This article studies the question and answer (Q&A) technology of electronic commerce platforms, an increasingly common form of user-generated content 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, the authors show that Q&As complement consumer reviews: unlike reviews, questions are primarily asked pre-purchase and focus on clarification of product attributes rather than discussion of quality; answers convey fit-specific information in a predominantly sentiment-free way. Based on these observations, the authors hypothesize that Q&As mitigate product fit uncertainty, leading to better matches between products and consumers, and therefore improved product ratings. Indeed, when products suffering from fit mismatch start receiving Q&As, their subsequent ratings improve by approximately 0.1 to 0.5 stars and the fraction of negative reviews that discuss fit-related issues declines. The extent of the rating increase due to Q&As is proportional to the probability that purchasers will experience fit mismatch without Q&A. These 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 of products that have incurred low ratings due to customer-product fit mismatch.
Research on Consumer Initial Trust Model Based on Internet of Things
