A Study of the Factors that Influence Engagement in ...

A Study of the Factors that Influence Engagement in University-Industry Collaboration Projects Petra Schubert

Nr. 12/2012

Arbeitsberichte aus dem Fachbereich Informatik

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The “Arbeitsberichte aus dem Fachbereich Informatik“ comprise preliminary results which will usually be revised for subsequent publication. Critical comments are appreciated by the authors. All rights reserved. No part of this report may be reproduced by any means or translated. Arbeitsberichte des Fachbereichs Informatik ISSN (Print): 1864-0346 ISSN (Online): 1864-0850 Herausgeber / Edited by: Der Dekan: Prof. Dr. Grimm Die Professoren des Fachbereichs: Prof. Dr. Bátori, Prof. Dr. Burkhardt, Prof. Dr. Diller, Prof. Dr. Ebert, Prof. Dr. Frey, Prof. Dr. Furbach, Prof. Dr. Grimm, Prof. Dr. Hampe, Prof. Dr. Harbusch, jProf. Dr. Kilian, Prof. Dr. von Korflesch, Prof. Dr. Lämmel, Prof. Dr. Lautenbach, Prof. Dr. Müller, Prof. Dr. Oppermann, Prof. Dr. Paulus, Prof. Dr. Priese, Prof. Dr. Rosendahl, Prof. Dr. Schubert, Prof. Dr. Sofronie-Stokkermans, Prof. Dr. Staab, Prof. Dr. Steigner, Prof. Dr. Sure, Prof. Dr. Troitzsch, Prof. Dr. Wimmer, Prof. Dr. Zöbel

Kontaktdaten der Verfasser Petra Schubert Institut für Wirtschafts- und Verwaltungsinformatik Fachbereich Informatik Universität Koblenz-Landau Universitätsstraße 1 D-56070 Koblenz E-Mail: [email protected]

Working Paper: Business Software Research Group, University of Koblenz‐Landau Date: 2012‐10‐01

Author: Prof. Dr. Petra Schubert petra.schubert@uni‐koblenz.de Institute for IS Research, University of Koblenz‐Landau, Koblenz, Germany Publication history: This working paper is a further development of a paper published at the Bled Conference 2009. The original paper received the “Outstanding Paper Award”. http://bledconference.org/index.php/eConference/2011/about/editorialPolicies Reference: Schubert, P., & Bjørn‐Andersen, N. 2012 . University‐Industry Collaboration in IS Re‐ search: An Investigation of Successful Collaboration Models. Proceedings of the Bled Con‐ ference 2012, 109‐126. This extended version contains a larger set of diagrams that could not be published in the original Bled paper due to space limitations. Additionally, five more interviews were in‐ cluded in the analysis. I was also asked to add an explanation of the IS Research Methods that were used in the interviews.

Table of Contents The imperative to publish..................................................................................................................................... 1 Setting the scene: attitude towards university‐industry collaboration............................................ 2 So, how is research with industry seen by IS researchers? .................................................................. 4 Themes in the literature on university‐industry collaboration ........................................................... 5 Scholarship of Engagement ............................................................................................................................. 6 Engaged Scholarship .......................................................................................................................................... 6 Real engagement through collaboration ................................................................................................... 6 Drivers for engagement: successful publications .................................................................................. 7 Barriers to engagement .................................................................................................................................... 8 Research methods in collaborative research .......................................................................................... 9 Design Science Research DSR : developing an artefact ............................................................................................... 9 Action Research: intervention in the company project ...................................................................................................... 9 Behavioural Research: study of human behaviour .............................................................................................................. 9 Grounded Theory: building theory from raw data ............................................................................................................... 9 Experiment/simulation: laboratory environment ............................................................................................................... 9 Case Study: deriving findings from real‐world phenomena .......................................................................................... 10 Conceptual development: creation of concepts based on reasoning ......................................................................... 10

Research approach ............................................................................................................................................... 10 Interview Guideline ......................................................................................................................................... 11 Taxonomy for university‐industry collaboration ............................................................................... 12 Findings from the interviews and discussion ........................................................................................... 13 Some final thoughts .............................................................................................................................................. 21 Acknowledgements .............................................................................................................................................. 22 References ................................................................................................................................................................ 23

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ABSTRACT Joint collaboration projects between University and industry partners have always been of particular importance to the IS community. The motivation to study current attitudes towards cooperation was inspired by recent discus‐ sions on “engaged scholarship” and “successful publication strategies” in which we can observe a difference in attitudes towards industry collabora‐ tion in different regions of the world. The author of this paper was particu‐ larly interested in exploring drivers and barriers for engagement with indus‐ try partners from the point of view of IS academics. This paper presents the findings from a qualitative in‐depth study, in which fourteen experienced re‐ searchers from four different continents were interviewed in order to under‐ stand the phenomenon of university‐industry collaboration in the context of different research traditions, different university environments and different geographical regions. The findings from the interviews show that research‐ ers have very different preferences regarding the ideal setup of such collabo‐ rative research projects. Findings also show that Case Study and Design Re‐ search are the most common research methods in such projects. Keywords: University‐industry collaboration, collaborative projects, practi‐ tioners, research programme, research funding Paper type: Research paper

The imperative to publish The IS research community has experienced a trend in recent years that seems to define “research” as equivalent to “publication output” and that accepts the number of journal publications in the best most cited international journals as the only “real” measurement

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Factors that Influence Engagement in University‐Industry Collaboration

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of research excellence Straub 2009 . Publication output is often the dominant metric for research assessment. There is growing concern that too strong a focus on journal publications may divert our attention from providing value to our key stakeholders: industry, students and society Davis et al. 2005 . As a response a number of researchers have proposed a “revival” of col‐ laborative research, which is now increasingly referred to as “engaged scholarship” Van der Ven 2007 . The purpose of this paper is to contribute to the understanding of university‐industry col‐ laboration within the discipline of Information Systems IS . The author investigates fac‐ tors that influence the decision of an IS researcher about whether or not to engage in joint projects with industry. The research data was drawn from current “best practice” collected from real‐life experiences of IS researchers. Specific focus is given to examining typical forms of collaborative projects, the applied research methods and outputs as well as barri‐ ers and drivers. The data was gathered in personal interviews with IS researchers who had previously engaged in industry collaboration and agreed to report on their experiences with such projects. A preliminary version of this study report shorter and based on a smaller number of in‐ terviews was presented at the 25th annual Bled Conference Schubert and Bjørn‐Andersen 2012 . Encouraged by the lively discussion and the encouraging feedback the author de‐ cided to extend the data to draw a more representative picture of the international com‐ munity of IS researchers and their attitudes and opinions toward industry research. In the months that followed the conference an additional five one‐hour interviews were con‐ ducted with researchers representing four new countries and three different continents.

Setting the scene: attitude towards university‐industry collaboration In preparation for the study on current practice the author searched for existing studies in the topic area. Several studies were identified in which either the unit of analysis in this study the “IS researcher” or the study object in this study “factors that either facilitate or impede collaboration” was different. The following sections review some of these studies. Bruneel et al. 2010 performed a study on the barriers to university‐industry collabora‐ tion from the point of view of companies: They found that “ ... relatively few studies have

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investigated the nature of the barriers and the factors that might mitigate them” Bruneel et al. 2010, p. 858 . Van de Ven and Johnson 2006, p. 802 point to rising concerns in the IS discipline stating “that academic research has become less useful for solving practical prob‐ lems and that the gulf between theory and practice in the professions is widening”. They raise specific concerns e.g. that “findings from academic as well as consulting studies are not useful to practitioners and do not get implemented” and they claim that academics are not aware of the relevant i.e. practice‐oriented research questions. They conclude that, “as a result, organizations are not learning fast enough to keep up with the changing times.” The key issue is to address what they call, the “transfer problem”. It can be observed that the IS research community appears divided over the question of whether collaboration with industry is something that IS researchers should embrace as a valuable way of knowledge co‐creation, or if it has too many distracting facets that it should be avoided. It can be argued we have to provide value to our key stakeholders industry, students and society at large if the IS field is to survive in the long run. In other words, we need to become engaged scholars. It can also be argued that there are forms of university‐ industry research collaboration that are highly rewarding for the researchers involved and that such research can lead to “value co‐creation” Sarker et al. 2012 . The author believes that the prevailing relationship between academia and industry is an important aspect in the discussion around the nature and fundamental understanding of the IS discipline. Provided that the scientific community understands the concept of “rele‐ vance” to a large extent as “relevant to practitioners” it becomes clear that a researcher’s attitude towards an engagement with industry has to be a defining characteristic of his/ her research stance. This question is isolated from the question of how to communicate the research findings to practitioners. Academic outlets are not suited for this communication because they are directed towards academic researchers and largely not relevant to the concerns of practitioners. This is emphasised by Straub and Ang 2008, p. v who state: “Any academic journal written by researchers for researchers as the primary audience is simply not targeted for practitioners.” In the study questions regarding outputs were in‐ cluded that are suitable to academic e.g. papers, articles as well as practitioners e.g. pro‐ ject reports, slides .

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So, how is research with industry seen by IS researchers? The motivation for the long‐term study on IS researchers’ engagement with industry was further spurred by a number of discussion streams in recent IS journals and at IS confer‐ ences. Among these are the following academic discourses:  The provocative opinion piece “Why the old world cannot publish?” Lyytinen et al. 2007 ,  the debate about the publication chances of design science articles in journal publica‐ tions Österle et al. 2011; Baskerville et al. 2011 ,  the value from research funding that stakeholders and society as a whole are deriving Davis et. al. 2005; Gibbons and Johnston 1974; Hall et al. 2003 ,  the stream about the concept of “Engaged Scholarship” which is meant to address the alleged gap between theory and practice “knowledge production problem”, Van de Ven and Johnson 2006, p. 802 ,  the Scholarship of Engagement, a movement reacting to the disconnect between aca‐ demics and the public Boyer 1996; Pettigrew 2001; Barker 2004 ,  the pros and cons of collaborative research endeavours such as interactive social science Orme 2000; Simmons and Walker 2000; Caswill and Shove 2000; Van Buuren and Edelenbos 2004; Hardy and Williams 2011 or Co‐operative Inquiry Heron and Reason 2001 . It can be argued that in principle, university‐industry collaboration can address all of the above thematic challenges, but it differs substantially depending on academic traditions and socio‐economic settings due to different sets of internal and external challenges. The author believes that we can learn from examples of “excellent” collaboration projects with industry and that these may help academics to write relevant and at the same time rigor‐ ous journal publications and contribute to high quality teaching. It can be assumed that research cultures that favour alternative research approaches Frank 2006 and more spe‐ cifically Design Science Hevner et al. 2004 are more likely to actively seek out opportuni‐ ties for university‐industry collaboration.

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This paper addresses the following research questions:

What are forms or “models” of successful university‐industry collaboration within IS? Additional questions that are addressed in this paper:  What is a typical setup of collaboration projects between IS researchers and industry funding sources and number of partners ?  What are barriers and drivers of such projects?  What research methods are used in these projects and what is the typical form of out‐ put? The remaining paper is structured as follows: In the next section, the literature which led to the research question and the interview guideline is reviewed. Drivers and barriers to en‐ gagement are discussed and different forms and methods of collaboration are introduced. The research steps and the development of the survey instrument are then explained. The main section presents the findings from the preliminary study. The author concludes with some thoughts on the current findings and suggestions for further studies.

Themes in the literature on university‐industry collaboration Before engaging in a study of university‐industry collaboration it is necessary to define the term more clearly. In the author’s understanding “university‐industry collaboration” de‐ scribes a research activity performed by a group of people containing academics and prac‐ titioners. The research is carried out together collaboratively or, as Heron and Reason 2001 aptly put it, as research “with” rather than “on” people. In doing so, academics and practitioners are co‐constructing knowledge Hardy and Williams 2011 . The practitioners in a company or government agency are engaged in the research process – they are not mere study objects. Accordingly, a study of the impact of a particular technology in an or‐ ganisation common to behavioural studies would not qualify as research collaboration in the definition of this paper. The focus is on research projects that are carried out as a joint work between researchers in universities and practitioners in companies or government agencies. The engagement can occur at all stages, from the definition of the research question and the development of the research design, to the actual research work and the interpretation

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of the findings. Pettigrew points out that it is essential that the practitioners are involved early in the research process in saying “The action steps to resolve the old dichotomy of theory and practice were often portrayed with the minimalist request for management re‐ searchers to engage with practitioners through more accessible dissemination. But dis‐ semination is too late if the wrong questions have been asked.” Pettigrew 2001, p. 67 . The following sections present and discuss some of the themes in the literature that are related to university‐industry collaboration.

Scholarship of Engagement The idea of collaboration between university and industry is not a new one. Boyer 1996 coined the term “Scholarship of Engagement” also see Barker 2004 . While the current traditional measures of research are the number of academic journal publications, and to some extent the number of citations, there is a growing demand in industry and society for research metrics of benefits to key stakeholders, students, industry and society at large. In line with this, in the next Research Assessment Exercise RAE in the UK in 2014, research‐ ers will also be evaluated on whether their research has had an impact on society and in‐ dustry Ref2012 .

Engaged Scholarship Van de Ven took up the Boyer idea and published a book entitled “Engaged Scholarship” Van de Ven 2007 . In his book he describes a research methodology for participatory re‐ search with stakeholders. The content is concerned with bridging the knowledge gap and engaging practitioners in parts of the research process. However, his work does not talk about academic‐industry collaboration as such, and it does not provide practical guidance on the necessary organisational framework for such projects.

Real engagement through collaboration However, whilst Van de Ven 2007 acknowledges the engagement of practitioners to en‐ sure a certain degree of relevance and practicability in the research he does not explicitly argue for collaboration with an industry partner over the period of a research project with a defined outcome. There are, however, successful forms of collaboration which have been described in the literature, e.g. Collaborative Basic Research Schubert and Fisher 2009 or Consortium Research Österle and Otto 2010 . Such forms of direct collaboration can vary depending on scope number of parties involved, project amount , length time in months/years , initiator research initiated by university or industry , research object ar‐

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tefact, process, data/information, behaviour, attitudes and research outcome software, technology component, method, report . These influential issues were added to the design of the interview guideline used for this study. It can be argued that collaboration can address many of the before‐mentioned prob‐ lems by engaging the practitioner in the research process as also argued by proponents of field work such as Schein 1987 and Whyte 1984 .

Drivers for engagement: successful publications It has been argued that engagement with industry can lead to successful academic publica‐ tions if well presented and carried out in a rigorous way Baskerville et al. 2011; Straub and Ang 2008 . Research with industry, when using a suitable research method, can be a very valuable basis for evidence‐based research following well accepted paradigms for how to deal with data, measurements, observations, testing, and validation. This perception is in accordance with statements from the provocative opinion piece “Why the old world cannot publish?” Lyytinen et al. 2007 in which the authors acknowledge a strong industry en‐ gagement of European Ph.D. students but criticise their lack of rigour in their academic writing. They say: “Many times, Ph.D. theses are produced to address practical problems within industry; for example, innovative workflow designs or modeling methods.” Lyyti‐ nen et al. 2007, p. 323 They further argue that these projects often end when the industry partner is satisfied with the research outcome, which is often a product, some software or a method without scientifically reporting the results in journal articles. The analysis of the interviews for this paper showed that collaborative research can indeed lead to high‐quality articles if the academic requirements are part of the research design. Rigour and relevance can be improved by raising substantive evidence data and monitoring the research process on a meta‐level, which might seem superfluous for the industry part‐ ner but is essential for high‐quality research publications. It is a prevailing criticism that design research projects often fall short of the last phase, the validation of the artefact in actual practice Hevner el al. 2004 and it can be argued that this is exactly where univer‐ sity‐industry cooperation can help filling the gap. One interview partner stresses the chance to look behind the scenes and see what is really going on in businesses by saying: “Research with industry partners gives us access to practical problems that we do not see from ‘outside the company’.” respondent #7

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Barriers to engagement Schubert and Fisher 2009 identify a number of factors that impede collaboration be‐ tween practitioners and academics from both of their respective points of view. Among the barriers for industry they mention 1 unclear relevance of research findings to industry Kabins 2011 , 2 lacking knowledge and interest in designing the research in‐ struments Amabile et al. 2001 , 3 lack of access to research results academic journals not attractive for practitioners , 4 different timescales, 5 different expectations from the research outcomes as well as 6 disagreement on intellectual property rights. For the academics they mention a belief that 1 industry is not interested or willing to work with universities Hall et al. 2003 , the 2 rather long timeframe for academic re‐ search products Pettigrew 2001 and the 3 tedious maintenance of relationships with industry partners over a long period of time Amabile et al. 2001 . The barriers for aca‐ demics were tested in the interviews and found some of them confirmed. Aspect three, the tedious building up of trust was confirmed by multiple respondents, one of them e.g. say‐ ing: “Research collaboration takes a lot of time and the mutual understanding and trust increases over time. It is an investment that you have to make.” respondent #7 . An ex‐ perienced EU project participant remarked: “There are path dependencies due to different expectations in different countries. You have to identify overlapping interests with your research partners and build up social capital over time.” respondent #9 . This assessment is shared by Van de Ven and Johnson ” 2006, p. 812 who say: “Time is critical for building relationships of trust, candour, and learning among researchers and practitioners”. A related barrier that emerged from the interviews was the time and energy that has to be invested by the senior researcher if Ph.D. students are involved in the project. Exemplary quotes are: ”Industry projects are labour‐intense for the senior researcher the professor because he has to lead and guide the project.” respondent #9 . These remarks are in ac‐ cordance with the observation made by Lyytinen et al. who argue: “ ... intense research‐ industry engagements sap time and energy away from publication and decreases Europe‐ ans’ motivation to publish in elite journals” 2007, p. 324 . Their conclusion, however, is not shared by the respondents. Most of them agree with the following assessment: “I gained access to valuable empirical data and was able to write a high quality paper with it.” respondent #9 . This is also confirmed by the high number of publications in top journals as will be shown in the findings section below .

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Research methods in collaborative research Part of the investigation was the study of research methods that are typically used for col‐ laboration with industry. The author assumed that approaches that call for an industry partner, such as Design Research or Action Research would score highest in the list. The following choice of research methods was derived from the literature and presented to the interview partners. Although interview partners were actively encouraged to name addi‐ tional methods, no further methods were added by the interviewees.

Design Science Research DSR : developing an artefact Design Science Research DSR represents the development of an IT artefact e.g. a soft‐ ware programme or method as the main objective of the joint project. DSR has been a topic of considerable interest in the IS literature in recent years Hevner et al. 2004, Österle et al. 2011, Gregor 2006, Lee and Hubona 2009 .

Action Research: intervention in the company project Action Research is used to describe a situation, in which a researcher is actively engaged in a company project and, instead of just being a mere observer, intervenes in it over time Baskerville and Wood‐Harper 1996, Gregor 2006, Lee and Hubona 2009 .

Behavioural Research: study of human behaviour The term Behavioural Research is used to indicate the study of human behaviour with quantitative methods. Research projects in this category make use of statistical analysis often in the form of hypotheses models showing causal relations e.g. Lee and Hubona 2009, Österle et al. 2011 .

Grounded Theory: building theory from raw data Grounded Theory is an inductive method for theory‐building. This kind of research is aimed at developing theory from data raised during the project “from scratch” i.e. without the existence of a prior model or theory Glaser and Strauss 1999, Urquhart et al. 2009 .

Experiment/simulation: laboratory environment The term experiment/simulation refers to the use of special simulation software or a labo‐ ratory setting to test or predict certain outcomes Hartmann 1996 . Simulation is typical for problem situations in which the algorithms are very complex e.g. dynamic system be‐ haviour .

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Case Study: deriving findings from real‐world phenomena Case Study is used when an object is either studied in‐depth single case, e.g. a company or organisational unit or when multiple cases multiple‐case study are compared to derive generisable conclusions Eisenhardt 1989, Yin 2009 . Case studies follow a holistic ap‐ proach, target multiple perspectives and describe real‐world phenomena. They are usually seen as descriptive and exploratory and ideally include an explanatory analysis.

Conceptual development: creation of concepts based on reasoning The development of ideas and concepts can be built on a “mere” formal or conceptual de‐ ductive analysis hermeneutics . This form of research is mostly fact‐based and makes use of reasoning with a strong focus on the explanation of semantics and the interpretation of correlations which results into the development of ideas and concepts. The author expected that some researchers work with mere formal or conceptual deduc‐ tive analysis hermeneutics but none of the participants selected this as an applied re‐ search method.

Research approach This section describes the steps that were followed in order to develop the research in‐ strument. In the first step an inductive, qualitative research approach was deployed in or‐ der to understand the factors surrounding successful collaboration with industry from the point of view of IS researchers. A semi‐structured interview guideline a questionnaire with closed and open questions based on the themes identified above was developed. Interviews were used in order to give the interview partners the opportunity to raise im‐ portant aspects and identify issues and factors during the conversation. The interviews were also used to identify categories and terminology. Uncertainty about terminology and semantics could directly be solved during the interview. In total, fourteen personal interviews were conducted in the years 2010 to 2012 of be‐ tween 30 and 60 minutes in length with researchers who had carried out collaborative re‐ search projects. The interviews were recorded and partly transcribed. In the interviews respondents were asked to indicate their level of agreement with the proposed statements contained in the interview guideline. These items were developed from the literature on drivers and barriers to university‐industry collaboration discussed above.

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Interview Guideline The content of the interview guideline is listed below cf. Table 1 . Some questions are simple selections from a list of items. Questions on opinions or attitude are phrased in statements and responses are measured on a five point Likert scale from “I fully agree” to “I fully disagree” or “no response” . The complete interview guideline is available from the author on request. 1. Demographic information

2. Types of industry collaboration

Position, role, size of group, academic age, country

Engagement in the past

3. One successful project

4. Project experience in general

Demographics of the successful project Initiating party Motivation to start project Research methods used Publication output Output for practitioners Success factors

Overhead Satisfaction of industry partners Drivers for engagement Barriers to engagement Problems/challenges encountered in the past Experience with of Ph.D. students Perceived trend towards more or less industry research

Table 1: Contents of the interview guideline Demographic information was collected in order to identify correlations between project characteristics and the outcome/attitude of the researcher. Previous experiences of the interviewee were investigated to understand the range of projects he or she had been en‐ gaged in. The main section of the questionnaire explores the respondent’s experiences with a “suc‐ cessful project”. The author believes that the motivation/output for the practitioners largely defines the research design and thus the choice of the research method. This part of the research draws on previous work by Perkman and Walsh 2009 , who performed an inductive study and identified four typical goals of collaboration from interviews with aca‐ demics. The findings showed that companies had approached the researchers with issues regarding 1 problem solving, 2 technology development, 3 ideas testing and 4 knowledge generation. Accordingly, this classification of motivations was used. The reasons why the selected project was considered successful “success factors” were also explored. The “general experience” section explores the drivers and barriers for collabora‐ tion as discussed in the literature review above and the role of Ph.D. students in response to the criticism voiced in Lyytinen et al. 2007 .

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Taxonomy for university‐industry collaboration The initial search in the literature for a useful classification scheme for collaboration pro‐ jects remained largely unsuccessful. As a consequence, the author used a self‐developed taxonomy in order to be able to identify archetypes Schubert and Bjørn‐Andersen 2012 . The following matrix was developed to classify research projects cf. Figure 1 . During the work on developing the taxonomy, it became apparent that in the academic literature some authors focus on the funding aspect Hall et al. 2001, 2003; Cohen et al. 2002; D’Este 2007 , while others focussed on the parties involved in the project Schubert and Fisher 2009; Österle and Otto 2010 . As a consequence, the starting point was put on the development of a classification scheme for parties involved and external funding sources. It was decided to only include external funding industry partner or government agency although the Uni‐ versity, in most cases, contributes to the overall cost either as “in kind contribution” e.g. their already employed staff or in the form of additional financial resources. In the end the taxonomy contained three dimensions: 1. Number of universities involved 2. Number of companies involved 3. Industry funded, government funded or co‐funded In order to illustrate the matrix, examples of typical projects are provided that would fall within each category. EU Integrated Projects (n:m) Nationally-funded Collaborative Research (n:1)

Government-funded

Nationally-funded Research Projects (n:m)

Many Universities (n)

Co-funded Collaborative Contract Research (n:1)

Collaborative Contract Research (n:m)

Nationally-funded Research Projects (1:1)

Nationally-funded Research Projects (1:m)

One University (1)

Industry-funded

Government-funded

Co-funded Contract Research (1:1)

Consortium Research (1:m)

Industry-funded

Figure 1: Taxonomy for University‐Industry Research parties and external funding One company (1)

Many companies (m)

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Information about how often the different types were encountered and which fields of the matrix were seen as the “most successful” project setups will be presented below.

Findings from the interviews and discussion The following section presents the findings from the study. The figures in round brackets n indicate the number of responses in this category. All respondents are either the head of their research unit 13 or the head of their own re‐ search group 1 . The majority of their research groups include 6 to 15 researchers 10 . They are experienced researchers; the average number of years since their Ph.D. gradua‐ tion is 21 years. The sample includes researchers from Europe, Australia, Asia and North America, i.e. Finland 2 , Germany 3 , Switzerland 1 , The Netherlands 1 , Australia 1 , South Korea 2 , Singapore 1 and Taiwan 1 , Canada 1 and USA 1 . The typical project budget has an average of 437’195 ranging from 7’718 to 1’500’000 Euros. Most academic institutions deduct overhead cost from the project funding, i.e. they charge the project for costs such as staff workplace, rent of rooms, telephone, stamps, library, maintenance, etc. The overhead rate ranges between zero and 43% with an average of 23% 9 . The classification for collaboration projects revealed typical combinations cf. Figure 2 . The “1:1, industry funded” emerged as the most frequently used type 7 . Generally, the respondents had been engaged in all funding forms of “1:1 projects” total of 14 . The col‐ laboration type “n:m, government‐funded” was placed second 6 . The n:1 combinations with one company and multiple Universities were the most uncommon 2 . The successful projects had an average of 1.7 universities minimum: 1, maximum: 4 and 3 industry partners minimum: 1, maximum: 10 . This suggests that most projects that are ranked as “successful” are run by only one university 9 but may well include more than one industry partner.

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What type of industry collaboration have you been personally engaged in over the last 10 years? (N=14) 1:1, industry‐funded

7

n:m, government‐funded

6

1:1, co‐funded

4

1:m, government‐funded

3

1:1, government‐funded

3

1:m, co‐funded

2

1:m, industry‐funded

2

n:m, industry‐funded

1

n:1, co‐funded

1

n:1, industry‐funded

1

n:m, co‐funded 0 n:1, government‐funded 0 0

1

2

3

4

5

6

7

8

Figure 2: Experiences: types of industry engagement The interviews showed that the respondents were not particularly driven by the financial prospect of the joint project. Only two interview partners named funding of staff or equip‐ ment as an important motivation to engage with the industry partner. In 60% of the cases the research challenge i.e. the research topic was the main driver for the engagement. The funding sources varied according to the different forms of collaboration Figure 3 . 41 % of the funding came from government sources, 43 % from industry and only 17 % were paid by the University itself. Which funding did you have for the project – please indicate approximate in % (N=14) 45

41

43

40 35 30 25 20

17

15 10 5 0 Government funding

Industry funding

University funding

Figure 3: Experiences: types of industry engagement

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The dominant research approach was Case Study 8 followed by Design Research 7 , Be‐ havioural Research 6 and Action Research 3 . Experiments/simulations 2 and Grounded Theory 1 were only named in two respectively one case. Mere deductive analy‐ sis was not used at all. The findings confirm that projects with industry partners are best suited to look at one par‐ ticular case in detail and develop artefacts and evaluate them in practice cf. Figure 4 . The single Case Study allows academics to transfer the qualitative results of the projects into an accessible form. Industry projects are also a source for empirical data, as the company/ government agency can also be seen as a study object for Behavioural Research Positivist Research. As expected we also see the application of Action Research in which the re‐ searcher is part of the research process and actively influences the outcome.

Research methods/approaches used in this project (N=14) Case Study

8

Design Research

7

Behavioural research

6

Action Research

3

Experiment/simulation

2

Grounded Theory

1

Deductive analysis

0 0

1

2

3

4

5

6

7

8

9

Figure 4: Research methods used Peer reviewed conference publications 44 % are the most frequent academic output of projects with industry. The high number of articles in the AIS senior scholar basket of eight 18 % followed by other peer‐reviewed journal articles 17 % demonstrates clearly that findings from industry projects can be used to produce high quality publications cf. Figure 5 . Where Ph.D. students were involved in the projects the senior researcher made sure that there was a high synergy between the project outcomes and the findings of the Ph.D.

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theses 15 % . “Other publications” 6 % are mostly books which were authored or edited by the research consortium. It is remarkable that basket‐of‐eight journal articles are in second place even ahead of “other” peer‐reviewed journals. This could confirm the increasing importance of journal rankings and the changed awareness for a need to publish in highly ranked outlets. Type of resulting publications in % (N=14) Peer‐reviewed conference

44

Top 8 journal

18

Other peer‐reviewed journal

17

Ph.D. theses

15

Other publications

6 0

10

20

30

40

50

Figure 5: Type of publication in % of total amount of resulting publications The findings of the study show that there is a wide spectrum of outputs that are relevant for the industry partner cf. Figure 6 . The most important outputs for practitioners are reports i.e. the actual findings, artefact documentation, figures, interpretation and advice for action and complementary sessions in which the findings are presented to senior man‐ agement. Direct support in the form of consulting 10 and presentations 10 was also highly welcome in the majority of the projects.

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Output for practitioners (N=14) Report

12

Sessions with senior manager(s)

11

Consulting

10

Speeches/presentations

10

Piloting

7

Method

6

Survey

6

Prototype

6

Training material

4 0

2

4

6

8

10

12

14

Figure 6: Output for practitioners total numbers The perceived value to the industry partners from collaborative projects varies substan‐ tially cf. Figure 7 . Almost half of the respondents felt that the partners received a value that was much greater than what they had invested 6 . Another four classified the per‐ ceived value still higher than the input 4 . In two cases, however, the respondents felt that the resulting value was much less than the industry partners had expected. Both research‐ ers said in the interview that such research projects carry the risk of failure and that in many cases the industry partner might pay for the development of an artefact that will never be successful in the market respondent #5 and #6 . Value compared to input (N=14) much more

6

more

4

equal

2

less 0 much less

2 0

1

2

3

4

5

6

7

Figure 7: Perceived value for industry partners However, a separate question on the satisfaction of the industry partners depicts an even more positive picture. With only one exception 1 the respondents agreed that their in‐

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Schubert

dustry partners are very satisfied 7 or satisfied 6 and are likely to engage in another industry project with this university group again 13 . Figure 8 shows the most important drivers for academics to engage in industry collabora‐ tion. The most important one is to “get access to empirical data” followed by “get access to relevant problems/research questions”. The responses to this question confirm the lack of financial orientation regarding funding for “academic staff for your group”. Not surpris‐ ingly, academics seem to be rather driven by their individual career than by the managerial urge to enlarge the existing research group. Other drivers that came up in the interviews but were not included in the original list are “the prestige that such projects give to the University”, “competitive advantage over other researchers”, that “findings are an important input for teaching” and that “such projects provide evaluation opportunities to try artefacts in practice ”. Most important drivers for academics involving themselves in industry collaborative projects (N=14) Get access to empirical data Get access to relevant problems / research questions Get research funds for your own personal use Contribute to effectiveness and growth & development in industry / society Get more academic faculty / staff for your group 0 important

rather important

neutral

2

4

6

rather unimportant

8

10

unimportant

12

14

16

Figure 8: Most important drivers for academics sorted by importance Figure 9 shows the most important barriers for academics. The first two barriers are re‐ lated to external factors and are hard to influence by the researcher. These are “Scepticism in industry towards academics” which scores as the number one barrier followed by “Ac‐ quisition cost of getting projects is too high or takes too long time and effort”. The respon‐ dents also see difficulties in obtaining funding for their work from external or public sources. Possible unfavourable conditions for acquiring collaboration projects are positioned in the mid‐field. They are: “Few opportunities in the environment of where my university is lo‐ cated”, “Lack of recognition from colleagues/deans” and “Unfavourable or negative repu‐

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Schubert

tation of industry research for personal promotion”. Especially in the German speaking countries, a positive general attitude towards cooperation can be identified. One respon‐ dent describes this as: “I think you only get an unfavourable reputation when you engage exclusively in research with industry partners. The portfolio has to be mixed. Universities are not meant to be consulting companies but joint projects with industry partners can lead to high quality findings and this is highly respected in our place.” respondent #8 It is interesting to note that the respondents did not feel that they lack expertise. The three questions that contain a self‐assessment of the researcher’s skills, i.e. “Lack of proper methods / ideas for how to do it”, “Lack of necessary practical knowledge” and “Inability to speak the practitioners’ language” were rated as unimportant. The one respondent who rated them “important” later explained that he was referring to other IS researchers and not to himself. All in all, the findings show that there is no perception of lack of personal skills to work with industry but that collaboration is rather impeded by external factors such as attitude of colleagues/practitioners and funding/acquisition costs. Additional barriers that were mentioned in the interviews are: “Board of University is now careful about direct collaboration with industry due to the risk of researchers becoming consultants and not doing research”; “It is too time consuming”; “Too short time perspec‐ tive from the company's side. Companies want too concrete results.”; “University admini‐ stration makes it difficult to run such projects too expensive .”; “Overhead costs on indus‐ try projects too high”. There is no agreement on a trend towards more or less research collaboration with indus‐ try. Six respondents perceive a trend towards less, five towards more collaboration. Two respondents do not perceive a trend at all. The interview partners were unanimous in their assessment to an increase in measuring and assessing of research output. One says: “We observe a drive to a metric‐based world. This makes it harder to succeed as a researcher, it reminds me of a tennis match” respondent #14 . Another researcher comments on the trend question: “With worries I perceive a trend to counting, weighing and measuring and an obligation to publish in A‐journals. This could lead to a trend of less engagement with industry” respondent #6 .

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What do you consider the most important barriers for academics involving themselves in industry collaborative projects in general (N = maximal 14) Scepticism in industry towards academics Acquisition cost of getting projects is too high or takes too long time and effort Difficulty in obtaining supporting external funds from public sources (e.g. low acceptance rate) Lack of access to industry partners Few opportunities in the environment of where my university is located Lack of recognition from colleagues/deans Unfavourable (or negative) reputation of industry research for personal promotion Lack of proper methods / ideas for how to do it Lack of practical knowledge Lack in‐ability to speak practitioners’ language 0 important

rather important

neutral

2

4

6

rather unimportant

8

10

12

14

16

unimportant

Figure 9: Most important barriers for academics sorted by the sum of important and rather important In the study of the “most successful setup for a collaboration”, the “1:1, industry funded collaboration” came out as “the most successful type”. It was favoured by five of the 13 re‐ searchers that responded to this question cf. Figure 10 . Two researchers favoured the “n:m, co‐funded model” and two others the “1:m, industry‐funded”. Four types were men‐ tioned only once. The ninth respondent stated that only a mix of types could sustain a suc‐ cessful research group and thus claimed that there was no unique most successful type re‐ spondent #9 . Exactly half of the respondents selected an industry‐funded model either 1:1 or 1:m as their most successful approach. The problem in acquiring public funding was unanimously expressed by all participants during the interview. It is not surprising that the findings

20


Schubert

show that researchers value projects in which the industry covers the full cost. Such pro‐ jects can start immediately after the decision has been taken by the industry partner and there is no need to go through the awkward grant application process that is in most cases connected with a long waiting time for an uncertain outcome. The 1:1 approach can, in these cases, be used for small, agile projects where confidential topics are researched or new innovations are developed that cannot be shared with competitors. It is interesting to note that of the three respondents of n:m projects two stem from Finland co‐funded and one from Singapore government‐funded . The n:m paradigm is a typical EU project profile which will most likely lead to large, complex projects with a lot of ex‐ change of ideas, possibly interdisciplinary groups and the need to openly discuss ideas and share thoughts. Which type of collaboration was the most successful for you? (N=13) 1:1, industry‐funded

5

n:m, co‐funded

2

1:m, industry‐funded

2

n:m, government‐funded

1

n:1, co‐funded

1

1:m, co‐funded

1

1:1, government‐funded

1 0

1

2

3

4

5

6

Figure 10: Most successful type of collaboration These findings were underlined by a respondent who argued for consortium projects as the best form because “researcher teams can achieve a higher efficiency and more interesting output with multiple partners” respondent #7 . The researchers responded unanimously to the last question: They would all engage in an industry collaboration project again.

Some final thoughts This study investigates attitudes and experiences of IS researchers from four different con‐ tinents North America, Europe, Asia and Australia regarding collaboration with industry.

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The findings show that the attitude of researchers is largely determined by the prevailing research culture and dependent on the personal experiences that an individual researcher has gained from such projects. As Baskerville et al. 2011 argue, top journals only accept top publications characterized by contribution to theory and novel findings based on a rigorous analysis of data evidence . It can be argued that with the right set of methods, researchers can derive findings that are highly relevant for academics and practice as well as highly rigorous at the same time. It is the author’s firm believe that the pursuit of relevance constitutes a conditio sine qua non for IS research. Without it industry will lose interest in research findings and the public spending on IS research will be wasted. A second implication of engaging in university‐industry collaboration is that we might avoid the many articles/projects, where the researcher is using a convenience sample of students from his/her class, which are often problematic proxies for real users/managers. Thirdly and most importantly, university‐industry projects are much more likely to pro‐ duce value to our key stakeholders industry and students than research carried out ex‐ clusively at the desk. In collaborative projects with practitioners in industry, value will al‐ most automatically become centre stage. Value to students is also likely to be higher. It is argued that in most situations, the marginal value to students of learning about one more taxonomy and/or one more theoretical perspective is likely to be substantially less than working with real cases/data/persons. In that way, students will be better prepared for the reality awaiting them after graduation, and they will be better able to survive, develop and contribute value indirectly justifying our role as teachers/co‐learners.

Acknowledgements The study of attitudes and experiences required to collect a lot of primary data which was quite labour‐intensive and also required a lot of travelling due to the international nature of the research. The author of this working report would like to give her thanks to a num‐ ber of people who contributed to this work. Firstly, I would like to thank the interviewers Niels Bjørn‐Andersen, Norbert Frick, Matthias Bertram and myself who conducted the interviews and turned in the recordings. I am also indebted to the research personnel who transcribed the interviews and made them ready for coding and analysis. My special thanks go to the research participants, the people who agreed to be interviewed and share their valuable experiences and attitudes.

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Marc Santos, Harald F.O. von Kortzfleisch, Shared Annotation Model – Ein Datenmodell für kollaborative Annotationen, Arbeitsberichte aus dem Fachbereich Informatik 10/2010 Gerd Gröner, Steffen Staab, Categorization and Recognition of Ontology Refactoring Pattern, Arbeitsberichte aus dem Fachbereich Informatik 9/2010 Daniel Eißing, Ansgar Scherp, Carsten Saathoff, Integration of Existing Multimedia Metadata Formats and Metadata Standards in the M3O, Arbeitsberichte aus dem Fachbereich Informatik 8/2010 Stefan Scheglmann, Ansgar Scherp, Steffen Staab, Model-driven Generation of APIs for OWL-based Ontologies, Arbeitsberichte aus dem Fachbereich Informatik 7/2010 Daniel Schmeiß, Ansgar Scherp, Steffen Staab, Integrated Mobile Visualization and Interaction of Events and POIs, Arbeitsberichte aus dem Fachbereich Informatik 6/2010 Rüdiger Grimm, Daniel Pähler, E-Mail-Forensik – IP-Adressen und ihre Zuordnung zu Internet-Teilnehmern und ihren Standorten, Arbeitsberichte aus dem Fachbereich Informatik 5/2010 Christoph Ringelstein, Steffen Staab, PAPEL: Syntax and Semantics for Provenance-Aware Policy Definition, Arbeitsberichte aus dem Fachbereich Informatik 4/2010 Nadine Lindermann, Sylvia Valcárcel, Harald F.O. von Kortzfleisch, Ein Stufenmodell für kollaborative offene Innovationsprozesse in Netzwerken kleiner und mittlerer Unternehmen mit Web 2.0, Arbeitsberichte aus dem Fachbereich Informatik 3/2010 Maria Wimmer, Dagmar Lück-Schneider, Uwe Brinkhoff, Erich Schweighofer, Siegfried Kaiser, Andreas Wieber, Fachtagung Verwaltungsinformatik FTVI Fachtagung Rechtsinformatik FTRI 2010, Arbeitsberichte aus dem Fachbereich Informatik 2/2010 Max Braun, Ansgar Scherp, Steffen Staab, Collaborative Creation of Semantic Points of Interest as Linked Data on the Mobile Phone, Arbeitsberichte aus dem Fachbereich Informatik 1/2010 Marc Santos, Einsatz von „Shared In-situ Problem Solving“ Annotationen in kollaborativen Lern- und Arbeitsszenarien, Arbeitsberichte aus dem Fachbereich Informatik 20/2009 Carsten Saathoff, Ansgar Scherp, Unlocking the Semantics of Multimedia Presentations in the Web with the Multimedia Metadata Ontology, Arbeitsberichte aus dem Fachbereich Informatik 19/2009 Christoph Kahle, Mario Schaarschmidt, Harald F.O. von Kortzfleisch, Open Innovation: Kundenintegration am Beispiel von IPTV, Arbeitsberichte aus dem Fachbereich Informatik 18/2009 Dietrich Paulus, Lutz Priese, Peter Decker, Frank Schmitt, Pose-Tracking Forschungsbericht, Arbeitsberichte aus dem Fachbereich Informatik 17/2009 Andreas Fuhr, Tassilo Horn, Andreas Winter, Model-Driven Software Migration Extending SOMA, Arbeitsberichte aus dem Fachbereich Informatik 16/2009 Eckhard Großmann, Sascha Strauß, Tassilo Horn, Volker Riediger, Abbildung von grUML nach XSD soamig, Arbeitsberichte aus dem Fachbereich Informatik 15/2009 Kerstin Falkowski, Jürgen Ebert, The STOR Component System Interim Report, Arbeitsberichte aus dem Fachbereicht Informatik 14/2009

Sebastian Magnus, Markus Maron, An Empirical Study to Evaluate the Location of Advertisement Panels by Using a Mobile Marketing Tool, Arbeitsberichte aus dem Fachbereich Informatik 13/2009 Sebastian Magnus, Markus Maron, Konzept einer Public Key Infrastruktur in iCity, Arbeitsberichte aus dem Fachbereich Informatik 12/2009 Sebastian Magnus, Markus Maron, A Public Key Infrastructure in Ambient Information and Transaction Systems, Arbeitsberichte aus dem Fachbereich Informatik 11/2009 Ammar Mohammed, Ulrich Furbach, Multi-agent systems: Modeling and Virification using Hybrid Automata, Arbeitsberichte aus dem Fachbereich Informatik 10/2009 Andreas Sprotte, Performance Measurement auf der Basis von Kennzahlen aus betrieblichen Anwendungssystemen: Entwurf eines kennzahlengestützten Informationssystems für einen Logistikdienstleister, Arbeitsberichte aus dem Fachbereich Informatik 9/2009 Gwendolin Garbe, Tobias Hausen, Process Commodities: Entwicklung eines Reifegradmodells als Basis für Outsourcingentscheidungen, Arbeitsberichte aus dem Fachbereich Informatik 8/2009 Petra Schubert et. al., Open-Source-Software für das Enterprise Resource Planning, Arbeitsberichte aus dem Fachbereich Informatik 7/2009 Ammar Mohammed, Frieder Stolzenburg, Using Constraint Logic Programming for Modeling and Verifying Hierarchical Hybrid Automata, Arbeitsberichte aus dem Fachbereich Informatik 6/2009 Tobias Kippert, Anastasia Meletiadou, Rüdiger Grimm, Entwurf eines Common CriteriaSchutzprofils für Router zur Abwehr von Online-Überwachung, Arbeitsberichte aus dem Fachbereich Informatik 5/2009 Hannes Schwarz, Jürgen Ebert, Andreas Winter, Graph-based Traceability – A Comprehensive Approach. Arbeitsberichte aus dem Fachbereich Informatik 4/2009 Anastasia Meletiadou, Simone Müller, Rüdiger Grimm, Anforderungsanalyse für RiskManagement-Informationssysteme (RMIS), Arbeitsberichte aus dem Fachbereich Informatik 3/2009 Ansgar Scherp, Thomas Franz, Carsten Saathoff, Steffen Staab, A Model of Events based on a Foundational Ontology, Arbeitsberichte aus dem Fachbereich Informatik 2/2009 Frank Bohdanovicz, Harald Dickel, Christoph Steigner, Avoidance of Routing Loops, Arbeitsberichte aus dem Fachbereich Informatik 1/2009 Stefan Ameling, Stephan Wirth, Dietrich Paulus, Methods for Polyp Detection in Colonoscopy Videos: A Review, Arbeitsberichte aus dem Fachbereich Informatik 14/2008 Tassilo Horn, Jürgen Ebert, Ein Referenzschema für die Sprachen der IEC 61131-3, Arbeitsberichte aus dem Fachbereich Informatik 13/2008 Thomas Franz, Ansgar Scherp, Steffen Staab, Does a Semantic Web Facilitate Your Daily Tasks?, Arbeitsberichte aus dem Fachbereich Informatik 12/2008 Norbert Frick, Künftige Anfordeungen an ERP-Systeme: Deutsche Anbieter im Fokus, Arbeitsberichte aus dem Fachbereicht Informatik 11/2008 Jürgen Ebert, Rüdiger Grimm, Alexander Hug, Lehramtsbezogene Bachelor- und Masterstudiengänge im Fach Informatik an der Universität Koblenz-Landau, Campus Koblenz, Arbeitsberichte aus dem Fachbereich Informatik 10/2008

Mario Schaarschmidt, Harald von Kortzfleisch, Social Networking Platforms as Creativity Fostering Systems: Research Model and Exploratory Study, Arbeitsberichte aus dem Fachbereich Informatik 9/2008 Bernhard Schueler, Sergej Sizov, Steffen Staab, Querying for Meta Knowledge, Arbeitsberichte aus dem Fachbereich Informatik 8/2008 Stefan Stein, Entwicklung einer Architektur für komplexe kontextbezogene Dienste im mobilen Umfeld, Arbeitsberichte aus dem Fachbereich Informatik 7/2008 Matthias Bohnen, Lina Brühl, Sebastian Bzdak, RoboCup 2008 Mixed Reality League Team Description, Arbeitsberichte aus dem Fachbereich Informatik 6/2008 Bernhard Beckert, Reiner Hähnle, Tests and Proofs: Papers Presented at the Second International Conference, TAP 2008, Prato, Italy, April 2008, Arbeitsberichte aus dem Fachbereich Informatik 5/2008 Klaas Dellschaft, Steffen Staab, Unterstützung und Dokumentation kollaborativer Entwurfsund Entscheidungsprozesse, Arbeitsberichte aus dem Fachbereich Informatik 4/2008 Rüdiger Grimm: IT-Sicherheitsmodelle, Arbeitsberichte aus dem Fachbereich Informatik 3/2008 Rüdiger Grimm, Helge Hundacker, Anastasia Meletiadou: Anwendungsbeispiele für Kryptographie, Arbeitsberichte aus dem Fachbereich Informatik 2/2008 Markus Maron, Kevin Read, Michael Schulze: CAMPUS NEWS – Artificial Intelligence Methods Combined for an Intelligent Information Network, Arbeitsberichte aus dem Fachbereich Informatik 1/2008 Lutz Priese,Frank Schmitt, Patrick Sturm, Haojun Wang: BMBF-Verbundprojekt 3D-RETISEG Abschlussbericht des Labors Bilderkennen der Universität Koblenz-Landau, Arbeitsberichte aus dem Fachbereich Informatik 26/2007 Stephan Philippi, Alexander Pinl: Proceedings 14. Workshop 20.-21. September 2007 Algorithmen und Werkzeuge für Petrinetze, Arbeitsberichte aus dem Fachbereich Informatik 25/2007 Ulrich Furbach, Markus Maron, Kevin Read: CAMPUS NEWS – an Intelligent Bluetoothbased Mobile Information Network, Arbeitsberichte aus dem Fachbereich Informatik 24/2007 Ulrich Furbach, Markus Maron, Kevin Read: CAMPUS NEWS - an Information Network for Pervasive Universities, Arbeitsberichte aus dem Fachbereich Informatik 23/2007 Lutz Priese: Finite Automata on Unranked and Unordered DAGs Extented Version, Arbeitsberichte aus dem Fachbereich Informatik 22/2007 Mario Schaarschmidt, Harald F.O. von Kortzfleisch: Modularität als alternative Technologieund Innovationsstrategie, Arbeitsberichte aus dem Fachbereich Informatik 21/2007 Kurt Lautenbach, Alexander Pinl: Probability Propagation Nets, Arbeitsberichte aus dem Fachbereich Informatik 20/2007 Rüdiger Grimm, Farid Mehr, Anastasia Meletiadou, Daniel Pähler, Ilka Uerz: SOA-Security, Arbeitsberichte aus dem Fachbereich Informatik 19/2007 Christoph Wernhard: Tableaux Between Proving, Projection and Compilation, Arbeitsberichte aus dem Fachbereich Informatik 18/2007

Ulrich Furbach, Claudia Obermaier: Knowledge Compilation for Description Logics, Arbeitsberichte aus dem Fachbereich Informatik 17/2007 Fernando Silva Parreiras, Steffen Staab, Andreas Winter: TwoUse: Integrating UML Models and OWL Ontologies, Arbeitsberichte aus dem Fachbereich Informatik 16/2007 Rüdiger Grimm, Anastasia Meletiadou: Rollenbasierte Zugriffskontrolle (RBAC) im Gesundheitswesen, Arbeitsberichte aud dem Fachbereich Informatik 15/2007 Ulrich Furbach, Jan Murray, Falk Schmidsberger, Frieder Stolzenburg: Hybrid Multiagent Systems with Timed Synchronization-Specification and Model Checking, Arbeitsberichte aus dem Fachbereich Informatik 14/2007 Björn Pelzer, Christoph Wernhard: System Description:“E-KRHyper“, Arbeitsberichte aus dem Fachbereich Informatik, 13/2007 Ulrich Furbach, Peter Baumgartner, Björn Pelzer: Hyper Tableaux with Equality, Arbeitsberichte aus dem Fachbereich Informatik, 12/2007 Ulrich Furbach, Markus Maron, Kevin Read: Location based Informationsystems, Arbeitsberichte aus dem Fachbereich Informatik, 11/2007 Philipp Schaer, Marco Thum: State-of-the-Art: Interaktion in erweiterten Realitäten, Arbeitsberichte aus dem Fachbereich Informatik, 10/2007 Ulrich Furbach, Claudia Obermaier: Applications of Automated Reasoning, Arbeitsberichte aus dem Fachbereich Informatik, 9/2007 Jürgen Ebert, Kerstin Falkowski: A First Proposal for an Overall Structure of an Enhanced Reality Framework, Arbeitsberichte aus dem Fachbereich Informatik, 8/2007 Lutz Priese, Frank Schmitt, Paul Lemke: Automatische See-Through Kalibrierung, Arbeitsberichte aus dem Fachbereich Informatik, 7/2007 Rüdiger Grimm, Robert Krimmer, Nils Meißner, Kai Reinhard, Melanie Volkamer, Marcel Weinand, Jörg Helbach: Security Requirements for Non-political Internet Voting, Arbeitsberichte aus dem Fachbereich Informatik, 6/2007 Daniel Bildhauer, Volker Riediger, Hannes Schwarz, Sascha Strauß, „grUML – Eine UMLbasierte Modellierungssprache für T-Graphen“, Arbeitsberichte aus dem Fachbereich Informatik, 5/2007 Richard Arndt, Steffen Staab, Raphaël Troncy, Lynda Hardman: Adding Formal Semantics to MPEG-7: Designing a Well Founded Multimedia Ontology for the Web, Arbeitsberichte aus dem Fachbereich Informatik, 4/2007 Simon Schenk, Steffen Staab: Networked RDF Graphs, Arbeitsberichte aus dem Fachbereich Informatik, 3/2007 Rüdiger Grimm, Helge Hundacker, Anastasia Meletiadou: Anwendungsbeispiele für Kryptographie, Arbeitsberichte aus dem Fachbereich Informatik, 2/2007 Anastasia Meletiadou, J. Felix Hampe: Begriffsbestimmung und erwartete Trends im IT-RiskManagement, Arbeitsberichte aus dem Fachbereich Informatik, 1/2007 „Gelbe Reihe“ (http://www.uni-koblenz.de/fb4/publikationen/gelbereihe) Lutz Priese: Some Examples of Semi-rational and Non-semi-rational DAG Languages. Extended Version, Fachberichte Informatik 3-2006 Kurt Lautenbach, Stephan Philippi, and Alexander Pinl: Bayesian Networks and Petri Nets, Fachberichte Informatik 2-2006 Rainer Gimnich and Andreas Winter: Workshop Software-Reengineering und Services, Fachberichte Informatik 1-2006 Kurt Lautenbach and Alexander Pinl: Probability Propagation in Petri Nets, Fachberichte Informatik 16-2005

Rainer Gimnich, Uwe Kaiser, and Andreas Winter: 2. Workshop ''Reengineering Prozesse'' – Software Migration, Fachberichte Informatik 15-2005 Jan Murray, Frieder Stolzenburg, and Toshiaki Arai: Hybrid State Machines with Timed Synchronization for Multi-Robot System Specification, Fachberichte Informatik 14-2005 Reinhold Letz: FTP 2005 – Fifth International Workshop on First-Order Theorem Proving, Fachberichte Informatik 13-2005 Bernhard Beckert: TABLEAUX 2005 – Position Papers and Tutorial Descriptions, Fachberichte Informatik 12-2005 Dietrich Paulus and Detlev Droege: Mixed-reality as a challenge to image understanding and artificial intelligence, Fachberichte Informatik 11-2005 Jürgen Sauer: 19. Workshop Planen, Scheduling und Konfigurieren / Entwerfen, Fachberichte Informatik 10-2005 Pascal Hitzler, Carsten Lutz, and Gerd Stumme: Foundational Aspects of Ontologies, Fachberichte Informatik 9-2005 Joachim Baumeister and Dietmar Seipel: Knowledge Engineering and Software Engineering, Fachberichte Informatik 8-2005 Benno Stein and Sven Meier zu Eißen: Proceedings of the Second International Workshop on Text-Based Information Retrieval, Fachberichte Informatik 7-2005 Andreas Winter and Jürgen Ebert: Metamodel-driven Service Interoperability, Fachberichte Informatik 6-2005 Joschka Boedecker, Norbert Michael Mayer, Masaki Ogino, Rodrigo da Silva Guerra, Masaaki Kikuchi, and Minoru Asada: Getting closer: How Simulation and Humanoid League can benefit from each other, Fachberichte Informatik 5-2005 Torsten Gipp and Jürgen Ebert: Web Engineering does profit from a Functional Approach, Fachberichte Informatik 4-2005 Oliver Obst, Anita Maas, and Joschka Boedecker: HTN Planning for Flexible Coordination Of Multiagent Team Behavior, Fachberichte Informatik 3-2005 Andreas von Hessling, Thomas Kleemann, and Alex Sinner: Semantic User Profiles and their Applications in a Mobile Environment, Fachberichte Informatik 2-2005 Heni Ben Amor and Achim Rettinger: Intelligent Exploration for Genetic Algorithms – Using Self-Organizing Maps in Evolutionary Computation, Fachberichte Informatik 1-2005