Product returns continues to mount operational challenges for the e-tailing firms due to both their sheer volume and the costs of processing returns. But returns also represent untapped opportunities to turn the dissatisfied customers into the loyal ones. Despite this, research on the topic of e-tailing is sparse and the little that is known about the quality of e-tailer’s service during product returns treat returns as recovery from failure. However, that view is outdated. E-tailers’ product return practices have substantially evolved and customers’ return behavior has considerably increased, influencing also their expectations. Thus, a need arises to revise our understanding of how customers evaluate the quality of e-tailers’ service during product returns. This study aims to propose a revised concept called e-return service quality (e-RetS-Qual) and develop a hierarchical measure of the concept that fit the current times.
We employed mix of qualitative and quantitative approaches to fulfill this objective. Firstly, an abductive approach was adopted, with a continuous back-and-forth movement between existing theory and two datasets (from two qualitative studies - sequential incident technique and critical feedback incident technique studies).This qualitative research resulted in the identification of the six dimensions important to the returning customers. Next, second-order factor model of e-return service quality (e-RetS-Qual) is proposed based on three quantitative studies. Lastly, using direct ranking and rank order logit modelling study, we assessed customers’ order of importance for six dimensions and further identify three return customer segment- Hassle haters, Reliable-response cravers and Refund warriors. Overall, the research suggests e-tailers, who are looking to differentiate themselves through superior e-return service quality, should focus on customizing their service by performing excellently on these dimensions.

Keywords– service quality, e-tailing, product returns, abductive logic, measure development, ranking, rank order logit model, cluster analysis