JOURNAL ARTICLE
Platform Financing vs. Trade Credit for Lending to Third-Party Sellers.
Published In: Management Science (INFORMS), 2025, v. 71, n. 7. P. 5589 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Huang, Rongyi; Lai, Guoming; Wang, Xiaofang; Xiao, Wenqiang 3 of 3
Abstract
This article examines platform financing—loans provided by e-commerce platforms to third-party sellers—in comparison with traditional trade credit from suppliers, focusing on scenarios with exogenous (external) and endogenous (decision-influenced) default risks. Under exogenous default risk, platform financing can subsidize sellers by offering lower interest rates and isolating suppliers from default risk, thereby increasing sales commissions and benefiting all parties, especially in small markets, high product costs, or high commission rate settings. In the endogenous default risk scenario, platform financing helps mitigate sellers’ opportunistic overordering and dampens suppliers’ strategic price hikes, preventing or reducing defaults and improving supply chain performance, though the effects of product cost and market uncertainty on equilibrium outcomes are more complex. The study also explores the roles of sellers’ initial capital—which generally expands the conditions favoring platform financing—and credit limits, finding that imposing credit limits is suboptimal in price-elastic demand environments. These findings provide nuanced insights into when and how platform financing can outperform trade credit, offering guidance for e-commerce platforms and third-party sellers in optimizing financing strategies.
Additional Information
- Source:Management Science (INFORMS). 2025/07, Vol. 71, Issue 7, p5589
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:0025-1909
- DOI:10.1287/mnsc.2022.00201
- Accession Number:187524650
- 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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