The Battle Against Rating Management – Does Lowering Barriers To Rate Improve The Informativeness of The Rating Consensus on Online Platforms?

Abstract

Online platforms such as Amazon.com, Google, and Glassdoor have to design policies for submitting ratings. We investigate how lowering barriers to rate affects the informativeness of the rating consensus - a crowd-sourced performance measure. In 2020, Amazon.com introduced a new one-tap rating system, whereas before, a written text review was required to rate a product. Changing the cost of submitting ratings may affect the underlying distribution of raters (i.e., satisfied and unsatisfied customers, sellers of managed ratings). Our analyses show that after the policy change, the average rating increases, and the effect is stronger for lower-rated products. Thus, the rating consensus becomes less informative for platform users to discriminate between products. A potential explanation is that lowering the barriers makes it cheaper to provide managed ratings which may outweigh a potential increase in authentic ratings. Alternatively, relatively more satisfied customers make use of the new option. The results of several additional analyses are consistent with both explanations.

Keywords: Wisdom of crowds, platforms, non-financial information, informativeness, rating manipulations, rating management, rating inflation, fake reviews, Amazon.com

JEL Classification: D20, D82, M10, M31, M41