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The Asymmetric Information Model of State Dependence

Author

Listed:
  • Nickolay V. Moshkin

    (Associate, Cornerstone Research, 599 Lexington Avenue, New York, New York 10022)

  • Ron Shachar

    (Tel Aviv University, Tel Aviv, 69978 Israel)

Abstract

Marketing researchers and practitioners are interested in consumer loyalty because of its managerial consequences. Previous empirical studies find that consumers are loyal not only to a brand, but also to a firm (umbrella brand). That is, even when firms offer products, consumers tend to continue to purchase from the same firm. This repeat-purchase behavior might result from or from . The meaning of state dependence is that the current choice behaviorally depends on the previous one. The traditional model of state dependence assumes that the previous choice affects the current . This study suggests another source of state dependence: The previous choice affects the current . Specifically, the model assumes that the consumer (a) knows the attributes of the product offered by the firm from which he/ she purchased in the previous period, (b) is uncertain about the attributes of the new products offered by the other firms, (c) can obtain full information about the attributes of all the products through a costly search, and (d) if the consumer decides not to search, he/she purchases the new product offered by the firm from which he/she purchases in the previous period. It is shown that state dependence can result either from the effect of previous choices on the current utility or from its effect on the current information set. This theoretical result raises the following question: What kind of data does a researcher need in order to distinguish between the two sources of state dependence? This study shows that the two sources can be distinguished with a standard panel data set. In other words, although the new source of state dependence is based on the search activity of consumers, there is an that enables a researcher to detect such activity even without direct data on search. The empirical distinction is possible because the behavioral implications of the two sources of state dependence are different. They differ in the effect of product attributes on the repeat-purchase probability. The following example partially illustrates this result: There are two firms and ; the consumer purchased a product from firm in period – 1; the only product attribute is ; and the utility is a linear function of . One aspect of our findings is that in the traditional model of state dependence a change in both and that leaves the difference between them, ( – ), unchanged (neutral change, hereinafter) has no effect on the repeat-purchase probability. However, such a change affect the repeat-purchase probability in the asymmetric information model of state dependence. This is only one aspect of the finding—the implications of the models differ in a more general fashion. The intuition of this result is the following. A neutral change has no effect on the repeat-purchase probability in the traditional model of state dependence, because it does not affect the difference between the utilities from both alternatives. In the asymmetric model of state dependence the consumer's decision process consists of two stages. First, he decides whether to search for information about the other alternative or not. Then, if he searches for information, he chooses the alternative that maximizes his utility. In the second stage, a neutral change has no effect on choices, since such a change does not affect the difference between the utilities from the two alternatives. In the first stage, the consumer knows , but does not know . It turns out that in this stage a neutral change affect the search decision. When, for example, both 's decrease and the utility is a positive function of , the probability of search increases, and thus the repeat-purchase probability decreases. The proposed source of state dependence is examined using structural estimation and panel data on television viewing choices in the United States. Controlling for both observed and unobserved heterogeneity, it is found that the suggested source is more important in creating repeat-purchase than the traditional one for most of the population (71%). This indicates that what was considered by previous studies to result from the dependence of consumer utility on their previous choices is at least partially due to the effect of the previous choices on consumers' information set. The distinction between the two sources of repeat-purchase is important because ignoring the informational explanation may lead to incorrect theoretical and empirical conclusions. For example, price discounts to induce trial are more important for consumers whose utility depends on previous choices, while advertising is more effective for those whose information set depends on previous choices.

Suggested Citation

  • Nickolay V. Moshkin & Ron Shachar, 2002. "The Asymmetric Information Model of State Dependence," Marketing Science, INFORMS, vol. 21(4), pages 435-454, August.
  • Handle: RePEc:inm:ormksc:v:21:y:2002:i:4:p:435-454
    DOI: 10.1287/mksc.21.4.435.136
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    1. Sha Yang & Vishal Narayan & Henry Assael, 2006. "Estimating the Interdependence of Television Program Viewership Between Spouses: A Bayesian Simultaneous Equation Model," Marketing Science, INFORMS, vol. 25(4), pages 336-349, July.
    2. Jungwon Yeo, 2017. "The Weekend Effect in Television Viewership and Prime-Time Scheduling," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 51(3), pages 315-341, November.
    3. Jeremy T. Fox, 2010. "Estimating the Employer Switching Costs and Wage Responses of Forward-Looking Engineers," Journal of Labor Economics, University of Chicago Press, vol. 28(2), pages 357-412, April.
    4. Sridhar Narayanan & Pradeep Chintagunta & Eugenio Miravete, 2007. "The role of self selection, usage uncertainty and learning in the demand for local telephone service," Quantitative Marketing and Economics (QME), Springer, vol. 5(1), pages 1-34, March.
    5. Yeo, Jungwon & Miller, Daniel P., 2018. "Estimating switching costs with market share data: an application to Medicare Part D," International Journal of Industrial Organization, Elsevier, vol. 61(C), pages 459-501.
    6. 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.
    7. Catherine Waddams Price & Minyan Zhu, 2016. "Empirical Evidence Of Consumer Response In Regulated Markets," Journal of Competition Law and Economics, Oxford University Press, vol. 12(1), pages 113-149.
    8. Brian Knight & Ana Tribin, 2019. "The Limits of Propaganda: Evidence from Chavez’s Venezuela," Journal of the European Economic Association, European Economic Association, vol. 17(2), pages 567-605.
    9. Celik, Levent, 2016. "Competitive provision of tune-ins under common private information," International Journal of Industrial Organization, Elsevier, vol. 44(C), pages 113-122.
    10. Levent Çelik, 2008. "Strategic Informative Advertising in a Horizontally Differentiated Duopoly," CERGE-EI Working Papers wp359, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    11. Levent Çelik, 2008. "Monopoly Provision of Tune-ins," CERGE-EI Working Papers wp362, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    12. Song, Lianlian & Shi, Yang & Tso, Geoffrey Kwok Fai & Lo, Hing Po, 2021. "Forecasting week-to-week television ratings using reduced-form and structural dynamic models," International Journal of Forecasting, Elsevier, vol. 37(1), pages 302-321.
    13. Wilson, Chris M., 2012. "Market frictions: A unified model of search costs and switching costs," European Economic Review, Elsevier, vol. 56(6), pages 1070-1086.
    14. Wilson, Chris, 2006. "Markets with Search and Switching Costs," MPRA Paper 131, University Library of Munich, Germany, revised 06 Oct 2006.
    15. Kenneth C. Wilbur, 2008. "A Two-Sided, Empirical Model of Television Advertising and Viewing Markets," Marketing Science, INFORMS, vol. 27(3), pages 356-378, 05-06.
    16. Steven M. Shugan, 2003. "Editorial: Defining Interesting Research Problems," Marketing Science, INFORMS, vol. 22(1), pages 1-15.
    17. Pradeep Chintagunta & Tülin Erdem & Peter E. Rossi & Michel Wedel, 2006. "Structural Modeling in Marketing: Review and Assessment," Marketing Science, INFORMS, vol. 25(6), pages 604-616, 11-12.
    18. Chen, Linfeng & Hu, Qibing & Lv, Qiang, 2020. "The economics of TV tune-in," Economic Modelling, Elsevier, vol. 89(C), pages 189-200.
    19. P. B. Seetharaman, 2004. "Modeling Multiple Sources of State Dependence in Random Utility Models: A Distributed Lag Approach," Marketing Science, INFORMS, vol. 23(2), pages 263-271, April.
    20. Peter J. Danaher & Isaac W. Wilson & Robert A. Davis, 2003. "A Comparison of Online and Offline Consumer Brand Loyalty," Marketing Science, INFORMS, vol. 22(4), pages 461-476, February.
    21. Wilson, Chris M, 2009. "Market Frictions: A Unified Model of Search and Switching Costs," MPRA Paper 13672, University Library of Munich, Germany.
    22. Jeremy T. Fox, 2009. "Estimating the Employer Switching Costs and Wage Responses of Forward-Looking Engineers," Working Papers 1113, Princeton University, Department of Economics, Industrial Relations Section..
    23. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 25-70, March.

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