On Information Distortions in Online Ratings

• 成果类型: Article
• 署名作者: Besbes, Omar; Scarsini, Marco
• 署名单位: Columbia University; Luiss Guido Carli University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2017.1676
• 发表日期: 2018
• 页码: 597-610
• 关键词: Social networks; DYNAMICS; BEHAVIOR; cascades; fads

摘要

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, customers overestimate the underlying quality of the product. We establish properties of the bias, stemming from the selection associated with observing only reviews of customers who purchase. Then, we show the existence of a simple non-Bayesian quality mference rule that leads to social learning when all customers use such a rule. The results point to the strong information content of even limited statistics of past reviews as long as customers have minimal sophistication.