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

In: The Evolution of Economic and Innovation Systems

Author

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  • Abiodun Egbetokun

    (Friedrich Schiller University and Max Planck Institute of Economics)

  • Ivan Savin

    (Ural Federal University)

Abstract

Cooperation can benefit and hurt firms at the same time. An important question then is: when is it better to cooperate? And, once the decision to cooperate is made, 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 (Econ J 99(397):569–596, 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. The results show that cognitive distance and appropriability conditions between a firm and its cooperation partner have an ambiguous effect on the profit generated by the firm. Thus, a firm chooses to cooperate and selects a partner conditional on the investments in absorptive capacity it is willing to make to solve the understandability/novelty trade-off.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:eccchp:978-3-319-13299-0_16
    DOI: 10.1007/978-3-319-13299-0_16
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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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    16. 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.
    17. 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.

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    More about this item

    Keywords

    Absorptive Capacity; Knowledge Spillover; Potential Partner; External Knowledge; Knowledge Stock;
    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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