Reviews and Self-Selection Bias with Operational Implications

• 成果类型: Review
• 署名作者: Chen, Ningyuan; Li, Anran; Talluri, Kalyan
• 署名单位: University of Toronto; University Toronto Mississauga; University of Toronto; University of London; London School Economics & Political Science; Imperial College London
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3892
• 发表日期: 2021
• 页码: 7472-7492
• 关键词: Social learning; product reviews; Consumer choice; Assortment Optimization and Pricing; process variability

摘要

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. Many empirical papers have documented a bias in the aggregate ratings, arising because of customers' inherent self-selection in their choices and bounded rationality in evaluating previous reviews. Although there is a vast empirical literature analyzing reviews, theoretical models that try to isolate and explain the bias in ratings are relatively few. Assuming consumers simply substitute the average rating that they see as a proxy for quality, we give a precise characterization of the self-selection bias on ratings of an assortment of products 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, which is more pronounced for niche products. 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.