Product Returns in a Digital Era: The Role of Multidimensional Cognitive Dissonance, Regret, and Buying Context in the Post-purchase Appraisal Process

Abstract

The retailing industry is battling a behemoth – the escalating problem of product returns. The problem is of a graver import for e-tailers. However, the underlying cognitive and affective appraisal process that leads to product returns in case of online purchase still remains unclear. The liberal product returns environment in the context of online purchase has led consumers to proactively consider the option of decision reversal. Nevertheless, the impact of the initial buying context on the post-purchase appraisal process has been neglected in previous studies. To bridge the gaps found after evaluating the current gamut of research work conducted on this topic, a mixed-method approach was employed in the present study. Using in-depth semi-structured interviews (N = 42), the first qualitative study identified three online purchase situations (unplanned, purchase-for-trial and opportunism buying) that frequently provoke product returns. Additionally, the qualitative uncovered the salient post-purchase appraisal factors. To empirically test the underlying appraisal process and the differences caused by the buying situations, a quantitative study was conducted, using scenario-based experiment (N = 620). Findings suggest that contrary to recent studies (e.g., Lee, 2015; Powers & Jack, 2013), cognitive dissonance is not the immediate cause of product returns. It is the affective factor, regret, which leads to decision reversal. Additionally, in opposition to the claim of previous literature that high coping potential reduces stress, this study suggests that the ability to reverse the decision actually increases regret and, in turn, leads to product returns. Results also indicate that buying context (e.g., different buying situations) causes difference in serial mediation pathways from both primary and secondary appraisal to product returns likelihood. E-tailers should utilise consumers’ behavioural profile in order to classify different consumer groups and tailor the means to manage product returns accordingly.