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Consumer satisfaction versus churn in the case of upgrades of 3G to 4G cell networks

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  • Steven D’Alessandro
  • Lester Johnson
  • David Gray
  • Leanne Carter

Abstract

The current use of 3G technologies has created significant demands for capacity, such as cell TV, and this needs to be balanced with the capital constraints of many firms. Providers face price pressures on margins and the need to update cell networks to 4G in the post-GFC era where capital is scarce. Understanding consumer behavior in this area by use of simulations may be a time- and cost-efficient method, but how accurate is it? This study demonstrates that the use of a simple, agent-based model can lead to accurate initial prediction of parameters of satisfaction with a cell phone provider, and provides a basis of understanding factors of cell phone subscriber choice in the context of the introduction of new technology. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Steven D’Alessandro & Lester Johnson & David Gray & Leanne Carter, 2015. "Consumer satisfaction versus churn in the case of upgrades of 3G to 4G cell networks," Marketing Letters, Springer, vol. 26(4), pages 489-500, December.
  • Handle: RePEc:kap:mktlet:v:26:y:2015:i:4:p:489-500
    DOI: 10.1007/s11002-014-9284-3
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    References listed on IDEAS

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    1. Garcia, Rosanna & Rummel, Paul & Hauser, John, 2007. "Validating agent-based marketing models through conjoint analysis," Journal of Business Research, Elsevier, vol. 60(8), pages 848-857, August.
    2. José M. Labeaga & Mercedes Martos-Partal & Nora Lado, 2007. "Testing the Double Jeopardy Loyalty Effect Using Discrete Choice Models," Working Papers 2007-21, FEDEA.
    3. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    4. Chuang, Yi-Fei, 2011. "Pull-and-suck effects in Taiwan mobile phone subscribers switching intentions," Telecommunications Policy, Elsevier, vol. 35(2), pages 128-140, March.
    5. Levesque, T.J. & McDougall, G.H.G., 1992. "Managing Customers Satisfaction : The Nature of Service Problems and Customers Exit, Voice and loyalty," Working Papers 92009, Wilfrid Laurier University, Department of Economics.
    6. Winer, Russell S, 1986. "A Reference Price Model of Brand Choice for Frequently Purchased Products," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(2), pages 250-256, September.
    7. Shaohui Ma & Joachim Büschken, 2011. "Counting your customers from an “always a share” perspective," Marketing Letters, Springer, vol. 22(3), pages 243-257, September.
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