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

• 成果类型: Article; Early Access
• 署名作者: Aziz, Arslan; Li, Hui; Telang, Rahul
• 署名单位: University of British Columbia; University of Hong Kong; Carnegie Mellon University
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2022.1134
• 发表日期: 2022
• 关键词: word-of-mouth; online; product; reputation; DYNAMICS; feedback; reviews; sales; uncertainty; performance

摘要

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.