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Estimating Consumer Inertia in Repeated Choices of Smartphones

Author

Listed:
  • Lukasz Grzybowski
  • Ambre Nicolle
Abstract
In this paper, we use a unique dataset on switching between mobile handsets in a sample of about 8,623 subscribers using tariffs without handset subsidies from a single mobile operator on a monthly basis between July 2011 and December 2014. We estimate a discrete choice model in which we account for disutility from switching to different operating systems and handset brands and for unobserved time-persistent preferences for operating systems and brands. Our estimation results indicate the presence of significant inertia in the choices of operating systems and brands. We find that it is harder for consumers to switch from iOS to Android and other operating systems than from Android and other operating systems to iOS. Moreover, we find that there is significant time-persistent heterogeneity in preferences for different operating systems and brands, which also leads to state-dependent choices. We use our model to simulate market shares in the absence of switching costs and conclude that the market shares of Android and smaller operating systems would increase at the expense of the market share of iOS.

Suggested Citation

  • Lukasz Grzybowski & Ambre Nicolle, 2018. "Estimating Consumer Inertia in Repeated Choices of Smartphones," CESifo Working Paper Series 7434, CESifo.
    • Handle: RePEc:ces:ceswps:_7434
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    References listed on IDEAS

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    1. Ying Fan & Chenyu Yang, 2020. "Competition, Product Proliferation, and Welfare: A Study of the US Smartphone Market," American Economic Journal: Microeconomics, American Economic Association, vol. 12(2), pages 99-134, May.
    2. Gautam Gowrisankaran & Marc Rysman, 2012. "Dynamics of Consumer Demand for New Durable Goods," Journal of Political Economy, University of Chicago Press, vol. 120(6), pages 1173-1219.
    3. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54, January.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    5. Arne Risa Hole, 2007. "Fitting mixed logit models by using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 7(3), pages 388-401, September.
    6. Grzybowski, Lukasz & Liang, Julienne, 2015. "Estimating demand for fixed-mobile bundles and switching costs between tariffs," Information Economics and Policy, Elsevier, vol. 33(C), pages 1-10.
    7. Steven Berry & James Levinsohn & Ariel Pakes, 2004. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 68-105, February.
    8. Jean‐Pierre Dubé & Günter J. Hitsch & Peter E. Rossi, 2010. "State dependence and alternative explanations for consumer inertia," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 417-445, September.
    9. Hiller, R. Scott & Savage, Scott J. & Waldman, Donald M., 2018. "Using aggregate market data to estimate patent value: An application to United States smartphones 2010 to 2015," International Journal of Industrial Organization, Elsevier, vol. 60(C), pages 1-31.
    10. Park, Yuri & Koo, Yoonmo, 2016. "An empirical analysis of switching cost in the smartphone market in South Korea," Telecommunications Policy, Elsevier, vol. 40(4), pages 307-318.
    11. Austan Goolsbee & Amil Petrin, 2004. "The Consumer Gains from Direct Broadcast Satellites and the Competition with Cable TV," Econometrica, Econometric Society, vol. 72(2), pages 351-381, March.
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    Cited by:

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    2. Griffin, Míde & Lyons, Sean & Mohan, Gretta & Joseph, Merin & Domhnaill, Ciarán Mac & Evans, John, 2022. "Intra-operator mobile plan switching: Evidence from linked survey and billing microdata," Telecommunications Policy, Elsevier, vol. 46(7).
    3. Massoud Moslehpour & Sahand E. P. Faez & Brij B. Gupta & Varsha Arya, 2023. "A Fuzzy-Based Analysis of the Mediating Factors Affecting Sustainable Purchase Intentions of Smartphones: The Case of Two Brands in Two Asian Countries," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    4. Kawaguchi, Kohei & Kuroda, Toshifumi & Sato, Susumu, 2023. "Relevant markets and market power of mobile apps," Japan and the World Economy, Elsevier, vol. 67(C).
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    6. Drouard, Joeffrey, 2022. "Content-distribution strategies in markets with locked-in customers," International Journal of Industrial Organization, Elsevier, vol. 80(C).
    7. Doan, Thanh & Manenti, Fabio M. & Mariuzzo, Franco, 2023. "Platform competition in the tablet PC market: The effect of application quality," International Journal of Industrial Organization, Elsevier, vol. 88(C).

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    More about this item

    Keywords

    smartphones; consumer inertia; switching costs; mixed logit; iOS; Android;
    All these keywords.

    JEL classification:

    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L50 - Industrial Organization - - Regulation and Industrial Policy - - - General
    • L96 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Telecommunications

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