On Information Distortions in Online Ratings

33 Pages Posted: 17 May 2013 Last revised: 17 Dec 2016

See all articles by Omar Besbes

Omar Besbes

Columbia Business School - Decision Risk and Operations

Marco Scarsini

LUISS, Dipartimento di Economia e Finanza

Date Written: December 10, 2016


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 consider a model with heterogeneous customers who buy a product of unknown quality and we focus on two different informational settings. In the first setting customers observe the whole history of past reviews. In the second one they only observe the sample mean of past reviews. We examine under which conditions, in each setting, customers can recover the true quality of the product based on the feedback they observe. In the case of total monitoring, if consumers adopt a fully rational Bayesian updating paradigm, then they asymptotically learn the unknown quality. With access to only the sample mean of past reviews, inference becomes intricate for customers and it is not clear if/when and how social learning can take place. We first analyze the setting when customers interpret the mean as the proxy of quality. We show that in the long run, the sample mean of reviews stabilizes and in general, they overestimate the underlying quality of the product in the long run. We establish properties of the bias, which stems from the selection associated with observing only reviews of customers who purchase. Then, we show the existence of a simple non-Bayesian quality inference rule that leads to social learning when all customers use such a rule. In this framework, when the population of consumers is mixed, we show that a subgroup of customers can learn the true quality and that this subgroup negatively affects the more naive customers.

Keywords: online reviews, quality biases, stochastic approximation, stochastic order, sequential analysis, manipulation

Suggested Citation

Besbes, Omar and Scarsini, Marco, On Information Distortions in Online Ratings (December 10, 2016). Columbia Business School Research Paper No. 13-36. Available at SSRN: https://ssrn.com/abstract=2266053 or http://dx.doi.org/10.2139/ssrn.2266053

Omar Besbes (Contact Author)

Columbia Business School - Decision Risk and Operations ( email )

New York, NY
United States

Marco Scarsini

LUISS, Dipartimento di Economia e Finanza ( email )

Viale Romania 32
Rome, RM 00197

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