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Emergence of Innovation Networks from R&D Cooperation with Endogenous Absorptive Capacity

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  • Ivan Savin
  • Abiodun Egbetokun

Abstract

This paper extends the existing literature on strategic R&D alliances by presenting a model of innovation networks with endogenous absorptive capacity. The networks emerge as a result of bilateral cooperation over time between firms occupying different locations in the knowledge space. Social capital is ignored, and firms ally purely on the basis of knowledge considerations. Partner selection is driven largely by absorptive capacity which is itself influenced by cognitive distance and investment allocation between inventive and absorptive R&D. Cognitive distance between firms changes as a function of the intensity of cooperation and innovation. Within different knowledge regimes, we examine the structure of networks that emerge and how firms perform within such networks. Our model replicates some stylised empirical results on network structure and the contingent effects of network position on innovative performance. We find networks that exhibit small world properties which are generally robust to changes in the knowledge regime. Second, subject to the extent of knowledge spillovers, certain network strategies such as occupying brokerage positions or maximising accessibility to potential partners pay off. Third and most importantly, absorptive capacity plays an important role in network evolution: firms with different network strategies indeed differ in the build-up of absorptive capacity.

Suggested Citation

  • Ivan Savin & Abiodun Egbetokun, 2013. "Emergence of Innovation Networks from R&D Cooperation with Endogenous Absorptive Capacity," Working Papers CEB 13-022, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:sol:wpaper:2013/143871
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    4. Abiodun Egbetokun & Ivan Savin, 2015. "Absorptive Capacity and Innovation: When Is It Better to Cooperate?," Economic Complexity and Evolution, in: Andreas Pyka & John Foster (ed.), The Evolution of Economic and Innovation Systems, edition 127, pages 373-399, Springer.
    5. Aistleitner, Matthias & Gräbner, Claudius & Hornykewycz, Anna, 2021. "Theory and empirics of capability accumulation: Implications for macroeconomic modeling," Research Policy, Elsevier, vol. 50(6).
    6. Michael P. Schlaile & Johannes Zeman & Matthias Mueller, 2021. "It’s a Match! Simulating Compatibility-based Learning in a Network of Networks," Economic Complexity and Evolution, in: Michael P. Schlaile (ed.), Memetics and Evolutionary Economics, chapter 0, pages 99-140, Springer.
    7. Ivan Savin, 2021. "On optimal regimes of knowledge exchange: a model of recombinant growth and firm networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(3), pages 497-527, July.
    8. Matthias Mueller & Kristina Bogner & Tobias Buchmann & Muhamed Kudic, 2017. "The effect of structural disparities on knowledge diffusion in networks: an agent-based simulation model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 613-634, October.
    9. Savin, Ivan & Egbetokun, Abiodun, 2016. "Emergence of innovation networks from R&D cooperation with endogenous absorptive capacity," Journal of Economic Dynamics and Control, Elsevier, vol. 64(C), pages 82-103.
    10. Wei Gao & Daojuan Wang, 2021. "Will Increasing Government Subsidies Promote Open Innovation? A Simulation Analysis of China’s Wind Power Industry," Sustainability, MDPI, vol. 13(23), pages 1-20, December.
    11. Juana Castro & Stefan Drews & Filippos Exadaktylos & Joël Foramitti & Franziska Klein & Théo Konc & Ivan Savin & Jeroen van den Bergh, 2020. "A review of agent‐based modeling of climate‐energy policy," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(4), July.
    12. Mundt, Philipp & Cantner, Uwe & Inoue, Hiroyasu & Savin, Ivan & Vannuccini, Simone, 2021. "Market selection in global value chains," BERG Working Paper Series 170, Bamberg University, Bamberg Economic Research Group.
    13. Johannes van der Pol, 2015. "Structural dynamics of the French aerospace sector: A network analysis," Working Papers hal-01284993, HAL.
    14. Tur, Elena M. & Azagra-Caro, Joaquín M., 2018. "The coevolution of endogenous knowledge networks and knowledge creation," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 424-434.
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    More about this item

    Keywords

    absorptive capacity; agent-based modeling; cognitive distance; dynamics; innovation; knowledge spillovers; networks;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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