CN EN
Advanced

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

Indexed by

SCIE Scopus ABDC-A

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).

Keyword

Author Community

[Ying, Hao] China Europe International Business School; Shanghai Jiao Tong University Shanghai Jiao Tong Univ

[Chen, Lujie] Xian Jiaotong Liverpool Univ, Int Business Sch Suzhou IBSS, Suzhou, Peoples R China

[Zhao, Xiande] China Europe International Business School


Related Article

Source

Industrial Management & Data Systems

ISSN:0263-5577

Year:2021

Issue:2

Volume:121

Page:498-518

ESI Discipline:ENGINEERING;

Cited Count
W
Loading... 3
C
Loading...
Get Fulltext
Rights and Licenses
Related Keywords
Communities & Collections
Access Stats
Creative Commons Licence
The content of CEIBS Research Online is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).