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dc.contributor.authorKumar, Vikas
dc.contributor.authorHolt, Diane
dc.contributor.authorGhobadian, Abby
dc.contributor.authorGarza-Reyes, Jose Arturo
dc.date.accessioned2018-07-17T15:45:58Z
dc.date.available2018-07-17T15:45:58Z
dc.date.issued2014-05-21
dc.identifier.citationKimar, V. et al (2014) 'Developing green supply chain management taxonomy-based decision support system', International Journal of Production Research, 53 (21):6372.en
dc.identifier.issn00207543
dc.identifier.doi10.1080/00207543.2014.917215
dc.identifier.urihttp://hdl.handle.net/10545/622821
dc.description.abstractThe 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.
dc.description.sponsorshipN/Aen
dc.language.isoenen
dc.publisherTaylor and Francisen
dc.relation.urlhttp://www.tandfonline.com/doi/full/10.1080/00207543.2014.917215en
dc.rightsArchived with thanks to International Journal of Production Researchen
dc.subjectSupply chain managementen
dc.subjectGreen operationsen
dc.subjectDecision supporten
dc.subjectTaxonomyen
dc.subjectEnvironmental agendasen
dc.subjectStructural equation modellingen
dc.titleDeveloping green supply chain management taxonomy-based decision support system.en
dc.typeArticleen
dc.identifier.eissn1366588X
dc.contributor.departmentUniversity of the West of Englanden
dc.contributor.departmentUniversity of Essexen
dc.contributor.departmentUniversity of Readingen
dc.contributor.departmentUniversity of Derbyen
dc.identifier.journalInternational Journal of Production Researchen
html.description.abstractThe 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.


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