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Consumer Online Search with Partially Revealed Information

Author

Listed:
  • Chris Gu

    (Scheller College of Business, Georgia Institute of Technology, Atlanta, Georgia 30308)

  • Yike Wang

    (Department of Economics, London School of Economics and Political Science, London WC2A 2AE, United Kingdom)

Abstract

Modern-day search platforms generally have two layers of information presentation. The outer layer displays the collection of search results with attributes selected by platforms, and consumers click on a product to reveal all its attributes in the inner layer. The information revealed in the outer layer affects the search costs and the probability of finding a match. To address the managerial question of optimal information layout, we create an information complexity measure of the outer layer, namely orderedness entropy, and study the consumer search process for information at the expense of time and cognitive costs. We first conduct online random experiments to show that consumers respond to and actively reduce cognitive cost for which our information complexity measure provides a representation. Then, using a unique and rich panel tracking consumer search behaviors at a large online travel agency (OTA), we specify a novel sequential search model that jointly describes the refinement search and product clicking decisions. We find that cognitive cost is a major component of search cost, while loading time cost has a much smaller share. By varying the information revealed in the outer layer, we propose information layouts that Pareto-improve both revenue and consumer welfare for our OTA.

Suggested Citation

  • Chris Gu & Yike Wang, 2022. "Consumer Online Search with Partially Revealed Information," Management Science, INFORMS, vol. 68(6), pages 4215-4235, June.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:6:p:4215-4235
    DOI: 10.1287/mnsc.2021.4104
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    References listed on IDEAS

    as
    1. 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.
    2. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    3. 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.
    4. Brockett, Patrick L. & Charnes, Abraham & Cooper, William W. & Learner, David & Phillips, Fred Y., 1995. "Information theory as a unifying statistical approach for use in marketing research," European Journal of Operational Research, Elsevier, vol. 84(2), pages 310-329, July.
    5. Babur De los Santos & Sergei Koulayev, 2017. "Optimizing Click-Through in Online Rankings with Endogenous Search Refinement," Marketing Science, INFORMS, vol. 36(4), pages 542-564, July.
    6. 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.
    7. 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.
    8. Andrew Caplin, 2016. "Measuring and Modeling Attention," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 379-403, October.
    9. 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.
    10. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    11. 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.
    12. Hauser, John R & Wernerfelt, Birger, 1990. "An Evaluation Cost Model of Consideration Sets," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(4), pages 393-408, March.
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