Neue Erkenntnisse zur Vorhersage von Retourenverhalten
Die Publikation gibt einen umfassenden Überblick über bestehende Forschungsansätze zur Prognose von Retouren im Online-Handel.
Vor dem Hintergrund steigender Retourenmengen untersucht die Literaturübersicht bestehende Ansätze zur Retourenprognose im E-Commerce. Hierzu werden 25 wissenschaftliche Beiträge hinsichtlich der Datenanforderungen, der relevanten Einflussgrößen und der Prognoseverfahren ausgewertet.
Abstract des Artikels: 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, focusing 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.
Zitation: Karl, D. (2024): "Forecasting E-Commerce Consumer Returns: A Systematic Literature Review", in: Management Review Quarterly, Vol. 75, S. 2369-2424, DOI: https://doi.org/10.1007/s11301-024-00436-x
