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Measuring Network Effects in Mobile Telecommunications Markets with Stated‐Preference Valuation Methods

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  • Czajkowski, Mikolaj
  • Sobolewski, Maciej

Abstract

This paper demonstrates how stated-preference methods can be applied to modeling consumers' preferences in the field of mobile telecommunications, and to measuring and the valuation of network effects. We illustrate this with a case study of mobile phone operators in Poland. We utilize the Choice Experiment method and present the respondents with hypothetical choices of mobile phone operators, while explicitly controlling for network effects in the form of other users in the same network. Based on the hypothetical choices consumers make we construct a conditional random parameters multinomial logit model to analyze their preferences. This approach allows us to calculate welfare effects associated with alternatives, as well as marginal rates of substitution (and hence implicit prices) of the attributes used to describe the choices, such as operator brand and distribution of family and friends between available mobile networks. The latter constitutes a network effect as consumer's utility is influenced by the number (or ratio) of members of his or her family, friends and other users subscribed to the same operator. Our results confirm the existence of a strong network effect, which is related to the size of the social network group a particular subscriber belongs to, rather than the absolute size of the mobile operator's customer base. We observe that there are two sources of this 'gross' network effect - pecuniary (arising from possible price discounts for on-net calls) and non-pecuniary, and demonstrate a way to disaggregate them. In addition, we find that brand perception and brand loyalty are important determinants of operator choice. Finally, through the application of a non-market valuation method we are able to calculate monetary values of the network effect and brand loyalty, and both turn out to be relatively high. The results might be of a particular interest to mobile phone operators and regulatory authorities - we find that the capacity for vigorous price competition between mobile operators is limited due to significant non-price barriers which mitigate subscribers' mobility in the market. We demonstrate a way to measure these effects in monetary terms based on modeling of consumer preferences.

Suggested Citation

  • Czajkowski, Mikolaj & Sobolewski, Maciej, 2010. "Measuring Network Effects in Mobile Telecommunications Markets with Stated‐Preference Valuation Methods," 21st European Regional ITS Conference, Copenhagen 2010: Telecommunications at new crossroads - Changing value configurations, user roles, and regulation 5, International Telecommunications Society (ITS).
  • Handle: RePEc:zbw:itse10:5
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    1. Liikanen, Jukka & Stoneman, Paul & Toivanen, Otto, 2004. "Intergenerational effects in the diffusion of new technology: the case of mobile phones," International Journal of Industrial Organization, Elsevier, vol. 22(8-9), pages 1137-1154, November.
    2. Michal Grajek, 2003. "Estimating Network Effects and Compatibility in Mobile Telecommunications," CIG Working Papers SP II 2003-26, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
    3. Richard Carson & Nicholas Flores & Norman Meade, 2001. "Contingent Valuation: Controversies and Evidence," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 19(2), pages 173-210, June.
    4. Laitila, Thomas, 2004. "Economic Valuation with Stated Preference Techniques: A Manual: Bateman, I.J., R.T. Carson, B. Day, M. Hanemann, N. Hanley, T. Hett, M. Jones-Lee, G. Loomes, S. Mourato, E. Ozdemiroglu, D.W. Pearce, R," Ecological Economics, Elsevier, vol. 50(1-2), pages 155-156, September.
    5. Erik Meijer & Jan Rouwendal, 2006. "Measuring welfare effects in models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 227-244.
    6. Hole, Arne Risa, 2008. "Modelling heterogeneity in patients' preferences for the attributes of a general practitioner appointment," Journal of Health Economics, Elsevier, vol. 27(4), pages 1078-1094, July.
    7. Brock, William A. & Durlauf, Steven N., 2007. "Identification of binary choice models with social interactions," Journal of Econometrics, Elsevier, vol. 140(1), pages 52-75, September.
    8. Stephen Hynes & Nick Hanley & Riccardo Scarpa, 2008. "Effects on Welfare Measures of Alternative Means of Accounting for Preference Heterogeneity in Recreational Demand Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 1011-1027.
    9. Farrell, Joseph & Klemperer, Paul, 2007. "Coordination and Lock-In: Competition with Switching Costs and Network Effects," Handbook of Industrial Organization, in: Mark Armstrong & Robert Porter (ed.), Handbook of Industrial Organization, edition 1, volume 3, chapter 31, pages 1967-2072, Elsevier.
    10. Knittel, Christopher R. & Stango, Victor, 2011. "Strategic incompatibility in ATM markets," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2627-2636, October.
    11. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    12. Christos Genakos & Tommaso Valletti, 2011. "Testing The “Waterbed” Effect In Mobile Telephony," Journal of the European Economic Association, European Economic Association, vol. 9(6), pages 1114-1142, December.
    13. Riccardo Scarpa & John M. Rose, 2008. "Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), pages 253-282, September.
    14. Christopher R. Knittel & Victor Stango, 2003. "Compatibility and pricing with indirect network effects: evidence from ATMs," Working Paper Series WP-03-33, Federal Reserve Bank of Chicago.
    15. Nick Hanley & Robert Wright & Vic Adamowicz, 1998. "Using Choice Experiments to Value the Environment," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 11(3), pages 413-428, April.
    16. Train, K. & Weeks, M., 2004. "Discrete Choice Models in Preference Space and Willingness-to Pay Space," Cambridge Working Papers in Economics 0443, Faculty of Economics, University of Cambridge.
    17. Colombo, Sergio & Calatrava-Requena, Javier & Conzalex-Roa, M.C., 2005. "Testing Choice Experiment for Benefit Transfer," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24747, European Association of Agricultural Economists.
    18. Edward Morey & Jennifer Thacher & William Breffle, 2006. "Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 34(1), pages 91-115, May.
    19. David Dranove & Neil Gandal, 2003. "The Dvd‐vs.‐Divx Standard War: Empirical Evidence of Network Effects and Preannouncement Effects," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 12(3), pages 363-386, September.
    20. Knittel, Christopher R. & Stango, Victor, 2011. "Strategic incompatibility in ATM markets," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2627-2636, October.
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    Cited by:

    1. Sobolewski, Maciej & Czajkowski, Mikołaj, 2018. "Receiver benefits and strategic use of call externalities in mobile telephony markets," Information Economics and Policy, Elsevier, vol. 44(C), pages 16-27.
    2. Czajkowski, Mikołaj & Sobolewski, Maciej, 2016. "How much do switching costs and local network effects contribute to consumer lock-in in mobile telephony?," Telecommunications Policy, Elsevier, vol. 40(9), pages 855-869.
    3. Basaran, Alparslan A. & Cetinkaya, Murat & Bagdadioglu, Necmiddin, 2014. "Operator choice in the mobile telecommunications market: Evidence from Turkish urban population," Telecommunications Policy, Elsevier, vol. 38(1), pages 1-13.
    4. Confraria, João & Ribeiro, Tiago & Vasconcelos, Helder, 2017. "Analysis of consumer preferences for mobile telecom plans using a discrete choice experiment," Telecommunications Policy, Elsevier, vol. 41(3), pages 157-169.

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    More about this item

    Keywords

    Network effects; mobile telecommunications; brand valuation; stated preference methods; non-market valuation methods; choice experiment; multinomial conditional logit model; preference heterogeneity; random parameters model;
    All these keywords.

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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