IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_7434.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp7434.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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, September.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Doan, Thanh & Manenti, Fabio M. & Mariuzzo, Franco, 2023. "Reprint of: Platform competition in the tablet PC market: The effect of application quality," International Journal of Industrial Organization, Elsevier, vol. 89(C).
    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).
    5. Luo, Jinjing & Moschini, GianCarlo & Perry, Edward D., 2023. "Switching costs in the US seed industry: Technology adoption and welfare impacts," International Journal of Industrial Organization, Elsevier, vol. 89(C).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luo, Jinjing & Moschini, GianCarlo & Perry, Edward D., 2023. "Switching costs in the US seed industry: Technology adoption and welfare impacts," International Journal of Industrial Organization, Elsevier, vol. 89(C).
    2. Grzybowski, Lukasz & Hasbi, Maude & Liang, Julienne, 2018. "Transition from copper to fiber broadband: The role of connection speed and switching costs," Information Economics and Policy, Elsevier, vol. 42(C), pages 1-10.
    3. Peter Davis & Pasquale Schiraldi, 2014. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products," RAND Journal of Economics, RAND Corporation, vol. 45(1), pages 32-63, March.
    4. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90059, University Library of Munich, Germany.
    5. Qizhong Yang & Keiichiro Honda & Tsunehiro Otsuki, 2019. "Structural demand estimation of the response to food safety regulations in the Japanese poultry market," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 9(3), pages 367-385, September.
    6. Amil Petrin & Kenneth Train, 2003. "Omitted Product Attributes in Discrete Choice Models," NBER Working Papers 9452, National Bureau of Economic Research, Inc.
    7. Robin S. Lee, 2013. "Vertical Integration and Exclusivity in Platform and Two-Sided Markets," American Economic Review, American Economic Association, vol. 103(7), pages 2960-3000, December.
    8. Békési, Dániel & Loy, Jens-Peter & Weiss, Christoph, 2013. "State Dependence and Preference Heterogeneity: The Hand of the Past on Breakfast Cereal Consumption," 87th Annual Conference, April 8-10, 2013, Warwick University, Coventry, UK 158699, Agricultural Economics Society.
    9. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    10. Pinar Karaca-Mandic, 2011. "Role of complementarities in technology adoption: The case of DVD players," Quantitative Marketing and Economics (QME), Springer, vol. 9(2), pages 179-210, June.
    11. Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021. "Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.
    12. Kim, Hyunchul & Kim, Kyoo il, 2017. "Estimating store choices with endogenous shopping bundles and price uncertainty," International Journal of Industrial Organization, Elsevier, vol. 54(C), pages 1-36.
    13. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2017. "The Probit Choice Model Under Sequential Search with an Application to Online Retailing," Management Science, INFORMS, vol. 63(11), pages 3911-3929, November.
    14. Hotle, Susan L. & Castillo, Marco & Garrow, Laurie A. & Higgins, Matthew J., 2015. "The impact of advance purchase deadlines on airline consumers’ search and purchase behaviors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 1-16.
    15. Maria Polyakova, 2016. "Regulation of Insurance with Adverse Selection and Switching Costs: Evidence from Medicare Part D," American Economic Journal: Applied Economics, American Economic Association, vol. 8(3), pages 165-195, July.
    16. Javier D. Donna, 2021. "Measuring long‐run gasoline price elasticities in urban travel demand," RAND Journal of Economics, RAND Corporation, vol. 52(4), pages 945-994, December.
    17. Deuflhard, Florian, 2018. "Quantifying inertia in retail deposit markets," SAFE Working Paper Series 223, Leibniz Institute for Financial Research SAFE.
    18. Hugo Molina, 2024. "Buyer Alliances in Vertically Related Markets," Working Papers hal-03340176, HAL.
    19. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," Cowles Foundation Discussion Papers 2301, Cowles Foundation for Research in Economics, Yale University.
    20. Adithya Pattabhiramaiah & S. Sriram & Shrihari Sridhar, 2018. "Rising Prices Under Declining Preferences: The Case of the U.S. Print Newspaper Industry," Marketing Science, INFORMS, vol. 37(1), pages 97-122, January.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ces:ceswps:_7434. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.