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Additional contract selection and parameter design under supplier credit guarantee financing.

Authors :
Kang, Kai
He, Mengyu
Shi, Bingjie
Li, Wenlu
Source :
Journal of Industrial & Management Optimization; Oct2024, Vol. 20 Issue 10, p1-30, 30p
Publication Year :
2024

Abstract

The essence of credit guarantee financing (CGF) is the risk sharing between the guarantor and financial institution. Inspired by the case of the CGF of Evergrowing Bank, this study aims to explore the risk compensation strategies and guarantee decisions for guarantors under CGF and provide decision support for the efficient operation of the supply chain CGF. Considering three kinds of risk compensation contracts, namely cost-sharing, revenue-sharing, and quantity-flexibility, the Stackelberg game method is used to construct the supplier CGF model under the retailer's financial constraints. The results show that additional contract parameters, interest rate, debtor's initial capital, and implicit bankruptcy cost are the main factors affecting the credit guarantee coefficient decision. Besides, we find optimal additional contract parameters that can optimize the profits of supply chain members and complete the contract parameter design. The quantity-flexibility contract is the most effective strategy for the supplier to compensate for guarantee risk. However, the retailer's choice of additional contract depends on the initial capital or implicit bankruptcy cost. The main innovation of this study is that it endogenizes the credit guarantee decision and explores the details of the risk compensation contract. Further, the impact of implicit bankruptcy cost on CGF decision is also mentioned for the first time in supply chain finance. Our findings lead to recommendations for the optimal credit guarantee scheme and contract design of compensation for guarantee risk in the supply chain. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15475816
Volume :
20
Issue :
10
Database :
Complementary Index
Journal :
Journal of Industrial & Management Optimization
Publication Type :
Academic Journal
Accession number :
178713837
Full Text :
https://doi.org/10.3934/jimo.2024048