Title: Bibliometric research indicators for green supply chain modelling

Authors: Mohammed Alkahtani; Shafiq Ahmad; Mohammed A. Noman; Husam Kaid; Ahmed Badwelan

Addresses: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia ' Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia ' Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia; Industrial Engineering Department, College of Engineering, Taiz University, Taiz, Yemen ' Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia; Industrial Engineering Department, College of Engineering, Taiz University, Taiz, Yemen ' Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia

Abstract: Bibliometric research is a statistical technique that analyses written publications like articles, reviews, or books from a quantitative perspective. This study provides an overall picture of the research in green supply chain modelling (GSCM) sciences. This paper proposes a methodology. First, the articles have been collected using Web of Science based on selected keywords from 1995 to 2018. Next, the most influential journals, articles, keywords, authors, and institutions have been determined in GSCM. Then, the country analysis has been performed to analyse GSCM studies with respect to its geographical distribution. Finally, the VOS viewer software has been used to visualise the bibliographic material through co-authorship, co-occurrence, citation, bibliographic coupling, and co-citation analysis. The indicators identify the fundamental research in this field. It can be concluded that there is a high level of scattering in this field, with several effective nations, such as the USA.

Keywords: bibliometric; green supply chain modelling; GSCM; sustainability.

DOI: 10.1504/IJISE.2020.107772

International Journal of Industrial and Systems Engineering, 2020 Vol.35 No.3, pp.314 - 344

Received: 16 Feb 2018
Accepted: 23 Nov 2018
Published online: 17 Jun 2020 *

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