Learning From Online Ratings

Xiang Hui, Tobias Klein and Konrad Stahl

CRC TR 224 Discussion Paper Series from University of Bonn and University of Mannheim, Germany

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.

Keywords: Online markets; rating; reputation (search for similar items in EconPapers)
JEL-codes: D83 L12 L13 L81 (search for similar items in EconPapers)
Pages: 81
Date: 2024-04
New Economics Papers: this item is included in nep-ind and nep-pay
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Related works:
Working Paper: Learning from Online Ratings (2024)
Working Paper: Learning from Online Ratings (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:bon:boncrc:crctr224_2024_532

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