Deciphering E-tail returns: A multi-study approach on the influence of transaction variables on returns

This study addresses the complexity of predicting returns and return categories in the e-tail sector by analysing key transaction variables, offering in-depth insights into returns management in e-tail. Two studies were conducted: Study 1 adopts binary logistic regression to identify factors influencing return rates. Study 2 applies multinomial logistic regression to identify factors influencing specific return categories such as wrong purchase, false returns and late deliveries. The findings, which are further validated against reputable industry reports, provide e-tailers and managers with enhanced decision-making tools to improve their returns management strategies, contributing a predictive model that enhances returns prediction in e-tail.