| Description |
Product fit uncertainty is cited as the top reason for high online product return rate. To deal with such a challenge, online retailers encourage the customers to post opinions about product fit in the purpose of helping the subsequent shoppers make better purchase decisions. Although the role of the overall online product rating has been widely studied, the impact of fit-related opinions on product purchase and return decisions is not fully understood. The goal of this dissertation is to address those issues. In Chapter 1, by applying text mining techniques to extract reviewers' product quality and product fit opinions to derive volume and valence of each type of opinions, we estimate and compare their impacts on online purchase and product return decisions. The results suggest fit-related product opinions have a stronger effect on purchases than quality opinions do. Customers who purchase products when the valences of fit opinions are high are less likely to return the products than customers who purchase products when the volume of fit opinions are high. However, customers in the first group are more likely to post reviews with fit related issues. In Chapter 2, we probe beyond the valence of fit opinions by considering the role of fit reference (i.e., preference information) in reducing product returns. Leveraging a quasi-experiment in the apparel category, we discover it is the combination of fit valence (e.g., whether the apparel is true to size or not) and fit reference (e.g., reviewers' body size) that drives the decrease of product returns. Moreover, when fit reference is provided, negative fit opinions become as useful as positive fit opinions. Chapter 3 is a research commentary that addresses the essential problem of online product uncertainty. Within the horizontal dimension of product attributes, we propose that it is necessary to differentiate the fit attributes from the taste attributes because the nature of their characteristics results in different challenges regarding the product description uncertainty. We argue that understanding the nature of the product attributes would help researchers and business practitioners develop more advanced online IT functions addressing the relevant problems in the long run. |
