Comparing world regional sustainable supply chain finance using big data analytics: a bibliometric analysis

Tseng, M.-L., Bui, T.-D., Lim, M. K. , Ming Tsai, F. and Tan, R. R. (2021) Comparing world regional sustainable supply chain finance using big data analytics: a bibliometric analysis. Industrial Management and Data Systems, 121(3), pp. 657-700. (doi: 10.1108/IMDS-09-2020-0521)

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Abstract

Purpose: Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement. Design/methodology/approach: A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context. Findings: The results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability. Originality/value: This study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF.

Item Type:Articles
Keywords:Big data, entropy weight method, fuzzy Delphi method, fuzzy decision-making trial and evaluation laboratory, sustainable supply chain finance.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lim, Professor Ming
Authors: Tseng, M.-L., Bui, T.-D., Lim, M. K., Ming Tsai, F., and Tan, R. R.
College/School:College of Social Sciences > Adam Smith Business School > Management
Journal Name:Industrial Management and Data Systems
Publisher:Emerald
ISSN:0263-5577
ISSN (Online):1758-5783
Published Online:17 February 2021

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