IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_11171.html
   My bibliography  Save this paper

Learning from Online Ratings

Author

Listed:
  • Xiang Hui
  • Tobias J. Klein
  • Konrad O. Stahl

Abstract

Online ratings play an important role in many markets. However, how fast they can reveal seller types remains unclear. To study this question, we propose a new model in which a buyer learns about the seller’s type from previous ratings and her own experience and rates the seller if she learns enough. We derive two testable implications and verify them using administrative data from eBay. We also show that alternative explanations are unlikely to explain the observed patterns. After having validated the model in that way, we calibrate it to eBay data to quantify the speed of learning. We find that ratings can be very informative. After 25 transactions, the likelihood of correctly predicting the seller type is above 95 percent.

Suggested Citation

  • Xiang Hui & Tobias J. Klein & Konrad O. Stahl, 2024. "Learning from Online Ratings," CESifo Working Paper Series 11171, CESifo.
    • Handle: RePEc:ces:ceswps:_11171
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp11171.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Imke Reimers & Joel Waldfogel, 2021. "Digitization and Pre-purchase Information: The Causal and Welfare Impacts of Reviews and Crowd Ratings," American Economic Review, American Economic Association, vol. 111(6), pages 1944-1971, June.
    2. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    3. Patrick Bajari & Ali Hortaçsu, 2004. "Economic Insights from Internet Auctions," Journal of Economic Literature, American Economic Association, vol. 42(2), pages 457-486, June.
    4. Tobias J. Klein & Christian Lambertz & Konrad O. Stahl, 2016. "Market Transparency, Adverse Selection, and Moral Hazard," Journal of Political Economy, University of Chicago Press, vol. 124(6), pages 1677-1713.
    5. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    6. Gary Bolton & Ben Greiner & Axel Ockenfels, 2013. "Engineering Trust: Reciprocity in the Production of Reputation Information," Management Science, INFORMS, vol. 59(2), pages 265-285, January.
    7. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    8. Sherry He & Brett Hollenbeck & Davide Proserpio, 2022. "The Market for Fake Reviews," Marketing Science, INFORMS, vol. 41(5), pages 896-921, September.
    9. Chris Nosko & Steven Tadelis, 2015. "The Limits of Reputation in Platform Markets: An Empirical Analysis and Field Experiment," NBER Working Papers 20830, National Bureau of Economic Research, Inc.
    10. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    11. Xiang Hui & Maryam Saeedi & Neel Sundaresan, 2018. "Adverse Selection or Moral Hazard, An Empirical Study," Journal of Industrial Economics, Wiley Blackwell, vol. 66(3), pages 610-649, September.
    12. Andrey Fradkin & Elena Grewal & David Holtz, 2021. "Reciprocity and Unveiling in Two-Sided Reputation Systems: Evidence from an Experiment on Airbnb," Marketing Science, INFORMS, vol. 40(6), pages 1013-1029, November.
    13. Michael Luca & Oren Reshef, 2021. "The Effect of Price on Firm Reputation," Management Science, INFORMS, vol. 67(7), pages 4408-4419, July.
    14. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    15. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    16. Luís Cabral & Ali Hortaçsu, 2010. "The Dynamics Of Seller Reputation: Evidence From Ebay," Journal of Industrial Economics, Wiley Blackwell, vol. 58(1), pages 54-78, March.
    17. Chrysanthos Dellarocas & Charles A. Wood, 2008. "The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias," Management Science, INFORMS, vol. 54(3), pages 460-476, March.
    18. Yi-Chun (Chad) Ho & Junjie Wu & Yong Tan, 2017. "Disconfirmation Effect on Online Rating Behavior: A Structural Model," Information Systems Research, INFORMS, vol. 28(3), pages 626-642, September.
    19. Steven Tadelis, 2016. "Reputation and Feedback Systems in Online Platform Markets," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 321-340, October.
    20. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2022. "Learning From Reviews: The Selection Effect and the Speed of Learning," Econometrica, Econometric Society, vol. 90(6), pages 2857-2899, November.
    21. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
    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. Xiang Hui & Tobias J. Klein & Konrad Stahl, 2021. "When and Why Do Buyers Rate in Online Markets?," CRC TR 224 Discussion Paper Series crctr224_2021_267v1, University of Bonn and University of Mannheim, Germany.
    2. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    3. Gesche, Tobias, 2018. "Reference Price Shifts and Customer Antagonism: Evidence from Reviews for Online Auctions," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181650, Verein für Socialpolitik / German Economic Association.
    4. Andreas J. Steur & Mischa Seiter, 2021. "Properties of feedback mechanisms on digital platforms: an exploratory study," Journal of Business Economics, Springer, vol. 91(4), pages 479-526, May.
    5. Apostolos Filippas & John Horton & Joseph M. Golden, 2017. "Reputation in the Long-Run," CESifo Working Paper Series 6750, CESifo.
    6. Paul Belleflamme & Martin Peitz, 2018. "Inside the Engine Room of Digital Platforms: Reviews, Ratings, and Recommendations," Working Papers halshs-01714549, HAL.
    7. Krügel, Jan Philipp & Paetzel, Fabian, 2024. "The impact of fraud on reputation systems," Games and Economic Behavior, Elsevier, vol. 144(C), pages 329-354.
    8. Georgios Zervas & Davide Proserpio & John W. Byers, 2021. "A first look at online reputation on Airbnb, where every stay is above average," Marketing Letters, Springer, vol. 32(1), pages 1-16, March.
    9. Yan Chen & Peter Cramton & John A. List & Axel Ockenfels, 2021. "Market Design, Human Behavior, and Management," Management Science, INFORMS, vol. 67(9), pages 5317-5348, September.
    10. Greiff, Matthias & Paetzel, Fabian, 2020. "Information about average evaluations spurs cooperation: An experiment on noisy reputation systems," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 334-356.
    11. Apostolos Filippas & John J. Horton & Joseph M. Golden, 2019. "Reputation Inflation," NBER Working Papers 25857, National Bureau of Economic Research, Inc.
    12. Balázs Kovács, 2024. "The Turing test of online reviews: Can we tell the difference between human-written and GPT-4-written online reviews?," Marketing Letters, Springer, vol. 35(4), pages 651-666, December.
    13. Lingfang (Ivy) Li & Steven Tadelis & Xiaolan Zhou, 2020. "Buying reputation as a signal of quality: Evidence from an online marketplace," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 965-988, December.
    14. Apostolos Filippas & John J. Horton & Richard J. Zeckhauser, 2020. "Owning, Using, and Renting: Some Simple Economics of the “Sharing Economy”," Management Science, INFORMS, vol. 66(9), pages 4152-4172, September.
    15. Peitz, Martin & Schwalbe, Ulrich, 2016. "Zwischen Sozialromantik und Neoliberalismus: Zur Ökonomie der Sharing-Economy," ZEW Discussion Papers 16-033, ZEW - Leibniz Centre for European Economic Research.
    16. Heeseung Andrew Lee & Angela Aerry Choi & Tianshu Sun & Wonseok Oh, 2021. "Reviewing Before Reading? An Empirical Investigation of Book-Consumption Patterns and Their Effects on Reviews and Sales," Information Systems Research, INFORMS, vol. 32(4), pages 1368-1389, December.
    17. Arslan Aziz & Hui Li & Rahul Telang, 2023. "The Consequences of Rating Inflation on Platforms: Evidence from a Quasi-Experiment," Information Systems Research, INFORMS, vol. 34(2), pages 590-608, June.
    18. Chris Nosko & Steven Tadelis, 2015. "The Limits of Reputation in Platform Markets: An Empirical Analysis and Field Experiment," NBER Working Papers 20830, National Bureau of Economic Research, Inc.
    19. Schwalbe Ulrich & Peitz Martin, 2016. "Kollaboratives Wirtschaften oder Turbokapitalismus?: Zur Ökonomie der Sharing economy," Perspektiven der Wirtschaftspolitik, De Gruyter, vol. 17(3), pages 232-252, September.
    20. Erfan Rezvani & Christian Rojas, 2022. "Firm responsiveness to consumers' reviews: The effect on online reputation," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(4), pages 898-922, November.

    More about this item

    Keywords

    online markets; rating; reputation; Bayesian learning;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L12 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Monopoly; Monopolization Strategies
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ces:ceswps:_11171. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.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.