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Tipping and Concentration in Markets with Indirect Network Effects

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Author Info

  • Jean-Pierre H. Dubé

    ()
    (Booth School of Business, University of Chicago, Chicago, Illinois 60637, and National Bureau of Economic Research, Cambridge, Massachusetts 02138)

  • Günter J. Hitsch

    ()
    (Booth School of Business, University of Chicago, Chicago, Illinois 60637)

  • Pradeep K. Chintagunta

    ()
    (Booth School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract

This paper develops a framework for measuring “tipping”—the increase in a firm's market share dominance caused by indirect network effects. Our measure compares the expected concentration in a market to the hypothetical expected concentration that would arise in the absence of indirect network effects. In practice, this measure requires a model that can predict the counterfactual market concentration under different parameter values capturing the strength of indirect network effects. We build such a model for the case of dynamic standards competition in a market characterized by the classic hardware/software paradigm. To demonstrate its applicability, we calibrate it using demand estimates and other data from the 32/64-bit generation of video game consoles, a canonical example of standards competition with indirect network effects. In our example, we find that indirect network effects can lead to a strong, economically significant increase in market concentration. We also find important roles for beliefs on both the demand side, as consumers tend to pick the product they expect to win the standards war, and on the supply side, as firms engage in penetration pricing to invest in growing their networks.

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File URL: http://dx.doi.org/10.1287/mksc.1090.0541
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Bibliographic Info

Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 29 (2010)
Issue (Month): 2 (03-04)
Pages: 216-249

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Handle: RePEc:inm:ormksc:v:29:y:2010:i:2:p:216-249

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Keywords: dynamic oligopoly; indirect network effects; tipping; standards war; high technology;

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Cited by:
  1. Timothy Derdenger, 2014. "Technological tying and the intensity of price competition: An empirical analysis of the video game industry," Quantitative Marketing and Economics, Springer, vol. 12(2), pages 127-165, June.
  2. Cantillon, Estelle & Yin, Pai-Ling, 2008. "Competition between Exchanges: Lessons from the Battle of the Bund," CEPR Discussion Papers 6923, C.E.P.R. Discussion Papers.
  3. Dae-Yong Ahn & Jason A. Duan & Carl F. Mela, 2011. "An Equilibrium Model of User Generated Content," Working Papers 11-13, NET Institute, revised Dec 2011.
  4. Stephen P. Ryan & Catherine Tucker, 2011. "Heterogeneity and the Dynamics of Technology Adoption," NBER Working Papers 17253, National Bureau of Economic Research, Inc.
  5. Chintagunta, Pradeep K. & Nair, Harikesh S., 2010. "Marketing Models of Consumer Demand," Research Papers 2072, Stanford University, Graduate School of Business.
  6. Pinar Karaca-Mandic, 2011. "Role of complementarities in technology adoption: The case of DVD players," Quantitative Marketing and Economics, Springer, vol. 9(2), pages 179-210, June.
  7. Grajek, Michał & Kretschmer, Tobias, 2012. "Identifying critical mass in the global cellular telephony market," International Journal of Industrial Organization, Elsevier, vol. 30(6), pages 496-507.
  8. Gretz, Richard T., 2010. "Hardware quality vs. network size in the home video game industry," Journal of Economic Behavior & Organization, Elsevier, vol. 76(2), pages 168-183, November.
  9. Catherine Tucker, 2011. "Network Stability, Network Externalities and Technology Adoption," NBER Working Papers 17246, National Bureau of Economic Research, Inc.
  10. Bart Bronnenberg & Jean Dubé & Carl Mela & Paulo Albuquerque & Tulin Erdem & Brett Gordon & Dominique Hanssens & Guenter Hitsch & Han Hong & Baohong Sun, 2008. "Measuring long-run marketing effects and their implications for long-run marketing decisions," Marketing Letters, Springer, vol. 19(3), pages 367-382, December.

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