Reviews and Self-selection Bias with Operational Implications
50 Pages Posted: 25 Jun 2019 Last revised: 28 Oct 2019
Date Written: June 20, 2019
Reviews for products and services written by previous consumers have become an influential input to the purchase decision of customers. Many service businesses monitor the reviews closely, for feedback as well as detecting service flaws, and they have become part of the performance review for service managers with rewards tied to improvement in the aggregate rating. It is therefore of great importance to understand how much the public ratings reflect true quality of the product or service. Many empirical papers have documented a bias in the aggregate ratings, arising due to various causes: an inherent self-selection in writing reviews, as well as customers’ bounded rationality in evaluating previous reviews. While there is a vast empirical literature analyzing reviews, theoretical models that try to isolate and explain the ratings bias are relatively few, and most are based on a rational Bayesian learning assumption on the part of consumers. However, writing a review requires some effort in itself and it seems unlikely that consumers would make the considerable effort to do a Bayesian update of their beliefs before making purchases. Assuming consumers simply substitute the average rating they see as a proxy for quality, we give a precise characterization of the acquisition bias, when consumers confound ex-ante innate preferences for a product or service with ex-post experience and service quality and do not separate the two. We develop a parsimonious choice model for consumer purchase decisions and show that the mechanism leads to an upward bias. Based on our theoretical characterization, we study the effect on pricing and assortment decisions of the firm, when potential customers purchase based on the biased ratings. Our results give insights into how quality, prices and customer feedback are intricately tied together for service firms.
Keywords: social learning, bounded rationality, choice probability, assortment optimization, pricing, process variability
JEL Classification: D83
Suggested Citation: Suggested Citation