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Search Duration

Author

Listed:
  • Raluca M. Ursu

    (Stern School of Business, New York University, New York, New York 10012)

  • Qingliang Wang

    (School of Management, Northwestern Polytechnical University, 710072 Xi’an, Shaanxi, China)

  • Pradeep K. Chintagunta

    (Booth School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract

In studying consumer search behavior, researchers typically focus on which products consumers add to their consideration sets (the extensive margin of search). In this article, we attempt to additionally study how much consumers search individual products (the intensive margin of search) by analyzing the time they spend searching (search duration). We develop a sequential search model by which consumers who are uncertain (and have prior beliefs) about their match value for a product search to reveal (noisy) signals about it that they then use to update their beliefs in a Bayesian fashion. Search duration, in this context, is an outcome of the decision by a consumer to seek information on the same product multiple times; with a unit of time corresponding to one signal, the more the number of signals sought greater is the search duration. We also show how the model can be used to study revisits, a feature not easily accommodated in Weitzman’s sequential search model. We build on the framework by Chick and Frazier for describing the optimal search rules for the full set of decisions consumers make (which products to search, for how long, in what order, and whether to purchase) and develop the model’s empirical counterpart. We estimate the proposed model using data on consumers searching for restaurants online. We document that search duration is considerable, even when consumers search few restaurants, and that restaurants that are searched longer are more likely to be purchased. Using our model, we quantify preferences and search costs, as well as consumer prior beliefs, providing additional insights into consumers’ search process. Finally, we develop managerial implications related to the amount of information companies should provide to consumers, given that this will affect search duration and thus search and purchase decisions.

Suggested Citation

  • Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
  • Handle: RePEc:inm:ormksc:v:39:y:2020:i:5:p:849-871
    DOI: 10.1287/mksc.2020.1225
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    References listed on IDEAS

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    1. Rothschild, Michael, 1974. "Searching for the Lowest Price When the Distribution of Prices Is Unknown," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 689-711, July/Aug..
    2. Mantian (Mandy) Hu & Chu (Ivy) Dang & Pradeep K. Chintagunta, 2019. "Search and Learning at a Daily Deals Website," Marketing Science, INFORMS, vol. 38(4), pages 609-642, July.
    3. Chunhua Wu & Hai Che & Tat Y. Chan & Xianghua Lu, 2015. "The Economic Value of Online Reviews," Marketing Science, INFORMS, vol. 34(5), pages 739-754, September.
    4. Stigler, George J., 2011. "Economics of Information," Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 5, pages 35-49.
    5. Sergei Koulayev, 2013. "Search With Dirichlet Priors: Estimation and Implications for Consumer Demand," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 226-239, April.
    6. T. Tony Ke & Zuo-Jun Max Shen & J. Miguel Villas-Boas, 2016. "Search for Information on Multiple Products," Management Science, INFORMS, vol. 62(12), pages 3576-3603, December.
    7. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    8. Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    9. José Luis Moraga-González & Zsolt Sándor & Matthijs R. Wildenbeest, 2015. "Consumer Search and Prices in the Automobile Market," Tinbergen Institute Discussion Papers 15-033/VII, Tinbergen Institute.
    10. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    11. Ke, T. Tony & Villas-Boas, J. Miguel, 2019. "Optimal learning before choice," Journal of Economic Theory, Elsevier, vol. 180(C), pages 383-437.
    12. Stephen E. Chick & Noah Gans, 2009. "Economic Analysis of Simulation Selection Problems," Management Science, INFORMS, vol. 55(3), pages 421-437, March.
    13. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
    14. 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.
    15. Babur De Los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2012. "Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior," American Economic Review, American Economic Association, vol. 102(6), pages 2955-2980, October.
    16. Fernando Branco & Monic Sun & J. Miguel Villas-Boas, 2016. "Too Much Information? Information Provision and Search Costs," Marketing Science, INFORMS, vol. 35(4), pages 605-618, July.
    17. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    18. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    19. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2014. "Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue," Management Science, INFORMS, vol. 60(7), pages 1632-1654, July.
    20. Moraga-González, José Luis & Wildenbeest, Matthijs R., 2008. "Maximum likelihood estimation of search costs," European Economic Review, Elsevier, vol. 52(5), pages 820-848, July.
    21. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    22. Tülin Erdem & Michael P. Keane & Baohong Sun, 2008. "A Dynamic Model of Brand Choice When Price and Advertising Signal Product Quality," Marketing Science, INFORMS, vol. 27(6), pages 1111-1125, 11-12.
    23. Fernando Branco & Monic Sun & J. Miguel Villas-Boas, 2012. "Optimal Search for Product Information," Management Science, INFORMS, vol. 58(11), pages 2037-2056, November.
    24. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    25. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    26. Elisabeth Honka, 2014. "Quantifying search and switching costs in the US auto insurance industry," RAND Journal of Economics, RAND Corporation, vol. 45(4), pages 847-884, December.
    27. Brezzi, Monica & Lai, Tze Leung, 2002. "Optimal learning and experimentation in bandit problems," Journal of Economic Dynamics and Control, Elsevier, vol. 27(1), pages 87-108, November.
    28. Daniel A. Ackerberg, 2003. "Advertising, learning, and consumer choice in experience good markets: an empirical examination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(3), pages 1007-1040, August.
    29. Michael Rothschild, 1974. "Searching for the Lowest Price When the Distribution of Prices Is Unknown: A Summary," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 1, pages 293-294, National Bureau of Economic Research, Inc.
    30. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2019. "Modeling Consumer Footprints on Search Engines: An Interplay with Social Media," Management Science, INFORMS, vol. 65(3), pages 1363-1385, March.
    31. Han Hong & Matthew Shum, 2006. "Using price distributions to estimate search costs," RAND Journal of Economics, RAND Corporation, vol. 37(2), pages 257-275, June.
    32. Thomas Blake & Chris Nosko & Steven Tadelis, 2016. "Returns to Consumer Search: Evidence from eBay," NBER Working Papers 22302, National Bureau of Economic Research, Inc.
    33. Stephen E. Chick & Peter Frazier, 2012. "Sequential Sampling with Economics of Selection Procedures," Management Science, INFORMS, vol. 58(3), pages 550-569, March.
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