JOURNAL ARTICLE
Offline Returns for Online Retailers via Partnership.
Published In: Management Science (INFORMS), 2025, v. 71, n. 1. P. 279 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Nageswaran, Leela; Hwang, Elina H.; Cho, Soo-Haeng 3 of 3
Abstract
This article examines the emerging business practice of "return partnerships," where online retailers collaborate with physical store retailers to enable customers to return online purchases offline at store locations. It analyzes when such partnerships benefit both parties by modeling customer purchase and return decisions across online and offline channels, considering product differentiation and store visit convenience. The study finds that partnerships are mutually advantageous when online and store retailers offer differentiated products with conveniently located stores, or when they offer similar products but the store network is limited, as seen in examples like Everlane-Cost Plus World Market and Amazon-Kohl’s. The research highlights that while return partnerships reduce online retailers’ reverse logistics costs and increase store foot traffic, they may also increase return rates if offline returns become too convenient, especially when products are similar and stores are numerous. Extensions of the model address valuation correlation, customer heterogeneity, multiple store locations, and consumers keeping both products, confirming the robustness of the main insights and providing guidance for retailers considering such partnerships.
Additional Information
- Source:Management Science (INFORMS). 2025/01, Vol. 71, Issue 1, p279
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2025
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.01291
- Accession Number:182281766
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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