2. Coopetitive strategy and performance

Editorial Board Wojciech Czakon (head), Aldona Frączkiewicz-Wronka, Janina Harasim, Grzegorz Kończak, Małgorzata Pańkowska, Andrzej Piosik, Adam Samborski, Sławomir Smyczek, Maja Szymura-Tyc, Tadeusz Trzaskalik, Urszula Zagóra-Jonszta, Patrycja Klimas (secretary) Programming Committee Danuše Bauerová, Antonio Cartelli, Józef Dziechciarz, Bohdan Gruchman, Günter Hofbauer, Natalia P. Ketova, Natalja Lace, Jorma Larimo, Victor N. Ovchinnikov, Maria Romanowska, Frederic Le Roy, Reiner Springer, Heinz-Dietrich Steinmeyer Publishing editor Patrycja Keller Printed by EXPOL P. Rybiński, J. Dąbek Spółka Jawna ul. Brzeska 4, 87-800 Włocławek © Copyright by Publishing House of The University of Economics in Katowice 2014

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Jerzy Niem mczyk, Ewa Stańczyk-Hu S giet COOPERA ATIVE AND COMPETITIV C VE RELATION NSHIPS IN HIGH EDUCATION E N SECTOR IN N POLAND ..................................................................

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Anna Lipk ka, Stanisław Waszczak, Alicja A Winnick ka-Wejs LOYALTY Y AND WORK KAHOLISM IN N THE METH HODS OF HU UMAN CAPIT TAL EVALUAT TION (IN) AN N ORGANIZA ATION – A CO OMPARATIVE E STUDY .................

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Dominikaa Latusek-Jurcczak, Kaja Prrystupa-Rząd dca COLLABO ORATION AN ND TRUST-B BUILDING IN N OPEN INN NOVATION COMMUN NITY .............................................................................................................................

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Frédéric Le L Roy, Famaara Hyacinthee Sanou DOES CO OOPETITION N STRATEGY Y IMPROVE MARKET M PE ERFORMANC CE? AN EMPIR RICAL STUD DY IN MOBIL LE PHONE IN NDUSTRY ........................................

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K Patrycja Klimas MULTIFA ACETED NAT TURE OF CO OOPETITION N INSIDE AN N AVIATION N SUPPLY CHAIN C – THE E CASE OF THE T AVIATIO ON VALLEY Y...................................

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Wojciech Czakon, C Karo olina Mucha--Kuś, Mariuszz Rogalski COOPETIITION RESEA ARCH LAND DSCAPE – A SYSTEMATIC S C LITERATU URE REVIEW W 1997-2010 ....................................................................................... 121

Jerzy Niemczyk Ewa Stańczyk‐Hugiet Wroclaw University of Economics, Poland

COOPERATIVE AND COMPETITIVE RELATIONSHIPS IN HIGH EDUCATION SECTOR IN POLAND

J ERZY N IEMCZYK , E WA S TAŃCZYK -HUGIET

Abstract Coopetition builds on the idea that firms – competitors cooperate to create values and to appropriate value. Despite extant research on this topic, our understanding about how firms are engaged in cooperative relationships with their rivals is still in its early stages. This paper explores the higher education sector in Poland from the perspective of cooperative and competitive relationships, and analyses its performance on three different levels, i.e. macro, meso, and micro using case-based insights to answer the question(s). We propose that cooperative relationships amongst a variety of different universities increase their competitiveness and enhance the diffusion of knowledge. In the long run this translates into benefits for all parties and into a rise in the efficiency of the entire education sector. Keywords: coopetition, competition, cooperation, strategy, university, resource heterogeneity, convergent goals.

Introduction We can observe increasingly innovative forms of relationships between competing organizations when we view them through the framework of the cooperative relationship (Czakon, 2012).This makes us believe that the source of competitive advantage, is a set of relationships between the firm and other market players. The organization that sets up such a relationship can be more beneficial in the market. As a result the competition for the relational value is treated as a third leg in the theory of strategy (Contractor, 2002). Research on coopetition is limited mainly to business organizations, however, relations of coopetition can easily be observed in the non-business sectors, for instance in higher education. Siregar, Dagnino, and Garraffo (2011) state: “Connectivity between the concepts of Relationships, Strategy and Resources brings perspectives such as resource based view and relational view into [our] consideration as potential theoretical perspective in explaining coopetition. All the results at the end may affirm coopetitive strategy as a new form of strategy, an alternative to the two other main paradigms – competition and cooperation – that are already corroborated in the field of strategic management”.

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COOPERATIVE AND COMPETITIVE RELATIONSHIPS…

The higher education is a unique sector that permits us to observe systems of cooperation and competition, both characteristics of so called coopetition strategy. Moreover, colleges and universities communicate with their environment in a natural way. This communication process takes place through: students, that in the vast majority are employed in surrounding areas; staff, who are carrying out research for outside companies and teams of researchers, which are working together with outside companies as well as within the framework of the university’s general procedures and so they are developing interconnected links with surrounding businesses and companies so establishing an cooperative relationships. In almost laboratory conditions, we mean case-based insights individual variants of relationships are possible to be analysed and assessed qualitatively in terms of their effectiveness and efficiency. This article aims to diagnose the current state of cooperative and competitive relationships within the academic environment and suggests what future might be. The reasoning visualizes that we undertake the identification of the type of the relationship between the organizations in the researched sector. In other words the concept of the paper refers to the identification of the types of relationships. This is particularly important since such a relationship has not been observed yet.

1. Coopetition in strategic management literature In the world of business many companies decide to take not only competitive actions, but also actions, which rely on cooperation with other competitors. Research conducted by A. Brandenburger and B. Nalebuff (1996) likewise G. Dagnino and G. Padula (2002), amongst others, present situations where competitive and cooperative actions appear simultaneously. Research on coopetition has been increasing rapidly in recent years and the very concept has been used to clarify the economic and social effects of networking in various sectors and countries (de Ngo, and Okura, 2008). Until 1996, studies on coopetition were limited to proposals of A. Brandenburger and B. Nalebuff (1996). Since the mid-90s of 20 century however many more publications focusing on this subject have become available, such as: dyadic coopetition between two entities (Bengtsson, Kock, 2000), heterogeneous coopetition (e.g. Luo, 2004) and inter-organizational coopetition (e.g. Amburgey and Rao, 1996; Tsai, 2002; Luo, Slotegraaf and Pan, 2006). G. Hamel et al. (1989) treat coopetition as a continuation and natural consequence of competition. Cooperation and competition can therefore be per

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ceived as the phases of the organization’s life cycle. M. Bengtsson and S. Kock (2000) suggest that the benefit companies derive from coopetition is an effect of the combination of the pressure of competitors (effect of the competition), with an ability to access greater resources (effect of the cooperation Coopetition is defined as "a system of actors operating on the basis of the partial compliance of interests and purposes". It is an approach that is still developing, allowing for new and different examinations into the field of strategic management (Dagnino et al., 2008). Coopetition, claims W.Czakon, is a particular object of study, requiring a specific theoretical approach (Czakon, 2009). In general terms, coopetition is a strategy of joint value creation, a strategy of competition in the distribution of values in conditions of a partial similarity of purposes and the changeable structure of the positive-sum game (Dagnino et al., 2008). In A. Lado’s, as well as M. Bengtsson and S. Kock’s (2000) opinion, it is these two significant forces, the pressure of competition and desire for cooperation – that constitute coopetition, allowing for a rare situations in which competitors show the initiative directed at rent-seeking (Lado et al., 1997). Coopetiton is also “driven” by the need for strategic flexibility. Many studies have focused on the search for an innovative benefit in innovation-related coopetition, or simply in innovation networks (Ritala, Hurmelinna-Laukkanen, 2009). These studies are conducted mainly in business enterprises and rarely in other types of organizations (Lundberg, Andresen, 2012). It is possible, however, to find examples of coopetition in the education sector in several research projects – International research collaboration: opportunities for the UK higher education sector, 2008; Review of closer collaboration between universities and major publicly funded research agencies, 2004 (Commonwealth of Australia, 2004); Lundberg, Andresen, 2012. How do we study co-opetition in practice? Research on coopetition has theoretical character largely. Listed authors attempt to offer coopetition classifications and models (Luo’s studies, 2004, 2006, 2007; Rusko, 2011, p. 311-320; Mention, 2011, p. 44-53).

2. Research concept In this study, Kenworthy’s method of distinguishing levels of micro, macro and meso coopetition is used (Kenworthy, 2005). The level of macro coopetition refers to the relation between groups of organizations, including those from various sectors. The meso level refers to vertical and horizontal relationships amongst organizations. The micro level concentrates on the entities within the organizations. 8


Most studies focus only on inter-organizational network ties and do not incorporate into their research the effect of interpersonal relationships, which can also facilitate economic interactions between organizations (Ingram and Roberts, 2000). The basic question concerns the benefits, in terms of knowledge and economic value that we receive from every type of relation. For the purpose of this study three important variables have been considered: interests and goals, relations, and resources. As Branderburger and Nalebuff propose coopetition/coopetition strategy is characterized by partially convergent interests and goals and this view has been widely accepted by researchers since 1996. The relations between partners are another important attribute determining cooperation. The importance of relations in building a competitive advantage is highlighted by a resource-based view (Barney, 1991). Relations are a source of competitive advantages. They who have the valuable resources win a competitive advantage. Relational capital between partners in a network of relationships can foster cooperative relationship, as it creates a basis for learning and knowledge transfer on the one hand, and curbs opportunistic behaviour so preventing the leakage of critical knowledge, on the other. Therefore, relational capital can enable competition and co-operation to co-exist. The overall goal for firms to cooperate with other firms is to strengthen their competitive positions by inter-partner learning and by obtaining valuable resources from their cooperative relationships. This is recognized in both the literature on alliances (e.g. Parkhe, 1993; Reuer and Tong, 2010) and coopetition (e.g. Gnyawali and Madhavan 2001; Luo, 2007). Papers written on alliances (thoroughly) described how firms achieve stronger competitive positions by cooperating with other firms through: internalizing partner skills and resources (Ahuja, 2000; Prahalad and Hamel, 1990; Oum et al., 2004), learning from partners (Dussauge, Garrette and Mitchell, 2000), knowledge sharing and creation (Inkpen, 2000; Khanna, Gulati and Nohria, 1998), growth in size and market share (Oliver, 2001; Reuer and Tong, 2010), protection from radical new innovations which will erode a firm’s competitive position (Rothaermel, 2001; Afuah, 2000), sharing the risks and costs of research and development (Hagedoorn, 2002; Ouchi and Bolton, 1988), raising entry barriers (Eisenhardt and Schoonhoven, 1996) and creating economies of scale (Koh and Venkatraman, 1991; Garrette, Castaner and Dussauge, 2009; and Yami et al., 2010). Gulati (Gulati, Singh, 1999) develops the notion of network resources, which refer to those resources that emerge from a firm being embedded in inter-

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firm networks. Other studies show that network resources are particularly important to a firm's acquisition of competitive capabilities (McEvily and Zaheer, 1999) because network resources offer valuable information about new business opportunities (Gulati, Singh, 1999). Regarding the resource-benefit of the cooperative relationship, the main aspect discussed by some researchers is, that it is best if the variety of resources available in the organisations environment is use together in order to survive the demanding and changing environment (Tsai, Fang, and Lin, 2005). Cooperation between firms occurs when they present economic benefits for each other rather than the result of the alliance meaning that costs associated with acquiring resources in the market or developing them internally are incurred (Williamson, 1991). Typically, the type of association in collaborations is tilted towards partial interdependence, in which the firm uses cooperative arrangements with other firms to attain its objectives. Heterogeneity in resources can foster coopetitive relationships, because unique and complementary resources can be advantageous both for co-operation and competition. In a typical network, three types of resource flows take place between partners – information flows, asset flows, and status flows – and firms’ ability to access and use network resources varies depending on their structural position in the network (Gnyawali and Madhavan, 2001).

3. Research framework and methodology The study assumes three levels analysis of the types of relationship. The first level is a macro level between universities and the environment, mainly business. The second level is between universities and the third is level is between university departments, inside organizations. On all these levels, factors that may affect the increase in the quality of the relationships will be examined. The research assumes that these factors are: the degree of similarity of the objectives, the type of relationship binding the main participants and the degree of resource diversity on both/all sides.

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Figure 1. Conceptual model Meso

Macro •similarity of the objectives •type of relation •resource diversity

•similarity of the objectives •type of relation •resource diversity

Micro •similarity of the objectives •type of relation •resource diversity

Coopetition

In particular, at the value of the macro level relies on communication and information flow as well as generating inter-sector knowledge and the transference of this information. This results in the possibility of accumulating knowledge. The value in this case is obtained by reducing aggressive and suboptimal rent-seeking, and by agreeing on sharing both benefits and funds. Relationspis of the university and contextual surrounding strengthens their competitiveness, but also affects the synergy between the competitive parties (Bizzi and Langley, 2012). Therefore, we ask the following question: Q1: Does a higher level of convergent interests and goals between the university and business environment lead to a higher level of cooperation? Q2: Does the sense of cooperation between the university and business environment dominate in terms of their relationship? Q3: Will heterogeneity in resources between the university and business environment lead to coopetition? In our opinion, answering these questions helps identify the types of relationships between universities and business organizations. The chosen way to identify this type of relationship enables us to determine whether in this case, the relationship between these entities is simultaneous competition and cooperation, or if there is only cooperation between them.

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At the meso level, the value can be seen in the results of intra-sector creation and transfer of new knowledge, deep communication and the flow of information as well as joint action on co-development. Coopetition of many differing varieties of universities increases their competitiveness and enhances the diffusion of knowledge, and in the long run, translates into benefits for all coopetitive sides and a rise in efficiency rise throughout the entire education sector. Therefore, we ask the following question: Q4: Will higher levels of convergent interest and goal between universities lead to higher level of cooperation? Q5: Will higher levels of cooperation between universities lead to increased level development throughout the education sector (meso coopetition level)? Q6: Will heterogeneity in resources between universities lead to coopetition (equal competition and collaboration) At the micro level, where value is added through an extensive system of communication and information flow, and through the creation and transfer of new knowledge within an organization, the economic benefits (economic value) are obtained through greater involvement of all stakeholders of the organization (Enz and Lambert, 2012; Bizzi and Langley, 2012). Cooperation between many different departments and units can influence the rise in their level of effectiveness, In order to answer this we must first look at the following questions: Q7: Will higher levels of convergent interest and goals between departments and researchers lead to higher level of cooperation between departments (cooperation – dominated)? Q8: Will higher levels of competition between departments and researchers lead to higher levels of faculty (school) effectiveness? Q9: Will heterogeneity in resources between departments and researchers lead to coopetition (equal competition and collaboration)? Referring to resource diversity we follow Bengtsson and Kock thesis: “Heterogeneity in resources can foster coopetitive relationships, as unique resources can be advantageous both for cooperation and competition” (2000, p. 421). Finding the complex results need in order to answer these questions, using the available data and information, is almost impossible. The main obstacle is the lack of possibilities in comparing in similar operating conditions. Practically 12


speaking, in order to fully answer these questions a sample of mature sectors in established Polish universities would have to be available for research. Such a situation will more than likely only be possible in about 10 years as currently the higher education sector and in particular the economics department is in a process of growth and development, triggered by implementing new changes in how it functions in Poland.

4. Sample characteristics and methods As of 1 March 2014 in Poland there were 5 public universities and 38 faculties of economics at other public universities (16 at universities, 18 at polytechnics and 4 at the agricultural colleges) operating. In addition, nearly 150 private higher education institutions educating in the field of economics and 25 public vocational schools educating at the first cycle of studied, mainly in the field of management were operating. The largest and most important “players” in the market include: University of Economics in Katowice, Cracow University of Economics, Poznan University of Economics, Warsaw School of Economics, Wroclaw University of Economics. As of 30 November 2010, 59,184 students were enrolled in these five universities. A total of 415,559 – students were studying in the field of economic and administrative sciences at public universities in Poland. This means ca. 2/3 of all students in Poland (the total of 1,841,251) were studying (CSO, 2011). Other statistics which show the study of economics in relation to the rest of the higher education sector are unable to be used for analysis owing to the specific nature of the study of economic. These specific features are: a very low level of expenditure needed for educating an individual student in comparison to other types of colleges, a high percentage of part-time students – nearly 50% and a low statistical citation rate in the field of social sciences. All these factors make the study of economic very hermetic in a sense. The five most important business schools in Poland are representative for the Polish higher education. These universities have a decisive influence on the level of both economics and management and the quality of those graduating with economics in Poland. They will also be subject for exploration in the context of the stated hypotheses. There are 23 departments in five business colleges in Poland. A further 38 economic departments exist in other private higher education institutions. In total, in public universities there are 51 departments grouping ca 200 chairs,

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bringing nearly 2,500 researchers and PhD students. These employees work independently conducting research, which may result in some 5,000 scientific articles, monographs and chapters in books per year. The HEI sector, in particular the economic department in Poland is a highly competitive sector. It is difficult for a natural cooperation. This follows from the fact that virtually all of its revenues comes from teaching activities (the subsidy and tuition fees from non-full-time students). Therefore, each student recruited actually means more revenue. And every school is a real competitor. The number of students ranges from 10 thousand at Warsaw School of Economics to 21 thousand at Cracow University of Economics (2010). This illustrates the differences in the activity of recruiting departments. Data used in the work was obtained from different research methods. They were primarily unstructured interviews with the staff of the analysed business schools, as well as primary data from different reports and financial statements from analysed business schools. The different data was then compared in order to eliminate factual inaccuracies. The study used comparative analysis and presentation of the usual techniques of quantitative data.

5. Macro level analysis The macro level concerns the analysis of the relationships between universities and the business environment. This relationship is still in its infancy and is mainly limited to the use of resources offered by both partners in teaching activities. A high degree of convergent interest and goals has to be dealt with. In most Western universities, this type of relation is also one of the most common. Analysis of five universities’ and careers offices’ websites selected for the study show that these universities are connected to about 20 global companies which offer their student work programs. The second area of cooperation is in applied research, undertaken at the request of individual companies. The situation of analysed universities differs substantially from other universities. In case of economic HEIs the competition for research grants is great and completely differs from the competition for grants from departments at other colleges. Apart from colleges, global consulting companies, small and medium enterprises advising in the area of finances, accounting, management, logistics, etc. and of course companies’ own developmental departments seek to receive contracts from the business domain.

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A report prepared by the staff of the University of Economics in Katowice emphasizes these facts and indicates that companies "are mostly not interested in the implementation of joint research (66% negative responses), nor in participating in the process of education (65%) or providing university students and graduates with placements (55%). They are moderately (30-40% of responses) interested in consulting, expertise and training conducted for their benefit by university staff” (Model współpracy uczelni..., 2010). A rapid development of consultancy conducted by university research staff outside the formal framework of the university is a significant specificity of Polish economic HEIs. Most research staff working for business colleges is or will be also working in a business or as a business consultant. This results from the specificity of teaching and research activity, as well as from an economic necessity caused by the low rate of pay for salaries, and competitive salaries in the private consulting sector. Such connections unquestionably improve the quality of research and teaching processes. Previous studies show the positive impact of better coordination and increased diversity of resources for the cooperating parties (Garcia and Velasco, 2002). Coopetition also means an access to external knowledge (Spence, Coles, and Harris, 2001) and coordination of organizational learning, particularly through an access to the partner's core competencies (Bengtsson and Kock 2000). From a strategic point of view the cooperation with competitors gives the opportunity to be more flexible and more responsive to the environment. Coopetition also has potential costs, such as losing control of key activities, information and resources (Håkanson and Ford, 2002). Similar challenges are faced with regards to relationships between colleges and business companies. The recruitment activity of global businesses is conducted virtually in all business colleges in Poland and is another example of cooperation, especially as a few from these global businesses have own colleges located outside Poland. It is also interesting to look at those companies whose business and capabilities prove to be a useful resource for the educational establishment they have a relationship with. The clearest example of this can be seen when looking at financial pulling power of both partners. The business is a party in possession of a financial surplus. According to research commissioned by EandY in 2008, Research and Development activities in Poland take place mainly in the public sector. 60% of the expenditure on Research and Development was financed by the state, while in the EU it was 40%, and in case of OECD countries – 34% (OECD, 2008; Wolszczak-Derlacz and Parteka, 2008).

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Universities in Poland have reduced financial resources. An interesting example of cooperation can be seen in research where scientists who have knowledge and skills that the company’s staff do not possess, benefit from the knowledge of these companies which is often stored in the form of databases, specific knowledge and secret knowledge. This is particularly true of research in the area of macro-economics and finance. Unfortunately, such forms of cooperation in Poland are still rare. Such relationship always benefits and costs both parties. Whilst there are many positive examples, it is also possible to find a lot of negative attempts to use “the competition and cooperation”. Many companies use their position of power to forcing universities which are in a disadvantage position to take certain actions, such as forcing a university to place their funds on developing practices and departments which focus on the needs and practices of that particular company. The observations carried out in five business universities after the reforms present a very high willingness to change; there is an annual double-digit rise in the share of the external research funding and a growing participation in the teaching process of business specialists. The identification of the types of relationships at the macro-level seems problematic, since the research activity outside the university cannot be a measure of coopetition between universities and organizations from the business environment. It is because the activity in this field indicates the relationship of cooperation.

6. Meso level analysis The area of research activities is becoming more competitive. Limited budgetary resources are being divided by the National Science Centre according to a criteria, which is based on multiple teams of researchers who existing in competition with one another. Though there are funding bodies which, favour research carried out jointly by several centres, however they are in the minority in Polish economical schools and almost seem like a symbolic gesture of cooperation rather than anything else. In Poland, research is customarily conducted individually, in contrast to other Western countries, where most research is collaborative. The activities of five major business schools give a few examples of cooperation, such as: joint research conferences, and textbooks and scholarly monographs written by research teams. The lack of joint research, as well as the small number of individual research programs, is the result of the fact that researchers are overloaded with 16


teaching work. This is particularly evident in the economic universities, which are oriented towards financing their activities with tuition fees. This is confirmed by the previously quoted evidence of studies carried out by EandY. A negative correlation between the teaching burden and research productivity of the staff has been observed. The conclusion is that research and teaching are in fact more competitive, than complementary (Wolszczak-Derlacz and Parteka, 2008).

7. Micro level analysis In conditions when autarky, but also maintaining high competitiveness is ineffective, a partnership can turn out to be the success factor between members of interacting organizations. According to P. Bourdieu, social capital, which came into existence as a result of having durable networks of relations, is a collection of resources supported by a mutual acquaintance and recognition, and participation in such an organized network provides each of its members support in the form of resources – including relational – which is owned by the whole group (Bourdieu, 1985). In this regard, each member of the network becomes a kind of node, the agent making their own relational resources available to other members of network. In this way an existing multi-directional plain of contact enhances the possibilities of the partnership, and each of the entities involved are a potential node, enabling the further expansion of the network. Anything which connects with other operators of within the same environment, regardless of their position and the nature of the relationship may become a node. Collaborating teams are most often grouped around independent researchers having considerable research achievements and/or academic position, and most often solve research problems from narrow research sub disciplines. More and more of these horizontal structures can be noticed among young staff working mainly in a virtual environment. In the future, there will be more such examples. Rules for financing research projects have impact on that. Both the financial resources coming from the government and the European Union prefer teams composed of researchers from different backgrounds. It is also possible to find examples of excessive competition (rat race) that blocks the creation of systems of cooperation. It is a syndrome of generation Y. Competition leads to the department’s development measured in: the number of research activities carried out, degrees obtained and the quality of the teaching process and position in the market. Unfortunately, in the majority of

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Polish economic universities two examples of activities can be observed. The first is limiting the faculties and departments ability to collaborate and pursue strategies of independence (autarky). Understanding the simple rules of synergy is often difficult. Increased ability of implementing the principle of synergy may result from of the competition between researchers for research grants. Analysis of applications submitted to the National Science Centre; show a dynamically growing research activity in academic staff. Also, activities within a network of contacts may surprisingly increase such activities. In practice, researchers, not only the Polish ones, have very narrow specializations. Such a solution should therefore contribute to linking diverse knowledge and skills while maintaining the possibility of competing for other resources. Examples of such processes can be observed in research and teaching. They are: joint research projects, faculties and teaching specialties led through different departments, joint scientific conferences, a voluntary association of units in the form of institutes, and cyclical administering of the departments’ affairs. However, the limited scope of such solutions, results from the general low financing of science in Poland, which has already been pointed out.

Conclusions, recommendations, limitations The aim of cognitive research is to formulate applications of testing the effectiveness of types of relationship at different levels of analysis. Being able to carry out examinations which go beyond the formal boundaries of an organization is especially valuable. Showing possible means of exploiting cooperation benefits and costs from the level meso and macro to the level of the micro organization is another conclusion of these examinations. The greatest difficulty is in proving that the benefits of such collaborations between different parties are in fact beneficial for all and that the sum of the whole is greater than its parts. Changes in the criteria for assessing the effectiveness of higher education, which are now moving in the direction of those criteria used in other Western universities, will foster the growth of cooperation in this regard. Apart from rare examples of collaboration in the form of joint conferences, incidental joint research projects and attempts to implement network possibilities, so as to exchange knowledge, cooperation is practically non-existent. The lowest level of cooperation exists at the micro level. Here too, it is possible to take note of only a few examples of institutional cooperation. Many different forms of cooperation occur at the level of departments and individual employees. However, there exist 18


the benefits which are offered by relationship networks between researchers and educators, though these exist at a personal/ individual level rather than owing to a formal contractual agreement. To some extent future research approach should be oriented to the network level. Studying coopetition at the network level and maybe the articulation of different levels within the network is good perspective to extent uor understanding of coopetition in higher education sector. Co-location, cluster formation, international and national networking, sharing of infrastructure, co-investment in infrastructure and research, are critical for collaboration. We recognize some limitations of the study, mainly due to the methodology adopted and context as well. This study addresses a specific context. Due to this there is a need for generalization of research finding to allow for further in depth research

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Anna Lipka, Stanisław Waszczak, Alicja Winnicka‐Wejs University of Economics in Katowice, Poland

LOYALTY AND WORKAHOLISM IN THE METHODS OF HUMAN CAPITAL EVALUATION (IN) AN ORGANIZATION – A COMPARATIVE STUDY

A NNA L IPKA , STANISŁAW W ASZCZAK , ALICJA W INNICKA -W EJS

Abstract The aim of the study is to show how selected methods of human capital evaluation determine loyalty and workaholism, the features affecting employees` efficiency. It was found that seven analysed methods of evaluation differ in terms of ways of defining generators and devaluators of human capital value. Based on the conducted analysis, it was found that the historical costs method, the discounted revenue streams method, the HR Balanced Scorecard and the Saarbrücken formula are not suitable for recognition of loyalty and workaholism as behavioral sources constituting the value of human capital. The most useful methods for their recognition were: Mayo monitor, risk – value method and personnel portfolio. The conclusion is that, with the use of the Mayo monitor, the dynamics of loyalty types and the types of workaholics, which correspond to the variable in the monitor, may be captured, forming the skills profile. Also the risk value method may depict a risk map for loyalty and workaholism, for example, to assess the quality of methods and their diagnosis. In turn, the personnel portfolio allows us to determine the dynamics of the share of different types of workaholism and loyalty. Keywords: methods of human capital evaluation, loyalty, workaholism, value generator, devaluator.

Introduction Both loyalty and workaholism are terms referring to the qualitative value of human capital – individual and team capital as well as the whole organization. Quality, on the other hand, determines human capital value which can be understood in a variety of ways (among others, as the replacement, strategic or market value). It is assumed that the way loyalty influences that value is different from the way workaholism affects it, i.e. loyalty is its generator, and workaholism its devaluator. The aim of the article is to verify the assumption on the basis of critical analysis and reference books as well as to deal with the following research issue: What are the similarities and differences between loyalty and workaholism in selected human capital evaluation methods and can one identify the methods which would be more and less useful to determine generators as well as devaluators of human capital?

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LOYALTY AND WORKAHOLISM IN THE METHODS…

In the first section of the paper the state of research on loyalty and workaholism as the features that constitute the quality of human capital were presented. The evolution and the current stage of development of human capital valuation methods were also presented in this section.

1. The state of research on loyalty and workaholism as determinants of human capital quality Loyalty and workaholism are not the only determinants of the human capital quality. However, these days they seem to be of great significance. Employee loyalty can be described as “the (perceived) probability of work continuance in an organization by an employee with greater or lesser commitment – and certain emotional attachment towards the organization regardless of its image on the market – thanks to employee`s or other staff`s well-being or due to lack of other opportunities to find a different job or high costs of changing the employer“ (Lipka, 2012, p. 20). Such a definition of loyalty reflects the following types (singled out according to such inner mechanisms as trust, habit, commitment): − partnership loyalty (there is trust, habit and positive organizational commitment), − commitment loyalty (there is trust and positive commitment), but it does not reflect the types of loyalty with no commitment, i.e. − loyalty of convenience (there is trust and habit), − conscious loyalty (there is only trust), − loyalty out of habit (there is only habit). It does not reflect those types of loyalty where employees` commitment is of negative nature (e.g. employees sabotage the company`s operations), i.e. − lenient loyalty (there is also trust and habit), − conditional loyalty (there is no habit, but there is trust), − helpless coercion loyalty (there is no trust, but there is habit), − unaccepted coercion loyalty (there is no trust or habit). The above definition refers to the most desired types of loyalty from the organization`s perspective. One should keep in mind that commitment, differentiating those types (apart from trust and habit), includes: − affective commitment, − continuance commitment, − normative commitment (Meyer, Allen, 1991, pp. 67-69).

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Partnership and commitment loyalty (and to a lesser degree loyalty of convenience, conscious loyalty and loyalty out of habit), especially that of indivisible and long-term nature, can be a behavioural boost to the human capital value as well as to the organisation itself for the following reasons: − the influence on employment stability (so difficult to achieve in times of increased competition, lack of MVEs and battling to get the best personnel, even intensified by demographics and changing values), which helps increase company profitability (Reichheld, Teal, 2007, p. 159), − smaller loss of customers (and its implications, e.g. increased turnover) (Gerpott, Paukert, 2011), which is of greater significance to services where tacit knowledge is vital (Schüller, Fuchs, 2005, p. 29), − loyalty effect (Schüller, Fuchs 2005, p. 189), i.e. good performance after a longer period of employment (gaining experience), − better work quality (the same reasons as in loyalty effect), − smaller costs for training courses, − greater resistance to marketing activities of competitive businesses among persons representing the above types of loyalty, and greater tolerance to lack of pay rise or even reduced salaries (Buchanan, Gilles, 1990), − good opinions and referrals contributing to the company`s human capital and clientele, − cost reduction relating to turnover and training programmes, − high staff morale despite witnessing (disloyal) employees leave, − the viability of planning, − cost reduction relating to controlling (Šmid, 2003, p. 141), − lack of limitations in applying the latest management methods (e.g. High Performance Work Systems). All in all, loyalty: − affects attitudes and values (loyalty itself can be labelled as good attitude or conduct), − prevents the loss of knowledge, aptitude and skills, − contributes to company`s healthy relationships, − improves motivation, i.e. improves all the components of human capital. Workaholism, the other qualitative feature of human capital, is described as “obsessive commitment which is characterized by high employee`s ambitions, inability to control habits in the workplace, and too much work (work dedication) connected with lack of the individual`s activity in other areas of life, which

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results in the deterioration of employee`s well-being and dysfunctional interpersonal relationships (Wojdyło, 2010, s. 18). This syndrome is considered a type of addiction (Oates, 1971), which in opposition to other addictions is socially approved and it is even associated with high job performance. Workaholism is also described in terms of behavioural aspect (it is represented by A behaviour, which is characterized by fast pace of life, taking up extremely difficult tasks and setting high standards), affective aspect (job satisfaction), cognitive aspect (automatic thinking, fixed opinions and assumptions concerning work), attitudes towards work (overzealous) or compulsive-obsessive personality (perfectionism, exaggerated thoroughness, rigidity, inability to delegate work). All the above factors foster excessive involvement in a working life. The main symptoms of workaholism include: − inner obligation to work with no outer coercion (inability to `cut off` from work and not to think about it), − recognizing work as the core of one`s life, which determines its purpose and establishes it as the main value as well as one`s identity and self-esteem, − loss of control over one`s life in a working environment due to excessive involvement in one`s work and setting too high, often unrealistic standards (“lost in work”), − working life takes over other areas of one`s life (lack of other activities, working time is extended to leisure), − erratic behavior which results from applying defensive mechanisms (among others, ignoring the problem, rationalising longer working hours, compensating all kinds of emotional deficiencies, denying bad effects of work addiction), − inability to “abstain” from work without some negative consequences, such as self-destructive thoughts and emotions (constant thinking about work, stress, guilt complex, anxiety) (Lipka, Waszczak, Winnicka-Wejs, 2013). The definition of workaholism and its symptoms imply that it is a syndrome and a problem determined by personality, entailing negative consequences for an individual, their family as well as for an organisation. However, is the abovementioned opinion on workaholism not one-sided (too clinical an approach) and radical? According to some researchers, such as Machlowitz, “workaholism is an employee`s commitment to career life”, “workaholics are passionate about their work” and therefore “the organisation reaps benefits employing such workers” (Golińska, 2011, p. 12). If one considers a broader context, including work as a priority in a value hierarchy and work ethics, then workaholism seems to be a natural consequence of high expectations from the family, working environment and society towards an individual.

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One of such expectations is conscientiousness, which in Five Factor Theory by Costa Jr. and McCrae constitutes a dimension of distinctive features – one of five personality dimensions (apart from neuroticism, openness to experience, extraversion and agreeableness). Those factors include six components, describing the aspects of a given factor in detail. When it comes to conscientiousness, its components are as follows: competence, propensity for tidiness, obedience, an aim for achievements, self-discipline and prudence – qualities which are very appreciated in employees. Conscientious individuals being dutiful and disciplined as well as ambitious and industrious, become workaholics at some point` (McCrae, Costa, 2005, p. 65). But only conscientiousness taking the form of compulsion/obsession with work – shown in diagnostic criteria ICD-10 and DSM-IV-TR as exaggerated experiencing of doubts, rigidity and perseverance, focusing on details, perfectionism interrupting tasks completion – poses a problem to an organisation because it badly affects its functioning and the quality of human capital. Therefore conscientiousness may lead to workaholism, but not necessarily – it depends, to a great degree, on the management style and the ability to take advantage of human potential, company culture and the situation on the job market. The consequences of workaholism are multidimensional. The following implications can be singled out here: personal well-being, the implications for workaholic`s social environment and risks for an organisation relating to employing workaholics. Workaholism affects all the components of human capital in terms of its value depreciation. And so, excessive involvement in a working life can badly affect knowledge, impairing individual`s memory, perception and focus as well as cognitive structures, including thought activation, hyperactivity of intellectual processes, escapism, reduced intellectual activity (Kalinowski et al., 2005, pp. 117-119). Accompanying anxiety may lead to certain losses (emotional atrophy) and a loss of certain skills, such as a skill of communicating clearly, a loss of empathy and sympathy (Killinger, 2007, pp. 111-153). Constant working typical of workaholism makes a human body function at full stretch at the cost of good health and well-being. The reduction of human capital value in this area may lead to bad health consequences and erratic behaviour – noticeable or inconspicuous (cf. Meissner, 2005, p. 49, after: Städele, 2008, p. 46). Workaholism can result in concentration problems, physical and psychological disorders, overtiredness, which can even lead to total depreciation of human capital (in the case of karoshi – an employee dies of overworking). Decreased value also concerns motivation. It is known that

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workaholics are driven by inner coercion. Their motivation for work is determined by, first of all, a hidden motivation for their ego protection and selfesteem or gaining recognition from their colleagues (Porter, 1996, pp. 70-84). Workaholism also has an effect on employees` attitudes towards the ways of performing tasks and their relationships with co-workers. Perfectionism as setting too high standards and rejecting imperfect solutions can make it impossible to take full advantage of certain attributes of human capital in an organisation. If work becomes a paramount value (it becomes “god”), work-life balance might be upset, which, in turn, affects a person`s functioning in various spheres of life. It seems, therefore, that the direction of the impact of workaholism is different from that of loyalty. The quoted examples of lowering the value of all the components of human capital seem to indicate that workaholism can be described as a devaluator of human capital value. To state that it is essential to use human capital valuation methods. These methods are – to an extent which is larger than ever before (cf. Table 1) – in the center of researchers’ interest due to the fact that they are treated as a component of business entities valuation – a component which is difficult to measure, but necessary to take into account, since without it a situation depicted as phase 6 (cf. Table 1) occurs. HR specialists and managers have to take into account a possibility of using the results of human capital evaluation in personnel controlling. If an organization uses the strategy of maximizing human capital value, and generally, the idea of value management, one has to keep in mind that certain measurement means, e.g. those concerning loyalty and workaholism must be generally accepted. Table 1. The evolution stages of human capital evaluation methods Stages – Period 1 mid 60s, 20th c 2 mid 60s – beginning of 70s, 20th c 3 beginning of 70s – mid 70s, 20th c 4 mid 70s – beginning of 80s, 20th c 5 80s, 20th c

6 beginning of 90s, 20th c until the present day

Description of the stage Theory of human capital and the psychology of an organization The development of the methods based on historical and replacement costs; Brummet, Flamholtz and Pyle introduce the notion of Human Resource Accounting Fist applications of the designed methods in companies Fewer researches, more combined methods, which leads to a decreased number of specialists for applying the methods Fierce competition between US and Japan leads to a greater interest in the field and greater significance of an employee in business operations; lack of solutions to the problems relating to the existing methods New wave of a greater interest due to the increasing role of human capital and the existing value gap; change of market-to-book ratio

Source: Based on: Gebauer (2005, pp. 18-230); Dudycz (2005, p. 216); Lipka, Król, Waszczak, Satoła (2008, p. 13).

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The choice of valuation methods must have regard to organization strategies and boundaries, also methodical ones, to their applicability. In the previous study (Lipka, Król, Waszczak, Satoła, 2008) it was found that methodology issues connected with determining human capital value depend on: − lack of justified assumptions and gaps in descriptions of research methodology, − difficulties with establishing the basis for determining the value and assuming research method (the basis is understood as individual or team or human capital as a whole organization; omitting or taking into consideration so-called contextual factors relating to human capital management, i.e. the strategy, structure, company culture), − problems referring to measuring the variables/ways of acquiring data (including economic as well as psychological and organizational data), − difficulties of temporal nature (depreciation of human capital, changeability of the value generated by human capital in time: using historical and/or prospective data with the aid of the methods: a need to take advantage of future values in reference to the present ones), − feedback problems: human resources management – human capital evaluation (narrowed down to the efficiency evaluation of HR practices), − issues referring to the economic situation and management trends (e.g. increasing variety of human capital in an organization, flexibility, the application of lean management and work-life balance and downward movement programmes).

2. Hypothesis and testing procedure The following hypothesis was formulated for the research problem specified in the introduction: H1: The methods of human capital evaluation differ in terms of loyalty and workaholism, which shows that some of them are less and others are more useful to specify given generators and devaluators of human capital. In the study, selected evaluation methods were included, belonging in the following categories: cost (historical costs method), revenue (discounted revenue stream method), multiplier (Mayo monitor, risk value method), indicator (Balanced Scorecard), mixed (Saarbrücken formula), alternative (personnel portfolio) (some authors take into consideration only the first two categories). Access to the detailed descriptions of these methods and the clarity of algorithms during their application were considered while choosing the methods.

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To verify the hypothesis by deduction based on critical analysis and reference books, the following sources of presenting evaluation methods were used: Brummet et al. (1968, pp. 221-223), Flamholtz (1975a), Mayo (2001, p. 14), Lipka (2007, p. 22-24), Scholz, Bechtel (2005, p. 32-36), Becker, Huselid, Ulrich (2002, pp. 65-89). The following procedure was assumed: − a description of loyalty and workaholism in terms of their contribution to a qualitative value of human capital and their influence on all the components of human capital, i.e. knowledge, aptitude, skills, health, motivation, attitude, values, − loyalty and workaholism as determinants of human capital value in seven selected evaluation methods, − comparative studies and the selection of the most useful method(s).

3. Loyalty and workaholism in selected methods of human capital evaluation in an organization 3.1. Historical costs method In the most popular cost method – historical costs method (Brummet, Flamholitz, Pyle, 1968) – in its “most updated” version, retention investment costs (i.e. loyalty investment) could be incurred as follows: − hiring – direct costs e.g. offering competitive remuneration as part of diversified personnel marketing, − hiring – indirect costs e.g. loyal MVEs promotion, − training – direct costs e.g. training programmes improving employee loyalty, − training – indirect costs e.g. productivity loss during retention investment. According to this method loyalty gauged by retention time increases human capital value. As far as workaholism is concerned direct hiring costs can be taken into consideration as in the case of loyalty, for example the costs related to hiring a non-workaholic, i.e. a person who can skilfully manage the time and take a rest after work, taking care of their health, knowledge, skills and motivation. Such costs are supposed to prevent or intervene in (reduce the effects of) workaholism. The same objective is ascribed to direct training costs (e.g. costs related to individual coaching enhancing time management), indirect hiring costs (e.g. employee reallocation costs preventing their workaholism) and indirect training costs (e.g. time spent by other workaholics talking to the workaholic employee, during which they fail to perform their duties). According to this method, like in the case of loyalty, the higher the incurred costs, the higher the value of human capital (the obvious flaw here is the assumption that costs reflect value).

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3.2. The discounted revenue streams This method, devised thirty years ago by Flamholtz (1975a), is being revived these days and it is used in human resources in a number of novel applications. It is strictly connected with the loyalty of employees whose value is determined by loyalty itself and revenues they generate on various positions they hold in an organization. According to this method, even those employees who are loyal with no organizational commitment are valuable provided their work performance is “average” and they are willing to be promoted. The employees who represent conditional loyalty are also valuable if their promotion is a precondition for staying in the company. The method shows that human capital value increases due to efficient retention practices (e.g. better fulfilment of employees` needs, such as authority, group identity or achievement) or a faster career path (when the probability of leaving the company is smaller than the values in Table 2 for u, w, p, s, v when other conditions are stable). Table 2. Employee evaluation changes according to discounted cash flow method – example Initial data Employment date S Position 31.12. 2012 Average employment period of the S employee in the M company: Forcast career path of the S employee

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Year

Possible positions

Probability of taking up the position by S (%)

2013 2013 2013 2014 2014 2014 2015 2015 2015 2016 2016 2016 2017 2017 2017

I II III I II III I II III I II III I II III

l 0 0 b c 0 k e f g h i j d z

01.01.2009 r. X 7 yrs Probability of leaving the company by S before the end of year (%) u u u w w w p p p s s s v v v


table 2 cont. Annual positive cash flow for the position I II III Discount rate Specifications

A euro B euro C euro m% Currents incurred costs Expected value (in money units)

Annual positive cash flow for the S employee in 2013 Annual cash flow for for the S employee in 2014

(100%-u%) * 100% * A

Annual cash flow for for the S employee in 2015

(100%-p%) * (k * A + e * B ) f * C)


(100%-s%) * (g * A + h * B + i * C)


(100%-v%) * (j * A + d * B + z * C)

Total

Total of the above: D

(100%-w%) * (b * A + c * B)

Discounted value (in money units) (100%-u%) * 100% * A [(100%-w%) * (b * A + c * B +: (1 + m: 100) [(100%-p%) * (k * A + e * B ) f * C)] : (1 + m: 100)2 [(100%-s%) * (g * A + h * B + i * C)] : (1 + m: 100)3 [(100%-v%) * (j * A + d * B + l * C)] : (1 + m: 100)4 Total of the above: W

The value of for the S employee on 31.12.2012. W Source: Own algebraic generalization based on the accounting example in: Bochniarz, Gugała (2005).

Reallocation to higher positions with potentially higher revenue may meet the expectations of some workaholics (e.g. workaholics with impaired focus), whereas some of them might not be satisfied (e.g. compulsive-addicted workaholics or those savouring work). The discounted revenue streams method does not differentiate among revenues brought by different employees following the same career paths. It is assumed that the revenues are the same regardless of the position. So, this method can distort the evaluation of those employees who are workaholics and whose revenue will be higher in the short run, but in the long run it will be lower than the assumed average (due to the above-mentioned somatic, mental and social effects of workaholism).

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2.3. Mayo Monitor This method uses the so-called individual asset multiplier including aptitude, potential, contribution to value and employee`s values compatibility with company`s values. If loyalty is a value (as in some examples of corporate culture, such as culture of power and role model (Harrison and Handy); process culture (Deal and Kennedy); hierarchy culture (Cameron and Quinn); productive culture (Snyder); harmony culture (Peters); communal culture (Goffee); Eiffel Tower culture (Trompenaars and Woolliams), and the employee represents the most desired types of loyalty, this kind of compatibility is scored the highest (2.0 according to the assumptions of the method). Theoretically, a type of loyalty affects the generated value. The type itself can be treated as a variable referring to aptitude profile. Its changing character can be interpreted in terms of potential (for example, from conditional loyalty to loyalty of convenience, from lenient loyalty to commitment loyalty, from helpless coercion loyalty to loyalty out of habit or from conscious loyalty to partnership loyalty). The above implies that loyalty can be subjected to evaluation with the aid of Mayo Monitor. Can workaholism be subjected to such evaluation? Certainly, a type of workaholism and its intensity (the level of its development) belong in the group of variables describing aptitude profile, that is one of the variables in Mayo Monitor. However, only in some types of corporate culture (e.g. in “aggressive” culture, Krawiec, 2010, p. 47), workaholism is considered a value. According to the assumptions of Mayo Monitor, this type of compatibility should be evaluated higher than incompatibility. As far as contribution to creating a value is concerned, it can be insignificant in the case of a relentless workaholic (because of rigidity while doing a job), bulimic workaholic (working unsystematically), savouring workaholic (“immersed” in details and failing to meet deadlines) or narcissistic controller (unable to work in a team). The issue concerning the factors which determine the possibility of transition between workaholic types is interesting. It seems that apart from personality qualities, there are other vital factors, such as the nature of tasks (among other things, the level of their complexity), the employee`s motivation and preferences (interests, coercion/satisfaction level), employer`s requirements (high performance, time pressure), a stage in the employee`s personal life (age) and career life (career path planning and its progress), and self-awareness (insight into a problem) as well as making the effort to tackle problems connected with workaholism (family pressure and support, professional therapy).

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2.4. The risk value method The method was devised a few years ago (Lipka, 2007) and it is constantly being improved. It is assumed that according to a selected analytical and point method of job evaluation, every employee should get a task/position of the highest point value which he is able to successfully perform (or meet given job requirements), having all the necessary skills (aptitude profile). This point value can be exchanged into money units (it is established how much one point is worth). However, this is not the whole procedure because it is thought that there might be specific types of personnel risk when an employee is not able to successfully perform the task even though they have the necessary skills and expertise. In other words, the risk destroys, i.e. reduces human capital value measured by the possibility of successful task completion at a specific difficulty level (criteria such as physical exertion, routine or working conditions, which are not related to job complexity, responsibility or team work, should not be taken into consideration when assessing the difficulty of the task). It seems that devising risk maps for both loyalty and workaholism is feasible (cf. Table 3) as well as determining its level (separately for loyalty and workaholism), for example with the aid of Martin and Heaulme`s equation (1998): j

i

r=

∑∑ r n i =1 j =1 i

ij

,

j

∑∑ n i =1 j =1

ij

ij

where: rij – risk value, nij – number of personal risk types in the field with a given value and a matrix where there is a risk level for specific probabilities relating to the occurrence and the volume of effects (in Table 3 loyalty risk is 3.66, and for workaholism risk is 8.80, which means that the risk is high only in the case of workaholism as it exceeds 5.0).

37


Table 3. Example map of loyalty and workaholism risks Type of risk related to loyalty (L) and workaholism (W) Quality of methods for diagnosing loyalty types in an organisation (L) Quality of methods for diagnosing workaholic types in an organisation (W) Work efficiency level for workaholics (W) Selection appropriateness of Employee Relationship Management strategy (ERM) (L) Communication skills in team work (W) Quality of method selection for preventing workaholism (W) Quality of method selection for workaholism intervention (P) Employee loyalty scheme profitability (L) Level of loyalty intensity (L) Loyalty types dynamics (L)

Probability of occurrence

Level of effects

Risk value

medium

medium

4.0

medium

medium

4.0

very high

high

8.0

high

high

7.0

very high

high

8.0

medium

extreme

7.0

medium

extreme

7.0

very low medium low

medium low high

1.5 1.8 4.0

So, employee value based on analytical and point job evaluation would be corrected according to risk value. The risk value method includes both loyalty and workaholism, which means it is an all-purpose method.

2.5. The Balanced Scorecard Indicator methods measure the level of goal achievement in specific perspectives. For example, HR Scorecard (Becker, Huselid, Urlich) provides information concerning human capital in terms of basic human resources management tools: recruiting, rewarding, development, retention. This evaluation method comprises both loyalty and workaholism (cf. Table 4). Table 4. HR Scorecard comprising selected aspects of loyalty and workaholism Process Evaluated aspect 1

Cost

38

Recruiting

Rewarding

Development

Retention

2 Cost of shaping working environment fostering new employees` loyalty (L) / Cost of hiring specialists identifying workaholics during recruitment (W)

3 Benefit costs (for loyalty) (L) / Costs of rewarding soft skills` (c.f. 2.6) (W)

4 Costs of employee training in the most desired type of loyalty (L) / Cost of intervention to change a workaholic (W)

5 Cost of employee retention(L) / Cost of investment in work-life programmes (W)


table 4 cont. 1

Time

Quantity

Quality

Feedback

2 Time for acquiring loyal (L) / nonworkaholic (W) employees

3 Motivation scheme period to improve loyalty (L) / reduce workaholism (W)

4 Period of time for loyalty stages (L) / workaholism stages (W)

Number of rejected jobhoppers in recruitment process (L) / Number of identified workaholics in the process (W) Quality of recruiting loyal employees (L) / non-workaholics (W)

Number of employees included in compensation system improving loyalty (L) / reducing workaholism (W) Quality of rewarding loyalty (L) / intervention to prevent workaholism (W)

Number of managers included in loyalty coaching programmes (L) / workaholism prevention (W)

Managers` satisfaction from acquiring loyal employees (L) / non-workaholics (W)

Employees` satisfaction from rewarding their loyalty (L) / from rewarding antiworkaholism (W)

Increasing percentage of employees highly assessing loyalty training courses (L) / antiworkaholism courses (P)

Differences in retention investments (L) / antiworkaholism (W)

5 Time implementation of employee loyalty scheme (L) / worklife programme (W) Number of employees included in employee loyalty scheme (L) / cognitive therapy to prevent workaholism (W) Quality evaluation of employee loyalty scheme quality (L) / Quality evaluation of behavioral therapy for workaholics (W) Retaining employees` trust included in loyalty schemes (L) / unchanging percentage of workaholic employees suffering from the withdrawal symptoms while at leisure (P)

Source: Our own additions to HR Scorecard shown in: Fitz-Enz (2001, p. 115).

The method can be used to compare value changes of indicators, yet it does not provide a result relating to human capital value determination.

2.6. Saarbrücken formula a) b) c) d) e)

The following parameters are taken into account in the formula: number of full-time employees, market remuneration rate, loss of expertise (determined by the ratio of the industry-determined period of expertise validity and years of service in an organisation), expertise compensation measured by the costs of investment in knowledge development, motivation.

39


Loyalty (in the meaning of attitude) is closely connected with motivation. Employees’ job resignations have an impact on the workforce volume, yet only up to a point as the latter depends on the number of the newly recruited employees. Loyalty measured by the years of service in the organisation results in loss of knowledge (thus, there is a different aspect of the connection between loyalty and expertise than the one indicated at the beginning). The remaining parameters do not seem to have any connection with loyalty. Workaholism, in turn, is connected only with motivation. Therefore, the formula encompasses more parameters related to loyalty than workaholism. Most certainly the formula does not measure the loss of human capital value caused by workaholism and/ /or indicate that the value is maintained due to its efficient prevention (e.g. replacement of traditional motivation systems sanctioning workaholism with “soft” criteria-orientated systems, such as customer satisfaction – Poppelreuter, 2007, p. 179).

2.7. Personnel portfolio Personnel portfolio, as opposed to the methods analysed so far, was drawn up with strategic human resources management in mind, however it may also be used for human capital evaluation. By means of the portfolio, it is possible to estimate (unfortunately only approximately, which is a drawback of the method) the share of individual employee segments, e.g. representing particular loyalty intensity (which is gradable) or the degree of workaholism (cf. Figure 1 and 2) among the employees in an organization.

Loyalty towards the organisation team

Loyalty only towards the team

Figure 1. Personnel portfolio of loyalty intensity

Z

V

X

Y

Divisible loyalty

Indivisible loyalty

Explanation: the most positive influence on building the value of human capital is depicted by square V.

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Workoholism – predicted state

Figure 2. Personnel portfolio of workaholism degree

III

A

B

C

II

D

E

F

I

G

H

K

I

II

III

Workoholism – present state

Explanations: Degree I – low. Degree II – medium. Degree III – high. Interpretation: A, D – unfavorable (from the point of view of human capital) workaholism development leading to the decrease in human capital value (with ceteris paribus), B, E, G – stopping the unfavorable dynamics of workaholism – without influence on the dynamics of human capital value (with ceteris paribus), C – the highest and constant workaholism having negative influence on human capital value F, H, K – achieving the effect of a converted workaholic – influence on the increase of human capital value (with ceteris paribus).

The method enables grasping the dynamics of different loyalty types or workaholics. Thus, it indicates their rising or falling share in individual segments of intra-organizational labor market. Distinguishing these segments may be helpful in optimizing the process of increasing human capital value. The following categories may be distinguished during the process of increasing the value in connection with shaping loyalty within the ERM (Employee Relationship Management) strategy: I. Most Valuable Employees, II. Most Growable Employees, III. Below Zero Employees, due to the fact that “not all internal clients will be satisfied with the same products and personal services” (Stotz, 2007, p. 32). In personal marketing particular attention needs

41


to be paid to the first of the segments – which is a value generator. Otherwise (even in a situation of a surplus of labour supply over labour demand, because it concerns persons who do not have problems finding a new job), the likelihood of job resignations will grow higher and an increase in human capital value will not take place. In the case of workaholism, transitions are also possible, e.g. from a committed workaholic to a workaholic who limits their commitment to work, which may be depicted by means of a portfolio. The size of segments used for indicating changes in the share of particular types of workaholics may be determined via such methods as Work-BAT scale by Spence and Robbins, WART by Robinson and Philips, SZAP by Golińska, KOP Hornowska and Paluchowski or UWES by Schaufeli and Bakker (Szpitalak 2012, pp. 73-79; Golińska, 2008, pp. 54-67). It must be emphasized that these segments may be determined in a quantitative aspect (percentages of employees who belong to them), which opens up the possibility of comparisons and evaluations – even monetary ones.

Conclusions Critical analysis and evaluation of reference books showed that human capital evaluation methods vary with respect to the possibility of including value generators connected with loyalty and devaluators connected with workaholism. In the seven selected evaluation methods, basing on the following groups: cost, revenue, multiplier, indicator, mixed and alternative, it was identified to what degree the determinants of human capital value such as loyalty and workaholism were taken into account and the suitability of the methods was evaluated (cf. Table 5). Table 5. Selected methods of human capital evaluation of/ in an organization and an inclusion of loyalty and workaholism in them Name of the method 1 historical costs method (R.L. Brummet, E.G. Flamholtz, C.P. Pyle) discounted revenue streams method (E.G. Flamholtz)

42

Loyalty Degree/taken Evaluation into account of suitability 2 3

Workaholism Degree/taken Evaluation into account of suitability 4 5

yes, indirectly

low

yes, indirectly

low

yes, directly

medium

yes, indirectly

medium


table 5 cont. 1 Mayo monitor, (A. Mayo) risk value method (A. Lipka)

2 yes, indirectly yes, directly (when the map of personnel risk includes loyalty)

HR Balanced Scorecard (B.B. Becker, M.A. Huselid, D. Urlich) Saarbrücken formula (Ch. Scholz, V. Stein, R. Bechtel) personnel portfolio (G.S. Odiorne)

3

4

5

high

yes, indirectly

high

high

yes, directly (when the map of personnel risk includes workaholism)

high

yes, directly (when it includes indicators concerning employee loyalty)

medium

yes, directly (when it includes the indicator of workoholics

medium

yes, directly in selected parameters

medium

yes, directly in one parameter

low

high

yes, directly (when it includes the degree of employees’ workaholism)

high

yes, directly (when it includes the degree of employee loyalty)

Evaluation basis The degree to which the determinant was considered: a) directly (the selected determinant is included in the formula, there is connection with loyalty or workaholism, b) indirectly (the selected determinant is not included in the formula; specification is required in relations to the selected component of evaluation; there is lack of close connection with loyalty or workaholism). Suitability evaluation: − low (due to false methodological assumptions; there is lack of formula measuring the increase in or loss of human capital value), − medium (distortion of the evaluation due to lack of diversification between revenues brought by a variety of loyal/ workaholic employee types; difficulty in delivering the result in the form of specified human capital value), − high (easiness in determining human capital value).

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Table 5 shows that loyalty is included directly in as many as five analysed methods and workaholism in four of them. Most of the methods takes into account both loyalty and workaholism, thus indicating their universal nature. However, the suitability for including particular generators and devaluators of human capital varies to a large extent. The least suitable method turned out to be the historical costs method whereas the highest suitability value was awarded to the monitor Mayo method, risk value method and personnel portfolio method. The remaining methods present a medium level of suitability for the determination of human capital value taking into account loyalty/workaholism. As part of further research on these issues different typologies of workaholism and loyalty could be taken into account. The hypothesis formulated and verified in the article could be tested by being applied to other generators and devaluators of human capital, such as different aspects of attitudes towards working time.

References Becker B.E., Huselid M.A., Ulrich D. (2002): Karta wyników zarządzania zasobami ludzkimi. Oficyna Ekonomiczna, Kraków. Bochniarz P., Gugała K. (2005): Budowanie i pomiar kapitału ludzkiego w firmie. Poltext, Warszawa. Brummet R.L., Flamholtz E.G., Pyle. C.P. (1968): Human Resource Accounting: A Tool to Increase Managerial Effectiviness. “Management Accounting”, Vol. 50. Buchanan, Gilles (1990): Value Manager Relationship: The Key to Customer Retention and Profit-Ability. “European Management Journal”, Vol. 8, No. 4. Dudycz T. (2005): Zarządzanie wartością przedsiębiorstwa. PWE, Warszawa. Fitz-Enz J. (2001): Rentowność inwestycji w kapitał ludzki. Oficyna Ekonomiczna, Kraków. Flamholtz E.G. (1975a): Assessing the Validity of Selected Surrogate Measures of Human Resource Value - A Field Study. “Personnel Review” Summer, pp. 37-50. Flamholtz E.G. (1975b): The Metaphysics of Human Resource Accounting and Its Implications for Managerial Accounting. “Accounting Forum”, December, pp. 51- 61. Flamholtz E.G. (1985): Human Resource Accounting. Advances in Concepts, Methods and Applications. Jossey-Bass, San Francisco. Gebauer M. (2005): Unternehmensbewertung auf der Basis von Humankapital. Josef Eul Verlag, Lohmar-Köln. Gerpott T.J., Paukert M. (2011): Der Zusammenhang zwischen Mitarbeiter- und Kundenzufriedenheit: Eine Metaanalyse. „Zeitschrift für Personalforschung“, J. 25.

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Golińska L. (2008): Pracoholizm. Uzależnienie czy pasja? Wydawnictwo Difin, Warszawa. Golińska L. (2011): Pracoholik a pracoholik entuzjastyczny – dwa światy? Wydawnictwo Uniwersytetu Łódzkiego, Łodź. Kalinowski M., Czuma I., Kuć M., Kulik A. (2005): Praca. Uzależnienia. Fakty i Mity. Wydawnictwo KUL, Lublin. Killinger B. (2007): Pracoholicy. Szkoła przetrwania. Dom Wydawniczy Rebis, Warszawa. Krawiec F. (2010): Kultura biznesu firmy. Szkoła Wyższa im. Bogdana Jańskiego, Warszawa. Lipka A. (2007): W poszukiwaniu metody wyceny kapitału ludzkiego. In: Ed. A. Lipka, S. Waszczak: Zarządzanie wartością kapitału ludzkiego. Wydawnictwo Akademii Ekonomicznej, Katowice. Lipka A. (2012): Pojęcie i ekonomiczna wartość lojalności pracowników. In: Ed. A. Lipka, A. Winnicka-Wejs, J. Acedański: Lojalność pracownicza. Od diagnozy typów lojalności pracowników do Zarządzania relacjami z pracownikami (Employee Relationship Management). Wydawnictwo Difin, Warszawa. Lipka A., Król M., Waszczak S., Satoła M. (2008): Wartościowanie kapitału ludzkiego organizacji (problemy metodyczne i próby ich rozwiązywania). Wydawnictwo Akademii Ekonomicznej, Katowice. Lipka A., Waszczak S., Winnicka-Wejs A. (2013): Aktywność twórcza a pracoholizm. Jak utrzymać kapitał kreatywności pracowników? Wydawnictwo Difin, Warszawa. Martin J.E., Heaulme P.-F. (1998): Risk Management: Techniques for Managing Project Risk. Field Guide to Project Management. Van Nostrand Reinhold, New York. Mayo A. (2001): The Human Value of the Enterprise. Valuing People as Assets: Monitoring, Measuring, Managing. Nivcholas Brealey Publishing, London. Mcrae R.R., Costa P.T. Jr (2005): Osobowość człowieka dorosłego. Perspektywa teorii pięcioczynnikowej. Wydawnictwo WAM, Kraków. Meissner U.E. (2005): Die „Droge” Arbeit: Unternehmen als „Dealer“ und als Risikoträger – personalwirtschaftliche Risiken der Arbeitsucht. Peter Lang Verlag, Bern. Meyer J.P., Allen J.N. (1991): A Three Component Conceptualization of Organizational Commitment. „Human resources Management Review“, Vol. 1, No. 1. Oates W. (1971): Confessions of a Workaholic. The Facts about Work Addiction. World, New York. Poppelreuter S. (2007): Arbeitssucht. Psychologie Verlag Union, Weinheim. Porter G. (1996): Organizational Impact of Workaholism: Suggestions for Researching the Negative Outcomes of Excessive Work. “Journal of Occupational Health Psychology”, No. 1. Reichheld F.F., Teal T. (2007): Efekt lojalności. Ukryta siła rozwojowa Twojej firmy. Helion, Gliwice.

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Scholz Ch., Bechtel R. (2005): Zehn Nutzen der Saarbrücker Formel. „Personalwirtschaft”, Nr 11. Schüler A.M., Fuchs G. (2005): Marketung lojalnościowy. Total Loyalty Marketing. Jak z zadowolonymi klientami i lojalnymi pracownikami osiągnąć sukces firmy. Akademia Sukcesu – HDT Consulting, Warszawa. Šmid W. (2003): Psychologia i socjologia zarządzania. Słownik terminów. Wyższa Szkoła Zarządzania i Marketingu, Sosnowiec. Städele M. (2008): Arbeitssucht und die zwanghafte Persönlichkeitsstörung. Eine theoretische und empirische Auseinandersetzung. VDM Verlag Dr. Muller Aktiengesellschaft, Saarbrücken. Stotz W. (2007): Employee Relationship Management. Der Weg zu engagierten und effizienten Mitarbeitern. Oldenbourg, München-Wien. Szpitalak M. (2012): Wielowymiarowy kwestionariusz oceny pracoholizmu. Wydawnictwo Uniwersytetu Jagiellońskiego, Kraków. Wojdyło K. (2010): Pracoholizm. Perspektywa poznawcza. Wydawnictwo Difin, Warszawa.

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Dominika Latusek‐Jurczak Kaja Prystupa‐Rządca Kozminsky University, Poland

COLLABORATION AND TRUST-BUILDING IN OPEN INNOVATION COMMUNITY

D OMINIKA L ATUSEK -J URCZAK , K AJA P RYSTUPA-R ZĄDCA

Abstract Growing popularity of open innovation communities poses various challenges for business practice. One of them is trust, which facilitates social interaction, provides basis for risk-taking and strengthens cooperation. In virtual environment traditional mechanisms of its development are unavailable. However, in many companies using virtual teams trust is created, maintained and capitalized, which provides indication that it may be developed in other ways. In this paper, the authors present a study of work within testing community in computer game industry based on two-year qualitative fieldwork, which may serve as an example of trust emergence in virtual environment. Keywords: open innovation community, trust, virtual teams.

Introduction The advancement of the web and mobile communications has led to a globally shifting movement away from business’ brick and mortar team structures to innovative technical teams working with more interactively connected technologies. The growing popularity of open innovation communities is grounded in the idea that “firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as they look to advance their technology” (Chesbrough, Vanhaverbeke, West, eds., 2006). However, the use of open innovation communities poses various challenges for all actors engaged. One of them is trust, which is particularly difficult to create, maintain and repair in virtual environments (Cook, Snijders, Buskens, Cheshire, eds., 2009; Knights, Noble, Vurdubakis, Willmott, 2001). This paper applies a two-year qualitative fieldwork within the computer game industry testing community to develop a newly applied understanding of trust challenges in the open innovation communities. Trust facilitates social interaction, provides basis for risk-taking and strengthens cooperation. Trust is a necessary component of team environments supporting and executing innovation. Traditionally, trust building processes are enabled by mechanisms such as repeated interaction (e.g. ensuing familiarity) and stabilizing third parties (e.g. institutions in various forms). In the context of distributed teams (Bosch-Sijtsema, Fruchter, Vartiainen, Ruohomäki, 2011) and

48

COLLABORATION AND TRUST-BUILDING…

cooperation taking place in virtual environments (Cook et al., eds., 2009), these traditional mechanisms are usually unavailable. The open innovation community can be defined as “as a group of unpaid volunteers who work informally, attempt to keep their processes of innovation public and available to any qualified contributor, and seek to distribute their work at no charge” (Flemming and Waguespack, 2007, p. 166). The model gained popularity in knowledge-driven sectors, inter alia through game development companies. Taking into account rapidly changing industry trends and customers’ preferences, the game development market is considered risky business venture, since ultimately the game may not meet customers’ preferences, and such preferences may be more nuanced and difficult to understand across virtually diverse communities (Prato, Feijoo, Nepelski, Bogdanowicz, Simon, 2010). To minimize such risk companies customarily test their products before officially launching them by engaging people from outside of the organization. Game production companies use different strategies of implementing external gamers’ into their projects. They vary in their decision about when to engage the outsiders, from where to acquire them, how to communicate with them, and how they should protect their product legally. The number of game testers in focus groups differs according to the size of the game; however, business practice suggests it not smaller than several dozen. Smaller organizations possessing limited budgets cannot afford to pay for testing, and they often seek volunteer testers. At the same time, for small companies, the need for rigorous testing phase is even more essential; due to limited resources, they depend much more than on the success of each single game than big companies do. This creates an interesting situation where, on the one hand, a company needs to guard its intellectual property since its loss would be equal to the failure of the product. On the other hand, however, the company needs to disclose sensitive information about the game to the group of volunteers who cannot be effectively monitored and controlled.. Prior research indicated that trust is the alternative mean of control in the case when legal or official protection is unavailable. However, the development of trust in virtual environment is more complicated than in traditional circumstances of cooperation. Therefore the aim of this research is to examine the how is trust created, maintained and capitalized on in open innovation communities. The study has exploratory nature as this issue has not been examined at large (Fleming and Waguespack, 2007). In the first section of this article we examine possible sources of trust in case of no prior experience with peers which is the situation common in open

49


innovation communities. In the empirical part we present the case of Cubicon, the company that engaged the community of gamers in testing of their new product. In the discussion part we use the concept of swift trust to explain the process of trust building in the specific circumstances of the case.

1. Types of trust Trust can be defined as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” (Mayer, Davis, Schoorman, 1995, p. 712). Trust is usually a product of repeated interaction; it is gained in time through mutual group member experience in collaboration and is supported by stable environments and third parties. But contemporary life has situations where participation and collaboration is required without any prior knowledge of partners (Latusek and Cook, 2012). Existing research indicates three possible sources for trust in situations when previous experience is unavailable: − Stable institutions (Latusek and Cook, 2012), − ‘Generalized trust’ (Sztompka, 1999), − ‘Swift trust’ (Meyerson, Weick, Kramer, 1996).

2. Stable Institutions Networks are a key source of social capital, and they reflect how rich in social capital that the society is (Cook, Hardin, Levi, 2005; Lin, 2001). In complex environments characterized by uncertainty, as are most contemporary societies, exchanges may occur among actors who are able to restrict or limit uncertainty to a level that makes the risk of cooperation acceptable. This security may be provided either by some form of reliable institutional backing when trust is unavailable. In modern societies, where most exchanges are impersonal, institutions play a crucial role (North, 1990). Note that in those settings where trust matters most (under high uncertainty and obvious risk) individuals are least likely to rely on trust and most likely to require more formal mechanisms of coordination and control (Cook, Rice, Gerbasi, 2004). ‘Reliability’, however, does not equate to requirement of trustworthiness, and we know that partners may be reliable due to institutional forces 50


(Cook et al., 2005). Supporting this claim empirically, Yamagishi, Cook, Watabe (1998) experimentally separated assurance relations from trusting relations. It is important to remember that those institutions may lead to more secure and cooperative behavior, they may not automatically produce trust (e.g. Latusek and Cook, 2012; Sitkin and Roth, 1993; Yang, 2007).

3. Generalized Trust People exhibit different levels of trust; some of people are more trusting, and some are less trusting (Fink and Kessler, 2010; Fukuyama, 1995; Putnam, 1993; Putnam, 2000; Yamagishi and Yamagishi, 1994). The propensity to trust which Tanghe Wisse and van der Flier (2010) define as the extent to which people have a general belief in the goodness of human nature is an amalgamation of various factors, but, to a large extent, it is also a culturally learned attitude (Mayer et al., 1995; Tanghe et al., 2010). Cultural attitudes emerge from accumulation of collective experiences shared by groups of people; in other words, they are a product of history (Sztompka, 1999). As part of collective framework of perception and interpretation, trust governs individuals’ behavior. In Poland, the level of ‘generalized trust’ is comparatively low (Gerbasi and Latusek, 2012). Such research finding of this is explained usually through the country’s history (Sztompka, 1999). Today, Poland is still transitioning out of its time under communist rule, even though it has been 20 years from the start of transformation. The socialist state fostered a culture of suspicion and hostility, and the little social capital that remained after decades of life under Soviet domination was subsequently destroyed by transformation to a capitalist state of the early 1990’s. Under the socialist state, there was a strong reliance on closed networks of trust. Individuals accomplished many everyday tasks outside of the state system through networks of trusted associates (Marin, 2002). The uncertainty that accompanied the transition away from socialist rule reinforced this reliance on interpersonal bonds, which provided security and continuity. While closed networks, such as a person’s relationship with their family members, provided a safety net during times of change and uncertainty, an individual’s reliance on these networks had negative consequences; this created a base for corruption and cronyism (Peev, 2002; Rose-Ackerman, 2001a; Rose-Ackerman, 2001b). The patterns of social life formed under Soviet domination turned out to be a two-folded problem, because, in one way, distrust towards the state and reliance on personal connections was a useful defense against oppression and

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provided shelter from totalitarian control. In comparison, it also did not contribute to building a more open society (Baier, 1986; Cook and Gerbasi, 2009; Hardin, 2002; Meyerson et al., 1996; Williamson, 1993)

4. Swift trust In an environment where traditional way of trust emergence is hindered by time restraints or limited interaction between actors (e.g. temporary groups), researchers have proposed a notion of ‘swift trust’ (Meyerson et al., 1996). It is portrayed as a unique form of collective group members’ perception and relating that is capable of managing issues of vulnerability, uncertainty, risk and expectations’ (Meyerson et al., 1996). ‘Swift trust’ emerges in circumstances where there is limited pool of possible coworkers, which increases the speed of diffusion of information about each performance; in turn, this makes an individual’s reputation more vulnerable. For instance, freelancers, who being part of an industry-specific network, are very vulnerable to others’ opinions because it may affect their future employment possibilities. Moreover, a contractor’s reputation, for instance team leader, plays an important role in the team development and acceptance process as he or she is entrusted with selection of team members (Kawin, 1992). In other words, team members presume that their engagement in a project was measured on conscious criteria. Task-related work enhances role-based interaction and emergence of more stable and standardized expectations based upon terms of task and specialties (Meyerson et al., 1996). Moreover, cooperation in such circumstances ensures more interactions that are frequent and provide immediate experience with another partner. If there was a possibility of many immediate disappointments in cooperation after this experience, a more rapid development of trust or distrust would be expected (Gambetta, 1988). ‘Swift trust’ will appear in situations where uncertainty is high and unacceptable, and there are premises showing trustworthiness, as social situations provide a cultural expectation of good will rather than ill will (Meyerson et al., 1996). To reduce uncertainty, people rely on predisposition, categorical assumptions and implicit theories to move them toward the greater certainty of trust or distrust (McKnight, Cummings, Chervany, 1998). Moreover, ‘swift trust’ emerges in situations where the risk of disappointment is smaller than the value of advantages associated with taking this risk

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5. Method The aim of this research was to discover how trust develops in open innovation community. The research question was exploratory in nature, as mechanisms of trust development in such environment were not examined in prior research. For this purpose, the authors used qualitative approach based on grounded theory (Glaser and Strauss, 1957; Konecki, 2000). According to this methodology, the development of theory is the derivative of empirical data analysis, which directly refers to observed reality. The nature of the research question that examines social process induced the author’s decision to use qualitative analysis. The authors decided to choose Cubicon case for several reasons. First, it describes the development of product which was later recognized by the public as one of the best games worldwide of in its’ niche. Second, it serves as example of successful virtual collaboration. Third, Cubicon embodies common ambitions of many indie developers a group of friends with ambitious goal to manage small development studio and design worldwide-known games. As the method of qualitative analysis, the authors followed a case study approach (Yin, 2003). The basic techniques of data collection were semi-structured interviews, a company blog, and an online forum. The interviewees were asked about: game development process, launch and functioning of the community, management of critical situations in the project and reactions of other members, premises of trust development towards others, and risk associated with cooperation. Blogs are particularly useful in qualitative research as they allow researchers to examine social processes over the time, having insight into everyday life of the team members (Hookway, 2008). Whereas, online forum was a rich source of evidence of community members interactions. The interviews were conducted in the period of April-June 2012 with all employees of the company; the documents used in the analysis were from the period 20.11.2006-29.07.2012, consisting of around 450 pages of documentation. Data was coded and analyzed with qualitative research software Dedoose. To maintain credibility of the results, the authors used the data triangulation method. The identities of the interviewees in the text are coded according to the agreement between the researchers and the organization under its study.

6. The case Cubicon was a small game development company with four full-time contractors based in Poland. A young Polish game designer Greg Grudzinski and his

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previous coworker, graphic designer, Lena Czerwona, launched it in 2011. The rest of the small team worked from a distance. Uwe Andreassen, a Norwegian programmer who became acquainted with Greg through an online community devoted to Wizzardy. They already had a chance to work on one project. The other specialist hired, Bob Eastman, was a British music composer who also worked earlier with Greg and maintained contact with him through community. The founder, Greg, himself was an experienced game designer who worked both for small and big Polish companies, starting his professional carrier at the age of 16. The company focused on development of games from the niche genre called visual novel, targeting the segment of well-educated women of the age 20-35. Despite being experienced in game development, the team disposed limited knowledge about this particular genre. However, the decision to enter the segment was based on several premises. Namely, visual novels had lower production cost and were developed quicker than other games, such as, for example, RPG*, the demand exceeded supply, and the team would be able to present their unique graphic skills. The team fought to compensate the lack of knowledge and experience by conducting market research through reading different forums, blogs and playing games. Because of the investigation results, they developed project called The Snow White. Not having enough experience in visual novels, Greg decided to engage a gamers’ community to test the project.

7. Motivations to engage Cubicon’s online gaming community Greg started active participation in the gamers’ community when he was 10 years old. He was not only playing games, but also commenting others work. He admired people who non-professionally developed games. Finally, Greg decided to give himself a chance and designed small RPG game, Wizzardy. In similar vein as other developers he consulted it with the gamers’ community. Finally, he published the game on his own website and launched forum, in order to receive more feedback. Online forum, where the game was discussed, was not only a place for bug reporting, but also served as a general discussion about Wizzardy and other interesting games. Some of forum members volunteered for beta testing. Among them was Uwe, who had dual motivation in helping. *

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RPG – role-playing game, where the gamers takes the role of a character in fictional setting.


“I was fascinated with that game, but it had many bugs. I wanted to see a better version of it. […] That time I planned to develop my own game. Beta-testing seemed good opportunity to get useful experience”. (Uwe) Uwe respected Greg for the effort and courage that he presented by improving his game. Meanwhile, Greg started his professional career at the biggest Polish game company as a game tester. The company presented a completely different strategy towards knowledge sharing than he had been accustomed to in his online Cubicon’s gaming community. His new employer also organized beta testing sessions before each product launch. However, the procedure looked different than he experienced earlier. Testers were invited to a big conference room; they could not leave the room unsupervised before the official end of game beta testing. Moreover, the company introduced various procedures to secure confidentiality and intellectual property of their products. Greg did not appreciate this approach and decided that he would have never followed such methods. When Greg decided to start his own business, he was sure about engagement of the gamers’ community in the project. The online community gathered around his earlier production was a ready-made solution.

8. Members of the Cubicon’s community forum The forum was opened for everyone interested in Greg’s productions. The primary condition permitting access to discussion was registration, which required electronic submission of a nickname, surname, and email address. The profile was verified through email confirmation. The demo version of the game was available for all after it was published on website of the company. However, the access to more advanced versions was restricted to those community members who expressed interested in testing. The finished game was not available online, but it was transmitted to each person individually via email. Participants of the forum were from different countries, backgrounds and professions (Table 1). Membership included gamers as well as small game producers of various experience levels and industries. As the forum was primary dedicated to a genre of RPG games, the fans of visual novels started to join gradually. Information about the new visual novel production was spreading through various online communities. Individuals fascinated with this genre and actively participating in online communities were a rather small group where most members knew each other through online interactions.

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Table 1. Resume of bios of some community members Participant A B C D E F G H I J

Description Game producer with 12 years of experience in the industry; journalist of one of the biggest international game portals; winner of East Design Contest 23-years old biology student; fan of RGP and visual novels living in U.S. California 23-years old American studying Japanese linguistic in Japan; fan of visual novels 18 year old American; started playing RPG games; presently fan of visual novels 20- years old; lives in the East of U.S.; poetry lover Australian who finished programming at the university; tried to develop games on his own 22-years old British programmer from big international game development company Norwegian consultant; afterhours game developer 16 years old American, developing games since the age of 10 Teacher of mathematics form Louisiana

As on most of online forums, Greg introduced a post calculator that enabled track frequency of each member’s participation in discussion. The number of posts written on the forum was deciding about the rank of each participant. Thus, testers could easily determine the engagement of others participating in discussions of community. Moreover, being active on the forum allowed to build reputation among gamers.

9. Rules of the community Greg did not impose any rules by himself to community operation. At the very early beginning of forum existence, one of the community members posted general rules of behavior, which were standard requirements in the virtual environment. “These are the general forum rules: no spamming, no insulting, no bad words, no flaming don't go off-topic in topics, no nudity, no other bad things you can think of that they're bad. Please follow these rules and everything will be fine”. (A, posted on 29.03.2007) Rules were not violated by the community members. Interestingly, the topic presenting the rules had been viewed only 2596 times (data from December 2012) which made it one of the least popular topics broadcasted by forum members. While working on The Snow White, Greg maintained a style of interaction on the forum as when he was working on Wizzardy, leaving space not only for his project. Often, as a seasoned game developer, he served as a mentor providing development advice and assistance. He kept his responses as immediate as possible, to

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maintain trust, and in cases of prolonged silence, always apologized. Open for criticism, Greg answered all questions and discussed reasoning of his decisions. “Hey, sorry for a bit late reply to this. Major thanks for the feedback. Posts like this are very useful for me as a developer. I can't promise I'll fix everything in The Snow White (I'll try though) (…) [responses to propositions –AUT]: 3) This is actually a technical limitation resulting from how the scene system was made. The game is able to rewind only within the current scenario (a mini segment of a scene). I know it can be a problem and I'll try to find a solution for it eventually. It's something that is more likely to happen in the future project, though. 4) Okay, I can add that. It's a bit more complex, so I'm not sure if I'll do it in The Snow White or in the next game” (Greg, posted 11.07.2012) In his opinion, any advice required comment since individuals devoted their time to prepare it. Following such principles required great effort from Greg; sometimes, he barely had time to implement recommended corrections. Greg tried to compensate from vague community support by sharing details of the game development and releasing upgraded versions. He discussed personalities of the characters and possible scenarios, as those were aspects of the community gamers’ interest. Participating in the decision-making process engaged community members even more in the game.

10. Findings For many game producers, the knowledge about product details is guarded by security systems. Employees are obliged not to disclose information about ongoing projects. Nevertheless, some small firms as Cubicon consciously take the opposite strategy and share their project with a wide range of people by engaging gamers’ communities in testing from the very beginning of production process. Yet, a company risks loss of their product. As discussed in prior sections, the company possesses little control over the behavior of community members. The relation between the company and community is based on mutual risk and trust bets. The risk that Greg took was quite immense. Seasoned community members could have easily stolen, copied, or illegally distributed his game idea. Although, formally, his intellectual rights were secured, within the legal jurisdiction system in Poland, potential misconduct of one of testers would most probably have gone

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unpunished. Greg’s forum for discussion about The Snow White enabled him to receive many helpful suggestions from the testing community. Moreover, he compensated his lack of knowledge about the visual novel niche through community members who provided him with segment preferences. As a result, the game won awards in various game contests and gained popularity among online gaming players. Moreover, Greg gained valuable information about marketing activities available for indie developers, like his company, Cubicon, and the methods of negotiation within different publishers. Thus, he was able to plan the promotion and effective development of the game, and he modified the overall company strategy more efficiently. Community members entrusted Greg with their time and online reputations. Some of them even financially contributed to the game by pre-ordering his game and paying online. When the company started lacking financial resources, they believed that Greg would finish the project, and their contribution would not be wasted. Their trust was put to the test a few times due to the often changing the project deadlines. “Do you still plan to release the Snow White? As a customer, which purchased the pre-order, i start to worry”. (online forum member, 12.04.2012) When some of the forum members started to ask whether the game was going to be published, Greg devoted a lot of attention to such posts and tried to explain the delays. “We’re very sorry that the development takes longer that it was planned. […] We try to make good game, and we are stuck with correcting it […] the money that you gave us allowed to pay electricity bills in November”. (Greg posted on 12.04.2012) In addition Greg started to publish on the company’s blog the descriptions of the project progress and more screens from the game. He also was transparent with the money he received providing financial information on how he spent the donations.

11. Discussion As the case indicates, in the open innovation community that we studied, the traditional mechanisms facilitating trust building were missing. The fieldwork, however, indicates that trust indeed existed between members of the online gaming community, and the collaboration resulted in the successful launch of the product, a visual novel named The Snow White. In this concluding section of the paper, we would like to describe how elements from three concepts related to trust building were creatively used by

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community. First, as far as institutions are concerned, the issue of reputation in online interactions should be discussed. People participating in the forum had risked their reputations online and offline, as they willingly disclosed their identities and made tangible contribution to the development of the game. Moreover, through participation in the forum, they built their recognition and credibility among other gamers occupying the niche (Jemielniak, 2013). The process was time-consuming as the registration on the forum required no confirmed credentials, which in prior research were indicated as a condition of effectiveness of social activities (Johnson, 1997). The recognition in the community is important from the point of creating an expert position, but also, it provides insight into possibilities of future cooperation within a team, such as in the case with Uwe and Bob. Second, considering “generalized trust”, the phenomenon of choosing environments that are collaboration friendly, where the available context, (in the case: Poland) is characterized by a low-level of generalized trust (Gerbasi and Latusek, 2012). Transfer of interactions to virtual reality and international composition of the group allowed the online community to overcome difficulties associated with the national cultural framework. Finally, “swift trust” interactions within the community we studied were focused on a role-task approach, which reinforced the professionalization processes within the group (Meyerson et al., 1996). Specific behaviors displayed by Greg and his online Cubicon gaming forum collaborators included keeping short-term promises, applying quick response time to messages between members, and having a goal-orientation motivation bring to mind the tools facilitating the emergence of “swift trust” within the online gaming community. This research brought interesting insight to the debate about necessity of development of interpersonal trust in online communities. While some authors (Jones and Bowie, 1998; O’Leary, Orlikowski, Yates, 2002) underline its’ significance other researchers (Jemielniak, 2013) claim that in may be substituted by bureaucratized procedures. The Cubicon case indicates that trust may be formed as a mixture of institutional measures and norms ruling the cooperation of open innovation community. Presented study has some limitations. The chosen method of inquiry, i.e. qualitative approach based on single case study method, does not allow for statistical generalization of the results. Therefore there should be conducted more elaborated research that would operationalize the presented model of trust development in open innovation communities and verify it on a larger population. Moreover, authors following this research method should be careful with pre-

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senting recommendations for practitioners as the obtained results may be context sensitive. Therefore, researcher should supply readers with dense description (Lincoln and Guba, 2009), in order to allow them to measure the degree of transferability of findings into particular context. Nevertheless, the result of this comprehensive literature review and analysis will help future virtual team leaders and gaming founders fully understand respond to the leadership challenges of trust and communication in open innovation communities, further opening expansive networks of connective solutions that encourage an inspire innovation. Acknowledgements: The project was financed from the funds of National Center of Science upon decisions no DEC- 2011/01/N/HS4/04414.

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Frédéric Le Roy University of Montpellier 1 and Groupe Sup de Co Montpellier

Famara Hyacinthe Sanou University of Montpellier 1 – ISEM

DOES COOPETITION STRATEGY IMPROVE MARKET PERFORMANCE? AN EMPIRICAL STUDY IN MOBILE PHONE INDUSTRY

F REDERIC L E ROY , F AMARA H YACINTHE S ANOU

Abstract A central question about coopetition is its impact on performance. Past researches on this question obtained mixed results. No past researches have attempted to evaluate the impact of coopetitive strategies on performance compared with other strategies vis-à-vis competitors: aggressive, cooperative or coexistence strategies. In addition, there have been few studies that attempt to establish a relationship between coopetitive strategy and market performance. In order to fill these gaps, this research studies the impact of coopetitive strategy on market performance, compared to the impact of aggressive, cooperative and coexistence strategies. An empirical study is conducted in the mobile telephony industry. The method is a structured content analysis that identifies the strategic movements of mobile operators from different countries and geographical regions. The results show, first, that only three strategies may be identified in the industry: aggressive, cooperative and coopetitive. The results show, second, that the market performance depends on the strategy adopted toward competitors. A coopetition strategy seems to perform better than either an aggressive or a cooperative strategy. An aggressive strategy is more effective than a cooperative strategy. Keywords: aggressiveness, cooperation, coopetition, market performance, mobile telephony industry.

Introduction Since the seminal book of Brandenburger and Nalebuff (1996), coopetition has been the subject of an increasing amount of research in the field of strategic management. Researches on coopetition have been developed in many directions, to the point that today it is difficult to make a complete synthesis (Yami et al., 2010; Bengtsson and Kock, 2014; Czakon et al., 2014). An essential question asked about coopetition is that of its impact on performance. From the beginning, coopetition theory has been resolutely normative. For Brandenburger and Nalebuff (1996), coopetition is a strategy that will lead to better performance. This normative point of view has not been questioned and is always considered as relevant in coopetition theory (Bengtsson and Kock, 2014; Czakon et al., 2014). Some past researches are dedicated to establish empirically a relationship between coopetition strategies and performance. In this way, some first studies

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DOES COOPETITION STRATEGY IMPROVE MARKET PERFORMANCE?…

highlight the impact of alliance between competitors on economic and financial performance (Luo et al., 2007; Ritala et al., 2008; Oum et al., 2004; Kim and Parkhe, 2009). Other studies attempt to show the impact of cooperation among competitors on innovation (Belderbos et al., 2004, Neyens et al., 2010; Nieto and Santamaria, 2007; Peng et al., 2011). Latest studies directly use the concept of coopetition to attempt to establish its impact on economic performance (Marques et al., 2009; Morris et al., 2007), on innovation (Quintana-Carcias and BenaviedsVelasco, 2004; Le Roy et al., forthcoming) or on market performance (Ritala, 2012). Researches which study the link between coopetition strategy and performances are however far from exhausting the subject. They do not identify the impact of coopetition strategies on performance compared with the impact of other strategies. The supposed superiority of coopetition strategies over other strategies, like aggressive strategy or cooperative strategy, has therefore never been tested empirically. It is also noteworthy, except Ritala (2012), that none of this research addresses the impact of coopetition on market performance. In the original definition of Brandenburger and Nalebuff (1996), coopetition is supposed to allow rivals to increase the overall value they create for the customer, which should enable them to develop their sales. But just one empirical study is dedicated to this central point of coopetition theory (Ritala, 2012). The present research therefore aims to fill this double gap, by trying to establish empirically the impact of coopetition strategy on market performance, and by comparing it with the impact of cooperative and aggressive strategies. To this end, this research analyzes the strategies implemented by firms in the sector of mobile telephony. The method used is structured content analysis. This method makes it possible to identify the strategic movements of mobile operators from different countries and geographic regions. The results show that mobile operators adopt different strategies in the same industry, deciding to follow an aggressive strategy, a cooperative strategy or a coopetitive strategy. There is also the possibility that firms might adopt a strategy that we describe as the coexistence strategy, where show neither a strong tendency to cooperate, nor a strong tendency to aggression. However, in our sample there appear to be no firms that adopt this strategy. The results also show that the market performance of a firm depends on the strategy it adopts toward its competitors. A coopetitive strategy appears to perform better than either an aggressive strategy or a cooperative strategy. An aggressive strategy appears to perform better than a cooperative strategy. The results obtained in this research contribute significantly to the literature on coopetition. This is the first study comparing the relative impact of aggres-

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sive, cooperative and coopetitive strategies on market performance. It is the first time that these three strategies have been clearly identified in a sector of activity. It is also the first time that the superiority of coopetitive strategy over the other two strategies has been shown empirically. Finally, this research shows that an aggressive strategy is the second best strategy, while a cooperative strategy appears less effective for market performance.

1. Theoretical background Coopetitive Strategies Coopetition theory was first formulated by Brandenburger and Nalebuff in the mid-1990s (Brandenburger and Nalebuff, 1996). Authors use game theory to propose a first model of coopetition centered on the “value network”. Coopetition appears when two rival actors decide to cooperate together to create value for customers. Coopetition is a reconciliation of interests between “complementors” who cooperate while remaining competitors. In this way of thinking, coopetition is “a dyadic and paradoxical relationship that emerges when two companies cooperate in some activities, and at the same time compete with each other in other activities” (Bengtsson and Kock, 1999). The idea of cooperating while remaining competitive is a break from the dominant conception (Yami et al., 2010; Czakon et al., 2014; Fernandez et al., 2014). In this dominant conception, competition and cooperation are seen as opposites, implying that as competition increases, cooperation decreases, and vice versa. The concept of coopetition introduces a cognitive revolution in which cooperation and competition can occur simultaneously between actors who become partner-adversaries, in other words coopetitors. The simultaneity of competition and cooperation is thus the foundation of the concept of coopetition (Czakon et al., 2014; Fernandez et al., 2014). This new conception and its implications have been initially developed by Lado et al. (1997), although, paradoxically, these authors did not use the term of coopetition. Lado et al. (1997) observe that more and more companies are combining aggressive and cooperative strategies. They rely on game theory, the Resource Based View and social network theory to show that, although competition and cooperation have been previously regarded as opposite ends of a continuum, they must now been understood as two independent dimensions. So firms could choice their cooperative orientation, high or low, independently from their com-

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petitive orientation, also high or low. In the same way, for Luo (2007), firms could choice independently to maintain or not a strong competition with rivals and to maintain or not a strong cooperation with these rivals. The recognition of this independence between competition and cooperation is fundamental because it leads to the idea that a company can have four types of strategies (Table 1). It could decide to be aggressive toward its competitors while limiting cooperation with them. This strategy is called “competitive rent seeking behavior” by Lado et al. (1997) and “contending situation” by Luo (2007). We called here this strategy “aggressive strategy”. Conversely, the company could decide to be less aggressive as possible with its competitors while cooperating strongly with them. This strategy is called “collaborative rent seeking behavior” by Lado et al. (1997) and “partnering situation” by Luo (2007). We call this strategy “cooperative strategy”. The company may also decide to be as less aggressive and as less cooperative as possible with its competitors. This strategy is called “monopolistic rent seeking behavior” by Lado et al. (1997) and “isolating situation” by Luo (2007). We call this strategy “coexistence strategy”. Finally, the company may choose to be very aggressive toward its competitors while cooperating also strongly with them. This strategy is called “syncretic rent seeking behavior” by Lado et al. (1997) and “adapting situation” by Luo (2007). We call this strategy “coopetitive strategy”. Table 1. Strategies vis-à-vis competitors Propensity to aggressiveness low

high

high

cooperative strategy

coopetitive strategy

low

coexistence strategy

aggressive strategy

Propensity to cooperation

Sources: Adapted from Lado et al. (1997) and Luo (2007).

The concept of propensity to aggressiveness has its roots in competitive dynamic researches (Smith et al., 1992; Young et al., 1996; Ferrier et al., 1999; Ferrier, 2001; Ferrier et al., 2002; Offstein and Gnyawali, 2005). This school of thought considers competition from a behavioral point of view. Strategy is defined as a set of competitive actions and reactions. The competitive aggressiveness of a company is a multidimensional concept that is defined as the propensity of the company to proactively and intensively initiate competitive actions and

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respond quickly to competitive actions of its rivals (Ferrier et al., 2002). According to competitive dynamic researches, a company will be considered as having a high propensity to aggressiveness if it initiates many competitive actions, more varied and faster than competitors, and respond rapidly to competitive actions initiated by competitors (Ferrier, 2001). The concept of propensity to cooperation has its roots in networks researches (Granovetter, 1985; Burt, 1992; Miles and Snow, 1992; Nohria, 1992; Baum and Dutton, 1996; Gulati et al., 2000). According to Granovetter (1985), Thorelli (1986) or even Jarillo (1988), firms are considered as part of a network of relationships that influence their behavior. The network itself refers to two or more organizations involved in cooperative relationships (Thorelli, 1986). This network provides a number of resources and allows the company to develop its own stock of resources and skills. The position in the network is considered as the key element that determines the resources and expertise that the company can control. This position is expressed in the form of centrality within the network. According to Faust (1997), centrality is defined as the ability to be active in a network, or “degree centrality”, the capacity to intermediate the flow of resources between actors, or “betweenness centrality”, or the ability to be in relationships with other actors who are themselves central, or “eigenvector centrality”. According to network researches, a company will be considered as having a high propensity to cooperation if occupy a central position in the network by multiplying formal and informal exchanges with competitors. Once defined propensity of aggressiveness and propensity to cooperation it’s possible to define aggressive, cooperative, coexistence and coopetitive strategies. 1. The aggressive strategy consists in having a high propensity to aggressiveness and a low propensity to cooperation. In this strategy firms 1) initiate many competitive actions, more varied and faster than competitors, and respond rapidly to competitive actions initiated by competitors while 2) not occupying a central position in the network and minimizing formal and informal exchanges. 2. The cooperative strategy consists in having a low propensity to aggressiveness and a high propensity to cooperation. In this strategy 1) firms initiate competitive actions on fewer occasions, with less variety and less rapidly than competitors while 2) occupying a central position in the network by multiplying formal and informal exchanges with competitors. 3. The coexistence strategy consists in having a low propensity to aggressiveness and a low propensity to cooperation. In this strategy 1) firms initiate

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competitive actions on fewer occasions, with less variety and less rapidly than competitors while 2) not occupying a central position in the network and minimizing formal and informal exchanges. 4. The coopetitive strategy consists in having a high propensity to aggressiveness and a high propensity to cooperation. In this strategy firms 1) initiate many competitive actions, more varied and faster than competitors, and respond rapidly to competitive actions initiated by competitors 2) while occupying a central position in the network and increasing formal and informal exchanges with competitors. In our framework, aggressive strategies, cooperative strategies, coexistence strategies and coopetitive strategies are conceptually distinct. However, there was no empirical study that determines if these strategies are significantly different in an industry. In line with theories of coopetition, we assume that the coopetitive strategy is a specific strategy, distinct from aggressive, cooperative and coexistence strategies. We formulate, thus, the following hypothesis: Hypothesis 1: Coopetitive strategy is a strategy significantly distinct from aggressive, cooperative and coexistence strategies.

2. Coopetitive strategy and performance The pioneering researches on coopetition consider that this strategy should become an alternative to strategies based on pure cooperation and strategies based on pure competition. Brandenburger and Nalebuff (1996), Lado et al. (1997) and Bengtsson and Kock (1999, 2000) agree that coopetition is a strategy that holds the greatest potential for firms’ performance or, at least, has the greatest impact on variables clearly identified as likely to make them more efficient. Cost savings, sharing of resources and stimulation that promote innovation are among the potential gains from this strategy. A company that follows a coopetitive strategy is in a position where it can benefit from the advantages of both competition and cooperation. Competition pushes firms to introduce new product combinations, to innovate, to improve products-services, and so on. It is therefore a progressive factor for companies. In addition, it enables companies to improve their market position and their performance at the expense of rivals (Lado et al., 1997). Cooperation, in turn, allows the company to have access to almost-free resources, skills and knowledge that are necessary or indispensable (Lado et al., 1997).

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Coopetition is therefore intended, in its foundations, as a normative theory which promises superior performance to the companies that adopt it as a strategy. This fundamental assertion of the theory of coopetition has resulted in some empirical verification. Several studies attempt to determine the impact of strategies of alliances between competitors on economic and financial performance (Luo et al., 2007; Ritala et al., 2008; Oum et al., 2004; Kim and Parkhe, 2009). Other studies try to determine the impact of cooperation between competitors on innovation performance (Belderbos et al., 2004, Neyens et al., 2010; Nieto and Santamaria, 2007; Peng et al., 2011; Le Roy et al., forthcoming). Last past researches directly use the concept of coopetition to attempt to establish its impact on economic performance (Marques et al., 2009; Morris et al., 2007), on innovation (Quintana-Carcias and Benavieds-Velasco, 2004) and on market performance (Ritala, 2012). Some studies establish a negative relationship between coopetition and performance (Nieto and Santamaria, 2007; Ritala et al., 2008; Kim and Parkhe, 2009). Nieto and Santamaria (2007) show that cooperation with competitors has a negative impact on the newness of innovation. Ritala and colleagues (2008) show that a relatively high number of alliances within a group of competing firms contributes negatively to performance. Kim and Parkhe (2009) show that competing similarity between alliance partners is negatively related to alliance outcomes. Another previous study shows first a negative, then a positive link between cooperation with competitors and innovation performance (Luo et al., 2007). Luo and colleagues, (2007) show that the impact of company alliances with a company’s competitors on performance is curvilinear. Oum et al. (2004) show that horizontal alliances have a positive impact on productivity but not on profitability. Some studies find a negative or positive impact depending contingency variables. Ritala (2012) shows that the relationship between coopetition strategy and market performance is moderated by market uncertainty, network externalities and competitive intensity. Le Roy et al. (forthcoming) found that, for French firms, coopetition strategy has a deep impact on innovation when coopetitors are located in other countries in Europe or in USA, and no impact when coopetitors are located in France. Other researches show a positive relationship between cooperation with competitors and performance (Belderbos et al., 2004; Quintana Carcias and Benavieds-Velasco, 2004; Marques et al., 2009; Morris et al., 2007; Neyens et al., 2010; Peng et al., 2011). Quintana Carcias and Benavieds-Velasco (2004) show that coopetition strategies increase technological diversity and the devel-

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opment of new products. Morris et al. (2007) show that there is a strong and positive relationship between coopetition strategies of SMEs and their performance. Belderbos et al. (2004) show a positive impact of coopetition on labour productivity and the number of sales per employee. Marques and colleagues (2009) show that coopetition between french football clubs does not improve their athletic performance, but does improve their economic performance. Neyens and colleagues (2010) show that there is a positive impact of “continuous strategic alliances” with competitors on the performance of radical innovation. Peng et al. (2011) show that cooperation with competitor leads to better performance. If investigations on the link between coopetition and performances lead to conflicting results, they share two limitations. On the one hand, they do not distinguish the impact of coopetition strategies on performance from the impact of other strategies toward competitors. The assumed superiority of coopetition strategies over pure cooperation and pure competition has never been tested empirically. On the other hand, these studies, at the exception of Ritala (2012), didn’t include market performance. However, in the initial definition of Brandenburger and Nalebuff (1996), coopetition is supposed to allow rivals to increase the overall value they create for customers, which should allow both partners to increase their sales. It is therefore necessary to try to establish empirically the impact of coopetitive strategies on market performance compared with the impact of aggressive, cooperative and coexistence strategies.

3. Coopetitive strategy and market performance One of the key issues raised by research on competitive aggressiveness is that of its impact on performance (Ferrier et al., 1999; Ferrier, 2000). It is argued that a company needs to be more aggressive than its competitors. In this purely competitive vision, only the most aggressive companies can hope to achieve market leadership and maintain their position. The most successful companies are those that have the greatest number of competitive actions; that respond more quickly to competitive actions of their rivals and are more unpredictable in their behavior. Conversely, the least successful are those that introduce the fewest competitive actions, which take more time to respond to competitive actions of their rivals and initiate predictable competitive actions and reactions (Mac Crimmon, 1993; Miller and Chen, 1996; Ferrier et al., 1999; Ferrier, 2001).

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In contrast, researchers on cooperative strategies agree that membership in a network has a significant impact on performance, mainly because the networks create asymmetrical access to resources (Granovetter, 1985; Nohria, 1992; Baum and Dutton, 1996; Gnyawali and Madhavan, 2001). Research suggests that the links between companies help to develop and “absorb” technology (Ahuja, 2000), to withstand environmental and technological shocks (Powell, 1990) and, most importantly, to increase performance (Hagedoorn and Schakenraad, 1994; Singh and Mitchell, 1996; Zaheer and Zaheer, 1997; Baum et al., 2000). For a company, making many alliances in the sector is a source of competitive advantage (Eisenhardt and Schoonhover, 1996; Galaskiewicz and Zaheer, 1999). The company will be more successful because it is involved in cooperative relationships and is located in the heart of cooperative relations that take place in its industry. By being central in the network, that is to say, at the centre of cooperative actions taking place in the industry, the company benefits from more resources than companies that are not central, and consequently they should perform better (Ibarra and Andrews, 1993). Research on aggressive strategies considers that these strategies have a positive impact on performance (Young et al., 1996; Ferrier et al., 1999, Ferrier, 2001; Ferrier et al., 2002; Offstein and Gnyawali, 2005). However, these strategies benefit the company only through the advantages of aggressiveness. Research on cooperative strategies also considers that cooperative strategies have a positive impact on performance (Granovetter, 1985; Nohria, 1992; Baum and Dutton, 1996; Gnyawali and Madhavan, 2001). However, these strategies benefit to the firm only through the advantages of cooperation. Coopetitive strategies combine aggressive and cooperative strategies. A priori, they should therefore allow the company to benefit simultaneously from the advantages of these two strategies. Overall, companies that follow coopetitive strategies should, thus, have better performance than companies that only follow aggressive strategies or cooperative strategies. So we formulate the following hypotheses: Hypothesis 2: Firms that follow coopetitive strategies have better market performance than firms that follow aggressive strategies Hypothesis 3: Firms that follow coopetitive strategies have better market performance than firms that follow cooperative strategies

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4. Method The mobile operators A mobile operator is a company that provides communications services remotely. It is a company that sells services using telecommunication infrastructures. It can be an independent company or a subsidiary of a constructor of a public company. There are several types of mobile operators. A simple classification contrasts traditional operators, who have telecommunication networks with virtual operators, who use the networks of traditional operators. It is not easy to identify the activity of virtual operators. The study therefore focuses only on traditional operators. Mobile telephony is a rapidly evolving industry, experiencing a spectacular development. The mobile operators industry is also a multi-market industry. Competition in the industry of mobile operators is both localized and globalized. Before 2000, national telecommunications markets were closed to competition and national operators were directly controlled by the state. However, the deregulation of the early 2000s resulted in the entry of new operators into domestic markets. These new operators are either creations ex nihilo or foreign competitors wishing to expand outside their domestic markets. These foreign competitors usually enter national markets by forming alliances with national operators. This situation makes it difficult to understand the relationship of competition in the industry. A narrow vision suggests that all the operators present in a single domestic market should be thought of as competitors. However, this vision does not take into account existing agreements between competitors in the national market, and ignores competitors that are not present on the market. Cooperation provides technology and product innovation for the competitors present in the domestic market. So there is indirect competition through cooperation agreements between operators who are not present on the same national markets. To take into account this specific characteristic of the sector, we have adopted a broad view of competition, considering that all the mobile operators are in situation of potential and indirect competition. Data collection Secondary data are widely used to observe companies’ competitive actions and their cooperative relationships. Here, we mainly use secondary data to detect companies’ strategic actions. As a first step, we conducted four semi-structured interviews with experts in the telecommunications sector and mobile telephony.

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We interviewed four IDATE (Institute of Audio Visual and Telecommunications in Europe) consultants, three consultants who had a thorough knowledge of mobile telephony and telecommunications and the head of studies on telecommunications. This enabled us to establish a list of periodicals that identify all the relevant strategic moves in the sector. Data on strategic actions were obtained from issues of Global Mobile and 3G Mobile or 3GWireless. All the strategic movements which took place in the sector have been identified. About 6,300 pages of documentation were analyzed. Mobile operators considered in the analysis are traditional operators who initiated at least one cooperative and/or competitive action between 2000 and 2005. During this period, the mobile industry experienced major changes with digitization and liberalization in the telecommunications sector. Around the period 2000 to 2005, a series of mergers and acquisitions had a particular impact on the industry. These changes led us to study this industry during this period. We considered the strategic actions of mobile operators, whatever their original focal/domestic market place (Europe, Asia / Pacific, Africa / Middle East, etc.). Indicators on the country and on the measures of performance were obtained from World Telecommunications International Data. Identification of strategic actions A competitive action is a direct external movement, specific and observable, initiated by a firm to enhance its competitive position or defend it (Smith et al., 1991; Smith et al., 1992; Miller and Chen, 1996; Grimm and Smith, 1997). Cooperative action is defined as any type of action that establishes a link between at least two firms and involves exchange, sharing, co-development, and so on (Gulati, 1995). It includes strategic alliances, joint ventures, research and development, national and international roaming agreements, participating in trade associations and technological consortia, and the like. To detect strategic movements, we proceeded by structured and detailed content analysis (Jaugh et al., 1980; Ferrier and Lyon, 2004) of each issue of Global Mobile and 3G Wireless. This method is effective and recommended for exploring the strategic processes of a large multivariate sample (Ginsberg, 1988). In a first step, we developed an annual directory of traditional operators in each country. We then distinguished between the strategic actions of mobile operators and those of their controlling telecom operators. For example, we recorded the competitive actions of Telefonica de Espana and not those of Telefonica, which is its

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controlling telecom operator, who also has a fixed line network, and provides other services. Then we made the distinction between cooperative and competitive actions by searching for keywords in articles (Grimm and Smith, 1997). For instance, “price cut”, the “launch of new service or new product”, have been associated to competitive actions while “roamings’deals”, “joint venture”, “alliances” have been associated to cooperative actions (see appendix for more details). Then, 706 cooperative actions and 2.595 competitive actions, divided between 190 mobile operators, were detected. Competitive actions were classified into six categories of competitive actions in accordance with the classification existing in previous research (Ferrier and Lee, 2002). Regarding cooperative actions, we considered only cooperative actions between two or more mobile operators. Those between a mobile operator and its controlling telecom operator, or with another telecom operator, were ignored. A cooperative action including several operators was recorded as a cooperative action of each of the operators involved (Fjeldstat et al., 2004). For operators who changed names during the period of study, we used the new name of the operator, while recognizing the competitive and cooperative actions that were carried out under the former name. Measurement of variables Aggressive Propensity The measure of the aggressive propensity of the operator includes the three main measures of competitive aggressiveness, namely the volume of competitive actions and reactions, the time it takes between each consecutive competitive action and reaction and the complexity of the competitive actions and reactions. The volume of competitive actions (CONC) of the operator is measured by the number of competitive actions initiated by it during the period of study (Ferrier, Smith and Grimm, 1999) and the number of responses to competitive actions of other operators. Competitive activity of the firm = Σ NT* The time of the competitive actions (TIME) is the average time it takes the firm between two consecutive competitive actions and/or reactions. We calculated it for a given operator by the annual average number of days separating two *

Where NTL = number of competitive actions of the firm.

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actions and/or reactions. Actions and reactions were treated as equivalent (Young, Smith and Grimm, 1996). The dates of competitive actions selected are those that were explicitly given in the articles. Where this was not available, we used the date of publication of the journal. When there were two dates for the same competitive action, we selected the earlier. Average time = ∑ (t – t’) / NTL* The complexity of competitive actions (COMPLEX) of the firm is evaluated using the method used by Ferrier and Lee (2002), Ferrier et al. (1999) and Nayyar and Bantel (1994), by a Herfindahl-type index. Complexity

= 1 − ∑ ⎛⎜⎜ N a / NT ⎞⎟⎟ 2 L⎠ ⎝

**

A high score indicates that the operator initiates complex sequences of competitive actions while a low score indicates that the competitive actions of the firm varied very little. Cooperative Propensity The cooperative propensity of the firm was calculated by measuring the centrality of each mobile operator in the network of cooperative actions that occurred in the sector. The concept of centrality has several meanings in network analysis. Our concept of centrality is adopted from Faust (1997), which is the most complete. Faust (1997) defines the centrality of an actor as its ability to be active in the network, or “degree centrality”, its ability to intermediate the flow of resources between actors, and its capacity to be in relationship with other actors that are themselves central, or “eigenvector centrality”. Three measures of centrality were identified: “degree centrality” (DC), “betweenness centrality” (BC) and “eigenvector centrality” (EC). The measurements were obtained from 6,178 observations and Ucinet Netdraw 2.069. Degree centrality reflects the direct relational activity of the firm with other members of the network. In this study, it is measured by all the direct links forged by a mobile operator with other mobile operators during the study period. * **

Where t and t’ are the dates of two consecutive competitive actions of the firm and NTL is the total number of competitive actions/reactions of the operator during the year. We first calculated the ratio that represents each type of competitive action as a proportion of all the competitive actions of the firm. Then, to take into account the weight of the distribution of each type of actions initiated (Na), these ratios were squared. Finally, we calculated the sum of the mean squares obtained, which gives us the complexity of the competitive actions of the firm.

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We considered one of the most widely used measure of centrality (that of Freeman, 1979) to capture it. C D = d ( ni ) = X i + = ∑ X ij . j

Betweenness centrality reflects the intermediate position occupied by a mobile operator in relationships between several other mobile operators; the ability of the operator acting to facilitate interactions between other operators. The Betweenness centrality of firm (ni) is obtained following Faust (1997) by the equation: C B ( ni ) =

∑

j