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"On Bias in Social Learning and Consumer Choice" by Dr. Ningyuan Chen

Event information
Innovation and Information Management
Monday, 15 April 2019
KK 1303, K.K. Leung Bldg
  • 2:30pm - 4:00pm


  • Dr. Ningyuan Chen
    Assistant Professor
    Department of Industrial Engineering & Decision Analytics
    The Hong Kong University of Science and Technology




Reviews for products and services, written by consumers and users, have become an influential input to the purchase decision. For many service businesses they have also become part of the performance review for 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 rating arising from various sources --- both consumers' self-selection bias in reporting reviews, as well as potential customers' bounded rationality in evaluating previous reviews. While there is a vast empirical literature, theoretical models that try to isolate and explain the ratings bias are relatively few, and most are based on rational Bayesian learning on the part of consumers. However, writing a review requires some effort (even if firms try to make it as painless as possible) and it seems unlikely -- as well documented and tested in the behavioral economics field -- that consumers make the effort to do a proper Bayesian update of their beliefs before making purchases. In this paper we investigate the nature of the self-selection bias: consumers confound ex-ante innate preferences for a product or service with ex-post experience and are unable or unwilling to separate the two. We develop a parsimonious dynamic choice model for consumer purchase decisions and show that the mechanism leads to an upward bias. We quantify the bias and show that it makes the tastes of consumers look more ``heterogeneous'', benefiting niche products and hurting popular products in terms of the choice probability. We investigate how this affects the firms' assortment and pricing decisions on an online platform.