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

Ming-Lang Tseng, Tat-Dat Bui, Ming Lim, Feng Ming Tsai, Raymond R. Tan

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)
    183 Downloads (Pure)

    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.

    Original languageEnglish
    Pages (from-to)657-700
    Number of pages44
    JournalIndustrial Management and Data Systems
    Volume121
    Issue number3
    Early online date17 Feb 2021
    DOIs
    Publication statusPublished - 2 Mar 2021

    Funder

    Chongqing Science and Technology Commission (Project no. cstc2019jscx-msxmX0189).

    Keywords

    • Big data
    • Entropy weight method
    • Fuzzy Delphi method
    • Fuzzy decision-making trial and evaluation laboratory
    • Sustainable supply chain finance

    ASJC Scopus subject areas

    • Management Information Systems
    • Industrial relations
    • Computer Science Applications
    • Strategy and Management
    • Industrial and Manufacturing Engineering

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