JOURNAL ARTICLE

Trade Credit and Bankruptcy Risk in Supply Chains: An Experimental Study.

  • Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2025, v. 27, n. 4. P. 1205 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Davis, Andrew M.; Huang, Rihuan; Hyndman, Kyle 3 of 3

Abstract

This article experimentally investigates how bankruptcy risk influences supply chain decisions and outcomes under a trade credit contract between a supplier and a capital-constrained retailer. Contrary to baseline theoretical predictions—which suggest that retailers facing higher bankruptcy risk should order larger quantities and suppliers should charge higher wholesale prices—the study finds that retailers exposed to bankruptcy risk significantly understock, while suppliers respond by lowering wholesale prices. These behavioral deviations lead to higher retailer profits and lower supplier profits than predicted, reversing the expected profit order across different bankruptcy risk levels. The authors propose a behavioral model where retailers exhibit reference dependence (using realized profit from ordering mean demand as a stochastic reference point) and suppliers display fairness concerns, which together explain the observed decision patterns. The findings highlight important managerial implications regarding financing and operational decisions in supply chains with bankruptcy risk and call for further behavioral research in supply chain finance.

Additional Information

  • Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2025/07, Vol. 27, Issue 4, p1205
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:1523-4614
  • DOI:10.1287/msom.2022.0653
  • Accession Number:187706278
  • Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.