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

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.

• 저널Information Systems Research · 34(2) · 590–608
• 토픽Digital Marketing and Social Media · Sociology and Political Science
• DOI10.1287/isre.2022.1134
• 저자Arslan Aziz, Hui Li, Rahul Telang