us to three of their peers.
M2. Every salon or spa who buys from us buys a second system in less than two years.
M3. Every prospect we have in the UK recognises our brand.
M4. Every woman over the age of 30 has a skin consultation twice a year.
M4. All salons and spas retain 100% of their customers.
M5. Every UK health magazine has an article on skin scanning four times a year.
M6. There is a stable of celebrity key opinion leaders who are followed by women aged 30.
M7. Doctors and medical practitioners recommend tracking photo damage as part of a woman’s health regimen.
M8. Company X takes 20 phone queries a day from prospects concerning its facial scanner.
M9. The salon and spa business is growing by 20% per annum
Each of the affirmations above translates or contributes towards an intangible market asset.
M1. Refers to a set of evangelists
M2. Is repeat business
M3 and M5. Refer to brand recognition
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M4. Refers to product ROI
M6 and M7 Will contribute to market pull
M8 Is market pull
M9 Shows the market is expanding not retracting, reflecting a business opportunity.
For company X if the above affirmations were all true it could be said that the business was in a favourable position to double its revenue. Below are outlined intellectual property assets for Company X.
IP1. The facial scanner is protected by a thicket of 5 patents.
IP2. The brand name of the facial scanner is trademarked in all target markets.
IP3. The patent portfolio is managed and measured for ROI every year.
IP4.’All software is patented
This set of assets would mean that the scanner would be have a strong competitive position from a technology perspective. With respect to human centred assets the following set would be desirable given the company wanted to double revenues.
H1. Every employee knows how to take a customer query and follow through
H2. Every employee understands the scanner technology and can explain its USPs
H3. All sales executives have deep product knowledge
H4. The marketing team are all intimate with the target market sector
H5. The company has a high staff retention rate
H6. All customer training staff have previously worked in salons or spas
H7. All knowledge relating to the manufacturing process is documented.
Human centred assets H1, H2, H3, H4 and H6 all demonstrate that staff is fit for purpose. H5 means the company probably has content employees and H7 means that tacit and explicit knowledge are documented for this task, this is desirable for a potential acquirer.. Finally with respect to infrastructure assets the following would be desirable:
I1. All products have FDA approval
I2. All products are CE marked
I3. All customers are correctly recorded in a database for customer support
I4. Customer supports sells support contracts to all customers
I5. Quality manuals are updated and reviewed monthly
I6. All prospects are tracked in a sales tracking system
The above affirmations relate to the strength of the infrastructure of a company intent on increasing sales by use of internal systems. FDA approval is a major asset for any company wanting to branch out in the USA and having clean databases is an important asset for any company wanting to both increase sales and harvest incremental revenues from the sale of support contracts to customers. The four sets of affirmations make up the Dream Ticket for company X. It’s easy to share this with employees who are then able to visualise where the business needs to go to achieve its goal. They understand the story. The next step is to measure the gap between where the business is now and the Dream Ticket. An index is assigned to each affirmation reflecting its relative strength, 5 being strong and 0 weak. The business creates methods for measuring each asset’s index. Customer surveys, percentage through a process such as CE marketing, staff questionnaires for competence, auditing databases or getting staff such as telesales and customer support to validate and clean databases are a few measures that are typically used.
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Annie Brooking Dream Ticket for Company X with Index (5 is strong) index/5 M1. Every customer who buys from us recommends to us three of their peers. 1+ M2. Every customer who buys from us buys a second system in under two years. 1+ M3. Every prospect we have in the UK recognises our brand. M4. Every woman over the age of 30 has a skin consultation twice a year. M4. All spas retain 100% of their customers. M5. Every health magazine has an article on skin scanning four times a year. 2+ M6. There is a stable of celebrity key opinion leaders who are followed by women aged 30 M7. Doctors and medical practitioners recommend tracking photo damage as part of a woman’s health regimen. M8. Company X takes 20 phone queries a day from prospects concerning its facial scanner. M9. The salon and spa business is growing by 20% per annum IP1. The facial scanner is protected by a thicket of 5 patents. IP2. The brand name of the facial scanner is trademarked in all target markets. IP3. The patent portfolio is managed and measured for ROI every year. IP4. All software is patented H1. Every employee knows how to take a customer query and follow through 2+ H2. Every employee understands the scanner technology and can explain its USPs 3+ H3. All sales executives have deep product knowledge H4. The marketing team are all intimate with the target market sector H5. The company has a high staff retention rate H6. All customer training staff have worked in salons or spas H7. All knowledge relating to the manufacturing process is documented. I1. All products have FDA approval 0+ I2. All products are CE marked I3. All customers are correctly recorded in a database for customer support 2+ I4. Customer supports sells support contracts to all customers I5. Quality manuals are updated and reviewed monthly I6. All prospects are tracked in a sales tracking system
2 2 3 0 0 1 5 5 1 2 5
4 4 5 4 2 5 2 3 2
In using this method for over ten years in numerous high technology businesses estimates by the management team have proven to be just as accurate as in-depth analysis, survey and measurement (which can take a team months to complete) especially in small technology companies where the staff tends to be both smart and insightful. The benefit of this is that the Dream Ticket can be brainstormed by the management team and indexed in the same session. Once the assets are indexed they can be plotted on a target. If the asset is strong it will have an index of five and be at the centre. If it is weak it will have a zero value and be at the outer edge of the target. At this stage it may be useful to note the assets direction of travel. If the asset is getting stronger, that is measures are underway to strengthen it, its arrow points toward the centre of the target, if it’s getting weaker the arrow points to the outside of the target as shown in Figure 1. Assets moving away from the target are not always a bad thing. In young companies they typically show something wrong with the business. In more mature companies they may show a change in strategy where perhaps a particular asset is no longer considered to be of value, or a patent has been in place for 18 years and has only two years of its life left. However the position of all assets on the target will be relative to the goal and also to the context of the business at the time.
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Figure 1: Direction of travel of assets This pictorial representation tells us a lot about the Company X. We can see that it has a number of assets in the middle of the target:
I2. All products are CE marked (5)
H5. The company has a high staff retention rate (5)
IP1. The facial scanner is protected by a thicket of 5 patents (5)
IP4. All software is patented (5)
M9. The salon and spa business is growing by 20% per annum (5)
From a strategic planning perspective this is good, and especially good as this company is small and early stage. Its target market is growing, it has taken care to file intellectual property and high staff retention tells us the company is stable from a critical knowledge function perspective. This is what we would expect to find in a high technology start-up. The issue for Company X is to build its strength in the market and fix its infrastructure so that it was able to both target and support its customers generating both loyalty and revenue. The asset M1 tells us that the company seeks to grow through referral, creating a tribe from its customer base and this is a good strategic move for a company that is resource constrained. M6 and M7 tell us that the business thinks that it understands who the key influencers in its target market are; the issue now is how to make these assets strong so the Dream Ticket becomes a reality. Once the target has been populated the next step is to design measures that will close the gap and move the assets toward the centre of the target as quickly as possible. In this particular Dream Ticket there are twenty six assets. This is quite a low number, as this business scenario is only looking at one product and one distribution channel in one country. When this business went global and included a full product range it had close to sixty assets it was managing. Designing sixty measures to grow sixty assets is too complex so assets need to be grouped and prioritised so that “super” measures can be designed to strengthen the Dream Ticket as a whole. In this particular scenario measures might include a KOL generation and management programme, or partnering with a medical organisation that would verify that increased photo aging is linked with other diseases like skin cancer. An example of grouping assets is shown in Figure 2.
4. Asset grouping This company is a biotechnology company that was seeking to generate revenue by licensing intellectual property. The patent portfolio is quite strong with the exception of one weak patent that can be viewed (IP7). However the key weakness here is the lack of staff with appropriate expertise and this can be seen from the number of human centred assets that remain on the outside of the target. Early stage high technology companies cannot afford to have either weak intellectual property or weak human centred assets. The cross target linking of human centred assets and infrastructure assets tells us the wrong people are in place to set up the infrastructures that are required. So people will need to be trained or replaced. The mix of market assets here: four strong the other seven weak is
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Annie Brooking in fact because whilst there is a market for the service this company wishes to offer it has neither the market presence, infrastructure nor people to grab it.
Figure 2: Asset coupling Once the measures have been designed to strengthen the assets their cost can be estimated and a budget drawn up. The measures become part of the tactical and timelines planned. If the business does not have the finance to fund the plan then either less expensive measures can be designed or a less ambitious goal agreed. Should that be the case the Dream Ticket may need to be redesigned completely or in part, and re-indexed. It is likely that some assets will change their relative strength if a less ambitious goal is chosen, for example the business may be in a stronger position to increase revenues by 50% rather than doubling them. Intangible assets have been shown to be the major driver for acquisition of high technology businesses. Sometimes this manifests as an intellectual property portfolio of patents, trademarks and design rights. Sometimes it’s access to market via a distribution channel or a network of KOLs that an acquiring organisation can leverage for their own product range. One thing is certainly clear to this author, if exit is to be by acquisition the value of assets changes depending upon the goals of the acquirer. Different context, different value, and for that reason research to put a monetary value on individual assets has ceased as it is impossible to predict who will acquire the business. The alternative strategy is to build a strong business composed of strong intangible assets in the hope that there is an acquirer who will consider the entire package valuable rather than just the intellectual property portfolio or the customer base. That said, building a Dream Ticket that describes the business as it will be in two or three years time gives the business the opportunity to build a profile of potential acquirers for the business as a whole or in part. This also opens up the opportunity to sell the business “many times over” by transferring ownership of intangibles to a holding company and licensing them all or in part to various subsidiaries then selling them off one at a time, in parallel building a valuable holding company that has a royalty stream from licensing, and consulting revenues using critical knowledge skills only found in the holding company, all of which are also intangible assets. This is a much more pro-active strategy with intangible assets that sets out from day one to plan for value and exit.
5. Conclusion In a knowledge based economy, where the growth of high technology companies is the business trend that delivers highest returns to investors, building valuable intangible assets is the corporate strategy. The Dream Ticket method allows the management team to describe and build a Dream scenario that is easy to understand and communicate to all employees and stakeholders in the business to both
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References Boekstein, B (2009) “Acquisitions reveal the hidden intellectual capital of pharmaceutical companies”, Journal of Intellectual Capital, Vol 10 Issue 3, pp 389-400. Brooking, A. (2009) “Dream Ticket: Using Intellectual Capital Management as a Strategic Planning Paradigm”, “Dream Ticket, Using Intellectual Capital Management as a Strategic Planning Paradigm”, 5th Workshop on Visualising, Measuring and Managing Intangibles and Intellectual Capital, Dresden, October 2009 Brooking, A. (1996) “Intellectual Capital: Core Asset for the Third Millennium Enterprise”, Thompson International Business Press, London Brooking, A (1999) “Corporate Memory: Strategies for Knowledge Management”, Thompson International Business Press, London. Brooking, A. Board, P, Jones, S. (1998) “The Predictive Potential of Intellectual Capital”, International Journal of Technology Management, Volume 16, Nos 1/2/3 pp 115-125. Durst, S (2008) “The Relevance of Intangible Assets in German SMEs, Journal of Intellectual Capital, Vol 9 Issue 3, pp 410-432.
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Sharing Knowledge in a Knowledge City Using CoPs Sheryl Buckley and Apostolos Giannakopoulos University of Johannesburg, South Africa
[email protected] [email protected] Abstract: In the pre-industrial age, communities existed to connect people. People joined guilds to find mentors who would help them master their crafts. During the industrial revolution, workplace tasks were divided into small chunks to help employers define their employees’ roles and responsibilities. With the advent of the knowledge worker, the workplace has undergone another transformation. Now, jobs that involve the most complex type of interactions make up the fastest-growing segments in many industries (Sauve, 2007). The reality is that in many industries in which situations change rapidly, formal learning once or twice a year doesn’t provide employees with the experience or knowledge they need to find ongoing success on the job. This means that organisations must revamp their budgets and shift their resources from formal learning settings to informal situations in which the majority of learning actually takes place. The rise of social computing based on highly innovative new Web 2.0 technologies such as MySpace.com, YouTube.com, Digg.com and Facebook.com, offers a new paradigm for how we approach learning and knowledge sharing and is beginning to have a powerful impact on corporate learning (Sauve, 2007). Business cultures are changing rapidly to take advantage of these new technologies. Today the concept of knowledge sharing through new interactive online tools is taking hold in more and more public and private organisations. The change from an industrial economy to a knowledge economy forced many organisations to change their modus operandi if they were going to survive in a sustainable way. The introduction of communities of practice (CoPs) by Lave and Wenger in 1991 shed new light on knowledge sharing and dissemination of information. Sharing, interacting, actively participating, collaborating and learning from one another become the central activities in a knowledge society. According to Wenger (1998), CoPs are everywhere. We are core members of some and we belong to others more peripherally. CoPs are informal, naturally occurring, spontaneously evolving groups and the sense of community comes from defining them in terms of practice (Kubiak, 2003). In a knowledge based development approach to modern societies as suggested by Ergazakis, Metaxiotis & Psarras (2006), CoPs can be used as the originators of change and innovation for a ‘knowledge city’. This paper will address the role that CoPs can play in the development of a ‘knowledge city’. Keywords: Knowledge sharing, Communities of Practice, knowledge cities, knowledge-based development
1. Introduction In any developed or developing country the shift from the industrial era to one of knowledge era has reached a point of no return. It has become now common knowledge that the only way to prosperity of any nation is through the use of knowledge (explicit/hard or implicit/tacit/soft) which is possessed by people. Knowledge then serves a dual purpose:
a) It is an asset, and perhaps the most valuable asset of an enterprise (Nonaka, 1991; Wiig, 1993; Ergazakis, Metaxiotis & Psarras, 2004; Polanyi, 1961) and,
b) An agent for change and innovation.
In the societal level, the industrial society is changing rapidly to a knowledge society: one which places an explicit and principal value on knowledge as the means to achieve economic and social well being. In such a society, knowledge represents a core national value: the means through which the citizens achieve (i) greater choice and opportunity (ii) deeper social integration and (iii) longer life expectancy, each across very many dimensions (Mallalieu, 2006). In the national level as more knowledge societies are formed the old industrial cities are transformed to “knowledge cities”, where knowledge-based development (KBD) is developed. Carrillo (2002) suggests that KBD is a theoretical and technical field which itself derived from the convergence of a discipline and a movement. According to him, it has three levels: (1) social knowledge infrastructure; (2) human capital development; and (3) development of the social capital system. For Ergazakis et al (2004) “a knowledge city is a city that aims at a knowledge-based development, by encouraging the continuous creation, sharing, evaluation, renewal and update of knowledge.” Such development could take place in two ways: a) by developing the individual to a knowledge worker and/or b) the city taking the initiative of becoming a knowledge city which can be achieved “through the continuous interaction between its citizens themselves and at the same time between them and other cities’ citizens. The citizens’ knowledge-sharing culture as well as the city’s appropriate design, IT networks and infrastructures support these interactions” (Ergazakis et al, 2004). For Tanedu (2007) a knowledge society is one that creates, shares, and uses knowledge for the prosperity and wellbeing of its people. It is a society where people associate formally having similar interests, who try to make effective use
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Sheryl Buckley and Apostolos Giannakopoulos of their combined knowledge about their areas of interest and in the process, contribute to this knowledge (Tanedu, 2007). However, interactions between people and sharing of their knowledge could happen by mutual need, a mutual goal, a mutual enterprise where they all benefit and more ideally in a voluntary manner. This ideal situation gave rise to Communities of Practice (CoPs), a group of people who share their knowledge voluntarily and they are all active participants. It can be argued then that a knowledge society is a society that a knowledge city will comprise of. The vision of the knowledge city then becomes the vision of that society. This paper aims at explaining the idea of a knowledge city by supplementing knowledge agents (K-agents) as suggested by Ergazakis et al (2004) with CoPs. Firstly, a brief description on CoPs will show there can be a link between CoPs and a knowledge city based on the main constructs, knowledge sharing. But knowledge could be tacit (implicit) or explicit (hard). Sharing could be voluntary or obligatory, like in teams. Secondly, a brief discussion on the different types of CoPs follows and the link is established. This paper will address briefly knowledge sharing in a KBD and the role that CoPs play in achieving knowledge management (KM) objectives of a “knowledge city” in its narrow sense and the objectives of a “global city” in its broader sense.
2. Communities of Practice It was Skyrme (1998) who said “knowledge sharing is power” and such sharing is possible through CoPs. Politis (2003) adds that modern KM approaches are about “creating a team work environment in which power is equated with sharing knowledge, rather than retaining it.” In the context of a “knowledge city” as indicated by Ergazakis et al (2004), a knowledge sharing culture is a prerequisite for a KBD. Therefore, in the same context a CoP can be seen as a very important K-agent. The term CoP was coined by Lave and Wenger (1991) and is attributed to the Institute for Research on Learning who first used the phrase in their book, Situated Learning (1991). Since the idea of CoPs was accepted as another way of collaborative learning, many authors have tried to add/justify/explain why the formation of CoPs are necessary in any organisation. The aims/functions began to be clearly defined and the benefits derived from them were widely accepted. Once CoPs were introduced, they began to evolve. Their functions started being better defined and this definition was accordingly refined by Wenger and Snyder (2000a:139, 2002). For the authors CoPs are “… groups of people informally bound together by shared expertise and passion for a joint enterprise”. They organise themselves, determine their own agendas and establish their own leadership. Members have a personal commitment to the work of the community. At a later stage the authors redefined CoPs as “… groups of people that share a concern, a set of problems or a passion about a topic and who deepen their knowledge and expertise in this area by interacting on an ongoing basis” (Wenger & Snyder, 2000b). It can be argued then that such communities are the epitome of, what Laszlo et al (2003) termed, “participatory democracy.” CoPs then can be used as the “seeds for a democratic knowledge based developing society”. There are three requisites for communities to exist: (1) a common cultural and historical heritage, including shared goals, negotiated meanings and practices; (2) an interdependent system, in that individuals are becoming a part of something larger than themselves; and (3) a reproduction cycle, through which “newcomers” can become “old-timers” and through which the community can maintain itself. These three criteria for the existence of a CoP are in line with KBD principles of socio-cultural progress and sustainability. Barab and Duffy (1998) state that, communities go beyond the simple coming together for a particular moment in response to a specific need that needs to be satisfied. Successful communities have a common cultural and historical heritage that partially captures the socially negotiated meanings. This includes shared goals, meanings, belief systems and practices. Second, individuals are becoming a part of something larger as they work within the context and become interconnected to the community, which is also a part of something even larger (the society through which it has meaning/value). It is through the legitimate participation in this greater community, and through the community’s legitimate participation in society that communities and
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Sheryl Buckley and Apostolos Giannakopoulos identities are formed (Barab & Duffy, 1998; Thorpe, 2003). However, it is not just the community members who are a part of something larger. The community itself functions within a broader societal role that gives it, and the practices of the community members, meaning and purpose. If the community isolates itself from the societal systems of which it is a part, then both the individuals and the community become weaker. The socio-cultural aspect of CoPs therefore can contribute to the local and regional development of the “knowledge city”, by aligning CoPs’ goals to that of the society they exist. Thirdly, it is important that communities have the ability to reproduce as new members engage in mature practice with near peers and exemplars of mature practice. This is this line of thinking that led to Lave and Wenger’s discussion (1991) of legitimate peripheral participation (LPP). This renewal aspect of CoPs makes it possible for shared knowledge to be renewed and be kept up-to-date with the new developments in the various fields. Furthermore, Lave and Wenger (1991) saw the creation of CoPs as a solution to cope with the knowledge economy, which was taking over the industrial one. The authors were convinced then that an organisation would have to reposition itself in the emerging knowledge society if it was to survive. Learning through knowledge sharing began to emerge as the way to cope with the rate at which information was increasing. Also, established CoPs are by nature collaborative; and collaboration is for van Winkelen (2003) a cooperative, inter-organisational relationship that does not rely on the market or the hierarchical mechanisms of control, but is negotiated in an ongoing communicative process. CoPs are a mechanism within which collaboration between organisations can occur. Collaboration between organisations has come into focus in recent years with the recognition that success in a global economy comes through innovation. Innovation depends on the exchange of ideas and insights through trusted relationships, which depends on knowing how to collaborate effectively. People working in the same specialty, the same “practice” – even though they are not usually on the same work team – also develops communications patterns that help spread common understandings about how work is done, what information is relevant and important, and other factors in the work environment (Sharp, 1997). Recently, we have seen contributions which suggest that CoPs can be cultivated and leveraged for strategic advantage (Saint-Onge & Wallace, 2003). Organisations create CoPs as part of their KM strategies and are often viewed as a supplementary organisational form (Swan, Scarbrough & Robertson, 2002). These recent developments about CoPs indicate that they can be responsible for achieving the strategic goals of KM not only of an organisation but regionally and globally as networks of CoPs are formed (Wenger & Snyder, 2000b). And as the link was established between KM and KBD (Ergazakis et al, 2006) then KM found application beyond the organisation and into education, government and international agencies in turn KM became the vehicle of achieving the strategic goals of KBD. CoPs have also been identified as effective loci for the creation and sharing of knowledge (Lave & Wenger, 1991). The development of a strong network of likeminded individuals who share a common understanding is conducive to the development of an environment typified by high levels of trust, shared behavioural norms, mutual respect and reciprocity (Lesser & Storck, 2001). Such an environment has been identified as being high in social capital, and has been linked directly with the processes of the creation and sharing of knowledge (Nahapiet & Ghoshal, 1998). Finally, CoPs are many things for many people. For management, they may serve as a locus of knowledge and mentoring. For members, they may serve as a networking forum and answer depot. To the organisation, they provide innovative solutions to problems or reduce turnover by providing “homes” for employees and by strengthening the social fabric of the organisation. Organisations have only begun tapping back into this basic structure, because the break-up into departments, locations and business units has isolated employees (Verstal & Lopez, 2004:147).
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3. Types of CoPs For Saint-Onge & Wallace, (2003:32) there is a wide range of communities: communities of interest, communities of purpose, knowledge networks, communities of commitment, communities of expertise, professional communities, learning communities and so forth. In a large organisation, according to Denning (2004), it is likely that many different types of communities will emerge, each having different life cycles and pathologies, including:
discipline-based communities;
cross-cutting communities;
time-bound communities which are similar to organisational initiatives and
umbrella-type communities which embrace a number of different sub communities.
For McDermott (1999), there are many different kinds of CoPs. Some develop “official” best practices, some create guidelines, some have large knowledge repositories and others simply meet to discuss common problems and solutions. Communities also connect in many different ways. Some meet faceto-face, others have conferences; others share ideas through a website. To decide what kind of community and what kind of connection is best for an organisation, three dimensions need to be understood: what kind of knowledge people need to share, how tightly bonded the community is and how closely new knowledge needs to be linked with people’s everyday work. Globalisation is forcing many companies to accelerate their ability both to innovate and to disseminate learning. The existence of different types of CoPs, evolved by the different purpose they serve, could serve as a catalyst for knowledge sharing in a “knowledge city” whereby CoPs could be created to achieve a particular goal of that community (e.g. alleviate poverty) but also contribute to the global community through collaboration with other CoPs that work for the same goal. But CoPs do not only exist in a face-to-face situation. The last two decades, improvement in communication technologies have eliminated the constraints of time and space in communication. People now can ‘meet’ also in cyberspace in real time, synchronously, or asynchronously using various communications tools (i.e. email) and create virtual CoPs.
4. Cops and knowledge sharing One of the most important aspects of CoPs is that a group of people share knowledge, learn together and create common practices. Community members frequently help each other solve problems, give each other advice and develop new approaches or tools for their field. Regularly helping each other makes it easier for community members to show their weak spots and learn together in the public space of the community. As they share ideas and experiences, people develop a shared way of doing things, a set of common practices. Sometimes they formalise these in guidelines and standards, but often they simply remain “what everybody knows” about good practice. Hew and Hara (2007) add that conversation is also an important conduit for knowledge sharing among members in CoPs. Batson, Ahmad and Tsang (2002), on the other hand, maintain that individuals are willing to help one another and share knowledge because of egoism, altruism, collectivism and principlism. The ultimate goal in egoism-related motive is to increase one’s own personal benefit (e.g. pay, prizes and recognition). Altruism is a motive that increases the welfare of one or more individual(s) other than oneself. Collectivism is a motive that aims to increase the welfare of the group. Principlism on the other hand is a motive with the end goal of upholding some moral principles. For example, individuals who have received help from a community in the past feel that they should contribute something they know (Cheung, Shek & Sia, 2004). This principle is commonly referred to in the literature as reciprocity (Nowak & Sigmund, 2000). Another important aspect for the successful functioning of a knowledge-sharing CoP is active participation of a substantial part (ideally, all) of its members. Dixon (2000) argues that the CoP model allows organisations to overcome barriers to sharing information that conventional technology-based KM systems often encounter (Dixon, 2000). This sentiment is shared by Hayes and Walsham (2000). For a community to be truly vibrant, there should be an active participation of members in other knowledge-exchange activities; engaging in live chats, Q&A sessions, providing asynchronous feedback on previous postings, etc. (Hayes & Walsham, 2000).
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Sheryl Buckley and Apostolos Giannakopoulos With respect to online sharing of knowledge, research shows that there are numerous reasons individuals could have for sharing their knowledge with other members of a CoP online. These range from boosting self-esteem to altruistic and conformist considerations. Posting of knowledge entries and other active contributions by some members of a community represent only one side of the equation: the supply of new knowledge. For a community to be vibrant, there should also be an active participation on the demand side. The second requirement for a successful CoP is its members’ willingness to use the CoP as a source of new knowledge. These two major requirements (willingness to share knowledge and willingness to use a CoP as a source of knowledge) apply to any CoP, be it face-to-face or virtual. Finally, Ardichvili, Maurer, Li, Wentling and Stuedemann (2006) say in recent years CoPs have gained increasing popularity as a way to manage the human and social aspects of knowledge creation and dissemination within organisations, and have also received significant attention in the KM literature (Ardichvili et al., 2006; Gourlay, 2001; Davenport & Prusak, 1998; Walsham, 2001; Wasko & Faraj, 2000; Wenger et al., 2002).
5. CoPs and “knowledge cities” If a knowledge city is to be created, it is not sufficient to have the necessary infrastructures as suggested by Egrazakis et al (2004). The various structures have to be maintained by human beings. Using the “team” approach as defined by McDermott (1999) the objectives of a knowledge city can be achieved to a certain extent but it will not be maintained as it will take place in a discontinuous and fragmented way; an objective is determined, the right team is assembled and achieves that objective and everybody ... goes home till the next objective comes up. To achieve continuity, a team can be replaced with a CoP. Furthermore, the minute the decision is taken to convert a predominately industrial city to a knowledge city there will be knowledge workers that work “smarter” and others (in the periphery) that work “harder”. As CoPs make use of Lave and Wenger’s legitimate peripheral participation (1991) idea, those in the periphery will be integrated into the main stream of knowledge workers. CoPs then are ideal for KBD since they could help “human societal systems to move to an ethical social innovation phase, to promote a simple, meaningful and productive way of life and to support participatory democracy” (Ergazakis et al, 2006). Cops are characterised by two important dimensions with respect to development: that of enabling “learning to be seen and analyzed within its social context” and that of “action to promote certain kinds of practice or changes in practice in development requires us to think about the nature of the learning process involved and how it occurs” (Johnson, 2007:281). The first dimension ensures that learning is embedded in a certain social context while the second ensures that learning is practice oriented. Furthermore, Wenger (1998) expanded on three other important concepts which contribute to development, that of participation, reification, and identity. Voluntary participation guarantees the member’s knowledge contribution to the joint repertoire. Reification deals with the “forms and means of designing, negotiating, agreeing, accounting for and evaluating interventions between donors and beneficiaries” and thus “reifications become part of the repertoire of a community of practice and have the potential to become institutionalized. Such a process can both promote development” (Wenger, 1998). Identity is at the centre of a CoP. Changing identities and new configurations are key issues in current analyses of globalization, movements and relocations of peoples. These important dimensions of a CoP then become the agents for sharing, creating and disseminating knowledge which are the main drivers in the human development. These functions of CoPs will supplement the K-agents. A CoP thus can be used as the “integrating factor” in the development of the “knowledge city”. For this to become a reality though, the idea of CoPs has to be promoted by the various K-agents. As CoPs are formed within the K-agents, they in return interact among themselves and begin to develop a “constellation of CoPs” as Wenger (1998) described them, locally or internationally.
6. Solutions and recommendations For the past fifteen years, with the information explosion, the world has been going through a very important shift, from “knowledge is power” (Allee, 2002) to “knowledge sharing is power”, Skyrme’s (1998) most famous saying. Readjusting from an industrial era to a knowledge era has been
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Sheryl Buckley and Apostolos Giannakopoulos problematic for many organisations, including higher learning institutions which were “the caretakers” as well as the “creators” of knowledge. In a way, higher learning institutions had the monopoly of knowledge. However, with the advent of the Internet, they began to lose that monopoly. Organisations too came to the realisation that, to survive in a knowledge era, they had to concentrate on the intangibles such as the human capital, the employees, rather than the financial capital and other tangibles. In order to sustain a competitive advantage, they had to concentrate on aspects that could not be copied by competitors. One such aspect was the tacit knowledge that employees possess. Accepting that for employees to share such knowledge, it can only happen in a voluntary way, a conducive atmosphere for such sharing has to exist. Furthermore, tacit knowledge is created either through experience or by synthesising existing knowledge and new knowledge through various cognitive processes. The minute the individual becomes aware that he or she possesses this new knowledge, on the one hand, can use it as a leverage to make demands on the organisation (knowledge is power) or share that knowledge with others (knowledge sharing is power). Organisations have to be aware of that. What should be borne in mind is that in this context, knowledge sharing implies reciprocity: that there is a mutual interchange of knowledge between two or more people. However, if voluntary sharing of knowledge is the key in a CoP, then neither would achieve permanent results since neither contains the most important ingredient: passion for what you doing. What is suggested by the authors here is that a drive should be initiated to promote the idea of CoPs and make people aware that, by forming CoPs, it will be to their benefit and by default to the benefit of the organisation. But contributing to the organisation over and above what is expected of them, if the organisation flourishes, so will they. Against this background, CoPs and their value to the development of a “knowledge city” cannot be underestimated. CoPs can become the catalysts in developing knowledge workers through LPP. This is equivalent to the Chinese proverb about giving someone a fish and teaching them how to fish. Thus a CoP can be used as the means of empowering people to close the gap between those that “have” and the “have nots”. The CoPs then can be viewed as the micro-knowledge societies who are able to create new ideas and new business. This capacity of CoPs stems out of its ability to transfer not only explicit knowledge, but also tacit knowledge which has been recognized as the most important intangible asset which creates a competitive advantage. In the model of a “knowledge city” as depicted by Ergazakis et al (2004), CoPs can develop initially among the K-agents. The K-agents can be seen as teams with pre-determined goals and functioning according to set rules and regulations. The CoPs on the other hand have their own rules but the same goals. As CoPs are formed among the different K-agents they can form a constellation of CoPs. K-agents of a city can collaborate with K-agents of other cities to solve common problems. This way a global community begins to form.
7. Conclusion Sharing of knowledge in the industrial era and in the current knowledge era is a prerequisite in any organisation if continuity is to be maintained. Secondly, in any organisation, if it is accepted that a CoP is a way to tap into individuals’ tacit knowledge, the onus could lie on the organisation to create awareness in the employees and encourage them to get involved in CoPs. Thirdly, sharing of tacit knowledge within a CoP has more benefits than being shared in an isolated, haphazard way. Friendships can be created and developed, which would not otherwise have happened. Fourthly, knowledge sharing is directly linked to knowledge creation, in a conscious as well as subconscious way. When tacit knowledge is shared, members become inspired and innovative. Knowledge is not acquired in a vacuum but is constructed in a certain social environment. It becomes what McElroy (2002) called the social capital. Finally, sharing of tacit or implicit knowledge in a knowledge society, it can only lead to a competitive advantage if it is converted to explicit knowledge and new knowledge to be created. Knowledge is dynamic and for people to work smarter and not harder, they have to upgrade their knowledge. An organisation has to become a learning community. In a formal as well as informal way (e.g. through CoPs) the organisation must invest on its human capital, its employees, in their development so they can face the ever-changing society with confidence. This way the conversion of a worker to a knowledge worker using CoPs will bring about a total transformation of the work force in the years to
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Sheryl Buckley and Apostolos Giannakopoulos come and as a result a “working city” will be converted to a “knowledge city”. A CoP then can be conceived as a catalytic agent that speeds up knowledge dissemination and sharing as well as a reagent that coverts tacit knowledge into explicit knowledge.
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Non-Rational Thinking in the Decision Making Process Aurel Burciu and Cristian Valentin Hapenciuc “Ştefan cel Mare” University of Suceava, Romania
[email protected] [email protected] Abstract: The present paper work represents an attempt to explain the way throughout which, top managers adopt/make efficient decisions by using experience, intuition, imagination and management of emotions. The paper is based on several years of observation, empirical evaluations and activity/research into the real economy developed by the authors. In the economy, there are few practical situations when top managers take into account theoretical models of decision making. Instead, they use to the “quantitative” thinking and “qualitative” thinking in order to valorise their experience, intuition and imagination for defining some decisional issues and selecting alternatives.“The line” sharing conscious mind and unconscious mind is yet little understood by the human thinking; we believe that for some people there is a thin “line” of demarcation between those two, while for other people the same “line” becomes wider. The human mind remains a unique processor of tacit/explicit knowledge. By reference to the basic mechanism of a computer, or AI techniques, the basic mechanism of the human mind remains, for the time being, completely different. (It is true that the computer also processes knowledge, but throughout an entirely distinct manner than the human.) We know that there are concepts as emotional intelligence, social intelligence or other alike, which are very helpful for management practice. Even if we are accepting such developments (EI, ES, etc.) we believe that the main role for efficient decision making process is granted to some skills/abilities next to “the demarcation line” between conscious mind and unconscious mind (for these skills/abilities we suggest the phrases non-rational thinking and/or second bounded rationality.) Keywords: Non-Rational Thinking (NRT), Second Bounded Rationality (SBR), knowledge dynamics, mix of conscious and unconscious
1. Introduction to decision making The decision making process includes a set of activities that a manager undertakes in order to solve a decisional matter/issue, referring to a certain system of values. Usually, any manager in the real economy makes decisions based on experience, logics, ration, intuition, emotions etc; each choice aims to solve a specific problem, noted Pi , which means reaching an objective Oi , targeted by the manager. For all situations where exist two or more alternatives to solve the problem Pi (n alternatives of solving, n ≥ 2) we are discussing about a decision making process. It has to be mentioned that choosing the alternative from n possible variants constitutes only a single step out of five/six stages of the decision making process; the most frequently, the decisional process is divided into six steps/stages, respective:
S.1. Defining the problem Pi;
S.2. Gathering / processing information;
S.3. Constructing n alternatives to solve the problem;
S.4. Choosing an alternative from the n (decision);
S.5. Implementation of the decision;
S.6. Evaluating the outcomes.
It has been argued, in theory, that the human activity system (HAS) is a type of holonic system to which is referring the optimization of all other systems (in the economy) (McHugh a.o., 1995). Therefore, any manager is referring to a system of values generated from social practice corresponding to a certain level of development of mankind. That mention is necessary, because the difference between rational, irrational and non-rational, including the case of analysing the decision making process cannot be done without taking into account a reference system.
2. From Kenneth Arrow to Karl Popper The human thinking is far from being precise, linear, structured and/or predictable; on the contrary, human thinking remains somehow „mysterious” and unpredictable; the rational, the irrational and the non-rational remain specific human attributes. When we say “mysterious” we think at a question that, while it makes perfect grammatical sense, cannot in principle be answered (Oakes, 1998). We further mention some scientists who argued for the existence/presence of non-rationality within some “shots”
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Aurel Burciu and Cristian Valentin Hapenciuc of human thinking; this type of non-rational thinking “shots” are common for decision making, scientific discovery and chess game.
American professor Kenneth Arrow has a theory about group decision making, also called “impossibility theorem”, according to which for a large number of cases, any decision making cannot satisfy the pre-established rationality frame. In order to develop his theory, the author begins with some concepts of classical logics demonstrating in a personal manner the fact that rationality criteria cannot be all reached in those situations which imply at least two options of choice (n ≥ 2) (Arrow, 1963).
“Decision making, say Maynard and Mehrtens, it’s presumed to be based exclusively upon rational criteria and gives too little credit to the possibility that decisions could be influenced by unconscious “scenarios” and “intuition” (Maynard/Mehrtens, 1993). According to the previous authors, the intuitive and non-rational processes have an important contribution to the decisional act; alongside with the conscious, the unconscious is more and more regarded at as a supporting factor for any rational behaviour; this implies synchronization between unconscious and conscious beliefs (Maynard/Mehrtens, 1993).
Economist Janos Kornai, in the prestigious work Anti-equilibrium, considers that in the real economy it has been observed that practically the rule is to make decisions based on routine, or even by random, respectively on empirical basis and without taking too seriously into account the models suggested by theory (Kornai, 1974).
Herbert Simon, Nobel winner, 1978, imposed the notion of bounded rationality, referring to the restrictions that the manager faces in the real economy; this concept is based on the fact that rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite time having to make a decision (Simon, 1957). Thus, by this approach, the decision making practice aims to find an acceptable solution rather then the best solution for the Pi problem (Simon compares the decision making with the chess game and the design in engineering; Simon, 1972). However, including in Simon's perspective, the most people are only partly rational, and they are in fact emotional/irrational in the remaining part of their actions (Simon, 1957).
Karl Popper, also a thinker, considers that the scientific discovery consists, in its essence, of a non-rational moment towards which there is no logical way/path, based exclusively on the purest intuition (Popper, 1975). Obviously, as far as the above mentioned author is right, meaning that the scientific discovery is reached by intuition and only secondly throughout rational/logical structures, then it implicitly arises a real parallelism between the problematic of the decision making process and the logical and/or empirical procedure that stands as basis for the scientific discovery (Tacu, 2003). „New ideas have a striking similarity to genetic mutations” (Popper/Eccles, 1977).
3. Human thinking and knowledge The invoked concepts, respectively human thinking and knowledge are, to a great extent, inseparable; naturally, only individuals gather/process knowledge. Thus, in order to understand how knowledge is being processed, how experience is being gathered, it is necessary first to understand the way human thinking mechanism is functioning. We presently discuss about natural intelligence, artificial intelligence, emotional intelligence, social intelligence and other similar concepts. Which are the implications of these developments of research for the effective management? There are many papers regarding the brain’s way of working and the human thinking mechanism, but only in the last decade neuroscience has made remarkable progresses with respect to the brains’ development. “At birth, say Buckingham/Coffman, the children’s brain contains 100 billions of neurons;” during lifetime their number remains approximately the same, and these neurons represent the raw material for the brain (Buckingham/Coffman, 1999). Therefore, „quantitatively” speaking we are all relatively equal with the respect to the thinking capacity (Wonder, 1985). The mind of the future adult is something else though; it consists of the connections between the neurons, connections which are named synapses. On Pinker’s opinion, certain human skills are innate, as language, while other human faculties (the mind) are the result of natural selection and individual’s adaptation to the environment (Pinker, 1997). The brain’s working mechanism, as we perceive it now, it is probably the result of natural selection and genetic mutations which happened during millions of years. As Dawkins proposes, the nature may be compared to a blind watchmaker, who supervised the gradual “finishing” of an extremely complex biological mechanism (Dawkins, 1986). Other authors discuss about a blind
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Aurel Burciu and Cristian Valentin Hapenciuc programmer, subsequently reaching to the eternal dispute con or pro Darwin (Oakes, 1998; Fodor, 2000; Pinker, 1997); we prefer to avoid the invoked dispute, reasoning for which we hold to the common approaches regarding the thinking mechanism. If we take into account the neuroscience, it results that starting from the age of 3 and until somewhere around the age of 16, the children’s structuring of the thinking mechanism is being developed; when the child reaches the age of 3, the connections between the neurons are numerous, meaning up to 15.000 synapses for each cell; due to the fact that at this age the brain is overloaded, it begins to sort among these links (Buckingham/Coffman, 1999). Over time, some connections/links become stronger and transform themselves into a highways system, each highway having four ways of “light traffic”; simultaneously, other connections/links are less used then the first category, and if they continue to be “neglected” they may transform into a “secluded ground” (Buckingham/Coffman, 1999). Throughout what we call natural intelligence, we comprehend the individual’s skill to gather/process knowledge, to recur to experience and to adapt to new contexts. There are various approaches pro and con the IQ (Intelligence Quotient), but the majority of scientists accept the idea that intelligence is also a matter of environment; some aspects as emotions, intuition, imagination, interpersonal skills and other individual characteristics cannot be evaluated (Richard Nisbett, University of Michigan). The phrase artificial intelligence (AI) was used in the ‘60es by Marvin Minsky, but nowadays exists an entire domain of informatics dealing with AI (this domain regards the copying and/or reproducing of the human thinking mechanism and transposing it into the software component of any computer) (Tacu, 2003). There are authors, as Howard Gardner, who discusses the multiple intelligences, with reference to the eight types of intelligence (kinaesthetic, interpersonal, intrapersonal, linguistic, logical, naturalistic etc.) (Gardner, 1983). A great number of authors argue about the favourable influence of emotional intelligence in the management area, including in decision making process; emotional intelligence reflects a person’s skills/capabilities to understand and administrate his/her emotions within the relationships with others (there are five components: self-awareness, empathy, self-management, awareness of others and relationship management; Goleman, 1995). The previously invoked concept derives from the social intelligence concept, the latter reflecting the skills we gather under the influence of social environment, family and school, even from the first day of life (Goleman, 2006; Scott-Ladd, 2004; Mellers, 2001; Sevdalis, 2007). As members of various organisations in society, individuals gather and process knowledge; starting with signs and/or data, they built “hierarchically” certain structures that we call information; the last ones are “hierarchically” mixed and become what we call knowledge (Brătianu/Andriessen, 2008). Nowadays, knowledge management became a „fashionable” phrase into the world of successful corporations; “knowledge represents the most valuable intangible resource” upon it’s based the contemporary social progress (Brătianu, 2009). The knowledge stored by a person (and/or organization) is and stays closely linked to the direct/indirect experience, to formal/informal learning which that person controls; it is imperative to distinguish between tacit and implicit knowledge, because they have different characteristics depending on their way of expression, as resulting from the next table (McInerney, 2002). Table 1: Some characteristics of tacit and explicit knowledge Implicit/tacit knowledge Subconscious Perceived Unaware Unspoken Experienced based Transferred through conversation Escapes observation Held within self Assumption
Explicit knowledge Formally articulated Elucidated Aware Fixed Codified Documented (written, taped etc.) Can be viewed or heard Shared with others Reports, lesson, learned
Source: Adapted from (McInerney, 2002), based on (Polayni, 1962, 1983)
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Aurel Burciu and Cristian Valentin Hapenciuc As we further notice, the conscious and unconscious manipulation and/or knowledge processing is associated to a great extent with explicit knowledge and, respectively, with tacit ones, but demarcation between conscious/unconscious is extremely indefinite for the functioning of the thinking mechanism. Abilities/skills as intuition, imagination or management of emotions are based upon the experience gathered by individual; this experience becomes “a mixture” of tacit and explicit knowledge. How does the individual’s mind work when it has to “extract” knowledge and use it together with intuition/imagination in order to generate new ideas? How clear is the demarcation between the logic and non-logic thinking structures when it is being generated a new idea?
4. Non-Rational Thinking (NRT) definitions Referring to human thinking and associated behaviour, we distinguish between rational, irrational and non-rational; the invoked distinction is to be found at Herbert Simon, also (he uses the phrase of nonrational for the situation when the manager intuits that certain objectives are “support instruments” for other objectives) (Simon, 1993). In our view, we define non-rational thinking (NRT) as an extension of rational thinking, respectively as a native reflex of individual for “mixing” thinking into n patterns, recurring to logic and extra-logic structures in order to reach an objective. From another point of view, we could say that logic structures and rational thinking mainly belong to the individual’s conscious, while non-logic structures and non-rational thinking mainly belong to the individual’s unconscious. Authors as Howard Gardner discuss about multiple intelligences (even if it seems to be more about the classification of certain mental/physical abilities of persons); others distinguish between general intelligence and emotional intelligence and argue that both types of intelligence are equally important/relevant for obtaining organisational success, inclusively for efficient decisions (Goleman, 1995; Scott-Ladd, 2004; Gardner, 1983). It is possible that, practically, they all strike towards a single type of natural intelligence, namely the human intelligence, the variety of theoretical developments being just different points of focusing upon the same reality. Throughout a person’s rational behaviour we understand the behaviour defined by the rationality at the social level, which takes into account the values accumulated by society; by rationality we understand a set of skills/aptitudes that we use to find a course of actions that will lead us to achieve our goals (Simon, 1993). Even if usually, the semantic sense of the phrase non-rational is perceived as the opposite of the phrase rational, for the present situation we will consider that the antithetical phrases are rational and irrational (in the grammatical sense). Since we know few things with respect to the human thinking mechanism, regarding the manner that tacit and explicit knowledge is mixed/combined within the humans’ activity, it exists a less known area referring to thinking, respectively the demarcation line/area between the conscious and unconscious. In our opinion, we may talk about a “mixture” between the diverse human skills, respectively experience, intuition, imagination, management of emotions and other skills. Throughout recurring to the stored experience, respective to knowledge, individuals establish objectives and solve the problems that interfere while reaching to them. How does it happen the appeal to intuition, imagination and management of emotions? How is being “excerpted” and processed the tacit/explicit knowledge from the whole experience in order to generate a new solution? To what extent do the “quantity” and the “quality” of thinking contribute in this kind of processes? By experience we understand a set of abilities/skills that a person accumulates while the formal and informal learning during time; we are talking about knowledge assimilated through practice, learning, observation etc. Therefore, the individual is being shaped by the biological DNA and the social DNA (family, school and social environment), which will equal a certain experience gathered in time, respectively a certain volume of explicit/implicit knowledge. As we perceive it, the experience constitutes/becomes the basic background/structure of mental stock that a person makes use of. Intuition still remains a strictly human ability which is extremely little understood/ comprehended; philosophers argue that it is a non-discursive form of knowledge, namely a form of knowledge based on ration; it is not the opposite of ration but is distinct from ration. Intuition reflects the individuals’ capacity of identifying solutions into new frameworks/contexts that he/she is supposed to adapt to; but these solutions derive out from that person’s experience and manipulation of explicit/tacit knowledge.
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Aurel Burciu and Cristian Valentin Hapenciuc Intuition remains an attribute associated exclusively to the human mind, to a person; by antithesis with the human mind, no sophisticated/complex software which is based on AI techniques can intuit if it refers to new contexts. Referring to intuition, it is adequate to remark that there are awfully few works with respect to the role of intuition in management and/or efficient decision making. Synthesizing Khatri’s and Alvin’s opinion we will say that intuition is essential for strategic decisions; „intuition is not an irrational process; it is based on a deep understanding of the situation; it is a complex phenomenon that draws from the store of knowledge in our subconscious and is rooted in past experience; it is quick...” (Khatri/Alvin, 2000). Some authors describe intuition by using the term “supra-conscious”; it is used even in the process of scientific discovery and other similar activities; sometimes this ability is included in the phrase of talent. (Coffman/Molina, 2002). By imagination we understand the human faculty/capacity to create new ideas based on knowledge accumulated in the past, respectively the capacity to learn by iteration; as a human skill, imagination is similar to intuition. We may say, to a certain extent, that a computer is able of “learning” by iteration (starting from the data/knowledge introduced into the database and from certain pre-established rules of learning); however it is little probable that a computer could recur to imagination while the chess game. Kaufmann says that imagination bases on a mechanism we still know very little about; it relays on the chaotic associating between ideas/knowledge, an association out of which sometimes comes up a new idea (Kaufmann, 1994). In our point of view (accepting the influence of emotional intelligence in management), we consider that management of emotions is an ability comparable to intuition and/or imagination; this ability differs from one person to another, can be improved by managers and already has an explanation connected to the working mechanism of the brain. Recently discoveries in the field of neurobiology show that at the moment of taking decisions, under the influence of emotions, tonsil is being activated (Coffman/Molina, 2002). Finally, we may discuss even about more skills similar to intuition/imagination/emotion (we talk about issues as self-motivation, affect, mood, etc.). Put into graphical form, the components of what we called NRT are suggested in figure1; at the base of the NRT structure stays the experience based upon explicit/tacit knowledge combination/mixing; the other four elements of NRT combine in a non-structural manner for each person in turn. Experience based on mix of tacit/explicit
Intuition
Imagination
knowledge
NRT Emotions
Other skills
Figure 1: Components of non-rational thinking (NRT) Therefore, when undertaking various human activities (decision making, chess game, scientific discovery, etc.) individuals recur to intuition/imagination/emotions as strictly human skills, basing on experience and knowledge which they stored during time (Burciu, 2008). We don’t know precisely how these skills influence the “manufacturing”/producing of a new idea/solution; which would be the connection/relation between these skills and conscious/unconscious thinking of the person, we don’t know precisely. We believe that certain aspects of the human thinking mechanism belong to an area we may call quantitative thinking (the predominant side of the conscious where are being processed both the explicit knowledge and the explicit-tacit “mix”), while other aspects of the same mechanism create an area we may call qualitative thinking (the unconscious area where are being processed both tacit knowledge and the tacit-explicit “mix”).
5. Knowledge dynamics and structure of the mind There is a multitude of approaches with respect to the classification of knowledge and its connection with the human mind working mechanism. As we previously showed, depending on the way knowledge is expressed, it divides into explicit knowledge and tacit knowledge (McInerney, 2002). Further more, the accumulation of both knowledge categories remains dependent on the directly
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Aurel Burciu and Cristian Valentin Hapenciuc experience of the individual (tacit knowledge) and, respectively, on formal learning (explicit knowledge); the processing of the two types of knowledge remains associated/linked to the conscious and unconscious levels of human thinking; in the following figure it is suggested the accumulation/processing of the two knowledge types (Brătianu, 2009; Brătianu/Andriessen, 2008). Explicit knowledge
Conscious Unconscious
Tacit knowledge
Mediated experience Direct experience
Figure 2: The global structure of knowledge source: (Brătianu, 2009; Brătianu/Andriessen, 2008) Explicit knowledge gathers in time into books/volumes, encyclopaedias, databases, inventions, innovations, both on a technical level as well as on a social level; they are extremely fluid, cannot be monopolised, they spread rapidly in organisations and society; they continuously change, evolve along with the accumulated human experience (McInerney, 2002). Tacit knowledge is more difficult to quantify, manipulate and transferre as they predominantly “live” in the mind of the organisation’s members (in organisations exists tacit knowledge as know-how); however they too have a dynamic character; we are witnessing diverse types of conversion between explicit and tacit (tacit-tacit; tacit-explicit; explicit-tacit; explicit-explicit; Brătianu, 2009; Brătianu, Andriessen, 2008). We do not know precisely how creative individuals recur to intuition/imagination in order to process different types of knowledge and to generate new ideas; according to Kaufmann, the human brain works within an entropy frame/range; to one of the border we find the thinking based on total order (the robot), to the other border we find the non-structured thinking (Kaufmann, 1994). As we perceive the localization of what we called NRT, by analogy to the knowledge structure proposed by Brătianu, we suggest in figure 3 another angle of approaching NRT (as a fuzzy, non-structured “mixture”, between conscious and unconscious thinking). Area of quantitative mind
Area of qualitative mind
Conscious (explicit knowledge)
NRT
(intuition, imagination, management of emotions)
Unconscious (tacit knowledge)
Figure 3: Localization of NRT between conscious and unconscious In figure 3, we empirically suggest that the main part of the conscious may be called quantitative thinking area (meaning that this side is compelling, linear and relatively quantifiable; it processes the most of explicit knowledge and a variable „piece” of tacit knowledge); a little “piece” of conscious and the whole unconscious could be called qualitative thinking area (meaning that this part is fuzzy, nonlinear, flexible/volatile and very hard to quantify; it processes all tacit knowledge and a little “piece” of explicit knowledge). The idea that intuition and/or imagination are abilities from beyond the rational/conscious it is not new; similarly, we know that the human mind and body are a unitary whole in the knowledge process. Through antithesis with the diverse types of intelligence to which we previously referred to (traditional, emotional, social, multiple etc.; Goleman, 1995; Gardner, 1993), maybe it is best to discuss about a single type of natural intelligence, namely the human intelligence. If this approach is being accepted, there may be formulated countless/many questions: Which is the relationship between the brains’
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Aurel Burciu and Cristian Valentin Hapenciuc working and recur to intuition/imagination/emotions administration? In which “area/part” of the thinking mechanism we locate abilities as imagination or intuition? In other words, maybe it is preferable trying to better understand the demarcation line between conscious mind and unconscious mind, between the logical and extra-logical structures of thinking.
6. Applications of NRT on management Formally we distinguish between logic rationing and intuition/imagination; this distinction can not be done though with maximal precision because the logic rationing is based on experience/knowledge, and during the mental processes tends to associate with intuition and imagination, and emotions etc. (Kaufmann, 1994). Consequently, the logical-rational structure of any efficient decision making process is “favourably marked” by a certain mixture of experience, intuition, imagination, emotions etc. (NRT). We believe that the image of this “mixture” of rational and non-rational (as thinking structures) will continue to be strongly influenced by the managers personality type and by other aspects which define the context he activates in (Burciu, 2008). We intuitively say that exists a multitude of n possible “mixtures” between rational and non-rational, inclusive for the same manager dealing with different decisional contexts. Further, we will return to the steps of the decision making process, noticing the fact that in practice the six stages are being ranked from S1 to S6, as there is a natural and temporary restriction for the structure of any process of this kind. Especially within complicated/complex processes, when the applying of decision (S4) happens during a time range (weeks, months etc.) we may talk about a feed back between the final stage/step (S6) and any other of the previous stages/steps; the invoked idea is graphically represented in figure no 4. Feed-back
S1
S2
S3
S4
S5
S6
decision Figure 4: Structure of decision making process Within which stage of the decision making process does the manager’s recur/appeal to NRT happen? Are all the stages of the decision making process equally affected by NRT? In our opinion, making the efficient decision in management implies the recur to group decisions; for making strategic decisions, the members of a management teamwork will always be more efficient comparing to a single person. First rule in making the efficient decision, says Drucker, is that it should be made throughout the conflict of opinions and not by consensus (Drucker, 1993). Secondly, we believe that adopting the efficient decision (step S4 from the previous figure) is strongly restricted by the way the manager defines the problem Pi and/or the objective Oi (step S1 from figure). In other words, efficient decisions solicit also un-orthodox questions, non-traditional approaches and/or the result of non-logical ways; to correctly formulate the question and to accept “unimaginable” solutions; these became nowadays restrictions of performance management (Drucker, 1997; Toffler, 1985). As long as we accept the idea that efficient managers usually recur to intuition, imagination and management of emotions in order to obtain performance (respectively to NRT), we may say that especially steps S1 and S4 are stronger affected by NRT, comparatively to the other four steps. In other words, defining problem Pi and choosing an alternative from the n possible solutions, the manager is somehow “forced” to recur to all resources/instruments that the organisation offers him and to his own experience; in figure 5 we explain the manager’s recur to NRT in the two stages. Therefore, any would be the recommendations formulated by theoreticians, the manager in the real economy frequently recurs, we believe, to non-rational thinking for the purpose to make efficient decisions. Managers always recur to logic thinking structures and extra-logic thinking structures, to conscious and subconscious, “mixing” explicit knowledge with tacit knowledge in order for the organisations they lead to preserve/maintain their competitive advantage. By analogy with the term bounded rationality, strongly argued by Herbert Simon, we may say that managers confront themselves with what we call double bounded rationality, respectively:
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Aurel Burciu and Cristian Valentin Hapenciuc Feed-back
S1
S2
S4
S3
S5
S6
Stages in wich we believe that the decision maker can use wider NRT (experience, intuition, imagination and management of emotions)
Figure 5: Use of NRT on decision making process
a first bounded rationality induced by constraints where, inevitably, the manager takes action within the organisation (organisational rules, costs, time, available information etc.) (Simon, 1972);
a second bounded rationality (SBR) induced by the managers’ thinking mechanism, respectively the native tendency to „mix” rational thinking and logical structures with non-rational thinking and extra-logical structures; this second bounded rationality represents also a type of „constraint” that exerts upon the manager; it is comparable to the first type of constraint, but also differs, in the sense that it is simultaneously a “strong” and “week” point, depending on as how ample is the demarcation line between conscious and unconscious mind (essentially speaking, SBR and NRT are two phrases suggested to define the same reality that we perceive in the decisional practice, namely the top managers’ use of experience, intuition, imagination and management of emotions).
For improving the organisations efficiency, of objective reached by different management teams, Edward de Bono proposes the six hats method, lateral thinking etc. (de Bono, 1999); the main difficulty of thinking, he says, is confusion; emotions, information, logic, hope and creativity all crowd in on us (de Bono, 1999). There are other works that recommend methods of developing the emotional intelligence of employees in organisations, of improving management styles, group relationships, the company relationships with its clients etc. (Goleman, 2006; Mellers, 2001; Sevdalis, 2007). We do accept at all this kind of approaches, as they predominantly aim the improvement of abilities/competences that employees dispose of in an organisation. More simply said, employees training and development throughout various methods will anyway include, to various extents, also components of what we called non-rational thinking (NRT).
7. Conclusions There is an obvious conditioning among three main aspects: description of effective decisions in management, human mind working and gathering/processing of knowledge (in a wider sense we unwillingly get to the brain’s working, but this aspect is extremely complex, and the dispute con/pro Darwin will also continue further in the future). Pertinent estimations show that nowadays we understand, maybe, around 10% of how the human brain working evolved; biologists, geneticists, anthropologists and other scientists try to understand the unique way the human brain is working (Darwin, 1986; Pinker, 1997). In the present work we trait the decision making process predominantly from a descriptive perspective, based on practically experience in economy, empiric evaluations and observations over time. The conclusion we may formulate is that, anyway, in decision making practice, top managers seldom take into consideration the complex models of decision and usually recur to experience/intuition/imagination and management of emotions. What reasonable explanations could be developed with respect to the ecart between decision making practice and normative perspective in theory? For the hypothesis that the suggested phrases NRT and/or SBR are being accepted, we get to the idea that in the organisations’ practice it is necessary the deliberate development of some abilities of managers, as intuition/imagination. As mentioned there aren’t many works trying to catch the role intuition/imagination play in management (both with respect to adopting decisions, and to leading teams, motivation, training etc.). Further more, the present methods of
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Aurel Burciu and Cristian Valentin Hapenciuc teaching in schools and universities seem to be realised especially to develop what we call the “quantitative area” of thinking (aim to induce the child a big volume of information/knowledge that should structure as precisely as possible into synapses of the brain). Therefore, we even may suggest many future directions of research regarding the applicability of NRT and /or SBR in the performance management (activating throughout education the “qualitative area” of thinking of the future employee; distinctive methods of training for improving the “strong points” of NRT/SBR; the extension of emotional intelligence through including intuition/imagination and developing a general model etc.).
References Arrow, K. (1963) Social Choice and Individual Values, Wiley and Sons Inc., New York. Brătianu, C., Andriessen, D. (2008) Knowledge as energy: a metaphorical analysis. Proceedings of the 9th Euroepan Conference on Knowledge Management, Solent Southampton University, 4-5 September, pp.7582. Reading: Academic Publishing. Brătianu, C. (2009) The frontier of linearity in the intellectual capital metaphor. Electronic Journal of Knowledge Management, Vol.7, Issue 4, pp.415-424 Buckingham, M. and Coffman, C. (1999) First Break All the Rules: What the World's Greatest Managers Do Differently, Gallup Organization. Burciu, A. (coord.) (2008) Introducere în management, Economică, Bucureşti Cherniss, C. a.o. (1998) Bringing emotional intelligence to the work place, The Consortium for Research on Emotional Intelligence in Organization Coffman, C. and Molina, G. (2002) Follow this Path, Gallup Organization, Warner Books, New York. Dawkins, R. (1986) The Blind Watchmaker, Penguin Books Ltd, England. de Bono, E. (2000) Six Thinking Hats, Penguin Books, Great Britain. de Bono, E. (2003) de Bono's Thinking Course, BBC Worldwide Publishing, London. Drucker, P. (1967) The Effective Executive, William Heinemann. Drucker, P. (1993) Managing for results, HarperCollins, New York. Fodor, J.(2000) The Mind doesn't Work that Way, Cambridge MIT Press. Gardner, H. (1983) Frames of Mind: The Theory of Multiple Intelligences, Basic Books, New York. Goleman, D. (1995) Emotional Intelligence, Bantam Books, New York. Goleman, D. (2006) Social Intelligence, Bantam Books, New York. Kaufmann, A. a.o. (1994) Creativitatea în managementul întreprinderilor, AIT, Laboratorie, Bucureşti Khatri, N. and Alvin, NG. H. (2000) Role of intuition in strategic decision making, Human Relations, Vol. 53, No. 1. Kornai, J. (1974) Anti–equilibrium, Ştiinţifică şi Enciclopedică, Bucureşti Maynard, H.B. and Mehrtens, S. (1993) The Fourth Wave, Berrett-Koehler, San Francisco McHugh, P., Merli, G. and Wheeler III, W. A. (1995) Beyond Business Reengineering – Towards the Holonic Enterprise, John Wiley&Sons Ltd., UK, USA McInerney, C. (2002) Knowledge Management and the Dynamic Nature of Knowledge, Journal of American Society for Information Science and Technology Mellers, B.A. and McGraw, A. P. (2001) Anticipated Emotions as Guides to Choice, American Psychological Society, vol. 10, no. 6. Oakes, E. (1998) The Blind Programmer, First Thinks Pinker, S. (1997) How the mind works, Norton, New York. Popper, K. (1975) The Logic of Scientific Discovery, Hutchinson, London Popper, K. and Eccles, J. (1977) The Self and its Brain, Springer-Verlag, Berlin, London Scott-Ladd, B. and Chan, C.C.A. (2004) Emotional intelligence and participation in decision-making: strategies for promoting organizational learning and change, Strategic Change, March-Aprilie 2004, Wiley InterScience. Sevdalis, N. a.o. (2007) Trait emotional intelligence and decision-related emotions, Personality and Individual Differences 42, 1347-1358. Simon, H. (1957) Models of Man, John Wiley & Sons, New York. Simon, H. (1972) Theories of bounded rationality, in volume Decision and organizations, North-Holland Publishing Company. Simon, H. (1993) Decision Making: Rational, Non-rational and Irrational, Educational Administration Quarterly, vol. 29, no. 3. Tacu, A. (coord.) (2003) Inteigente Systeme in der Optimierung von Entsheidungen, KMV, Germany Toffler, A. (1985) The Adaptive Corporation, Bantam Wonder, J. a.o. (1985) Whole Brain Thinking, Balantine Books, New York. www.en.wikipedia.org/wiki/Portal www.scifietc.com http://hum.sagepub.com www.firstthings.com/article www.interscience.wiley.com www.sciencedirect.com www.eiconsortium.org
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A Strategic Model for Intellectual Capital Reporting: Study of Service Industry in Serbia Sladjana Cabrilo1 and Leposava Grubic-Nesic2 1 Educons University, Novi Sad, Serbia 2 University of Novi Sad, Serbia
[email protected] [email protected] Abstract: This paper presents an empirical research study and analysis of intellectual capital (IC) within the Serbian service industry. The purpose of this study was to scrutinize organizational IC within service oriented companies in order to facilitate the fine-tuning of IC reporting according to particular facets of the industry. The primary research objective was to define a group of relevant indicators of intellectual capital (IC indicators), i.e. to conceptualize an adequate model for IC reporting adapted to Serbian service companies. The existing methods of IC reporting as well as key value drivers and unique features of IC, identified by questionnaires designed for top managers within the Serbian service industry, represent the fundamentals of an adequate model for IC reporting. Identified IC indicators differ from typical relevant indicators mainly due to the specifics of the environment and service industry in Serbia. The research findings have revealed a lack of employee’s innovativeness, and permanent competence development of both, top managers and employees, which may be a significant problem concerning potential growth of Serbian service industry especially during the global economic crisis. The defined model simplifies IC measuring and reporting and provides a deeper understanding of strengths and weaknesses of IC within the observed industry. Based on a strategy map that bridges a valuesbased strategy with IC perspective, this model has been linked to strategic management of IC and value creation within the service industry. Keywords: Intellectual capital reporting, IC value driver, IC indicator, strategy map, service industry, Serbia
1. Introduction Measuring of intellectual capital (IC) is a complex problem. With regard to its properties, IC completely differs from its tangible counterparts. As a result, intellectual capital is not being monitored by the traditional bookkeeping system. „If we measure the new with tools with the old, we won’t be able to see the new” (Sveiby, 1997, p. 155). However, not knowing their value and performance leads to poor valuation (external communication problems) and poor usage (internal management problems) on the other. The belief is that systematic and standardized reporting about IC, both internally and externally, will lead to better organizational performance. During the global financial crisis, exploring the energy of IC seems to be even more important. Perhaps the best reason to measure intellectual capital is to consider the risks of not measuring it, such as unpredicted labor shortages, skills mismatches that curtail growth, talent fleeing to competitors and productivity levels that are only 70 percent of what they could be (Schiemann, 2008). Adequate IC indicators may provide key information about emerging trends, such as declining talent availability, higher turnover of strong performers and unionization vulnerability. Instituting these measures may be a competitive advantage of the enterprises in a world of increasing competition and scarce resources. IC statements supplement traditional financial reports. By having both accounting’s backward report on recent results and IC statement’s forward view of what might be coming, “management has a better sense of what to do next to develop or maintain a competitive advantage” (Fintz-Enz, 2000, p.148). “Good metrics facilitate implementation of strategy” (Allio, 2005, p.255). Measuring intellectual capital brings managerial, cultural and organizational changes. As a process, it permits planning and managing intangible resources consistently with the enterprise strategy for creating value. Therefore, the most important benefit obtained from measuring intellectual capital is the spread of the “intellectual capital culture” within an organization (Chiucchi, 2008, p. 229).
2. Context for the research The world is experiencing an increasing interest in IC measuring and reporting that is rooted in business evaluation and planning activities. Over the last decade, there has been a minor explosion in the intangible capital metrics industry. One recent study categorized 12 different approaches to measuring intellectual capital, and another identified more than 30 (Pike and Roos, 2004; Andriessen,
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Sladjana Cabrilo and Leposava Grubic-Nesic 2004a; Andriessen, 2004b). These new measurement approaches differ widely in objectives, methodology and solutions. Unfortunately most Serbian industries are still using traditional financial accounting and performance measurement methods for tangible assets. A lack of awareness of the importance and the nature of IC, measuring methods and IC reporting in Serbia has diminished the ability of companies to choose a proper method and apply it successfully in order to achieve strategic goals (Cabrilo, 2005). Taking into account that in Serbia IC measurement has not been considered important, the data gathering systems are not customized to the known IC measuring methods. The knowledge-based environment of Serbia and other transition economies requires a new model that encompasses intellectual capital and includes the specific environment. In this way, new topics for research emerged – conceptualizing the models for reporting organizational IC in Serbia adapted to the environmental features as well as industrial facets, in order to simplify IC reporting and make it more accurate in the observed environment and industry (Cabrilo et al., 2009; Cabrilo, 2009). Considering the economic role and importance of services, both on a global scale, as well as relations to economic growth and social development of Serbia, the topic of research in this paper is IC and its influence on performances of the service companies in Serbia. Major goals of the empirical research are to distinguish specific features of IC and knowledge flows, identify key value drivers of IC in the service industry in Serbia, and based upon those, define a group of relevant IC indicators which will make the basis for conceptualizing a model for IC reporting of Serbian service companies. For the purpose of our empirical research, we follow the well-accepted classes of intellectual capital: human, relational, and structural (Bontis, 1998; Meritum, 2002; Roos, 2004; Marr, 2004; Tovstiga and Tulugurova, 2007). Value creation is the product of interaction between the different classes of IC (Roos et al., 1997; Sanchez et al., 2000). IC renders the best possible value to organisations through the combination, utilisation, interaction, alignment, and balancing of the three IC categories as well as through the managing of the knowledge flow between them. Roos et al (1997, p. 417-423) stated that the vehicle for measuring intellectual performance is a set of indicators used for each intellectual capital category.
3. Research methodology The research methodology was based on interviews with 79 managers holding key managerial positions in 12 service companies (diverse with regard to ownership structure, number of employees and geographic location). In the selected sample the starting point was the structure of Serbian service industry in 2006 by divisions. An additional source for the selection process was a ranking of the 300 most successful enterprises in Serbia in 2006 by operating revenues, according to the Economist Journal. Research design. The main goals of the survey have been to identify key IC value drivers and knowledge flows in Serbian service companies. A questionnaire has been designed based upon analysis of the most common scorecard methods embodying Sveiby’s Intangible Assets Monitor (IAM), Danish Guidelines, Meritum Guidelines and Wissensbilanz. Within the group of IC value drivers suggested in the initial methods, 32 value drivers were chosen: 12 of human capital, 10 of structural, and 10 of relational capital. Each of the aforementioned value drivers was determined by a group of questions. Moreover, questions were clustered around three primary IC categories and the value drivers they comprise. In order to identify the IC value drivers as well as knowledge flows, the survey would need to include top and medium-level managers. This choice ensures a relevant and sensible response of participants who are sufficiently familiar with the operational characteristics of the targeted companies. The survey was conducted directly in order to avoid any behavioral biases in the responses. The vast majority of participants (40%) come from small organizations, 32% from medium-size and 28% from large organizations. Results of manager’s profile examinations have shown that the managerial structure in Serbian service industry is mostly made of people aged 26-55, having an university education and between 11-20 years of working experience. Considering the previous results, the sample can be seen as representative.
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Sladjana Cabrilo and Leposava Grubic-Nesic Data analysis. Pareto analysis of cumulative frequencies and the analysis of the percentage of occurrence in certain answers were used. In Likert-type questions, the data was analyzed by using factor analysis of principal components (Kaiser criteria). After extracting the factors, orthogonal analytical rotation (varimax criterion) was applied. Pareto analysis was used to determine the impact level of particular IC value drivers on goal achievement and business success. Measures of suggested drivers’ importance in Serbian service companies were identified based on their impact level.
4. Research findings and discussion Questionnaire is adequate for investigating phenomena of IC in service companies (Cronbach’s alpha is 0.8099). The answers to the proposed questionnaire can explain dominant characteristics of IC in service industry thus helping identification of relevant IC indicators. Empirical research results have been presented according to the aforementioned groups of intellectual capital. HC value drivers (employee’s expertise, experience, skills, innovativeness, motivation and competence development) and their relevance for business success of Serbian service companies were firstly explored. When the managers were asked to rank employee’s characteristics they appreciate the most, the following results were gathered: competence, cooperativeness, commitment, efficiency, experience, education, initiative, innovativeness, loyalty, competence development. Furthermore, analysis of managers' competence development in Serbian service companies revealed that almost one half of the managers in Serbian service companies developed their competences insufficiently, neglecting training and education. What is particularly worrying is a high percent of managers who have never taken part in a competence development program (16%), which is absolutely inadmissible in today’s knowledge economy. In order to overcome new challenges imposed by the knowledge economy, we have to accomplish obligatory pre-conditional factors: that of managing competences and leadership. They contribute to the key factors in the success of modern companies. According to these results – top managers do not seem to recognize the importance of innovativeness and continuous competence development, it is possible to draw the following conclusions:
top managers in Serbian service industry still largely apply principles typical for the industrial era,
the level of awareness of changes in the environment and new demands of knowledge management is very low in this industry,
the reasons for the lack of competitiveness within the Serbian service industry as a whole are to be examined in the obtained results.
Analysis of motivational factors showed that managers see money as the main motivator for their employees (77% of managers). Promotion is seen as a considerably less important motivator (10%), as well as public praise (7%) and the quality of the working environment (5%). Non-financial incentives, is considered to have an almost negligible influence on motivation (1%). This conclusion goes in line with the theory that money has a significant impact on people's motivation and their workrelated behavior in companies (Opsahl and Dunnette, 1966; Whyte, 1955) which has been the approach to industrial motivation. However, according to available research (Herzberg et al., 1959; Kovach, 1987; Linder, 1998), work variety, promotion, advancing possibilities and job security are the most important motivating factors, while monetary factors are ranked as third or fourth. These results seem to be more adequate for workers with a knowledge driven motivation. In order to identify the key HC value drivers, the participants in the survey were asked to choose, without ranking, the 5 out of 9 HC value drivers which have the greatest impact on company’s business success. Value drivers’ importance in Serbian service companies were identified based on their impact level. Based on cumulative frequencies (Pareto analysis), the following ranking of HC value drivers (decreasing importance) was acquired in service industry:
1. employee efficiency -85%,
2. employee motivation -73%,
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3. employee experience -69%,
4. employee expertise -53%,
5. management competence and leadership -48%,
6. education and knowledge-sharing -46%,
7. employee innovativeness -43%,
8. strategic alignment -37%,
9. social skills -33%.
By distinguishing value drivers with the largest influence (the “Pareto rule”), the following key HC value drivers were determined: efficiency, motivation, experience and expertise. Interestingly, the most relevant group of HC value drivers neither includes education and knowledge sharing nor innovativeness. Efficiency, being on top of the list, innovativeness close to the last position, reflects the existence of industrial, rather than the knowledge era in Serbia. Furthermore, the managers find education and knowledge-sharing, as well as innovation, being of less importance for business performance, and that is quite discouraging. Such attitudes are certainly not in accordance with the modern economy in which innovation and life-long learning have become a company’s ultimate tools in their attempts to cope with the dynamics as well as global competition in business. In comparison with the most important HC factors delineated in German InCaS (Intellectual Capital Statements) pilot project applied in 27 German SMEs from service sector (Mertins et al., 2009), the following conclusions could be drawn:
In both studies, 4 the key HC value drivers (named as HC factors in German pilot project) have been delineated. The most important HC factors in German service sector are: employee motivation, professional competence, leadership ability and social competence (Mertins et al., 2009).
Professional competence in German pilot project has comprised formal qualification as well as experiences gained in practice, while in this study employee efficiency, experience and expertise have been considered as individual HC value drivers. Taking these three HC value drivers as one common HC value driver - professional competence, it is possible to conclude that whereas professional competence plays the major role for service sector in Serbia, it is employee motivation which has been perceived as the most important HC factor in German SMEs from service sector. Though, service companies seem to rely mostly on motivated employees and their expertise, as the orders of the HC value drivers in both studies show.
Leadership ability (named as management competence and leadership in this study) is in the group of the most important HC factors in surveyed German service companies, while it has not been perceived as strategically important HC value driver in Serbian service sector. However, considering its importance in Serbian service sector, it follows immediately after professional competence (comprising efficiency, experience and expertise) and employee motivation, as in the case in German pilot project.
Social skills has been perceived by German services as considerably more important regarding business success than by Serbian service companies.
Analysis of structural capital (SC) included knowledge flows (knowledge creation, acquisition, codification, sharing and storage) related to structural capital, SC value drivers, organizational routines and attributes (including the attributes of organizational culture), managerial mechanisms, as well as processes and procedures. Processes of knowledge creation, acquisition, codification and sharing have been related to the innovation value chain. It is made of three basic links: source of knowledge, transformation of knowledge into innovation and exploiting of innovation. Results gained by surveying top managers about sources of knowledge showed that Serbian service companies acquire the deficient knowledge mostly by employing individuals with proper competence, training and education and cooperation with customers. Less common ways are learning from experience gained in more successful companies, cooperation with scientific research institutes as well as universities.
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Sladjana Cabrilo and Leposava Grubic-Nesic The analysis of manager’s attitudes towards transformation of knowledge into innovation and exploiting of innovation in their companies showed the following:
innovations in Serbian service industries are exploited to the largest extent spontaneously -53%, since there are no procedures for introducing innovations in business processes,
in 20% of cases, innovations are implemented according to formal procedures. Bearing in mind that innovation makes continuous contributions only within organizations that have an innovation system (formal procedures and rules), this result is discouraging,
considering the process of transforming knowledge into innovation, the conclusion is drawn that 37% of innovations in services are codified, but only 7.5% of innovations are patented,
about 10% of ideas remain in tacit form, since employees are afraid to bring them up in groups (problems of knowledge sharing, since the organizational culture is not characterized by the atmosphere of trust).
By considering what employees in Serbian service companies document in the work process, the level of knowledge codification in this industry was determined. Results showed that employees in services mostly document the work process (53%), which is only significant for process innovations. In 43% of cases, employees write down specific experiences (specific problems and ways of dealing with them), and in 32% of cases they document the way they work. This is how employees’ experience gets codified and then shared within the company. In only 20% percent of cases, employees write down new ideas for the work process improvement, which is a very small percent of innovation codification. Analysis of employee data bases in Serbian service companies has been focused in identifying whether and to what extent these companies create knowledge bases. Employee data bases in Serbian service companies mainly contain information on the employee’s formal education, personal information and information on service length, whereas information on the employee actual experience, as well as their knowledge and skills, is less documented. Based on these results, it is possible to come to the conclusion that the observed Serbian companies are still not focused enough on the identification of valuable knowledge, skills and experience, their codification and storage through the knowledge base creation. Then, in order to identify the key SC value drivers, we offered the top managers 10 SC value drivers to select four, without ranking, which have the greatest impact on company’s business success. The following is the ranking of SC value drivers, relative to their importance in decreasing order:
1. employees’ communication and interaction -74%,
2. managerial processes -63%,
3. information-communication technology (ICT) -54%,
4. data bases -48%,
5. process and procedural innovation development -39%,
6. brands and trade marks -32%,
7. product innovation development -21%,
8. research and development (R&D) -20%,
9. corporate culture -18%,
10. technological opportunities for knowledge transfer and acquisition -17%.
The following SC value drivers were highlighted as the key ones: employees’ communication and interaction and managerial processes. All the value drivers related to innovation and development (R&D, product innovation development, process and procedural innovation development) were not seen as critical value drivers. We come to the conclusion that innovation and development components are again neglected which goes in line with the previous results of ranking key HC value drivers, in which innovation and competence development are omitted. These findings reveal obvious deficiencies in business innovation in Serbian service industry. It can be a result of poor employee innovativeness, lack of managing initiatives aimed to encourage innovation, or insufficient implementation of innovations.
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Sladjana Cabrilo and Leposava Grubic-Nesic In comparison with the results delineated in German service sector (Mertins et al., 2009), it seems that differences between service sectors in Germany and Serbia regarding SC factors are visible, but less significant observing their influence on business success in service companies. Both service sectors, German and Serbian, have perceived employees’ communication and interaction including all structures for face-to-face knowledge sharing (named as Internal Co-operation and Knowledge Transfer in German pilot project) as the most important SC value driver. Managerial processes named in German pilot project as Management Instruments are perceived as having a high impact on business success in both, Serbian and German services (it has been on the third place considering its strategic importance in German service sector). All electronic information, ICT and data bases (summarized as IT and Explicit Knowledge in German pilot project) have a much higher impact on the strategic business success in Serbian services. On the contrary, Corporate culture is much more relevant in German service industry. The differences for Process innovation as well as Product innovation are visible, but less significant. Analysis of relational capital (RC) included the companies’ relational networks with external stakeholders (customers, partners, suppliers, regulatory institutions, universities, etc.). Out of 10 offered stakeholders (customers were not listed, since these relations were examined separately within other items), the key stakeholders in Serbian service industry, according to top managers, are business partners, industrial associations and unions, state and local administration. Banks and unions are significantly less important stakeholders, whilst the smallest degree of cooperation observed companies have with shareholders, research institutes, investors and universities. These findings are not surprising. Due to the fact that most observed companies have just recently finished or yet not finished the process of privatization, they have not developed the proper relationship with shareholders and investors so far, unaware that these relations are extremely important for their business activities. Orientation towards the bank sector as the main source of financing is dominant. A small degree of cooperation with scientific research institutes and universities reveals that service companies in Serbia are not sufficiently geared towards innovation and knowledge updating. According to top managers from the observed companies, key sources of competitiveness (most appreciated by users/consumers) are: the service quality, long tradition and reliability. Among the key sources of competitiveness; image and innovativeness are not listed. The managers do not believe that the competitiveness of their companies is based on innovation, which is completely inconsistent with the results of numerous researches world-wide that found innovation as the key driver of corporate value and competitiveness (Aramburu et al. 2006, Castellacci 2008, Cefis and Marsilli 2005). These attitudes towards innovativeness could be one of the reasons for the uncompetitive nature of Serbian services. In order to identify the key RC value drivers, we offered the top managers 10 RC value drivers to select four, without ranking. Cumulative frequencies determined the following ranking of RC value drivers, according to decreasing influence on business achievements and success:
1. customer relationships -96%,
2. perceived image -66%,
3. relationships to media -54%,
4. relationships to local community -48%,
5. integration of external knowledge -35%,
6. relationships with competitors -24%,
7. supplier relationships -21%,
8. social involvement -15%,
9. relationships with banks and other financial institutions -11%,
10. relationships with shareholders and investors -10%.
The key RC value drivers in Serbian service companies are: customer relationships and image. It is strange that image isn’t a key source of competitiveness according to top managers, but it is the key value driver having significant influence on business performance and success. This finding reveals
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Sladjana Cabrilo and Leposava Grubic-Nesic that managers are aware of the positive impact of image (it is also revealed by the high rank of relationship with media), but they are not aware of its relevance for competitiveness. In comparison with the most important RC factors in German service sector, delineated in above mentioned German pilot project (Mertins et al., 2009), it is remarkable that both service sectors, in Germany as well as in Serbia, have perceived Customer relationships as the most important and Investor relationships as the least important RC value driver for business success. As the orders of the RC value drivers in both studies have showed, considering formal and informal relations with stakeholders, service companies seem to rely mostly on customer relationships. Taking all individual RC value drivers related to public relations (e.g. perceived image, relationships to media and local community, as well as social involvement) as one common RC value driver – Public relationships (as it has been done in German pilot project), it is possible to conclude that major differences in the relational capital lie in Public relationships (higher strategic relevance in Serbian service sector) and Relationships to Co-operation partners and Supplier Relationships (higher strategic relevance in German service SMEs). From the above analysis, the key value drivers of human, structural and relational capital, or the key IC value drivers of Serbian service companies were established (Table 1). These value drivers, according to managers, impact business achievements and success to the largest extent. Table 1: Key IC value drivers of Serbian service companies IC categories
Key value drivers Employee efficiency Employee motivation Employee experience Employee expertise
Cumulative frequencies 84,8 73,4 68,4 53,2
Structural Capital
Employees’ communication and interaction
74,7
Managerial processes
63,3
Relational Capital
Customer relationships
96,2
Image
65,8
Human Capital
Table 2 shows the three most important IC value drivers for achieving strategic business success in Serbian (according to Table 1) and German service sectors (according to Mertins et al. (2009, p. 119)). Table 2: Strategically most important IC value drivers in service sector Rank 1. 2. 3.
Serbia Customer relationships (RC) Employee efficiency (HC) Employees’ communication and interaction (SC)
Germany Employee Motivation (HC) Professional Competence (HC) Leadership Ability (HC)
According to Mertins et al. (2009) human capital plays the major role within the analyzed German SMEs’ business activity in service sector. Whereas all three most important factors in the surveyed German service companies are HC factors, Serbian service companies perceive Customer Relationships (RC) as the most important and employee efficiency (HC) as the second one, according to the relevance for business success. Furthermore, there is one SC factor among the three most important factors in Serbian service industry – Employees’ communication and interaction, giving a hint to the higher importance of formal structures in Serbian service sector.
5. A strategic model for IC reporting in Serbian service industry Basic assumptions stated that dominant characteristics of IC, as well as key IC value drivers, largely select the group of relevant IC indicators. By combining measures of IC value drivers’ influence (defined by Pareto analysis) and dominant IC characteristics (identified by factor analysis), the group of relevant IC indicators within Serbian service companies was defined (Table 3). For each key IC value driver there are many indicators by which it can be appropriately highlighted and measured. However, specific features of IC and knowledge flows in observed companies affected the choice of the final group of relevant IC indicators. In this way, a model for IC reporting within Serbian service companies was defined (Table 3).
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Sladjana Cabrilo and Leposava Grubic-Nesic Table 3: Model for IC reporting in Serbian service industry IC category
Key IC value driver
Indicator Value added per employee Employees with part of the salary based on performance (proportion) Number of employees rewarded for outstanding results Employee turnover Employee satisfaction index (survey) Absenteeism Employees with a large number of working hours (proportion) Average seniority of employees Growth in professional experience Rookie ratio Average competence index Professional development per employee (days) Versatility and creativity index Number of exchanged documents Number of internal meetings Number of informal meetings and social events Number of conflict situations Average managerial experience of managers Quality of managing activities (employees’ survey) Number of managers’ meetings Training days per manager Proportion of new customers Customer satisfaction index Share of marketing and PR costs in turnover Reputation index (survey) Presence in media Number of image enhancing customers’ complaints
Employee efficiency
Employee motivation Human capital Employee experience Employee expertise Employees’ communication and interaction Structural capital Managerial processes
Customer relationship Relational capital Image
These indicators suggest that many types of metrics are possible and may vary across enterprises. There is no unique model for IC reporting, and the proposed model does not provide a bottom-line indicator of the value of intellectual capital of service companies in Serbia. Indicators are not concerned merely with metrics, but always with change activities. Intellectual capital is a powerfull source of sustaining a competitive advantage. ”If managers find the way to measure IC, then they could more accurately measure and manage their organisation’s competitive position” (Kaplan and Norton, 2004, p.52). Measuring isn’t a goal within itself. The main purpose of measuring IC is to link it to value creation. ”It is necessary to conceptualize the strategic model that bridges a vision and values-based strategy with the IC perspective” (Rylander and Peppard, 2003, p.318). One of the tools for that is a framework such as a strategy map. Overall assessment of human capital may embody links to operations management, customer management, innovation as well as social dimension. Also, top managers, and/or human resource managers need to identify target values for each of the human capital categories. Regarding structural capital, in order to compose a strategy map, managers need to create a report which contains relevant organisational characteristics (e.g. culture, leadership, teamwork, communications etc.), strategic measures similar to key value drivers and indicators in table 2, and target values. The same process is being repeated for the creation of relational capital portfolio. Following the results of our empirical research, we propose a strategy map for service companies in Serbia (see figure 1). Intellectual capital is the foundation of every enterprise’s strategy. ”The measurement and management of IC has a vital role in the transformation of an enterprise into a strategy-focused organisation” (Kaplan and Norton, 2004, p.63).
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Customer relationships Image
Managerial processes
Efficiency, motivation, expertise and experience of employees
Employees’ communication and interaction
Figure 1: Strategy map for service companies in Serbia
6. Conclusion Intellectual capital forms the essence of the knowledge economy. This hidden treasure is what really matters in a society in constant turmoil, especially during the global financial crisis. With respect to previously mentioned factors, the primary conditions for sustaining competitive advantage is the effective measurement of intellectual capital and the management practices that affect employees’ performance. The gathered research results should be considered whilst keeping in mind the following:
The primary research objective was to identify specific features and define general measures that are applicable in Serbian service industry by scrutinizing organizational IC from a broad perspective. These would in turn prescribe applications of IC reporting and management in Serbian service industry and possibly enhance its competitive edge;
The identification of key value drivers and specific features of IC was not only concentrated on IC reporting, but also on managerial control and decision-making, based upon identified strengths and weaknesses of IC. According to Kaplan and Norton (2001), “it is not only what is measured, but also how the measurements are used that determines organizational success” (p.158). The process of selecting relevant IC indicators and defining the model for IC reporting in Serbian service industry presents an opportunity for companies within this industry to assess their IC and integrate the findings into their IC management strategies;
The implementation of the defined model (Table 3) and the strategy map (Figure 1) are limited to service companies in Serbia due to the fact that the surveyed participants are environment and industry-specific.
Although German and Serbian ‘The Intellectual Capital Statement’ (ICS) methodology as well as IC value drivers’ taxonomy differ, comparison of the strategic impact of IC value drivers in German and Serbian service industry allows drawing qualitative conclusions about strategic relevance of particular IC value drivers in service industry. It is remarkable how many similarities there are across the service industries of different countries pointing at common specifics of service industry. This study presents preliminary IC diagnostics of Serbian services companies and points to important problems (competence development, innovation, leadership) in this industry and the possible consequences. Facing the fierce international competition, Serbia’s economy can grow by relying on top-quality performance (new knowledge, competence development through training and education) and innovation. However, as noted previously, these possible catalysts are at the same time identified as major deficiencies in Serbian service industry, which should be the mainspring of transition to the effective creation of a knowledge-based economy. The results gained present a starting point for further research, which can provide service companies in Serbia with an opportunity to use knowledge that creates value in a better way and to develop this knowledge much faster. This would increase their competitiveness and ensure further prosperity.
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The Role of Creative Industries in Stimulating Intellectual Capital in Cities and Regions Maria Rosário Cabrita1 and Cristina Cabrita2 1 Universidade Nova de Lisboa, Portugal 2 Universitat Politècnica de Catalunya, Spain
[email protected] [email protected] Abstract: The knowledge economy has put the focus on innovation, creativity and networks as drivers of competitiveness and economic growth. This has shifted development perspectives from tangibles-based competitiveness to knowledge-driven competitiveness transforming the way the economy is organised and putting the emphasis on the emergence of a new type of capital. Arts and cultural-related industries, also known as “creative industries”, represent a form of capital that provides direct economic benefits to cities and regions. This creative capital, defined as the intrinsically human ability to create new ideas, new technologies, new business models, new cultural forms, and new industries can fuel the regional intellectual capital. One of the knowledge economy dominant paradigms is the active role that places – states/cities/regions – assume in the attraction and development of talented and competent people. The competition is fierce at a global level to attract talents generating a widespread consensus that economic prosperity and concentration of creative people go together. In such context, cities and regions all over the world devote a large number of works to be purposely designed for encouraging and cultivating the collective knowledge or intellectual capital, as capabilities to shape efficient and sustainable actions of welfare over time. Thus it is important for regional managers to be able to describe and nurture their regional knowledge base. This leads to an increased interest in the development of methods and tools for analysis and promotion of regional intellectual capital, as a capacity of a city/region to create wealth and intangible assets. A critical need exists for understanding creative industries and its implications for economic development. There is also a high demand of new approaches to intellectual capital (IC) assessment and valuation at the macro level. We argue that the creative industries, characterised by the generation and exploitation of intellectual property, may contribute to better understand and assess intellectual capital in cities and regions. In turn, the models and frameworks on regional intellectual capital already in place may help to frame the foundations of creative industries and to stress the major role they play in building and sustaining their intellectual capital. This paper first explores the importance of creative industries for intellectual capital growth at a macro level. Next, the paper develops and integrates concepts from both areas of research demonstrating how creative industries can benefit from the developments already made in the regional intellectual capital field. Keywords: Regional intellectual capital, creative economy, creative industries, creative capital, regions, cities
1. Introduction Knowledge, innovation and creativity are the main driving forces behind economic development in today’s world. This has shifted development perspectives from tangibles-based competitiveness to knowledge-driven competitiveness giving birth to new development paradigms. The World Bank has introduced a knowledge-based framework at the national level called the Knowledge-Based Economy (KBE) which consists of four pillars – education; science and technology, and innovation; Information and Communication Technology (ICT) infrastructure; and economic incentive regime. The Asian Development Bank expanded this KBE framework to include the application of Knowledge Management (KM) to socio-cultural and natural-environmental domains. The resulting framework is called Knowledge-Based Development (KBD) which intends to reflect the combination of two powerful development paradigms: sustainable development and knowledge-based management. Evidences in the field of KBD are flourishing day by day at different levels: urban (Singapore, Barcelona); regional (Veneto, Basque Country); national (Denmark, Australia, New Zealand); and supranational (European Union) The Knowledge Based Development (KBD) approach will have profound implications for trade, growth and prosperity of regions, cities and communities. Most advanced economies rely heavily on services, information, technology and intellectual property. Along with the dominance of these industries comes the need for greater creativity and innovation in their human capital. The more traditional industries can also benefit from the existence of talented and creative people. Generally speaking, the quantity and quality of human capital will determine the parameters for success. Such phenomenon breeds competition for people, ideas, businesses and quality of life, influencing and shaping the competitive performance of regions. The availability and quality of the local cultural resources and offer can
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Maria Rosário Cabrita and Cristina Cabrita determine whether a city is a “good place to live”. This is why culture and creativity is increasingly associated with quality of life. In this environment, a state’s arts and cultural resources become economic assets. Arts and culturalrelated industries are viewed as anchors for attracting and improving these creative assets. Therefore, governors increasingly recognize the importance of the creative sector to their states’ economy, and incorporate the arts and culture into state economic development strategies. The increasing relevance of creative industries is evidenced in numerous studies devoted to regeneration of cities, entrepreneurialism and creative cluster. According to the United Nations Conference on Trade and Development (UNCTAD, 2004), the global market value of industries with strong creative and cultural components has grown since 2000 at an annual compound rate of over 7%. The Economy of Culture in Europe report, developed by KEA, European Affairs (2006), shows that in the EU30 the cultural and creative sector contributed to 2.6% of EU GDP in 2003. In addition, the overall growth of the sector’s value-added was 19.7% in the period 1999-2003, which was 12.3% higher than the growth of the general economy. At a global level, UNESCO (2005) shows that trade in cultural goods (heritage goods, books, newspapers and periodicals, other printed matter, recorded media, visual arts and audiovisual media) has increased over the last decade from US$39.3 billion in 1994 to US$59.2billion in 2002. At a policy level, a considerable number of countries are pursuing the creative industries as an opportunity for economic growth, sustainability or social cohesion. However, the relevance of these industries for states’ economies contrasts with serious omissions and lacks in terms of methodologies and classification systems for identifying, measuring and monitoring those creative assets. The research in this field is in its infancy and much more evidence is needed for understanding how creative assets determine economic growth and competitiveness. Tepper (2002:159) discusses the complexity of understanding the creative economy and suggests that “rather than spend time calculating the impact or size of the creative economy we should direct our analytical and policy energies toward better understanding how creative work and institutions are changing and what might be done to foster a more robust, more creative and more diverse economy and cultural life”. The developments in adjacent areas, such as regional IC, can help to clarify some concepts, and contribute to frame the foundations of creative industries. Furthermore, the developments in the creative sector approach may also contribute to better understanding of the major role of creative assets in building and sustaining IC in regions and cities. This paper first explores the importance of creative industries for socio-economic growth and development in a globalized world increasingly dominated by creativity and technology. Next, the paper develops and integrates concepts from both areas of research – creative economy and IC exploring how creative industries can benefit from the developments already made in the regional IC field.
2. Understanding the creative economy There is no unique definition of the “creative economy”. It is a subjective concept that is still being shaped. For some, it is a holistic concept embracing complex interactions between economics, culture and technology that are dominated by symbols, texts, sounds and images. Others, more sceptical, advocate some concerns about its exacerbated importance. Meanwhile, the creative economy has become a hot topic on the international economic and development agendas in both developed and developing countries.
2.1 The dynamics of the creative economy The term “creative economy” was coined by John Howkins in 2001 in a book devoted to the study of the relationship between creativity and economics and the way they combine to create extraordinary value and wealth.
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Maria Rosário Cabrita and Cristina Cabrita UNCTAD (2004) refers to the creative economy as an evolving concept based on creative assets potentially generating economic growth and development. It embraces economic, cultural and social aspects interacting with technology, intellectual property and tourism objectives. It is a set of knowledge-based economic activities with a development dimension and cross-cutting linkages at macro and micro level to the overall economy. The creative economy is broadly defined as the sum of economic activity arising from a highly educated segment of the workforce encompassing a wide variety of creative individuals – like artists, architects, computer programmers, university professors and writers from a diverse range of industries such as technology, entertainment, journalism, finance, high-end manufacturing and the arts. The term “creative economy” is seen as an umbrella to encompass economic development initiatives with arts or cultural dimensions. The literature shows that states define their creative economies in a variety of ways, depending on the composition and character of businesses, non-profit enterprises, individuals, and venues that exist in any given area. Therefore, there is a consensus that individual states should develop definitions that are appropriate to their unique circumstances and own reality. Particular care must be taken in creating taxonomy of these assets since definitions of “creative economy” often vary by state or country irrespective of how we define creative economy in our context. The most relevant is that it contains numerous sub-sectors (or nodes) that contribute to the production of cultural and creative products, and individuals may work in more than one sub sector at a time. Sub-sectors within the creative economy work together within and outside fields of creative practice, in many differing combinations, and produce consumer products and cultural goods. As with the IC concept, the notion of creative economy is context-dependent and given its multidisciplinary structure, each state should focus in what they have as unique and valuable. Most of the recent interest in creative economy among policy-makers has been inspired in the work of Florida (2002). The author argues that a number of trends are emerging giving rise to a new geography of creativity. Place remains important as a locus of economic activity because of the tendency of firms to cluster together. In the creative economy creative people prefer places that are innovative, diverse and tolerant. According to Florida (2005:34) “the distinguishing characteristic of the creative class is that its members engage in work whose function is to create meaningful new forms. The super-creative core of this new class includes scientists and engineers, university professors, poets and novelists, entertainers, actors, designers, and architects, as well as the thought leadership of modern society: nonfiction writers, editors, cultural figures, think-tank researchers, analysts, and other opinion-maker”. The author also includes those professionals that work in a wide range of knowledge-intensive industries such as high-tech sectors, financial services, the legal and healthcare professions, and business management. These people engage in creative problem-solving, drawing on complex bodies of knowledge in seeking innovative solutions. From the various definitions in the literature three main components of the creative economy may be emphasized:
The importance of place: "Place", includes the natural, physical and social environments. Securing a vibrant sense of place is crucial to sustainable economic vitality. The creative economy thrives in places where people want to work, live, and participate as active citizens.
Creative partnerships: States can increase their capacity to develop a creative workforce through partnerships with key area industries, including arts, heritage, place-based, and other not-forprofit organizations. By collaborating with the private sector, government and non-profit organisations create and maintain creative partnerships opening the doors to new resources and people.
Creative assets: The creative economy relies on innovative individuals to generate new ideas that give local businesses a competitive edge and build a larger culture of ingenuity. Leveraging cultural assets generates income for a region, while forming a community environment that inspires creative thinking in everyone and encourages people to explore diverse skills.
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2.2 Creative industries and creative capital With the increasing knowledge intensity of our economy and the need for innovation to maintain a sustainable competitive advantage, it has become imperative for states to tap into their reserves of creativity and promote a policy framework that coordinates activity and maximises connectivity to build markets, exchange knowledge and generate economies and efficiencies of scale. Thinking about creativity as an economic resource or creative capital means that creativity refers to the formulation of new ideas and to the application of these new ideas to produce original works in terms of art and cultural goods, scientific inventions, functional creations and technological innovations. Economic creativity is closely linked to gaining competitive advantage in the economy. This competitive advantage is demonstrated in the way it contributes to entrepreneurship, fosters innovation, enhances productivity and competitiveness, and promotes economic growth. As mentioned by KEA, European Affairs (2006), the creativity in today’s economy may be considered a phenomenon that encompasses various dimensions (Figure 1).
Scientific creativity
Technological creativity
Economic creativity
Cultural creativity
Figure 1: Creativity in today’s economy source: KEA, European Affairs (2006:42) Scientific creativity involves curiosity and a willingness to experiment and make new connections in problem solving. Economic creativity is a dynamic process leading towards innovation in technology, business practices, marketing, organisational models, etc, leading to competitive advantage. Cultural creativity involves imagination and a capacity to generate original ideas and novel ways of interpreting the world, expressed in text, sound and visual objects. Finally, technological creativity in today’s economy is interrelated to the three other types of capital as it offers new distribution channels for creative content, allows the adoption of entrepreneurial business models and strengthens the links between cultural, scientific and economic development. Reich (1991:159) argues that “knowledge and creativity will underpin prosperity and living standards in the new economy”. The author emphasizes the relevance of an increasingly valued group of workers and suggests that creativity is an attribute dispersed across a range of occupations rather than confined to those involved in works related to arts, craft or cultural industries. Drucker (1999) coined the term “knowledge worker” and asserts that knowledge and creativity are pivotal resources in the contemporary economy. However, Reich advocates that a great portion of knowledge is today codified and then easily replicated. Therefore, the real value derived from knowledge workers is their ability to be creative in the application of their knowledge no matter their field. In addition to Reich’s argument, Bilton (2007) asserts that individual creativity needs to be integrated with organisational resources, capacities and systems if we want that new ideas bear fruit. In such environment creative industries are increasingly cited as one of the sectors likely to be a future source of jobs, innovation and productivity. Their potential to enhance the quality of life in cities, and to stimulate new ideas and thinking within communities are sufficient arguments to capture the attention of policy-makers.
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Maria Rosário Cabrita and Cristina Cabrita Definitions of the scope of “creative industries” vary. The Creative Industries Task Force (CITF, 2001) defines creative industries as “those industries which have their origin in individual creativity, skill and talent and which have a potential for wealth and job creation through the generation and exploitation of intellectual property”. Emphasizing the concept of intellectual property, Howkins (2001) classifies creative industries into four broad subsectors: copyright, patents, trademarks and design. According to British classification, used in government-organized mapping exercises on the subject, the field of “creative industries” includes the following sectors: advertising, architecture, the art and antique market, crafts, design, designer fashion, film and video, interactive leisure software, music, the performing arts, publishing, software and computer games, television and radio (DCMS, 2005). No matter the classification adopted, what seems clear is that the creative industries sector lies at the crossroads between the arts, business and technology and deals with the interplay of various subsectors. UNCTAD (2004) associates the creative industries to the cycles of creation, production and distribution of goods and services that use creativity and IC as primary inputs. According to its classification, creative industries comprise a set of knowledge-based activities that produce tangible goods and intangible intellectual or artistic services with creative content, economic value and market objectives. To gauge the contributions to and potential impact of creative industries on a state’s economy it is important for each state to identify its creative industries and measure its creative economy. The creative economy capitalizes on cultural assets including buildings, institutions, organisations and people. States should then conduct a comprehensive scan of their cultural assets and include creative industries in their cluster analyses. To get started, approaches should be addressed for better identifying and analysing the cultural resources so that state policy-makers may better understand the existing creative industries in their state and the dynamic roles that these enterprises play in the state’s economy. To fully understand the economic contributions of those industries it is crucial to categorise the relevant group of assets in creative industries. In this context, we propose that creative industries include human, institutional, physical, organisational and social assets. Creative Industries
Human assets
Institutional
Organisational
assets
assets
Physical assets
Social assets
Figure 2: Conceptualizing creative industries Human assets comprise talented individuals and creative professionals who work in a wide range of knowledge-intensive industries. For the purpose of this paper, we adopt the Florida’s definition of creative class. Institutional assets refer to cultural and government institutions that study, encourage and support the integration of culture-related industries into their state’s economic development strategies. These include offering incentives targeted at the arts and creative industries as well as development initiatives, entrepreneurial training and marketing programs. Organisational assets are those related to companies, economy and management. Physical assets include buildings, museums, gardens, etc. Social assets are the relationships established between the governors, individuals, institutions and companies. It is about communities, collaborative teams and public-private collaborations. The creative industries, while economically important, in their own right, are also about:
Resources of identity
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Resources of social inclusion/cohesion
Economy of symbols, values and meanings
Quality, vitality and conviviality of inhabited human environments
Resources of a sustainable and creative new economy
Development of distinctive local, regional and national identities in the context of globalisation and potential homogenisation of cultures. Regeneration of cities
Economic and social
Responding to the recession
Impact of creative industries
Developing skilled workforce
Preparing for the recovery
Helping weak economic areas
Figure 3: Potential impact of creative industries
Economic and social: creative industries can contribute to generating jobs, innovation and productivity, as well as to enhance the quality of life and stimulate new ideas and thinking within communities.
Responding to recession: creative industries provide opportunities for local authorities to respond to challenges.
Preparing for recovery: creative industries can enrich local amenities contributing to improved quality of life for residents and increased attractiveness of places for investment.
Regeneration of cities: creative industries have the potential to contribute to physical and social regeneration as well as community cohesion.
Developing skilled workforce: creative industries have the potential to attract and retain talented young people becoming an important complement to community development.
Helping weak economic areas: the decentralised nature of the creative industries can benefit residents of areas often thought to lack economic strength – such as rural areas and the urban core.
Ideas are the raw material of the creative industries, and are generated by individual and collective talent and innovation. The creative economy is about transforming ideas into creative capital. Landry (2000) supports the notion of the critical value of the creative individual to the “new economy” and adopts the term “creative capital” emphasizing the idea that creative capacity should be captured and transformed into economic and social wealth. Florida (2005) launched the basis for a creative capital theory which differs from the human capital theory in two aspects:
it identifies a type of human capital, creative people as being key to economic growth; and
it identifies the underlying factors that shape the location decisions of people, instead of merely saying that regions are blessed with certain endowments of them.
Jacobs (1961, 1984) has long pointed to the role of places as incubators of creativity, innovation and industries. More recently, the idea of a creative economy has also been applied to the economy of cities, leading to the emergence of the concept of “creative city”.
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2.3 Creative cities In an industrial society, borders between nations, institutions, organisations and regions largely determined the position of regions. In a global economy, however, borders are fuzzier than ever before, and activities and processes are increasingly organised in networks. Creativity is replacing location, natural resources and market access as a principal key to urban dynamism. The term “creative cities” describes an urban complex where cultural activities of various sorts are an integral component of the city’s economic and social development. These cities are built upon strong social and cultural infrastructure, to have relatively high concentrations of creative employment, and to be attractive to inward investment because of their well-established cultural facilities. Cities have one crucial resource: their people. To attain a competitive advantage in the knowledge economy, urban centres must access, create and utilize human capital. In his popular work The Rise of the Creative Class, Florida (2002) argues that the creative class or knowledge workers are more discerning about the cities in which they choose to live. He asserts that the creative class is more likely to settle in cities that are recognized for their tolerant environments and diverse populations, in preference to cities that offer the highest paying jobs. In addition, Florida suggests that the “most successful places” are those that combine “all three T’s”, talent, technology and tolerance. Inspired by his seminal work some policy-makers alert to the importance of attracting and retaining highly skilled and experienced knowledge workers. Landry (2000) suggests that creative capital is an essential asset for the regeneration of cities and economies. There is considerable value in exploring the richness of local culture as it leads to the development of unique products that have a competitive advantage within the market place. Many successful creative sector products such as film, music, or literature succeed specifically because they are rich in local cultural references. An increasing number of states are recognizing a creative sector approach as a useful and timely part of the solution to a changing economy. London was one of the first cities that understood creative industries could potentially create wealth and jobs. Since 1994, programmes provide funding in order to support projects targeting cultural and creative industries. Many other initiatives take place to improve regional and local policies addressing the promotion and support of creative and cultural industries, as one of the most significant growth sectors for the European economy in terms of GDP and added value. CITIES – Creative Industries in Traditional Intercultural Spaces is a joint initiative generated by ten partners from seven countries, funded by the EU’s INTERREG IV C programme. The duration of the project is 36 months, from October 2008 to September 2011.
3. Intellectual capital in regions Today the position of both organisations and regions are more than before determined by their competencies and skills to learn and develop themselves in a continuous process to cultivate some specific, differentiated and locally rooted knowledge, and to foster linkages with other knowledge pools in the world. Consequently, local initiatives and an enterprising disposition are becoming more and more important in urban competitiveness. A central determinant of a knowledge economy is its dynamics. Flows pass through certain nodes and hubs but if a city or region becomes less attractive or some other area becomes more attractive, the routes of flows may change quiet rapidly. Ståhle and Bonfour (2008:166) explain how to tackle IC dynamics of nations. The authors argue that “when moving to national level IC dynamics, the organisation level definitions of IC can be partly taken as point of departure”. It is accepted among economists that economic growth is regional. Studies of national growth find a clear connection between the economic success of nations and their knowledge base – its regional IC. Regional IC is viewed as a capacity of a region to create wealth and intangible assets. Some studies adopt the taxonomy used at organisational level including both people/human capital and the region’s capacity to make use of the human capital i.e. the opportunity for people to be creative and
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Maria Rosário Cabrita and Cristina Cabrita productive/ Structural capital and Relational capital. An IC analysis aims to visualising and measuring the intangible assets and production factors of a specific company or economic area. The IC of a nation or region includes the hidden values of individuals, enterprises, institutions and communities that are the current and potential sources of wealth creation. The system used to capture the drivers and describe the constructs of national/regional IC can be presented in the shape of a modified IC navigator for nations/regions (Bontis, 2004; Pasher and Shachar, 2005). The literature refers to a few frameworks to assess IC in regions and cities. Perhaps the most influential are Martins and Viedma’s (2006) Regional Intellectual Capital Benchmarking System (RICBS) and Viedma’s (2003) Cities’ Intellectual Capital Benchmarking System (CICBS). The general structure of the RICBS is grounded in regional innovation systems theory. RICBS aims to help regions assess their capacity to create and exploit new opportunities in the knowledge economy context. It consists of two main subsystems and the interrelations between them: (a) the region’s global competitive capacity to create the foundations that will sustain growth and support microclusters’ activities, and (b) the region’s wealth creation capacity as it resides in the microclusters. A crucial element in the model is the Region’s Competitiveness Intellectual Capital Platform (RCICP) which represents the bundle of core resources and competencies that are bound together by core activities. This platform considers four blocks:
Institutions and regional governance: includes norms, guides and principles set by public and private institutions;
Technology: technological skills and capabilities;
Living-environment-based resources: environmental quality of life, as determined by public services, cost of living, and other territorial endowments;
Human capital and social capital: educated, skilled and values-nurtured human broad base with the aim of creating, sharing and using knowledge.
These core resources and competencies condition economic actors’ patterns of behaviour, shape the region’s culture, and determine the extent to which the region as a whole is capable of supporting and fostering an innovative and competitive productive system as displayed by the microclusters. In essence, the RCICP represents the intricacies of resources and relationships that, assuming macroeconomic stability - economy performance block -, can either boost or hinder microclusters’ wealth creation capacity. Creative industries reside in diverse microcluters differentiating between industries which draw on different knowledge bases. The nature of the knowledge bases determines the importance of attracting and retaining talents. Howkins (2001) defines a creative industry as one where brain work is preponderant and where outcome is intellectual capital. Thus, we argue that the focus in creative industries, mapping and clustering them will stimulate the development and growth of IC in regions and cities. In turn, the models and frameworks on regional intellectual capital already in place may help to frame the foundations of creative industries and to stress the major role they play in building and sustaining their creative capital.
4. Conclusion This article provides some insights into the conceptual and methodological framework for understanding the creative economy, with a view to develop analytical tools conducive to identify the role of creative industries in stimulating IC in cities and regions. It aims at adapting the insights from a creative economy approach to the insights of an IC approach. The creative industries may be seen as the means by which communities have the diversity, talent and resourcefulness to respond to rapid, global technological and political shifts. At the heart of the creative economy are the creative industries. The arts and cultural industries provide jobs, attract investments, and stimulate local economies. Perhaps more significant is the fact
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Maria Rosário Cabrita and Cristina Cabrita that creative industries can make creative contributions to industries’ products and services fuelling the renewal of IC in states. In order to better inform policy-makers’ thinking about how best to foster creativity and creative industries development as part of a wider strategy to promote innovation, it is critical that a more nuanced understanding of the dynamics, role and definition of the creative industries is fostered. There is a need to better understand how creative capital is applied – from arts and culture to science and technology. The challenge is to capture the IC flows and to define the intellectual property output on which cultural products are increasingly based, and not focus only on the physical and tangible support material. IC frameworks can provide an important contribution for this understanding.
References Bilton, C. (2007). Management and creativity: From creative industries to creative management. New York: Blackwell. Bontis, N. (2004). National intellectual capital index: A United Nations initiative for the Arab Region. Journal of Intellectual Capital, Vol.5, Nº1, pp.13-39. Creative Industries Task Force (2001). Creative Industries Mapping Document, Department of Culture, Media and Sport, [on line] http://www.culture.gov.uk/creative/pdf/part1.pdf DCMS (2005). Creative industries Section. [UK Government] Department for Cultural, Media and Support. Available online at: http://www.culture.gov/ukcreative_industries Drucker, P. (1999). Beyond the information revolution. The Atlantic Monthly, 2484, 4. Florida, R. (2002). The rise of the creative class – and how it’s transforming work, leisure, community and everyday life. New York: The Perseus Books Group. Florida, R. (2005). Cities and the creative class. New York: Routledge. Howkins, J. (2001). The creative economy: How people make money from ideas. London: Penguin. Jacobs, J. (1961). The death and life of great American cities. New York: Random House. Jacobs, J. (1984). Cities and the wealth of nations. New York: Random House. KEA, European Affairs (2006). The economy of culture in Europe. Study prepared for the European Commission (Directorate-General foe Education and Culture. Brussels. [online] http://www.keanet.eu/ecoculture/studynew.pdf Landry, C. (2000). The creative city: A toolkit for urban innovators. London: Earthscan Martins, B. and Viedma, J.M. (2006). The region’s intellectual capital benchmarking system: Enabling economic growth through evaluation. Journal of Knowledge Management, Vol.10, Nº5, pp.41-54. Pasher, E. and Shachar, S. (2005). The intellectual capital of the state of Israel. In Bounfour, A. e Edvinsson, L. (Ed.), Intellectual capital for communities – nations, regions, and cities. Oxford: Butterworth-Heinemann, pp.139-149. st Reich, R (1991). The work of nations: Preparing ourselves for 21 century capitalism. New York: A.A.Knopf. Ståhle, P. and Bonfour, A. (2008). Understanding dynamics of intellectual capital of nations. Journal of Intellectual Capital, Vol.9, Nº2, pp.164-177. Tepper, S. (2002). Creative assets and the changing economy. The Journal of Arts Management, Law, and Society, Vol.32, Nº2, pp.159-68. United Nations Educational, Scientific and Cultural Organisation (UNESCO), Institute for Statistics (2005). International flows of selected cultural goods and services, 1994-2003. Defining and capturing the flows of global cultural trade. Montreal, Canada [online] http://www.uis.unesco.org/template/pdf/cscl/IntlFlows_EN.pdf UNCTAD (2004). Creative industries and development (document TD(XI)/BP/13). Geneva, United Nations. [online] www.unctad.org/en/docs/tdxibpd13_en.pdf Viedma, J.M. (2003). CICBS: Cities’ Intellectual Capital Benchmarking System. Methodology and a Framework for Measuring and Managing Intellectual Capital of Cities. A Practical Application in the City of Mataró [online] http://www.intellectualcapitalmanagementsystems.com.
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Emotional Capital for Building Sustainable Business Performance Dan Candea and Rodica Candea Technical University of Cluj-Napoca, Romania
[email protected] [email protected] Abstract: The paper relies on an interdisciplinary approach to treating a source of business performance that is often overlooked or superficially acknowledged by practicing managers: developing the emotional knowledge (EK) and expanding the emotional capital (EC) of organizations. We show that EK and EC also influence the sustainability of business performance. Sustainable business performance is defined as the capability to deliver profits over the long term to shareholders, for which purpose the short term and the long term have to be integrated. EK is considered an integral of emotional information and abilities, prior emotional experiences, and emotional insights that provide a framework for evaluating and incorporating new experiences. It resides at the level of individuals, groups, organization, and their interactions. We maintain that when EK is put to work by exercising emotional competencies, EC is generated as an important intangible asset of an organization. EC affects directly the work climate and the organizational development process, spurring work performance and implicitly business performance on. Traditional approaches to business performance focused exclusively on private interest cannot guarantee long-term success. When aspiring to sustainable business performance, other factors should be considered too. Those factors originate from the organization’s responsibilities that extend beyond its economic purpose and function, to protecting and developing its operating environments, social and natural, which could support in return the organization for long-term prosperity. EC is a tremendous asset for carrying out those responsibilities. In an organization’s interactions with its internal and external stakeholders EC plays mediator role. The stakeholders are individuals and groups that have needs and expectations relative to the organization and can influence, directly or indirectly, its long-term performance. The employees, as individuals and groups who are intimately tied to the workings of the organization, are its internal stakeholders. The external stakeholders can be shareholders, customers, suppliers, financial institutions, local communities, authorities, etc. A business addresses the needs and expectations of stakeholders by exercising corporate social responsibility, whose effectiveness hinges on the emotional competencies involved in the interactions. The EC can therefore be regarded as a two-component asset: the internal and external EC. The internal EC bears on the goodwill, enthusiasm and loyalty of the internal stakeholders, reflected in their work performance. Strongly connected with internal EC is the external EC, which has an impact on the perception of the external stakeholders of the organization’s behavior and brand value, on their understanding and goodwill, and on their support. All these affect the sustainability of business performance. Consequently, both the internal and external EC have to be managed strategically. The paper advances a conceptual model, derived from the above considerations, that presents how EC can add to the organization’s energy for pursuing sustainable business performance. The model helps managers set strategic objectives and conceive practical approaches for taking charge of the process. The paper also highlights EC’s support for the creation of benefits for both business and society, as an approach to better prospects for sustainable business performance. Keywords: Emotional capital, emotional knowledge, emotional competencies, sustainable business performance
1. Sustainable business performance (SBP) This work is part of a larger project that researches the organizational factors that influence the sustainability prospects of organizations, having as the main goal to identify approaches and to formalize tools for managerial intervention. Our paper focuses on identifying factors that affect business performance and sustainability. We have in view factors that are located in the area of organizational emotional knowledge (EK) and emotional capital (EC) development. By sustainable business performance (SBP) we understand the capability to generate profit both in the short and long term. An organization undertakes various actions that lead to SBP of which some target immediate business performance, others aim at building organizational capability to produce long-term efficiency. Although it is not a sound managerial practice to think about the short term and the long term separately, for the purpose of analysis we will identify the two perspectives distinctly and emphasize that the integrated approach leading to SBP requires an extra effort. We will show that EC is a potent influence factor for both perspectives. Management has always felt responsible for business performance and, many times, that concern proves to be short-term oriented. Even under these circumstances, in pursuit of efficiency enlightened
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Dan Candea and Rodica Candea managers look upon employees as individuals who “lend” their intellectual and emotional capabilities to the organization to the extent they are stimulated to do so. Employees can offer the company more than their physical strength, knowledge and rational thinking and, for this to happen, they should be energized by positive complex emotions of enthusiasm, passion for their work, pride, devotion, etc. The organization, through its management, plays an important role in impelling employees to work decisively towards business performance, for which purpose the aforementioned emotions are a prerequisite. When the sustainability of business performance is at stake, however, managers’ focus has to broaden from narrow business self-interest to taking into account societal concerns related to the social and physical environments. An organization has a variety of stakeholders, individuals, groups, and organizations, who have diverse interests, needs and expectations relative to the organization’s activities and who can affect, directly or indirectly, SBP. The group of internal stakeholders consists mainly of employees (managers and non-managers, unions, special interest groups in the organization), who are intimately involved with the workings of the organization. The employees are a non-homogeneous group of stakeholders who place specific demands and expectations on how they are treated, on how responsible the organization is relative to the workplace health and safety, on the organization’s moral stand on societal issues. The external stakeholders are the shareholders, other investors, customers, suppliers, NGOs, public authorities, communities, individuals and groups, who affect or can be affected by the actions of the organization. These stakeholders exert pressure on the organization to assume responsibilities for:
protecting the natural environment by reducing the pollution generated by the company’s processes and by diminishing non-renewable resource consumption,
proper consideration for the welfare of the employees, who run the company’s processes,
ethical business conduct,
contributing to the prosperity of the surrounding community and the society at large.
A summary of the preceding views is presented in Figure 1, which affirms that SBP has good prospects when the organization creates simultaneously benefit for the company and society while protecting the environment. Responsibility to create benefit for the company
SUSTAINABLE BUSINESS PERFORMANCE [SBP] Responsibility to protect the physical environment
Responsibility to create benefit for society
Figure 1: Three determinants of Sustainable Business Performance (SBP) EK plays a role in almost any factor that influences people performance and organizational effectiveness. In what follows, we will show how using EK and emotional competencies can lead to developing an organizational asset, the EC, which, if invested in the interactions inside and with the outside of the organization, contributes to SBP.
2. Emotional Knowledge (EK) People are physical, intellectual and emotional beings, they first “feel” and then “think” and cannot always be rational. Emotions can support or sabotage our thinking depending on the emotional competencies we have. Emotional competencies are manifestations of the emotional intelligence
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Dan Candea and Rodica Candea defined as the ability to carry out accurate reasoning about emotions and the ability to use emotions and EK to enhance thought (Mayer, et al., 2008). Emotions motivate our actions, help us make decisions, be creative, relate better to other people. They help us learn from our previous experiences and change. They are what energize us, what urges us to act. They influence how we process information, affect our interpersonal relations and create the interface between the stimuli from the environment and the urge to adopt a certain behavior towards work and work-related performance. Emotions help us harmonize with ourselves and with the social environment. The emotional memory determines our mentality, prejudices and value system. EK is a „dynamic mix of framed emotional experiences and abilities, emotional information and its understanding, values, beliefs, feelings, and emotional insights that provides a framework for evaluating and incorporating new experiences” (Davenport & Prusak 1998). EK is essential in knowing how to label emotions and recognize relations among emotions, how to interpret the meaning emotions convey, how to understand complex feelings and recognize likely transitions among emotions, how to identify the causes of emotions, how to foresee the consequences of emotions and how emotions evolve (Mayer, et al., 2001). EK acquisition involves complex perceptual processes, experiencing and learning, communicating, associations, and reasoning. EK can be developed through a set of mechanisms and organizational practices and activities that include facilitating relationships and dialogue, teamwork and openly discussing emotional states, joint problem solving, sharing EK across organization. The EK found at the level of individuals builds mutual influences and integrates at group level, generating an emotional climate that in turn affects the individual EK. EK influences information processing by making it profound or superficial, accelerated or slower, correct or distorted, depending on the emotional climate (Druskat & Wolff 2001). Emotional toxicity is a performance „killer” and emerges primarily through toxic relationships, the same way emotional intelligence and competencies emerge primarily through productive relationships. Organizations should have in place policies and practices to ecologize the work climate (Frost, 2003), and emotional competent managers. Accumulating organizational EK takes place in three stages, which can be either sequential or can overlap: acquisition, transfer, and exercising the EK. Acquiring EK is the stage in which the organization’s members accumulate information of an emotional nature, individually and in groups, originating from their own and others’ experiences, from their own and others’ successes and failures, and by training and experimenting. The stage of EK transfer (dissemination) occurs by open dialogue on the emotional experiences encountered in the work process, in problem solving or in decision making (Candea, 2008). Exercising the knowledge is the last stage, in which EK emerges through the practice of emotional competencies. The individual and collective EK, explicit or implicit, constitutes the emotional memory of the organization. The unfolding of the EK accumulation processes is bolstered by communication both at the cognitive and affective levels. The psychologically complex nature of communication can create many problems because of emotional distortions, which raise perceptual filters in the communication process (Candea & Candea 2008).
3. Emotional competencies and Emotional Capital (EC) Emotional intelligence consists in the capability to perceive, access, control and generate emotions in a controlled manner. The existence of a superior emotional intelligence assumes an extensive EK and manifests itself by significant personal and social (interpersonal) emotional competencies. These refer to self-awareness and social awareness, and self-management and the management of interactions
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Dan Candea and Rodica Candea (Goleman, et al., 2002). Emotional competencies allow the individual to think, feel and act in a way that integrates the information contained in the emotions. Individuals are born with a certain emotional potential (demonstrated by emotional sensitivity, emotional memory, emotional processing, and emotional learning), which can be developed or inhibited over lifetime by education in the family, learning institutions or the work place, depending on the lived emotional experiences. Emotional competencies are shaped by the innate emotional potential and its augmentation by learning and experimentation, by the accumulation of EK. EC gets generated when the organization’s members are motivated to exercise their EK, to practice their emotional competencies. EC is the source of the collective emotional energy, which reveals itself through the employees’ level of passion and enthusiasm for their work, through their work performance, loyalty level and attachment to the organization, involvement and availability to assume responsibilities. The emotional energy of the organization is a measure of the EC and reflects in the internal and external interactions of the organization’s members, in the organization’s behavior in its operating environment. Figure 2 presents the processes that can magnify the employees’ emotional potential in the work place and turn it into the EC that feeds the organization’s emotional energy. ORGANIZATION’S EMOTIONAL ENERGY
EMOTIONAL CAPITAL
EMOTIONAL COMPETENCIES
Presence of emotional competencies at the managerial level A stimulating climate for practicing emotional competencies
MANAGEMENT OF INTERACTIONS SOCIAL CONSCIENCE SELF-AWARENESS
-Learning about emotions
SELF MANAGEMENT Motivation and support for externalizing and putting the emotional knowledge to use
EMOTIONAL KNOWLEDGE
-Expressing and experimenting emotions -Association and reasoning about emotions
EMOTIONAL POTENTIAL
Figure 2: Mechanisms for generating organizational EC and emotional energy The dashed lines indicate the conditions under which these processes can take place. Organizational policies and practices, and the management style can create the conditions. The emotional competencies are displayed in the hierarchical order of their interdependency (Cherniss & Goleman 2001). In the current business context, the interactions among individuals, groups and organizations are factors that affect the evolution of an organization. Business performance is thus related to interactions, and better interactions can bolster competitive advantage.
4. Communication at emotional level Any interaction takes place through verbal and nonverbal, oral and written communication. There are three kinds of interactions depending on the nature of the work (Bradford, et al., 2005):
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transformational – take place in the processes of extracting raw materials or converting them into finished goods, which are specific to operators performing industrial types of jobs,
transactional – routine (rule-based) interactions, which stay the same over time and are typical of clerical jobs, and the like,
tacit interactions – complex, requiring a higher level of judgment, characteristic of jobs in which the employee repeatedly faces one of a kind situations.
The tacit interactions originate in the heads of people, requiring employees to analyze rational and emotional information. This means that these interactions must often draw on emotional knowledge and competencies. They are complex, ambiguous, requiring a high level of experience, instinct, judgment at rational and emotional levels. While one can improve transformational interactions through process design, and transactional interactions by providing procedures and structures, tacit interactions require a flat hierarchy, individual empowerment, EK and emotional competencies, and an emphasis on networking. Tacit interactions are becoming central to economic activity today because workers involved in tacit interactions make up the fastest-growing segment of employees. The shift from transactional to tacit interactions requires organizations to think differently about interactions and transparency, about management style and how to improve performance, and also about their communication technology investments (Candea & Candea 2009). Emerging technologies (e.g., web interactive social communication tools including intranet, wikis, collaborative software, multiple-source videoconferencing) offer the technical support for organizations to extend the impact of tacit interactions. These technologies can facilitate, speed up, and progressively cut the cost of such interactions. The tacit interactions within an organization and between an organization and its external stakeholders open up the opportunity to create capabilities that competitors can not easily duplicate. Advantages built on tacit interactions are long-term. To illustrate, a company could use tacit interactions between its marketing staff and customers to comprehend customers’ needs and preferences and then cater to customers’ expectations more effectively. However, most of today's organizational models aim to maximize the performance of transactional or transformational interactions. The rigidity of traditional organizational models too often limits innovation and learning (Bradford, et al., 2005). Interactions are performed through communication that can take place at three levels (Candea & Candea 2005):
Cognitive level – logical, rational, objective. It aims at thinking and communicates, for example, facts, data, numbers, and technical information.
Emotional level – subjective, symbolic, having a certain emotional charge. Opinions, perceptions, experiences, reactions, values and the like are communicated at this level.
Behavioral level – observable as the decision to act in a specific way. It is a manifestation of what we think and feel which reflects the actual feelings and thoughts closer or in a distorted way depending on the emotional filtering.
In some organizations, communicating at the cognitive level is preferred because of the widespread belief that emotions are undesirable in the workplace. The proponents of this belief disregard the fact that all normal individuals experience emotions, which accompany any interaction. Denying or prohibiting those leads to dissimulated emotions and distorted communication. Reluctance to accept emotions as a source of information, influence or motivation originates in emotional incompetence, in the inability to understand and control one’s own emotions and to understand and act effectively on others’ emotions.
5. Emotional climate, emotional energy and the attitude towards Corporate Social Responsibility (CSR) An important class of interactions in which the emotional energy plays a major role is the class of interactions with the stakeholders, which, in part, appear as CSR activities.
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Dan Candea and Rodica Candea CSR is a responsibility assumed voluntarily by an organization that materializes as respect for human rights in general and employee rights in particular, environmental protection, community involvement and corporate volunteerism, supplier relations and minority purchasing, corporate philanthropy, corporate reporting and transparency, lack of corruption, adoption of moral principles and codes, consumer education, product and service stewardship, collaboration with stakeholders, etc. CSR may be regarded as a “contract” with the stakeholders, freely undertaken by the organization, from which, in exchange for the incurred expenses, the organization gains advantages in terms of improved image, higher sales, lower costs, mitigation of risks, etc. CSR activities can contribute towards establishing a social dialogue, developing relationships and even partnerships that can lead not just to social cohesion but can also bring important benefits to both sides, such as mutual support, anticipation of future changes and risks etc. A socially responsible corporation can create value for society in various ways, besides bringing products and services to markets. There is a strong connection between an organization’s attitude towards social responsibility and the condition of its emotional energy. The literature suggests that organizations can have reactive (R), defensive (D), accommodative (A) or proactive (P) attitudes towards CSR (Clarkson 1995). The reactive organizations deny their responsibility and do less than what stakeholders require. The defensive organizations admit responsibility but fight it by doing the least amount required. The accommodative organizations accept responsibility by doing all that is required. The proactive organizations anticipate the expectations and needs of stakeholders and do even more than it is required. Porter and Kramer (2006) discuss the organization that accepts and anticipates social responsibility, and integrates it in its core business strategy. As a result, CSR becomes an asset for the organization, a core value, and enhances the prospects for sustainable business performance. We denote this case by (S).
Intensity of emotions
The emotional energy of an organization can register several “states” defined by the intensity and type of the dominant emotions (Bruch & Ghoshal 2003): the comfort state, the resignation state, the aggression state and the passion state (Figure 3). An organization’s different parts can have differing energy states depending on the prevailing emotional climate. Aggression state
Passion state
[RR-D; A-P for unilateral advantage]
[A-P, S]
Resignation state
Comfort state [R-D, residual A-P]
[R-D]
Type of emotions
Figure 3: States of the organizational emotional energy and the attitude towards CSR (R-D: reactivedefensive attitude; A-P: accommodative-proactive attitude; S: CSR objectives intertwined with business strategy) The comfort state is distinguished by an emotional climate dominated by positive emotions, although low in intensity, such as calm, contentment, relaxation, tranquility. In this state, an organization lacks the vitality and the emotional tension required for initiating major changes or significant interactions. Employees are satisfied with their condition even if business performance (financial results included) is inadequate. Interactions with stakeholders continue with inertia but are sporadic and motivated by company self-interest or by the pressure from some stakeholders. The attitude towards CSR is most likely of a reactive-defensive or residual accommodative-proactive type, depending on the activities that were carried on in the prior successful period. CSR actions will continue for a while but lose momentum and fade away. Without an intervention the emotional energy will regress to resignation.
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Dan Candea and Rodica Candea The characteristic of the resignation state are the negative emotions (frustration, disappointment, suffering, lethargy etc.) of reduced intensity. Employees tend to be indifferent to the organization’s objectives and lost their hope for better days. Interactions with the stakeholders are, in the best case, limited to the internal stakeholders. The attitude towards CSR is reactive-defensive, CSR initiatives tend to be neglected or, if pressure is exerted, they will be maintained at the lowest possible level. Organizations in the aggression state have a tensioned emotional climate, charged with strong negative emotions, which feed the organization’s competitive spirit. This negative tension could be targeted against the “common” enemy in order to overtake it. Relations with stakeholders are used as a way to bring immediate profit and unilateral advantage. It is unlikely that the aggressive organization will invest in strategic CSR activities. The attitude towards CSR is of a reactive-defensive or accommodative-proactive kind. This state cannot last for a long time. Success is threatened by the regression to a comfort state, then failure by resignation. In the passion state organizations have an emotional climate dominated by strong positive emotions, by self-confidence, pride, enthusiasm for what people intend to achieve, enjoyment for completed work, esprit de corps. Interactions with stakeholders are multiple and complex, often in the form of partnerships, and target joint benefits. The attitude towards CSR is accommodative-proactive and the tendency is to incorporate CSR in the business strategy. Organizations in this state can weather crises easier and emerge stronger. Employees can withstand failures better because they are sustained by their relations.
6. Strategies for SBP buildup by employing emotional capital Emotional states exist at individual, group and organizational levels and affect the capability of people to become self-aware, high performing, to relate and collaborate, and generate a collective emotional intelligence. Emotions affect all processes either targeted directly to the market or aimed at internal functions, such as organizational communication, human resource activities, managing change, etc. They influence the organization’s capability to strengthen its business performance while acting responsibly towards its social and physical environments. Instead of disregarding them, the emotional states of an organization should always be taken into account when managing that organization’s processes. We elaborate on the two ways in which emotions can help organizations achieve SBP (Figure 4):
by investing the emotional energy in pursuing market-related performance, and
by applying the emotional competences in the interactions with relevant internal and external stakeholders.
The scheme in Figure 4 starts off from the EC generated by capitalizing on the EK (as described in Figure 2), from which the emotional energy stems and reflects in the four states presented in Figure 3. Using the internal EC to improve the emotional work climate serves a company’s business objectives through better employee performance; business growth and market-related performance are expected. At the same time, the EC invested in the interactions with the stakeholders will favor the adequacy of CSR actions. The two directions in employing the EC contribute to increased prospects for the sustainability of business performance, so we recommend that any business strategy implementation should use and monitor the EC and the organization’s emotional energy. It is a generally held view that negative emotions are counterproductive and, consequently, human resource management actions focus on reducing or eliminating them. However, research on emotions in organizations suggests that in major change processes negative emotions make an important contribution, together with developing positive emotions (Quy Nguyen Huy, 2005). Therefore, the strategies for developing and mobilizing the emotional energy required to achieve SBP can be grouped in two diametrically opposed categories: strategies based on negative emotions and on positive emotions. The strategies founded on negative emotions intend to channel the EC associated with the comfort and resignation states towards the aggression state by focusing attention and efforts on the threats to the organization, and by concentrating negative emotions and the associated energy on overcoming obstacles.
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Dan Candea and Rodica Candea
SUSTAINABLE PERFORMANCE: BUSINESS – ENVIRONMENT – SOCIETY
Emotional energy invested, through emotional competencies, in the interactions with internal and external stakeholders
Emotional energy invested, through emotional competencies, in marketrelated-performance and business th
EMOTIONAL ENERGY
EMOTIONAL CAPITAL Figure 4: SBP buildup mechanism based on managing organizational emotional energy The strategies based on positive emotions aim at directing the EC towards the passion state by awakening and feeding positive emotions and enthusiasm for an attractive and inspiring vision. These strategies bank on strong positive emotions, capable to engage the dreams of people and bring about willingness for heroic effort. Leaders should harness employees’ passion with such a force as to overcome passivity, resignation, complacency with existing conditions. The strategies should be used in the context of the existing condition. When an organization is in a critical situation, such as the risk of bankruptcy, it is unlikely to induce an emotional climate of enthusiasm. Similarly, if no imminent threat is in sight aggressiveness is not justified, not credible. When an organization is in a state of resignation, experiencing negative low intensity emotions, and is confronted by a threat, it is unlikely that employees are able to push themselves to the limit of the emotional energy required to fight the danger. They need to develop positive emotions and the vision of a better future that would energize them out of the current state. They need leaders who can generate enthusiasm and sustain it. The case is different with organizations in a state of comfort. Employees experience relative satisfaction and the level of emotional energy generated by the weak positive emotions is low. Under this condition, by promising a better future it is difficult to induce in them a positive energy strong enough and lasting. They should rather be energized by sensitizing them to an imminent threat, which should stimulate negative emotions of aggressiveness. All these situations, characterized by various emotional charges, reflect in an organization’s attitude towards its internal and external stakeholders and in the way the organization exercises its responsibility regarding the social and physical environments. They all affect SBP.
7. Conclusions We treated the sustainable business performance in an interdisciplinary manner, from within the fields of emotional capital and management science using practical organizational sociology and psychology approaches. We highlighted the relationship among EK, EC and the emotional energy of an organization. The organization’s emotional energy can be regarded as a measure of its EC. A practical implication is that EC can be and should be developed as a collective, organization-wide capability, through individuals relating with each other at emotional level. Management communication, internal and public, plays a crucial role in this endeavor and the competency of communication at the affective level can be turned into a competitive factor. A key aspect for increasing the prospects for business performance sustainability is the interaction with the internal and external stakeholders. Embedding CSR and ethics in all organizational processes, infusing the CSR spirit into all departments, engaging stakeholders through leadership,
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Dan Candea and Rodica Candea CSR reporting and web social interactions can benefit from the managers’ emotional competencies and the organization’s emotional capital. The paper calls attention to the fact that traditional approaches to business performance focused exclusively on private interest cannot guarantee long-term success. When aspiring to sustainable business performance, other factors should be considered too. By identifying those factors our conceptual model helps managers set strategic objectives and conceive practical approaches for taking charge of the process.
References Bradford, C. , James, M. and Lareina, A. Y. (2005) „The next revolution in interactions”, [online], http://www.mckinseyquarterly.com/Organization/Strategic_Organization/The_next_revolution_in_interaction s_1690?gp=1 Brown, J.S, and Hagel, J. (2005) „From Push to Pull: The Next Frontier of Innovation”, [online], The McKinsey Quarterly, October 19 pp 83-91 http://www.johnseelybrown.com/pushpull.pdf Bruch, H. and Ghoshal, S. (2003) ”Unleashing organizational energy”, MIT Sloan Management Review, Fall pp 45-51. Candea R.M. (2008) “Comunicarea manageriala etica in dezvoltarea organizatiei sustenabile”, Revista de management si inginerie economica, Vol 7, Nr 1, pp 27-40. Candea, R.M. and Candea, D. (2008) “Dimensiuni emoţionale pentru creşterea perspectivelor de sustenabilitate ale organizaţiei”, in Inovare si buna guvernare organizationala pentru sustenabilitate, Editura UTPRES, Cluj-Napoca, pp 237-263. Candea, R.M. and Candea, D. (2009) „Banking on web 2.0 approaches to build a sustainable enterprise”, Review of International Comparative Management, Vol 10, No 2, pp 95-104. Candea, R.M. and Candea D. (2005) Competentele emotionale si succesul in management, Editura Economica, Bucuresti, pp 156-175. Cherniss, C. and Goleman, D. (2001) The emotionally intelligent workplace, Jossey-Bass, San Francisco, pp 209-233. Clarkson, M.B.E. (1995) “A stakeholder framework for analyzing and evaluating corporate social performance”, Academy of Management Review, Vol 20, No 1, pp 92-117. Cross, R., Baker, W., and Parker, A. (2003) ”What creates energy in organizations?”, MIT Sloan Management Review, Summer, p.51-66. Davenport, T. H. and Prusak, L. (1998), Working knowledge: how organizations manage what they know, Harvard Business School Press, Cambridge, MA. Druskat, U.V. and Wolff, S.B. (2001) „Building the emotional intelligence of groups”, Harvard Business Review, No 3, pp 81-90. Frost, P.J. (2003) „Emotions in the workplace and the important role of toxin handlers”, Ivey Business Journal, November / December, Reprint #9B03TF05, pp 1-6. Goleman, D., Boyatzis, R. and McKee A. (2002) Primal leadership: realizing the power of emotional intelligence, Harvard Business School Press, Boston, pp 39-51 and 253-256. Huy, Q.N. (2005) “An Emotion-Based View of Strategic Renewal”, Advances in Strategic Management Vol 22 pp 3–37. Huy, Q.N. and Mintzberg, H. (2003) ”The rhytm of change” MIT Sloan Management Review, Vol 44, No 4, pp 79-85. Mayer, J.D., Perkins, D.M., Caruso, D.R. and Salovey, P. (2001) A unique accession number assigned to each record in the database; also referred to as ERIC Document Number (ED Number) and ERIC Journal Number (EJ Number).” The name assigned to the document by the author. This field may also contain subtitles, series names, and report numbers.Emotional Intelligence and giftedness” Personal author, compiler, or editor name(s); click on any author to run a new search on that name. The entity from which ERIC acquires the content, including journal, organization, and conference names, or by means of online submission from the author.Roeper Review, Vol 23, No 3, pp 131-137. Mayer, J., Roberts, D. and Barsade, G. (2008) “Human abilities: emotional intelligence”, Annu.Rev.Psychol., Vol 59, pp 507-536. Porter, M.E. and Kramer, M.R. (2006) “Strategy & Society” Harvard Business Review, December 2006, pp. 7892.
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Organizational Commitment, Knowledge Management and Social Economy: An Empirical Study Leonor Cardoso, Andreia Meireles and Joana Marques University of Coimbra, Portugal
[email protected] [email protected] [email protected] Abstract: We studied the relationship between Knowledge Management (KM) and Organisational Commitment (OC) based on ASH-ICI Questionnaire of Quijano, Navarro and Cornejo (2000) and the KM Questionnaire of Cardoso (2003). In an empirical study carried out in 9 Private Social Solidarity Institutions with a sample of 217 collaborators, we tried to empirically sustain the prediction capability of OC and organisational variables (length of time in the institution and post-held) in relation to KM. We conducted a statistical analysis of three models, which make up a general model of multiple hierarchic regressions with moderation of discrete variables. We concluded that only the instrumental commitment of exchange was statistically significant in all KM dimensions. The affective and values commitment showed a predictive capability in cultural orientation to knowledge and KM practices. However, instrumental commitment of necessity showed a negative impact on knowledge management practices. Lastly, we considered the post-held variable, which appears as a predictor of two knowledge management dimensions to group 1 (directors, professionals, teachers, animators, therapists and others): social and discursive knowledge and KM practices. The theoretical and practical implications of these results are analysed and discussed. Keywords: Knowledge Management, Organizational Commitment, Social Economy, PSSI’s
1. Introduction Knowledge management (KM) has gained prominence in academic circles and the organizational community as a key factor to face up the current organizational context. KM – the capacity to generate, renew and use organizational knowledge – constitutes an important strategic resource, being valuable and irreplaceable as a driving force in organizations that face uncertain situations, taking on the role of a principle source of sustainable competitive advantage for companies (Nonaka 1991). In this context, companies survive and prosper because they are continuously generating new knowledge. Organizational commitment is considered human and/or social variables that predict behaviour in organizations (Abbott, White and Charles 2005). The relationship (or psychological bond) between Human Resources and its organization is a subject that has been studied by various investigators. In assessing this relationship, studies have focused on questions of OC, identification with the organization and job involvement. Several authors have shown interest in studying the importance of the relationship between KM and OC, having arrived at convergent results about a positive relationship between levels of OC and desirable attitudes for KM processes. However, as stated by Hislop (2003:183), “whether commitment levels affect attitudes towards, or participation in, KM initiatives is an open question, as no research has been done in this area”. So, we propose to study KM and OC relationships, to understand how specificities inherent in the bond established by the collaborator have repercussions in the way KM processes occur or are fulfilled. Our investigation takes the social economy sector as its subject of study.
2. Background 2.1 2.1 Social economy organizations – IPSS’S Social economy has gained increasing prominence in the European context, particularly with the growing value of the role of social economy organizations in reforming systems of social protection. Sometimes designated as the tertiary sector, these organizations operate as a complement or substitute for State action in implementing social policies (Namorado n.d.). Social economy organizations combine a group of people, more than shareholders, whose purpose is to satisfy the needs of members of the community, more than making a profit.
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Leonor Cardoso et al There are various types of social economy institutions, with multiple legal statutes, such as associations, charities, foundations and Private Institutions of Social Solidarity (IPSS). The last-named are bodies with the legal status of "grouping of people for public service" and which pursue goals in the area of social security/social action. According to the Statute of IPSS’s they are defined as “nonprofit making institutions, formed by private initiative, with the aim of giving organized expression to the moral need for solidarity and justice among individuals, as long as they are not administrated by the State or by local government”, contributing to well-being in areas such as: support for children and young people, help for senior citizens and the infirm, health promotion and protection, education, safety, improvement of public places (Filho 2002; Namorado n.d.).
2.2 Knowledge management and social economy According to Lee and Yang (2000) there is a consensus that considers KM as a set of processes/activities that generates the creation, spread and influence of knowledge to fulfil organizational goals. KM involves knowledge processes that are distinct but interdependent: creation, storage and retrieval, transfer and application/use (Alavi, Cook, Cook and Leidner 2001). Much of the investigation has focused on the processes of creation, transfer and utilization of knowledge inside and between organizations. In a generic overview, knowledge management can be defined as an “approach to add or create value through the activation of know-how, experience inside and outside the organization” (Ruggles, 1997, p.81). In the social economy sector, compared to other sectors, we can infer that KM processes arising from this sector lie in internal management of organizational knowledge, both formal and informal, with a view to continuous development of a culture of support and knowledge-sharing in order to maintain quality performance, rather than orientation to outside and to use of organizational knowledge for competitiveness (Albuquerque 2008).
2.3 Organizational commitment The bond the individual forms with his work is complex and multidimensional and can present important implications for understanding organizational behaviour. The collaborator does not consider exclusively his relationship with the work itself, but also with work-team, career, clients, supervisors and trade unions (e.g., Reichers 1995). Commitment is perceived as unleashing actions in a certain types of people, being viewed by O’Reilly and Chatman (1986:493) as “…the psychological attachment felt by the person for the organizations; it will reflect the degree to which the individual internalizes or adopts characteristics of the organizations”. We anchor our study on the model developed by Quijano, Navarro and Cornejo (2000), based on the proposals by Meyer and Allen (1990) and those by O’Reilly and Chatman (1988), and consider the existence of four dimensions of commitment included in two categories: instrumental (or calculating) which is divided in two sub-groups: commitment of need (regulated by the need to keep the job) and exchange (relating to a type of commitment where the bond between the individual and the organization is established by exchange, specifically relationships based on extrinsic and intrinsic rewards), and personal, which is also divided in two sub-groups: affective commitment (a bond between the person and the organization that shows feelings of need and affiliation) and internalization of values and goals (acceptance of the organization’s values and objectives, since they agree with the employee’s personal values).
2.4 Relationships between Knowledge Management and Organizational Commitment The connection between these two concepts is difficult to construct, becoming a current challenge for researchers. Nonaka (1991) considers that personal commitment and employees’ identification with the organization and its mission are crucial for knowledge creation. According to Javenpaa and Staples (2001:156), “greater commitment may engender beliefs that the organization has rights to the information and knowledge one has created or acquired”. Cabrera, Collins and Salgado (2006) consider that individuals who present great internalization of commitment to their organization will probably make more effort to share their knowledge. More recently, Rocha (2007) concludes that collaborators’ level of OC to their organizations influences their participation positively, as well as their perception of the functioning of processes and activities related to knowledge at their place of work. The results of these studies converge on the existence of a positive relationship between levels of OC and desirable attitudes for knowledge management processes. Anyway, we cannot ignore the shortage of conceptual foundation and empirical support regarding the relationship between these variables. 190
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3. Empirical study 3.1 Objectives The objectives are: a) to contribute to greater understanding of the relationship between KM and OC, aiming for greater empirical support; b) evaluation of the relevance of the different dimensions of OC for each of the KM dimensions; c) empirical support for how the relationship formed between collaborator commitment and organizational variables (length of time working in the institution and function) influence KM. Therefore, we formulate the following hypotheses: Hypothesis 1a (H1a): affective and value commitment is a statistically significant predictor of cultural orientation to knowledge. Hypothesis 1b (H1b): affective and value commitment is a statistically significant predictor of knowledge management practices. Hypothesis 1c (H1c): affective and value commitment is a statistically significant predictor of social and discursive knowledge management. Affective and value commitment can reveal predictive capacity for the different dimensions of KM. A strong affective relationship with the institutions, will more easily be framed in a culturally oriented organization, absorbing and adopting values that are assumed and shared in the organization and that guide the way of being, acting and working, besides looking for sense in the function, considering internal orientation to the practices, rules and norms institutionalized in a store of collective memory, where good practices are kept and stored. Hypothesis 2a (H2a): instrumental exchange commitment is a statistically significant predictor of cultural orientation to knowledge. Hypothesis 2b (H2b): instrumental exchange commitment is a statistically significant predictor of knowledge management practices Hypothesis 2c (H2c): instrumental exchange commitment is a statistically significant predictor of social and discursive knowledge management. In this case, the collaborator has a bond with his organization based on exchange carried out with it, with varying degrees of satisfaction. The incorporation by collaborators of practices adopted by the organization, which allow creation/acquisition of knowledge, its share and use, as well as construction of networks of social relationships, can be carried out and influenced as a function of gratuities and rewards (both extrinsic and intrinsic) that collaborators can receive from the institution. Hypothesis 3a (H3a): instrumental need commitment is a statistically significant predictor with a negative impact on cultural orientation to knowledge. Hypothesis 3b (H3b): instrumental need commitment is a statistically significant predictor with a negative impact on knowledge management practices. Hypothesis 3c (H3c): instrumental need commitment is a statistically significant predictor with a negative impact on social and discursive knowledge management. Instrumental need commitment can function as a predictor – with a negative impact – of the different dimensions of KM. Here, the bond between the individual and the institution is regulated only by the need to keep the job to ensure self-sufficiency. IPSS collaborators do not work only as a function of their needs, but also as a function of affective links they may develop with their institution. The greater the instrumental commitment oriented to the need to keep the job, the lesser the occurrence of a set of practices focusing on knowledge of a mostly explicit nature, in formal processes of creation/acquisition, share and use. Hypothesis 4 a (H4a): the function variable is a statistically significant predictor of cultural orientation to knowledge. Hypothesis 4b (H4b): the function variable is a statistically significant predictor of knowledge management practices. Hypothesis 4 c (H4c): the function variable is a statistically significant predictor of social and discursive knowledge management. Hypothesis 5 a (H5a): the length of employment variable is a statistically significant predictor of cultural orientation to knowledge. 191
Leonor Cardoso et al Hypothesis 5 b (H5b): the length of employment variable is a statistically significant predictor of knowledge management practices. Hypothesis 5 c (H5c): the length of employment variable is a statistically significant predictor of social and discursive knowledge management. Organizational variables (length of employment and function held) may be statistically significant predictors of the various dimensions of KM. We also admit that those organizational variables can be statistically significant moderators of the relationship between the dimensions related to OC and each of KM dimensions.
4. Methodology 3.2 Type of study This study presents a correlational design, adopting the survey research and the technique of selfadministered questionnaire.
3.3 Sample The sample is composed of 217 subjects. 3.3.1 Length of service 136 subjects (60.7%) have been in the organization for more than 5 years and 81 subjects (39.3%) have been there for less than 5 years. 3.3.2 Function held 54.6% of subjects is included in the Group 2 (Administration workers, monitors, helpers, general service auxiliaries, cooks , drivers) and 45.4% of subjects is included in the Group 1 (Directors, specialists, teachers, activity workers, therapists and others).
3.4 Instruments 3.4.1 Questionnaire on Organizational Commitment and Identification (ASH-ICI) Elaborated by Quijano et al. (2000) and validated by Félix (2008) for our sample. This instrument aims to: evaluate the types of commitment that predominate in an organization; the degree of identification of collaborators with the organization; the degree of collaborators’ involvement with their job. Subjects are presented with five optional answers: 1- Strong Disagreement; 2- Disagreement; 3Neither Agreement nor Disagreement; 4- Agreement; 5- Strong Agreement. In this study we only used the sub-scale made up of 8 items concerning OC (referring to need commitment, exchange commitment and affective and value commitment). 3.4.2 Knowledge management questionnaire (KMQ) KMQ was originally constructed by Cardoso (2003), being validated for the social economy sector by Albuquerque (2008) with the following dimensions: I – Cultural orientation to knowledge; II – Knowledge management practices; III – Social and discursive knowledge management. The KMQ aims for organizational assessment and diagnosis through the perception organizational actors have of the operation of processes related to management and creation of knowledge, facilitating intervention in this area. The questionnaire is composed of a simplified version of 27 items with with five optional answers: 1 – Almost never applies; 2 – Applies little; 3 – Applies moderately; 4 – Applies a lot, and 5 – Applies almost totally.
3.5 Statistical procedures We used SPSS, version 15.0. The main techniques applied for data treatment were hierarchical multiple regression and multiple regression with interaction of discrete variables (Cohen, Cohen, West and Aiken 2003).
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Leonor Cardoso et al Afterwards, we carried out a process of re-categorization of the organizational variables at two levels, “0” and “1”. Therefore, in order to carry out this re-categorization we used the unifactorial Anova, assuming KM total as dependent variable and the organizational variables as independent variables.
4. Results 4.1 Questionnaires validity First of all, we will show the factor analysis concerning the questionnaires validation. Tables 1 and 2 reveal the factor analysis and Cronbach Alpha of ASH-ICI dimensions and KMQ dimensions, respectively. Table 1: Factor analysis and Cronbach alpha of ASH-ICI 5. ASH- ICI Items 11. I am proud of working in this Institution 15. I am proud when I say to others that I work in this Institution 10. I feel as a part of this institution 8. If other job would appear some people in this Institution woul immediately leave their position 5. I believe some members of this Institution would leave immediately if they could find another job 2. Some people is in this Institution because they cannot find another job 3. I am not willing to give more than I receive from this Institution 9. Unless they reward me for that, i do not intend to extra efforts to benefit the Institution
Factor I .940 .743
Factor II .203 .167
Factor III .132 .171
.661
.120
.287
-.151
.899
.099
-.139
.783
.209
-.172
.658
.130
-.207
.181
.961
-.303
.198
.476
Dimension I -Affective and value commitment (alpha: .858 Explained variance= 25.96%)
II – Need commitment (alpha: .845 Explained variance=25.11%)
III – Exchange commitment (alpha: .715 Explained variance= 16.86)
Table 2: Factor analysis and Cronbach alpha of KMQ KMQ
Itens
Factor 1
We act according to the way we are organized
.70
193
Factor 2 .09
Factor 3 .19
Dimension
Leonor Cardoso et al We know that our clients have an idea about us We know what is expected from each one of us and from the institution What we know is seen in the best service we render
.61
.25
.11
.59
.22
.00
.57
.25
.04
Each one of us has a task to fulfil
.56
.10
.06
What we know is seen in the way we perform our work
.56
-.04
.24
We reflect about the way we solved our problems in the past We all are responsible for what we should know to work with quality We try to realize the important things that are happening in this institution We use the records we have done along the time
.55
.16
.39
.55
.19
.28
.53
.22
.12
.49
.32
.21
We act according to certain principles
.48
.24
.36
Our superiors call our attention to what is important to know
.47
.33
.07
We attend training courses or we have training on the job We collaborate with other institutions to gather more information We attend to seminars/conferences, we read publications or we hire experts We use the information saved in our informatics systems We have ways to register the more important things that we know or learn We are attentive to what other institutions are doing
.06
.80
.08
.22
.76
.14
.19
.73
.18
.20
.67
.23
.39
.60
.28
.23
.57
.20
Those who know are rewarded
.18
.53
.11
We know that other institutions have information about us We talk one with each others about subjects that we don’t understand well We discuss our tasks
.30
.44
.26
.21
.25
.66
.22
.32
.62
We talk about our institution We tell to each other funny stories that happened in our work We talk about our work when we casually meet each other We ask our colleagues how they solved their problems that are similar to ours
.20
.18
.61
.08
.20
.60
.02
-.03
59
.30
.28
.56
I – Cultural orientation to knowledge (alpha: .880 Explained variance= 17.32%)
II – KM practices (alpha: . 880 Explained variance= 16.57%)
III – Social and discursive KM (alpha: .822 Explained variance= 11.94%)
5.1 Descriptive statistics Table 3 presents the results obtained for the average and standard deviation in each OC dimensions. The dimension of affective and value commitment (Factor 1_OC) presents the highest average (A=4.10; SD=.73) and the average found for the dimensions of instrumental need commitment (Factor 2_OC) [A=3.52; SD=.87] and instrumental exchange commitment (Factor 3_OC) [A=3.43; SD=.85) indicates once again a perception corresponding to agreement with the propositions included in these components. Table 3: Factors of OC (N=210) Factor 1_OC Factor 2_OC Factor 3_OC
Average 4.10 3.52 3.43
Stand. Dev. . 73 . 87 . 85
Table 4 shows the results obtained for the average and standard deviation in each KM dimensions. The first dimension, cultural orientation to knowledge (Factor 1_KM) presents the highest average (A=3.67; SD=.61), however, the dimensions of knowledge management practices (Factor 2_KM) and social and discursive knowledge management (Factor 3_KM), also show a high average (above 2.5), indicating that participants have a perception of applicability/operation of the processes in question in these institutions. Table 4: Factors of KM (N=210) Factor 1_OC Factor 2_OC Factor 3_OC
Average 3.67 . 3.03 . 3.43 .
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Stand. Dev. .61 .85 .75
Leonor Cardoso et al
5.2 Relationship between OC and KM, considering the influence of organizational variables Considering KM processes, we analyzed the influence of OC dimensions and of the organizational variables related to length of service and the function occupied, in cultural orientation to knowledge, KM practices and social and discursive KM. We carried out statistical analysis of six models, making up a general model of hierarchical multiple regressions with moderation of two discrete variables. We tried to find the predictive capacity of the three dimensions obtained for OC for each of the KM dimensions. Then, we analyzed the predictive power of those same dimensions of OC, together with the measurements obtained for each of the organizational variables. Finally, we added a third block of interaction, considering the moderating effect of each of the organizational variables in the relationship between OC and KM. 5.2.1 Cultural orientation to knowledge Taking as the criterion cultural orientation to knowledge and as predictors the three OC dimensions and the organizational variables, we found that individuals’ perception regarding the set of three OC dimensions considered as a whole explains significantly 50.1% [R2=.501] of the total variance of the dimension of KM under analysis. Introduction of organizational variables did not mean a significant increase of the explanatory power of the regression equation [R2 =. 003; F (2,175) =. 592: p=.554]. As showed in Table 5, the set of predictive variables considered in this regression equation explains around 50.4% of the total variance of the dimension related to cultural orientation to knowledge, and is considered statistically significant [R2=. 504; F (5,180) = 35.578, p
