Consumer Returns in Electronic Commerce

This dissertation has two empirical essays that explore consumer returns in electronic commerce. The goal of this work is to help online retailers to improve customer satisfaction, enhance customer loyalty, and eventually increase profitability by optimizing consumer returns management strategies.

The first essay explores the effects of customer procrastination and reverse logistics time on customer loyalty from the customer co-production perspective. We construct econometric models for the entire online return process. Using a proprietary dataset from one reverse logistics management firm, we apply logistic regression and the Cox proportional hazards regression models to verify our hypotheses. We find that both customer procrastination and reverse logistics time negatively affect customer loyalty. Our results suggest that reducing customer procrastination or shortening reverse logistics time can decrease consumer return rates and enhance customer retention. Further analysis using consumer psychology theories indicates that customer procrastination is associated with demographic factors like gender and ethnicity. These results suggest that online retailers should reconsider consumer returns not merely as an unavoidable cost of doing business but as a strategic opportunity for fostering positive customer engagement. By leveraging returns as a touch-point for enhancing customer experience, retailers can drive future sales and achieve long-term profitability.

Motivated by differences in customer procrastination across gender and ethnicity, the second essay examines how gender and ethnic diversities impact consumer returns in the luxury fashion industry. Although both gender and ethnicity are well studied in consumer behavior and retail operations, the academic literature lacks insights into how a consumer’s gender and ethnicity drive their return behaviors. We address this gap by analyzing 1.8 million online transactions across the US, using algorithms to predict customers' gender and ethnicity from their names and neighborhood demographics. Through logistic regression and mediation analysis, we identify distinct return patterns across different demographic groups. These findings enable targeted operational strategies to reduce returns and increase retention, allowing firms to tailor marketing approaches to accommodate varying return tendencies among consumer groups, ultimately improving operational efficiency and supporting localized marketing strategies.