Abstract
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 language | English |
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Pages (from-to) | 498-518 |
Journal | Industrial Management & Data Systems |
Volume | 121 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2021 |
Corresponding author email
jogayh@sjtu.edu.cnKeywords
- Computer-aided text analysis
- Risk management
- Supply chain finance
Indexed by
- ABDC-A
- SCIE
- Scopus
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Ying, H., Chen, L., & Zhao, X. (2021). Application of text mining in identifying the factors of supply chain financing risk management. Industrial Management & Data Systems, 121(2), 498-518. https://doi.org/10.1108/IMDS-06-2020-0325