IDEAS home Printed from https://ideas.repec.org/a/inm/ormsom/v28y2026i3p895-916.html

The Economics of Bestsellers: Consumer Search, Sales Ranking, and Social Learning

Author

Listed:
  • Wentao Lu

    (Faculty of Business for Science & Technology, School of Management, University of Science and Technology of China, Anhui 230052, China)

  • Man Yu

    (School of Business and Management, Hong Kong University of Science and Technology, Hong Kong)

Abstract

Problem definition : Motivated by major e-commerce platforms’ diverse practices in bestseller information provision, this paper examines consumers’ learning, searching, and purchasing behavior under uncertainty about products’ values, whereas a revenue-maximizing platform strategically decides whether and, if so, how to disclose products’ past sales information to consumers. Methodology/results : We analyze a two-period Bayesian learning model that embeds consumers’ sequential product search in a social learning framework and shows how the interaction between bestseller information and consumer search impacts sales and welfare. We find that a bestseller list constitutes an informative yet noisy signal about the products’ values. The informativeness of the signal is determined by the granularity of the bestseller information. By evaluating bestseller information of two levels of granularity, sales ranking and sales volume, we discover that, although consumers benefit more from information of a higher granularity (i.e., sales volume), the platform may prefer providing information of a lower granularity (i.e., sales ranking), suggesting that the platform may withhold information at the cost of consumers. In particular, an inference effect unique to multiproduct Bayesian learning gives rise to the possibility that disclosure of sales volume backfires and hurts the platform. We demonstrate significant sales implications of bestseller information granularity and show that concave distribution functions for consumers’ search cost, a stochastic increase in product values, or a growth in consumer population can tilt the platform’s preference toward displaying bestseller rankings without revealing sales volumes. Furthermore, we show that bestseller information may lead to lower purchased value or higher search cost, the latter implying that public learning may stimulate rather than substitute private learning. Managerial implications : The paper cautions retail platform practitioners about a pitfall associated with disclosing bestseller sales volume and presents guidelines on the timing and granularity of sales information provision. The findings also suggest e-commerce platforms with consumer-centric goals enhance bestseller information transparency on their marketplaces.

Suggested Citation

  • Wentao Lu & Man Yu, 2026. "The Economics of Bestsellers: Consumer Search, Sales Ranking, and Social Learning," Manufacturing & Service Operations Management, INFORMS, vol. 28(3), pages 895-916, May.
  • Handle: RePEc:inm:ormsom:v:28:y:2026:i:3:p:895-916
    DOI: 10.1287/msom.2023.0583
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/msom.2023.0583
    Download Restriction: no

    File URL: https://libkey.io/10.1287/msom.2023.0583?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jerry Anunrojwong & Krishnamurthy Iyer & Vahideh Manshadi, 2023. "Information Design for Congested Social Services: Optimal Need-Based Persuasion," Management Science, INFORMS, vol. 69(7), pages 3778-3796, July.
    2. 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.
    3. Kenneth Hendricks & Alan Sorensen & Thomas Wiseman, 2012. "Observational Learning and Demand for Search Goods," American Economic Journal: Microeconomics, American Economic Association, vol. 4(1), pages 1-31, February.
    4. Saed Alizamir & Francis de Véricourt & Shouqiang Wang, 2020. "Warning Against Recurring Risks: An Information Design Approach," Management Science, INFORMS, vol. 66(10), pages 4612-4629, October.
    5. Ozan Candogan & Kimon Drakopoulos, 2020. "Optimal Signaling of Content Accuracy: Engagement vs. Misinformation," Operations Research, INFORMS, vol. 68(2), pages 497-515, March.
    6. Yan Liu & William L. Cooper & Zizhuo Wang, 2019. "Information Provision and Pricing in the Presence of Consumer Search Costs," Production and Operations Management, Production and Operations Management Society, vol. 28(7), pages 1603-1620, July.
    7. Alan T. Sorensen, 2007. "Bestseller Lists And Product Variety," Journal of Industrial Economics, Wiley Blackwell, vol. 55(4), pages 715-738, December.
    8. Manuel Mueller-Frank & Mallesh M. Pai, 2016. "Social Learning with Costly Search," American Economic Journal: Microeconomics, American Economic Association, vol. 8(1), pages 83-109, February.
    9. Ming Hu & Zizhuo Wang & Yinbo Feng, 2020. "Information Disclosure and Pricing Policies for Sales of Network Goods," Operations Research, INFORMS, vol. 68(4), pages 1162-1177, July.
    10. Callander, Steven & Hörner, Johannes, 2009. "The wisdom of the minority," Journal of Economic Theory, Elsevier, vol. 144(4), pages 1421-1439.2, July.
    11. Hongbin Cai & Yuyu Chen & Hanming Fang, 2009. "Observational Learning: Evidence from a Randomized Natural Field Experiment," American Economic Review, American Economic Association, vol. 99(3), pages 864-882, June.
    12. Mahsa Derakhshan & Negin Golrezaei & Vahideh Manshadi & Vahab Mirrokni, 2022. "Product Ranking on Online Platforms," Management Science, INFORMS, vol. 68(6), pages 4024-4041, June.
    13. 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.
    14. Octavian Carare, 2012. "The Impact Of Bestseller Rank On Demand: Evidence From The App Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 717-742, August.
    15. 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.
    16. Raphael Boleslavsky & Christopher S. Cotton & Haresh Gurnani, 2017. "Demonstrations and Price Competition in New Product Release," Management Science, INFORMS, vol. 63(6), pages 2016-2026, June.
    17. Yiangos Papanastasiou, 2020. "Fake News Propagation and Detection: A Sequential Model," Management Science, INFORMS, vol. 66(5), pages 1826-1846, May.
    18. Daniel Garcia & Sandro Shelegia, 2018. "Consumer search with observational learning," RAND Journal of Economics, RAND Corporation, vol. 49(1), pages 224-253, March.
    19. Chen Jin & Luyi Yang & Kartik Hosanagar, 2023. "To Brush or Not to Brush: Product Rankings, Consumer Search, and Fake Orders," Information Systems Research, INFORMS, vol. 34(2), pages 532-552, June.
    20. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    21. Man Yu & Laurens Debo & Roman Kapuscinski, 2016. "Strategic Waiting for Consumer-Generated Quality Information: Dynamic Pricing of New Experience Goods," Management Science, INFORMS, vol. 62(2), pages 410-435, February.
    22. José Luis Moraga‐González & Zsolt Sándor & Matthijs R. Wildenbeest, 2013. "Semi‐Nonparametric Estimation Of Consumer Search Costs," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1205-1223, November.
    23. Giovanni Compiani & Gregory Lewis & Sida Peng & Peichun Wang, 2024. "Online Search and Optimal Product Rankings: An Empirical Framework," Marketing Science, INFORMS, vol. 43(3), pages 615-636, May.
    24. Eduard Calvo & Ruomeng Cui & Laura Wagner, 2023. "Disclosing Product Availability in Online Retail," Manufacturing & Service Operations Management, INFORMS, vol. 25(2), pages 427-447, March.
    25. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    26. Jacob Glazer & Ilan Kremer & Motty Perry, 2021. "The Wisdom of the Crowd When Acquiring Information Is Costly," Management Science, INFORMS, vol. 67(10), pages 6443-6456, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin Huang, 2017. "To Glance or to Peruse: Observational and Active Learning from Peer Consumers," Working Papers wp2018_1716, CEMFI.
    2. Ata Jameei Osgouei & Andrew T. Ching & Brian T. Ratchford & Shervin Shahrokhi Tehrani, 2026. "Estimating Position and Social Influence Effects in Online Search," Marketing Science, INFORMS, vol. 45(1), pages 203-223, January.
    3. Jin Huang, 2017. "To Glance or to Peruse: Observational and Active Learning from Peer Consumers," Working Papers wp2017_1716, CEMFI.
    4. Liangfei Qiu & Arunima Chhikara & Asoo Vakharia, 2021. "Multidimensional Observational Learning in Social Networks: Theory and Experimental Evidence," Information Systems Research, INFORMS, vol. 32(3), pages 876-894, September.
    5. Ming Hu & Joseph Milner & Jiahua Wu, 2016. "Liking and Following and the Newsvendor: Operations and Marketing Policies Under Social Influence," Management Science, INFORMS, vol. 62(3), pages 867-879, March.
    6. Zachary Mahone & Filippo Rebessi, 2024. "Observational learning and firm dynamics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 57(3), pages 989-1027, August.
    7. Zachary Mahone & Filippo Rebessi, 2019. "Consumer Learning and Firm Dynamics," Department of Economics Working Papers 2019-08, McMaster University.
    8. Kimon Drakopoulos & Ali Makhdoumi, 2023. "Providing Data Samples for Free," Management Science, INFORMS, vol. 69(6), pages 3536-3560, June.
    9. Song, Yangbo, 2016. "Social learning with endogenous observation," Journal of Economic Theory, Elsevier, vol. 166(C), pages 324-333.
    10. Bobkova, Nina & Mass, Helene, 2022. "Two-dimensional information acquisition in social learning," Journal of Economic Theory, Elsevier, vol. 202(C).
    11. Ali, S. Nageeb, 2018. "Herding with costly information," Journal of Economic Theory, Elsevier, vol. 175(C), pages 713-729.
    12. Herrera, Helios & Hörner, Johannes, 2013. "Biased social learning," Games and Economic Behavior, Elsevier, vol. 80(C), pages 131-146.
    13. Mark Armstrong, 2017. "Ordered Consumer Search," Journal of the European Economic Association, European Economic Association, vol. 15(5), pages 989-1024.
    14. Daniel Garcia & Sandro Shelegia, 2018. "Consumer search with observational learning," RAND Journal of Economics, RAND Corporation, vol. 49(1), pages 224-253, March.
    15. Kamal Bookwala & Caleb Gallemore & Joaquín Gómez‐Miñambres, 2022. "The influence of food recommendations: Evidence from a randomized field experiment," Economic Inquiry, Western Economic Association International, vol. 60(4), pages 1898-1910, October.
    16. James C. D. Fisher & John Wooders, 2017. "Interacting information cascades: on the movement of conventions between groups," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 63(1), pages 211-231, January.
    17. Parakhonyak, Alexei & Vikander, Nick, 2023. "Information design through scarcity and social learning," Journal of Economic Theory, Elsevier, vol. 207(C).
    18. Jacob Glazer & Ilan Kremer & Motty Perry, 2021. "The Wisdom of the Crowd When Acquiring Information Is Costly," Management Science, INFORMS, vol. 67(10), pages 6443-6456, October.
    19. Kaufman, Noah, 2014. "Overcoming the barriers to the market performance of green consumer goods," Resource and Energy Economics, Elsevier, vol. 36(2), pages 487-507.
    20. Hyun-Soo Ahn & Christopher Thomas Ryan & Joline Uichanco & Mengzhenyu Zhang, 2026. "Valuing Influence with Social Learning," Manufacturing & Service Operations Management, INFORMS, vol. 28(4), pages 1133-1153, July.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormsom:v:28:y:2026:i:3:p:895-916. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.