Interacting User Generated Content Technologies: How Q&As Affect Ratings & Reviews

Abstract

In this paper, we study the question and answer (Q&A) feature of electronic commerce platforms, an increasingly common form of user-generated content (UGC) that allows consumers to publicly ask product-specific questions and receive responses, either from the platform or from other customers. Using data from a major online retailer, we show that Q&As complement reviews and ratings: unlike reviews, Q&As primarily happen pre-purchase, focus on clarification of product attributes (rather than discussion of quality), and convey fit-specific information in a sentiment-free way. Our main hypothesis is that Q&As mitigate product fit uncertainty, leading to better matches between products and consumers, and therefore improved product ratings. We show that when low-rated products start receiving Q&As, their subsequent ratings improve by approximately 0.5 stars. We further show that the extent of the rating increase due to Q&As is moderated by the degree of ex-ante fit uncertainty. Overall, our findings suggest that, by resolving product fit uncertainty in an e-commerce setting, the addition of Q&As can be a viable way for retailers to improve ratings and sales of low-rated products, particularly those products that have incurred low ratings due to customer-product fit mismatch.