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Green supply chain management enablers: Mixed methods research

Dubey, Rameshwar, Gunasekaran, Angappa, Papadopoulos, Thanos, Childe, Stephen J. (2015) Green supply chain management enablers: Mixed methods research. Sustainable Production and Consumption, 4 (1). pp. 72-88. ISSN 2352-5509. (doi:10.1016/j.spc.2015.07.001) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:53042)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1016/j.spc.2015.07.001

Abstract

This paper contributes to the literature on green supply chain management

(GSCM) by arguing for the use of mixed methods for theory building. The

literature has identified antecedents and enablers for the adoption of GSCM

practices. Nevertheless, there is relatively little research on building robust

methodological approaches and techniques that take into account the dynamic

nature of green supply chains. To address this gap, the paper firstly reviews

systematically the literature on GSCM enablers; secondly, it argues for the use

of mixed methods research to address questions related to GSCM enablers;

thirdly, it uses interpretive structural modeling (ISM), MICMAC analysis, and

confirmatory factor analysis (CFA) to illustrate the application of mixed

methods in GSCM by testing a model on the enablers of GSCM; and fourthly,

highlights the influence of enablers including, inter alia, top management

commitment, institutional pressures, supplier and customer relationship

management on financial and environmental performance. Finally, we conclude

with limitations and further research directions.

Item Type: Article
DOI/Identification number: 10.1016/j.spc.2015.07.001
Uncontrolled keywords: Green Supply Chain Management (GSCM), Environmental Management (EM), Institutional Theory, Interpretive Structural Modeling (ISM), MICMAC analysis
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Kimberley Attard-Owen
Date Deposited: 10 Dec 2015 14:01 UTC
Last Modified: 17 Aug 2022 10:59 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/53042 (The current URI for this page, for reference purposes)

University of Kent Author Information

Papadopoulos, Thanos.

Creator's ORCID: https://orcid.org/0000-0001-6821-1136
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