Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/330438 
Authors: 
Year of Publication: 
2024
Citation: 
[Journal:] Management Review Quarterly [ISSN:] 2198-1639 [Volume:] 75 [Issue:] 3 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2024 [Pages:] 1-56
Publisher: 
Springer International Publishing, Cham
Abstract: 
The substantial growth of e-commerce during the last years has led to a surge in consumer returns. Recently, research interest in consumer returns has grown steadily. The availability of vast customer data and advancements in machine learning opened up new avenues for returns forecasting. However, existing reviews predominantly took a broader perspective, focussing on reverse logistics and closed-loop supply chain management aspects. This paper addresses this gap by reviewing the state of research on returns forecasting in the realms of e-commerce. Methodologically, a systematic literature review was conducted, analyzing 25 relevant publications regarding methodology, required or employed data, significant predictors, and forecasting techniques, classifying them into several publication streams according to the papers' main scope. Besides extending a taxonomy for machine learning in e-commerce, this review outlines avenues for future research. This comprehensive literature review contributes to several disciplines, from information systems to operations management and marketing research, and is the first to explore returns forecasting issues specifically from the e-commerce perspective.
Subjects: 
Consumer returns
Product returns
Forecasting
Prediction
Literature review
E-commerce
JEL: 
L81
C53
M10
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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