Impact of Enterprise Supply Chain Digitalization on Cost of Debt: A Four-Flows Perspective Analysis Using Explainable Machine Learning Methodology

Hongqin Tang, Jianping Zhu (), Nan Li and Weipeng Wu
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Hongqin Tang: School of Managment, Xiamen University, Xiamen 361005, China
Jianping Zhu: School of Managment, Xiamen University, Xiamen 361005, China
Nan Li: School of Managment, Xiamen University, Xiamen 361005, China
Weipeng Wu: Collegue of Bussiness, Huaqiao University, Quanzhou 362011, China

Sustainability, 2024, vol. 16, issue 19, 1-27

Abstract: Rising costs, complex supply chain management, and stringent regulations have created significant financial burdens on business sustainability, calling for new and rapid strategies to help enterprises transform. Supply chain digitalization (SCD) has emerged as a promising approach in the context of digitalization and globalization, with the potential to reduce an enterprise’s debt costs. Developing a strategic framework for SCD that effectively reduces the cost of debt (CoD) has become a key academic challenge, critical for ensuring business sustainability. To this end, under the perspective of four flows, SCD is deconstructed into four distinct features: logistics flow digitalization ( LFD ), product flow digitalization ( PFD ), information flow digitalization ( IFD ), and capital flow digitalization ( CFD ). To precisely measure the four SCD features and the dependent variable, COD , publicly available data from Chinese listed manufacturing enterprises such as annual report texts and financial statement data are collected, and various data mining technologies are also used to conduct data measurement and data processing. To comprehensively investigate the impact pattern of SCD on CoD, we employed the explainable machine learning methodology for data analysis. This methodology involved in-depth data discussions, cross-validation utilizing a series of machine learning models, and the utilization of Shapley additive explanations (SHAP) to explain the results generated by the models. To conduct sensitivity analysis, permutation feature importance (PFI) and partial dependence plots (PDPs) were also incorporated as supplementary explanatory methods, providing additional insights into the model’s explainability. Through the aforementioned research processes, the following findings are obtained: SCD can play a role in reducing CoD, but the effects of different SCD features are not exactly the same. Among the four SCD features, LFD , PFD , and IFD have the potential to significantly reduce CoD, with PFD having the most substantial impact, followed by LFD and IFD . In contrast, CFD has a relatively weak impact, and its role is challenging to discern. These findings provide significant guidance for enterprises in furthering their digitalization and supply chain development, helping them optimize SCD strategies more accurately to reduce CoD.

Keywords: supply chain digitalization; cost of debt; machine learning; Shapley additive explanations; business sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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