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

doi:10.1108/IMDS-09-2020-0521

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

Faculty Research Centre for Business in Society

Research output: Contribution to journal › Article › peer-review

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Abstract

Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners due to the indicators are still underdeveloped in achieving sustainable supply chain finance. This study proposes a bibliometric data-driven from the literature to illustrate a clear overall concept of sustainable supply chain finance that reveals hidden indicators for further improvement.

Original language	English
Pages (from-to)	(In-Press)
Journal	Industrial Management and Data Systems
Volume	(In-Press)
Early online date	17 Feb 2021
DOIs	https://doi.org/10.1108/IMDS-09-2020-0521
Publication status	E-pub ahead of print - 17 Feb 2021

Keywords

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

Access to Document

10.1108/IMDS-09-2020-0521

Post-PrintAccepted author manuscript, 371 KBLicence: CC BY-NC

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Cite this

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abstract = "Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners due to the indicators are still underdeveloped in achieving sustainable supply chain finance. This study proposes a bibliometric data-driven from the literature to illustrate a clear overall concept of sustainable supply chain finance that reveals hidden indicators for further improvement.",

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