文献类型：期刊文献

英文题名：A Dynamic Credit Evaluation Approach Using Sensitivity-Optimized Weights for Supply Chain Finance

作者：Zhang, Haoyue[1];Tian, Ran[1];Wang, Qi[1];Wu, Dongxiao[1]

通讯作者：Wu, DX[1]

机构：[1]Beijing Union Univ, Sch Management, 97 North Fourth Ring East Rd, Beijing 100101, Peoples R China

第一机构：北京联合大学管理学院

通讯机构：[1]corresponding author), Beijing Union Univ, Sch Management, 97 North Fourth Ring East Rd, Beijing 100101, Peoples R China.|[1141755]北京联合大学管理学院;[11417]北京联合大学;

年份：2023

卷号：30

期号：6

起止页码：1951-1958

外文期刊名：TEHNICKI VJESNIK-TECHNICAL GAZETTE

收录：;EI(收录号：20234414994410);Scopus(收录号：2-s2.0-85175149486);WOS:【SCI-EXPANDED(收录号:WOS:001096963800029)】；

基金：This research was funded by the Humanities and Social Sciences Research Youth Project of Chinese Ministry of Education (grant number 20YJC630175) , Beijing Municipal University Excellent Young Talents Cultivation Plan (grant number BPHR202203222) , and General Items of Social Science Plan of Beijing Municipal Commission of Education (grant number SM202211417007) .

语种：英文

外文关键词：-commerce supply chain financing; dynamic credit evaluation; micro and small enterprises (MSEs); weight optimization

摘要：Supply chain financing provides important funding channels for micro and small enterprises (MSEs), but effectively evaluating their creditworthiness remains challenging. Past methods overly rely on static financial indicators and subjective judgment in determining credit evaluation weights. This study proposes a dynamic credit evaluation approach that uses sensitivity analysis to optimize the weighting scheme. An indicator system is constructed based on the unique characteristics of e-commerce MSEs. The weight optimization integrates subjective, objective, and sensitivity-based methods to reflect specific financing scenarios. A system dynamics model simulates the credit evaluation mechanism and identifies the sensitivity of each influencing factor. The resultant comprehensive weights are applied in a TOPSIS-GRA dynamic evaluation model to assess MSE credit levels over time. An empirical analysis of 20 online stores demonstrates the proposed model's advantages in accurately revealing credit rankings relative to conventional static models. This research provides an effective data-driven weighting technique and dynamic evaluation framework for supply chain finance credit assessment.