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Memory and Organizational Evolvability in a Neutral Landscape

Author

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  • Amit Jain

    (National University of Singapore, Singapore 117574)

  • Bruce Kogut

    (Columbia University, New York, New York 10027)

Abstract

Many organizational theories are not sanguine over the chances of organizations to adapt and evolve, even if they should learn from the past through memory. Innovative search in the adaptive biology tradition leads quickly to dead ends. However, memory is useful for rendering innovative search more efficient. The concept from evolutionary biology of neutrality and drift along neutral pathways introduces the possibility that organizations are robust to local innovations and therefore potential candidates for evolvability. Through simulations in a neutral NK hypercube, our analysis shows that neutrality does not create value when future payoffs are discounted and change is costly. Here is the role for memory. Memory enables the faster development of better capabilities and reverses the negative assessment of evolvability. Even when allowing for forgetting, memory is a positive capability that improves evolvability of organizations so they can achieve better performance and better ways of doing so. Memory and neutrality are complementary for creating organizational evolvability, a finding consistent with the overwhelming evidence that organizations are more productive today than before because of innovation.

Suggested Citation

  • Amit Jain & Bruce Kogut, 2014. "Memory and Organizational Evolvability in a Neutral Landscape," Organization Science, INFORMS, vol. 25(2), pages 479-493, April.
    • Handle: RePEc:inm:ororsc:v:25:y:2014:i:2:p:479-493
    DOI: 10.1287/orsc.2013.0841
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    References listed on IDEAS

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    Cited by:

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    2. Hui Zhang & Jin-Biao Yi & Qian Wang, 2021. "Research on the Collaborative Evolution of Blockchain Industry Ecosystems in Terms of Value Co-Creation," Sustainability, MDPI, vol. 13(21), pages 1-17, October.
    3. Richard J. Arend, 2022. "Balancing the perceptions of NK modelling with critical insights," Journal of Innovation and Entrepreneurship, Springer, vol. 11(1), pages 1-15, December.
    4. Friederike Wall, 2021. "Modeling Managerial Search Behavior based on Simon's Concept of Satisficing," Papers 2104.14002, arXiv.org, revised May 2021.
    5. Sai Yayavaram & Sasanka Sekhar Chanda, 2023. "Decision making under high complexity: a computational model for the science of muddling through," Computational and Mathematical Organization Theory, Springer, vol. 29(2), pages 300-335, June.
    6. Amit Jain, 2016. "Learning by hiring and change to organizational knowledge: Countering obsolescence as organizations age," Strategic Management Journal, Wiley Blackwell, vol. 37(8), pages 1667-1687, August.
    7. Linda Argote & Sunkee Lee & Jisoo Park, 2021. "Organizational Learning Processes and Outcomes: Major Findings and Future Research Directions," Management Science, INFORMS, vol. 67(9), pages 5399-5429, September.
    8. Luo, Jianxi, 2018. "Architecture and evolvability of innovation ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 132-144.

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