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Absorptive Capacity and Innovation: When Is It Better to Cooperate?

Author

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  • Abiodun Egbetokun

    (Graduate College "Economics of Innovative Change", Friedrich Schiller University Jena)

  • Ivan Savin

    (Graduate College "Economics of Innovative Change", Friedrich Schiller University Jena)

Abstract

Cooperation can benefit and hurt firms at the same time. An important question then is: when is it better to cooperate. And how can an appropriate partner be selected? In this paper we present a model of inter-firm cooperation driven by cognitive distance, appropriability conditions and external knowledge. Absorptive capacity of firms develops as an outcome of the interaction between absorptive R&D and cognitive distance from voluntary and involuntary knowledge spillovers. Thus, we offer a revision of the original model by Cohen and Levinthal (1989) accounting for recent empirical findings and explicitly modeling absorptive capacity within the framework of interactive learning. We apply that to the analysis of firms' cooperation and R&D investment preferences. While the focus of this paper is limited to a static scenario, where the cognitive distance between cooperating firms is fixed and given exogenously, in Savin and Egbetokun (2012) we address the dynamic approach and provide more extensive simulation results.

Suggested Citation

  • Abiodun Egbetokun & Ivan Savin, 2012. "Absorptive Capacity and Innovation: When Is It Better to Cooperate?," Jena Economics Research Papers 2012-056, Friedrich-Schiller-University Jena.
    • Handle: RePEc:jrp:jrpwrp:2012-056
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    Cited by:

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    2. 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.
    3. 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.
    4. 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.
    5. d’Andria, D. & Savin, I., 2018. "A Win-Win-Win? Motivating innovation in a knowledge economy with tax incentives," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 38-56.
    6. Eun Hwa Lee & Choo Yeon Kim & Jae Wook Yoo, 2020. "Relationship between User Innovation Activities and Market Performance: Moderated Mediating Effect of Absorptive Capacity and CEO’s Shareholding on Innovation Performance," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    7. Oleg S. Mariev & Karina M. Nagieva & Viktoria L. Simonova, 2020. "Managing innovation activity factors in Russian regions through econometric modeling," Upravlenets, Ural State University of Economics, vol. 11(1), pages 57-69, March.
    8. Sohaib S. Hassan & Konrad Meisner & Kevin Krause & Levan Bzhalava & Petra Moog, 2024. "Is digitalization a source of innovation? Exploring the role of digital diffusion in SME innovation performance," Small Business Economics, Springer, vol. 62(4), pages 1469-1491, April.
    9. D. Blueschke & I. Savin, 2017. "No such thing as a perfect hammer: comparing different objective function specifications for optimal control," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 377-392, June.
    10. Yun, JinHyo Joseph & Ahn, Heung Ju & Lee, Doo Seok & Park, Kyung Bae & Zhao, Xiaofei, 2022. "Inter-rationality; Modeling of bounded rationality in open innovation dynamics," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    11. Medase, Kehinde, 2019. "The Impact of the Heterogeneity of Employees’ Qualifications on Firm-level Innovation Evidence from Nigerian Firms," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203563, Verein für Socialpolitik / German Economic Association.
    12. Diego d'Andria & Ivan Savin, 2015. "Motivating innovation in a knowledge economy with tax incentives," Jena Economics Research Papers 2015-004, Friedrich-Schiller-University Jena.
    13. Ángela González-Moreno & Pablo Ruiz-Palomino & Francisco J. Sáez-Martínez, 2019. "Can CEOs’ Corporate Social Responsibility Orientation Improve Firms’ Cooperation in International Scenarios?," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    14. Uwe Cantner & Ivan Savin, 2014. "Do Firms Benefit from Complementarity Effect in R&D and What Drives their R&D Strategy Choices?," Jena Economics Research Papers 2014-023, Friedrich-Schiller-University Jena.
    15. Ivan Savin & Dmitri Blueschke, 2013. "Solving nonlinear stochastic optimal control problems using evolutionary heuristic optimization," Jena Economics Research Papers 2013-051, Friedrich-Schiller-University Jena.
    16. Ivan Savin & Dmitri Blueschke, 2016. "Lost in Translation: Explicitly Solving Nonlinear Stochastic Optimal Control Problems Using the Median Objective Value," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 317-338, August.
    17. Yingkai Tang & Yaozhi Chen & Kun Wang & He Xu & Xiaoqi Yi, 2020. "An Analysis on the Spatial Effect of Absorptive Capacity on Regional Innovation Ability Based on Empirical Research in China," Sustainability, MDPI, vol. 12(7), pages 1-23, April.

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    More about this item

    Keywords

    inter-firm cooperation; absorptive capacity; cognitive distance; innovation; knowledge spillovers;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • 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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