A Dynamic Structural Model of Endogenous Consumer Reviews in Durable Goods Markets

Abstract

Consumers commonly seek quality information from product reviews when their perceived quality of new products remains uncertain. However, consumer-generated product reviews are endogenous and may be subject to potential biases that impact consumer's purchase behavior. In this paper, We develop a dynamic structural learning model and a consumer review-reporting model to quantify the causal impacts of critic reviews and consumer reviews biases on forward-looking consumer adoption behavior in the U.S. video-game market. Our datasets consist of a novel component of pre-order sales (sales realized prior to product launch), post-release sales and prices at product level from 2009 to 2012, and individual-level online critics and consumer ratings. Our study includes two parts. First, we leverage pre-order information to separately identify the impacts of critics and consumer's prior expectation on purchase behavior. Second, we find systematic differences between critics and consumer ratings: the average of consumer ratings tends to become lower (higher) when critics are higher (lower). Therefore, a standard Bayesian learning model cannot be directly applied since such systematic differences are not merely driven by sampling errors when quality signals are drawn from distributions of true product quality. We propose that the observed differences could be driven by consumer review biases due to a combination of self-selection effect and reference point effect. We propose a novel review-reporting model that rationalizes the observed discrepancies between critics and consumer ratings. We then implement a joint estimation strategy to estimate the combined model using Bayesian Markov chain Monte Carlo (MCMC) algorithm that is applicable to a non-stationary dynamic discrete choice model with potential price, and consumer review endogeneity problems. Using structural model estimates, we examine the causal impacts of critics and consumer reviews on firms' profits. To assess these impacts, we conduct counterfactual experiments by sequentially eliminating critics and consumer reviews. The results of the proposed model can provide important managerial implications with respect to dynamic pricing and preorder strategies.