This study investigates manufacturer guarantor financing (MG) and third-party logistics (3PL) guarantor financing (LG) in a four-party supply chain game that features a manufacturer, a 3PL, a capital-constrained retailer, and a bank. The manufacturer or 3PL can act as the guarantor for the retailer who borrows bank credit. Two different leadership structures are investigated, namely, Nash game and manufacturer leadership Stackelberg game, where the manufacturer and 3PL decide simultaneously and sequentially, respectively. The supply chain under both leadership structures prefers guarantor financing to traditional bank financing when the supply chain is sufficiently cost-efficient. In the Nash game, however, firms encounter a free-rider dilemma when choosing between MG and LG, wherein both potential guarantors prefer the other to be the guarantor. This free-rider dilemma can be resolved in the Stackelberg game. We also observe the follower–guarantor advantage in the Stackelberg game, wherein all firms favor the follower to provide guarantor financing. Our analysis shows that the supply chain under guarantor financing with a longer decision hierarchy (i.e., the Stackelberg game) can be conditionally more effective than that with a shorter one (i.e., the Nash game). By further analyzing different cost structures, pricing mechanism, and retailer’s initial capital, we find that most of our qualitative results remain accurate under more sophisticated conditions. These findings enhance our understanding of the value of guarantor financing in a capital-constrained supply chain and the impact of leadership structure on financing decisions.
Article
Guarantor Financing in a Four-Party Supply Chain Game with Leadership Influence
Information Systems and Analytics
Document Type
Article
Publication Date
9-1-2020
Publisher
John Wiley & Sons, Inc.
Disciplines
Abstract
Citation Information
Zhou, W., Lin, T., & Cai, G. (George). (2020). Guarantor Financing in a Four-Party Supply Chain Game with Leadership Influence. Production and Operations Management, 29(9), 2035–2056. https://doi.org/10.1111/poms.13196