Developing green supply chain management taxonomy-based decision support system

Kumar, V.; Holt, D.; Ghobadian, A.; Garza-Reyes, J. A.

Abstract

© 2014 Taylor & Francis. The aim of this paper is to develop a comprehensive taxonomy of green supply chain management (GSCM) practices and develop a structural equation modelling-driven decision support system following GSCM taxonomy for managers to provide better understanding of the complex relationship between the external and internal factors and GSCM operational practices. Typology and/or taxonomy play a key role in the development of social science theories. The current taxonomies focus on a single or limited component of the supply chain. Furthermore, they have not been tested using different sample compositions and contexts, yet replication is a prerequisite for developing robust concepts and theories. In this paper, we empirically replicate one such taxonomy extending the original study by (a) developing broad (containing the key components of supply chain) taxonomy; (b) broadening the sample by including a wider range of sectors and organisational size; and (c) broadening the geographic scope of the previous studies. Moreover, we include both objective measures and subjective attitudinal measurements. We use a robust two-stage cluster analysis to develop our GSCM taxonomy. The main finding validates the taxonomy previously proposed and identifies size, attitude and level of environmental risk and impact as key mediators between internal drivers, external drivers and GSCM operational practices.

• Journal Article Type: Article
• Publication Date: Nov 2, 2015
• Deposit Date: Apr 9, 2015
• Publicly Available Date: Feb 10, 2016
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Electronic ISSN: 1366-588X
• Publisher: Taylor & Francis
• Peer Reviewed: Peer Reviewed
• Volume: 53
• Issue: 21
• Pages: 6372-6389
• DOI: https://doi.org/10.1080/00207543.2014.917215
• Keywords: green supply chain management, taxonomy, decision support, environmental attitude, structural equation modelling
• Public URL: https://uwe-repository.worktribe.com/output/842845
• Publisher URL: http://dx.doi.org/10.1080/00207543.2014.917215
• Additional Information: Additional Information : This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 21 May 2014, available online: http://wwww.tandfonline.com/10.1080/00207543.2014.917215
• Contract Date: Feb 10, 2016