The effect of online reviews on addressing endogeneity in discrete choice models

Abstract

This paper investigates the effectiveness of online reviews on addressing price endogeneity issue in an application to consumer demand for smartphone. We consider review variables as the substitutes of unobserved product quality in terms of a scalar variable as seen in previous methods. An aspect-based sentiment classification technique is designed to construct feature-related review variables from millions of review contents. We discuss the performance of review variables both in a hedonic pricing model and a conditional logit discrete choice model. Our results demonstrate that review variables show a good performance either as instruments for price or as explicit control variables in demand models. In detail, the pricing prediction accuracy increases 3.4%, which is considered as a significant improvement in the practice of forecasting. In the discrete choice model, the estimated price coefficient is biased in the positive direction without endogeneity correction. It is adjusted in the expected way after including review variables. The findings indicate that online reviews provide alternative sources of information in dealing with endogeneity in discrete choice models. We also analyze the differences in the preferences and needs of individual consumers to provide some practical implications of marketing.