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About Cooperation, Selfishness and Joint Risks in Clusters


  • Gedai, Endre
  • Kóczy, László Á.
  • Meier zu Köcker, Gerd
  • Zombori, Zita


This study introduces an entirely novel way to study the cooperative and noncooperative nature of clusters by looking at the selfish, profit-seeking interests of the actors within cluster initiatives. The approach provides a game theory inspired framework to study the dilemma of cluster actors between the fruitful cooperation with other actors and their own selfish – and possibly short-term – interests at three levels: intensity focussing on the overall cooperation effort; structure looking at the network of cooperation and balance discussing good ways to allocate resources. Characteristic models of cluster behaviour have been developed for all these aspects. Interviews have been conducted among cluster actors of two cluster initiatives. Both were quite matured and well managed with similar core objectives. The methodology applied has revealed that the nature of cooperation among the actors and how the cluster initiative is managed is of surprisingly different nature although both cluster initiatives provide high added value by the cluster actors perspective. One cluster initiative can be characterised as a “managed cooperation cluster”, where the management has a central role to match actors, while the nature of the other cluster initiative is more “a peer-to-peer cooperation cluster” where cooperation emerges directly between cluster actors and the cluster management has another role. The results of the study lead to conclusions that there is not one ideal way how to manage cluster initiative. Furthermore the cluster actors cannot be seen as a homogenous group. Even if all of them have similar objectives like increased innovation capabilities, higher competitiveness, higher profitability etc. their intension why joining a cluster initiative and the readiness to contribute or just to benefit is very different. The cluster management has to understand what are the particular interests and to what extent a dedicated cluster is ready to contribute. Applying the game theory inspired analytical approach helps to gain important inside views for cluster management. Furthermore the study shows that the way how cluster initiatives are set up and supported by public authorities does have a strong implication on the nature of cooperation and selfishness among the cluster actors. The conclusion from the study is, among others, that high public funding facilitates the creation of cluster initiatives, but also attracts free riders to join since the barriers to enter are quite low. Having such an interest group “on board” within a cluster initiative hampers further trust building and cooperative framework conditions since selfish actors dominate. Low public funding at the beginning of the life of a cluster initiative leads to higher barriers due to higher mandatory investments of cluster participants, but creates a cooperative environment since mainly those actors have joint that are really interested to cooperate and take common risks. However, the study has shown that good cluster managements can deal with different cooperative natures among cluster participants, if they are aware of this and implement proper actions.

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  • Gedai, Endre & Kóczy, László Á. & Meier zu Köcker, Gerd & Zombori, Zita, 2015. "About Cooperation, Selfishness and Joint Risks in Clusters," MPRA Paper 65053, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:65053

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    References listed on IDEAS

    1. P. Herings & Gerard Laan & Dolf Talman, 2005. "The positional power of nodes in digraphs," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 24(3), pages 439-454, June.
    2. Gaspar, Jess & Glaeser, Edward L., 1998. "Information Technology and the Future of Cities," Journal of Urban Economics, Elsevier, vol. 43(1), pages 136-156, January.
    3. Borm, P.E.M. & Owen, G. & Tijs, S.H., 1992. "On the position value for communication situations," Other publications TiSEM 5a8473e4-1df7-42df-ad53-f, Tilburg University, School of Economics and Management.
    4. Copic Jernej & Jackson Matthew O. & Kirman Alan, 2009. "Identifying Community Structures from Network Data via Maximum Likelihood Methods," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 9(1), pages 1-40, September.
    5. László Kóczy, 2007. "A recursive core for partition function form games," Theory and Decision, Springer, vol. 63(1), pages 41-51, August.
    6. Iammarino, Simona & McCann, Philip, 2006. "The structure and evolution of industrial clusters: Transactions, technology and knowledge spillovers," Research Policy, Elsevier, vol. 35(7), pages 1018-1036, September.
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    More about this item


    industrial cluster initiative game theory cooperation effort cooperation structure balanced cooperation managed cooperation cluster peer-to-peer cooperation cluster;

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations
    • M19 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Other
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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