On Information Distortions in Online Ratings
Columbia Business School - Decision Risk and Operations
LUISS, Dipartimento di Economia e Finanza
Columbia Business School Research Paper No. 13-36
Consumer reviews and ratings of products and services have become ubiquitous on the Internet. This paper analyzes, given the sequential nature of reviews and the limited feedback of such past reviews, the information content they communicate to future customers. We focus on the informational setting in which customers only observe the sample mean of past reviews, and ask if customers can recover the true quality of the product based on the feedback they observe. We first analyze the benchmark setting, in which customers interpret the mean as the proxy of quality. In such a case, we show that in the long run, the sample mean of reviews stabilizes and two cases may arise. If customers are relatively homogeneous, then social learning takes place. If customers are sufficiently heterogeneous, then they consistently overestimate the underlying quality of the product in the long run. This bias stems from the selection associated with observing only reviews of customers who purchase. We show, however, that if customers are sophisticated, then there exists a simple quality inference and purchasing rule that corrects for the selection bias and leads to social learning. In addition, we show that the cumulative consumer surplus losses scale with the square root of the number of customers who have considered a purchase to date, which is of the same order as when customers observe the reviews of all preceding customers. In this framework, we also analyze the externality of sophisticated customers on more naive ones and quantify the impact that manipulated reviews may have.
Number of Pages in PDF File: 28
Keywords: online reviews, quality biases, stochastic approximation, stochastic order, sequential analysis, manipulation
Date posted: May 17, 2013 ; Last revised: March 3, 2015
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