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

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.

Citation

Tseng, M.-L., Bui, T.-D., Lim, M.K., Tsai, F.M. and Tan, R.R. (2021), "Comparing world regional sustainable supply chain finance using big data analytics: a bibliometric analysis", Industrial Management & Data Systems, Vol. 121 No. 3, pp. 657-700. https://doi.org/10.1108/IMDS-09-2020-0521