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Learning From Online Ratings

Author

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

Abstract

Online ratings play an important role in many markets. However, how fast they can reveal seller types remains unclear. We propose a simple model of rating behavior where learning about the seller type influences the rating decision. We calibrate the model to eBay data and 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 Stahl, 2024. "Learning From Online Ratings," CRC TR 224 Discussion Paper Series crctr224_2024_532, University of Bonn and University of Mannheim, Germany.
    • Handle: RePEc:bon:boncrc:crctr224_2024_532
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    File URL: https://www.crctr224.de/research/discussion-papers/archive/dp532
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    More about this item

    Keywords

    Online markets; rating; reputation;
    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

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