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)

Research output: Contribution to journalJournal

4 Citations (Web of Science)


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).
Original languageEnglish
Pages (from-to)498-518
JournalIndustrial Management & Data Systems
Issue number2
Publication statusPublished - 2021

Corresponding author email


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

Indexed by

  • ABDC-A
  • SCIE
  • Scopus


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