Fewer Reviews but Better Content: When an Online Review Platform Disables Downvotes

Most user-generated content platforms utilize peer evaluation systems that incorporate upvotes and downvotes to distinguish high-quality from low-quality content. However, downvotes are often misused to target others and discourage content creators, and some platforms, such as Amazon and TripAdvisor, have removed the downvote option altogether. Meanwhile, the real-world impact of this platform-level intervention on user contributions remains underexplored, and we fill this gap by analyzing a restaurant review platform that disabled downvotes. We apply the regression discontinuity in time (RDiT) method and find that removing downvotes reduces the quantity of reviews but increases their quality. Further mechanism analysis suggests that the former may stem from perceived unfairness, and the latter from a motivation of continuing reviewers to stand out. Additionally, our heterogeneity analysis shows that the decline in review quantity is stronger among long-tenure reviewers, whereas the improvements in multiple quality dimensions are concentrated among reviewers with fewer followers. Our study reveals the nuanced behavioral effects of a platform-level design change and offers practical insights for designing peer evaluation systems that balance fairness, motivation, and content value.

• 저널Journal of Management Information Systems · 43(2) · 654–679
• 토픽Meta-analysis and systematic reviews · Statistics, Probability and Uncertainty
• DOI10.1080/07421222.2026.2647471
• 저자Yinan Yu, Dezhi Yin, Warut Khern-am-nuai