Advertisement

Effects of Innovation Efficiency and Knowledge on Industry-University Collaboration: An Evolutionary Game Perspective

  • Yang Song
  • Zhiyuan Zhang
Chapter
Part of the Studies on Entrepreneurship, Structural Change and Industrial Dynamics book series (ESID)

Abstract

This paper studies the “evolutionarily stable strategy” (ESS) between industry and university during collaborative innovation processes based on evolutionary games. By designing knowledge sharing models, we analyze the impact factors of knowledge input, knowledge transfer, and innovation cost on collaborative innovation. Furthermore, we use simulation to verify the knowledge sharing model. Our results suggest that the “open innovation strategy” is actually the fact that players choose “evolutionarily stable strategy” in the long-term collaborative innovation process. When the number of game players is different, the small group takes the lead in achieving stabilization strategy. When the number of game players is similar, both groups adopt the “open strategy” at the same speed. Besides, we also suggest that increasing knowledge spillover will contribute to innovation efficiency and stabilization. Theoretically, our study explains the stabilization strategy of the game and provides reasonable recommendations for policy makers.

References

  1. Abramo, G., D’Angelo, C. A., & Di Costa, F. (2011). University-industry research collaboration: A model to assess university capability. Higher Education, 62, 163–181.  https://doi.org/10.1007/s10734-010-9372-0 CrossRefGoogle Scholar
  2. Al-Ashaab, A., Flores, M., Doultsinou, A., & Magyar, A. (2011). A balanced scorecard for measuring the impact of industry–university collaboration. Production Planning and Control, 22, 554–570.  https://doi.org/10.1080/09537287.2010.536626 CrossRefGoogle Scholar
  3. Amann, E., & Possajennikov, A. (2009). On the stability of evolutionary dynamics in games with incomplete information. Mathematical Social Sciences, 58, 310–321.  https://doi.org/10.1016/J.MATHSOCSCI.2009.08.001 CrossRefGoogle Scholar
  4. Anbarci, N., Lemke, R., & Roy, S. (2002). Inter-firm complementarities in R&D: A re-examination of the relative performance of joint ventures. International Journal of Industrial Organization, 20, 191–213.  https://doi.org/10.1016/S0167-7187(00)00081-3 CrossRefGoogle Scholar
  5. Ankrah, S., & AL-Tabbaa, O. (2015). Universities–industry collaboration: A systematic review. Scandinavian Journal of Management, 31, 387–408.  https://doi.org/10.1016/J.SCAMAN.2015.02.003 CrossRefGoogle Scholar
  6. Asheim, B. T., Smith, H. L., & Oughton, C. (2011). Regional innovation systems: Theory, empirics and policy. Regional Studies, 45, 875–891.  https://doi.org/10.1080/00343404.2011.596701 CrossRefGoogle Scholar
  7. Bruneel, J., D’Este, P., & Salter, A. (2010). Investigating the factors that diminish the barriers to university–industry collaboration. Research Policy, 39, 858–868.  https://doi.org/10.1016/J.RESPOL.2010.03.006 CrossRefGoogle Scholar
  8. Calvert, R. L. (1995). The rational choice theory of institutions: Implications for design. In Institutional design (pp. 63–94). Dordrecht: Springer Netherlands.CrossRefGoogle Scholar
  9. Cohen, W. M., Nelson, R. R., & Walsh, J. P. (2002). Links and impacts: The influence of public research on industrial R&D. Management Science, 48, 1–23.  https://doi.org/10.1287/mnsc.48.1.1.14273 CrossRefGoogle Scholar
  10. Conceicao, P., & Heitor, M. V. (1999). On the role of the university in the knowledge economy. Science and Public Policy, 26, 37–51.  https://doi.org/10.3152/147154399781782617 CrossRefGoogle Scholar
  11. Cooke, P., Gomez Uranga, M., & Etxebarria, G. (1997). Regional innovation systems: Institutional and organisational dimensions. Research Policy, 26, 475–491.  https://doi.org/10.1016/S0048-7333(97)00025-5 CrossRefGoogle Scholar
  12. D’Aspremont, C., & Jacquemin, A. (1988). Cooperative and noncooperative R & D in duopoly with spillovers. The American Economic Review, 78, 1133–1137.  https://doi.org/10.1016/0014-2921(88)90202-4 CrossRefGoogle Scholar
  13. De Silva, M., & Rossi, F. (2018). The effect of firms’ relational capabilities on knowledge acquisition and co-creation with universities. Technological Forecasting and Social Change, 133, 72–84.  https://doi.org/10.1016/J.TECHFORE.2018.03.004 CrossRefGoogle Scholar
  14. Eichengreen, B. (2004). Productivity growth, the new economy, and catching up. Review of International Economics, 12, 243–245.  https://doi.org/10.1111/j.1467-9396.2004.00446.x CrossRefGoogle Scholar
  15. Guerrero, M., Urbano, D., Fayolle, A., et al. (2016). Entrepreneurial universities: Emerging models in the new social and economic landscape. Small Business Economics, 47, 551–563.  https://doi.org/10.1007/s11187-016-9755-4 CrossRefGoogle Scholar
  16. Kamien, M., Muller, E., & Zang, I. (1992). Research joint ventures and R&D cartels. The American Economic Review, 82, 1293–1306.Google Scholar
  17. Lee, S., & Ngo, T. (2012). The capitalization of knowledge: A triple helix of university-industry-government. Higher Education, 63, 161–163.CrossRefGoogle Scholar
  18. Mansor, Z. D., Mustaffa, M., & Salleh, L. M. (2015). Motivation and willingness to participate in knowledge sharing activities among academics in a public university. Procedia Economics and Finance, 31, 286–293.  https://doi.org/10.1016/S2212-5671(15)01188-0 CrossRefGoogle Scholar
  19. Marco, J., & Goetz, R.-U. (2017). Tragedy of the commons and evolutionary games in social networks: The economics of social punishment. FEEM Working Papers.  https://doi.org/10.2139/ssrn.2998546
  20. Mazzoleni, R., & Nelson, R. R. (2005). The roles of research at universities and public labs in economic catch-up (No. 2006/01). LEM Working Paper Series.Google Scholar
  21. Mazzoleni, R., & Nelson, R. R. (2007). Public research institutions and economic catch-up. Research Policy, 36, 1512–1528.  https://doi.org/10.1016/j.respol.2007.06.007 CrossRefGoogle Scholar
  22. Nowak, M., & Sigmund, K. (1989). Game-dynamical aspects of the prisoner’s dilemma. Applied Mathematics and Computation, 30, 191–213.  https://doi.org/10.1016/0096-3003(89)90052-0 CrossRefGoogle Scholar
  23. Pardalos, P. M., Migdalas, A., & Pitsoulis, L. (2008). Pareto optimality, game theory and equilibria. New York: Springer.Google Scholar
  24. Romer, P. M. (1994). The origins of endogenous growth. The Journal of Economic Perspectives, 8, 3–22.  https://doi.org/10.1257/jep.8.1.3 CrossRefGoogle Scholar
  25. Rosenberg, N., & Nelson, R. R. (1994). American universities and technical advance in industry. Research Policy, 23, 323–348.  https://doi.org/10.1016/0048-7333(94)90042-6 CrossRefGoogle Scholar
  26. Shibayama, S. (2015). Academic commercialization and changing nature of academic cooperation. Journal of Evolutionary Economics, 25, 513–532.  https://doi.org/10.1007/s00191-014-0387-z CrossRefGoogle Scholar
  27. Wright, M., Clarysse, B., Lockett, A., & Knockaert, M. (2008). Mid-range universities’ linkages with industry: Knowledge types and the role of intermediaries. Research Policy, 37, 1205–1223.  https://doi.org/10.1016/J.RESPOL.2008.04.021 CrossRefGoogle Scholar
  28. Yamaguchi, Y., Fujimoto, J., Yamazaki, A., & Koshiyama, T. (2018). A study of the factors influencing industry-academia collaboration activities in private universities. In 2018 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 1–10). Honolulu, HI: IEEE.Google Scholar
  29. Zeng, W., Li, M., & Feng, N. (2017). The effects of heterogeneous interaction and risk attitude adaptation on the evolution of cooperation. Journal of Evolutionary Economics, 27, 435–459.  https://doi.org/10.1007/s00191-016-0489-x CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yang Song
    • 1
  • Zhiyuan Zhang
    • 2
  1. 1.Jilin UniversityChangchunChina
  2. 2.Jilin University of Finance and EconomicsChangchunChina

Personalised recommendations