The Optimization of Supply Chain Financing for Bank Green Credit Using Stackelberg Game Theory in Digital Economy Under Internet of Things

IF 3.6 3区 管理学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of Organizational and End User Computing Pub Date : 2023-02-24 DOI:10.4018/joeuc.318474
Hui Zhang, Fengrui Zhang, Bing Gong, Xuanjian Zhang, Yi Zhu
{"title":"The Optimization of Supply Chain Financing for Bank Green Credit Using Stackelberg Game Theory in Digital Economy Under Internet of Things","authors":"Hui Zhang, Fengrui Zhang, Bing Gong, Xuanjian Zhang, Yi Zhu","doi":"10.4018/joeuc.318474","DOIUrl":null,"url":null,"abstract":"The aim is to improve small and medium-sized enterprises (SMEs)' core competitiveness and financing attainability using deep learning (DL) under economic globalization. Accordingly, this work constructs a supply chain symbiosis system based on DL, economics, and Stackelberg game theory following a status quo analysis of the financing status of SMEs. Afterward, a structural framework of supply chain financing (SCF) is designed. Further, it verifies the effectiveness of the proposed back propagation neural network (BPNN) credit evaluation model through specific enterprise data. The results show that the proposed internet of things (IoT)-based SCF SMEs-oriented BPNN credit evaluation model reaches a prediction accuracy of 91.4%. It effectively eliminates information asymmetry between banks and various capitals. As a result, banks can guarantee operation funds for the supply chain SMEs and help them minimize project risks by lowering financing leverage and through information transparency.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"2 1","pages":"1-16"},"PeriodicalIF":3.6000,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Organizational and End User Computing","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.4018/joeuc.318474","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 7

Abstract

The aim is to improve small and medium-sized enterprises (SMEs)' core competitiveness and financing attainability using deep learning (DL) under economic globalization. Accordingly, this work constructs a supply chain symbiosis system based on DL, economics, and Stackelberg game theory following a status quo analysis of the financing status of SMEs. Afterward, a structural framework of supply chain financing (SCF) is designed. Further, it verifies the effectiveness of the proposed back propagation neural network (BPNN) credit evaluation model through specific enterprise data. The results show that the proposed internet of things (IoT)-based SCF SMEs-oriented BPNN credit evaluation model reaches a prediction accuracy of 91.4%. It effectively eliminates information asymmetry between banks and various capitals. As a result, banks can guarantee operation funds for the supply chain SMEs and help them minimize project risks by lowering financing leverage and through information transparency.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
物联网下数字经济下基于Stackelberg博弈论的银行绿色信贷供应链融资优化
其目的是在经济全球化背景下,利用深度学习(DL)提高中小企业的核心竞争力和融资可达性。因此,本文在分析中小企业融资现状的基础上,基于DL、经济学和Stackelberg博弈论构建了供应链共生系统。然后,设计了供应链融资的结构框架。进一步,通过具体企业数据验证了所提出的反向传播神经网络(BPNN)信用评价模型的有效性。结果表明,提出的基于物联网(IoT)的SCF中小企业面向BPNN信用评价模型预测准确率达到91.4%。它有效地消除了银行与各类资本之间的信息不对称。因此,银行可以通过降低融资杠杆和信息透明,为供应链中小企业的运营资金提供保障,帮助其最大限度地降低项目风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Organizational and End User Computing
Journal of Organizational and End User Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
6.00
自引率
9.20%
发文量
77
期刊介绍: The Journal of Organizational and End User Computing (JOEUC) provides a forum to information technology educators, researchers, and practitioners to advance the practice and understanding of organizational and end user computing. The journal features a major emphasis on how to increase organizational and end user productivity and performance, and how to achieve organizational strategic and competitive advantage. JOEUC publishes full-length research manuscripts, insightful research and practice notes, and case studies from all areas of organizational and end user computing that are selected after a rigorous blind review by experts in the field.
期刊最新文献
Cross-Checking-Based Trademark Image Retrieval for Hot Company Detection E-Commerce Review Sentiment Analysis and Purchase Intention Prediction Based on Deep Learning Technology Financial Cycle With Text Information Embedding Based on LDA Measurement and Nowcasting Enhancing Innovation Management and Venture Capital Evaluation via Advanced Deep Learning Techniques Going Global in the Digital Era
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1