The Consequences of Rating Inflation on Platforms: Evidence from a Quasi-Experiment

Arslan Aziz (), Hui Li () and Rahul Telang ()
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Arslan Aziz: Sauder School of Business, The University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
Hui Li: HKU Business School, The University of Hong Kong, Hong Kong
Rahul Telang: Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Information Systems Research, 2023, vol. 34, issue 2, 590-608

Abstract: Informative online ratings enable digital platforms to reduce the search cost for buyers to find good sellers. However, rating inflation, a phenomenon in which average rating increases and rating variance across listings decreases, threatens the informativeness of ratings. We empirically identify the consequences of rating inflation by conducting a quasi-experiment with a digital platform that exogenously changed its rating display rule in a treated neighborhood, which resulted in rating inflation. Using a differences-in-differences approach, we find that platforms benefit from one aspect of rating inflation: user purchases and seller sales increase because of the increased average rating. However, they also face negative consequences: rating inflation causes a decrease in user trial and a greater concentration of sales among popular restaurants. Overall, our results illustrate the potential consequences of rating inflation that platforms need to consider when designing and managing their rating system.

Keywords: rating inflation; online marketplace; quasi-experiment; economics of IS (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:34:y:2023:i:2:p:590-608

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