Application of text mining in identifying the factors of supply chain financing risk management

Hao Ying (First Author), Lujie Chen (Participant Author), Xiande Zhao (Participant Author)

科研成果: 期刊稿件期刊论文

4 引用 (Web of Science)

摘要

This study aims to clarify the risk management practices of banks as supply chain finance (SCF) service providers. Design/methodology/approach Using 4,014 evaluation and approval reports, this study constructed five risk management factors and examined their functions with secondary data. Two text-mining techniques (i.e. word sense induction, TF-IDF) were used to equip the classic routine of dictionary-based content analysis. This research successfully identified four important risk management factors: relationship-based assessment, asset monitoring, cash flow monitoring and supply chain collaboration. The default-preventing effect of these factors are different and contingent on the type of financing contexts (i.e. preshipment, postshipment).
源语言英语
页(从-至)498-518
期刊Industrial Management & Data Systems
121
2
DOI
出版状态已出版 - 2021

Corresponding author email

jogayh@sjtu.edu.cn

关键词

  • Computer-aided text analysis
  • Risk management
  • Supply chain finance

成果物的来源

  • ABDC-A
  • SCIE
  • Scopus

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