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

Anukesh Valase & Arshinder Kaur

IIMB Management Review2026https://doi.org/10.1016/j.iimb.2025.100637article
ABDC C
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1016/j.iimb.2025.100637

Or copy a formatted citation

@article{anukesh2026,
  title        = {{Deciphering E-tail returns: A multi-study approach on the influence of transaction variables on returns}},
  author       = {Anukesh Valase & Arshinder Kaur},
  journal      = {IIMB Management Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.iimb.2025.100637},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.