Developing green supply chain management taxonomy-based decision support system.
|Authors||Kumar, Vikas, Holt, Diane, Ghobadian, Abby and Garza-Reyes, Jose Arturo|
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.
|Keywords||Supply chain management; Green operations; Decision support; Taxonomy; Environmental agendas; Structural equation modelling|
|Journal||International Journal of Production Research|
|Publisher||Taylor & Francis|
|Digital Object Identifier (DOI)||https://doi.org/10.1080/00207543.2014.917215|
|Web address (URL)||http://hdl.handle.net/10545/622821|
|Publication dates||21 May 2014|
|Publication process dates|
|Deposited||17 Jul 2018, 15:45|
Archived with thanks to International Journal of Production Research
|Contributors||University of the West of England, University of Essex, University of Reading and University of Derby|
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