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Interdependency, Competition, and Industry Dynamics

Author

Listed:
  • Michael J. Lenox

    (Fuqua School of Business, Duke University, One Towerview Drive, P.O. Box 90120, Durham, North Carolina 27708)

  • Scott F. Rockart

    (Fuqua School of Business, Duke University, One Towerview Drive, P.O. Box 90120, Durham, North Carolina 27708)

  • Arie Y. Lewin

    (Fuqua School of Business, Duke University, One Towerview Drive, P.O. Box 90120, Durham, North Carolina 27708)

Abstract

Asystematic understanding of industry dynamics is critical to strategy research because individual firm performance dynamics both reflect and affect change at the industry level. Descriptive research on industry dynamics has identified a dominant pattern where prices fall, output rises, and the number of firms rises and then falls over time. Several models have been advanced to explain these patterns, with a particular focus on explaining why a shakeout in the number of firms occurs. In the most prominent models, shakeout is generated by rising realized heterogeneity among firms that either is assumed to be unrecognized but determined ex ante or is generated by stochastic innovation outcomes coupled with convex adjustment costs and scale advantages in innovation and learning. In this paper, we develop an alternative model where heterogeneity develops among firms over time (leading to a shakeout) because firms must make choices about highly interdependent productive activities where the ideal combinations cannot be easily deduced or imitated. By combining two established models (a Cournot model of competition with an NK model of interdependency in production activities), we are able to advance an alternative explanation for the observed patterns of industry behavior, including shakeout. We show that variation in the potential for interdependency in activities among industries is able to explain varying levels of shakeout as well as differing patterns of entry and exit among industries. Notably, the model generates several empirical predictions not apparent in past research and several that directly conflict with the results of prominent alternative models of industry dynamics. Specifically, we show that when the potential for interdependency within an industry is low, entry slows down and incumbent survival is all but assured, whereas in industries where the potential for interdependencies is high, shakeouts are severe and the rates of entry and exit remain high over longer time periods, with decreasing survival rates for incumbents.

Suggested Citation

  • Michael J. Lenox & Scott F. Rockart & Arie Y. Lewin, 2007. "Interdependency, Competition, and Industry Dynamics," Management Science, INFORMS, vol. 53(4), pages 599-615, April.
    • Handle: RePEc:inm:ormnsc:v:53:y:2007:i:4:p:599-615
    DOI: 10.1287/mnsc.1060.0679
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    20. 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.
    21. William M. Tracy & Dmitri G. Markovitch & Lois S. Peters & B. V. Phani & Deepu Philip, 2017. "Algorithmic Representations of Managerial Search Behavior," Computational Economics, Springer;Society for Computational Economics, vol. 49(3), pages 343-361, March.
    22. Joachim Stonig & Torsten Schmid & Günter Müller‐Stewens, 2022. "From product system to ecosystem: How firms adapt to provide an integrated value proposition," Strategic Management Journal, Wiley Blackwell, vol. 43(9), pages 1927-1957, September.
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