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Developing green supply chain management taxonomy-based decision support system

Author

Listed:
  • V. Kumar
  • D. Holt
  • A. Ghobadian
  • J.A. Garza-Reyes

Abstract

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.

Suggested Citation

  • V. Kumar & D. Holt & A. Ghobadian & J.A. Garza-Reyes, 2015. "Developing green supply chain management taxonomy-based decision support system," International Journal of Production Research, Taylor & Francis Journals, vol. 53(21), pages 6372-6389, November.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:21:p:6372-6389
    DOI: 10.1080/00207543.2014.917215
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    Citations

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    Cited by:

    1. Kumar, V.N.S.A. & Kumar, V. & Brady, M. & Garza-Reyes, Jose Arturo & Simpson, M., 2017. "Resolving forward-reverse logistics multi-period model using evolutionary algorithms," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 458-469.
    2. Muhammad Waqas & Qian-li Dong & Naveed Ahmad & Yuming Zhu & Muhammad Nadeem, 2018. "Critical Barriers to Implementation of Reverse Logistics in the Manufacturing Industry: A Case Study of a Developing Country," Sustainability, MDPI, vol. 10(11), pages 1-25, November.
    3. Saghiri, Soroosh Sam & Mirzabeiki, Vahid, 2021. "Buyer-led environmental supplier development: Can suppliers really help it?," International Journal of Production Economics, Elsevier, vol. 233(C).
    4. Graham, Stephanie & Graham, Byron & Holt, Diane, 2018. "The relationship between downstream environmental logistics practices and performance," International Journal of Production Economics, Elsevier, vol. 196(C), pages 356-365.
    5. Wensi Zhang & Jing Xiao & Lingfei Cai, 2020. "Joint Emission Reduction Strategy in Green Supply Chain under Environmental Regulation," Sustainability, MDPI, vol. 12(8), pages 1-24, April.
    6. Yuan Chen & Qinghua Zhu & Joseph Sarkis, 2024. "Heterogeneity in corporate green supply chain practice adoption: Insights from institutional fields," Business Strategy and the Environment, Wiley Blackwell, vol. 33(2), pages 389-406, February.
    7. Asif Khan & Chih-Cheng Chen & Kuan-Hua Lu & Ardy Wibowo & Shih-Chih Chen & Athapol Ruangkanjanases, 2021. "Supply Chain Ambidexterity and Green SCM: Moderating Role of Network Capabilities," Sustainability, MDPI, vol. 13(11), pages 1-13, May.
    8. Francesca Ciulli & Ans Kolk & Siri Boe-Lillegraven, 2020. "Circularity Brokers: Digital Platform Organizations and Waste Recovery in Food Supply Chains," Journal of Business Ethics, Springer, vol. 167(2), pages 299-331, November.
    9. Thomas F. Gattiker & Julia Hartmann & Finn Wynstra & Mark Pagell & David Cantor & Tingting Yan & Wendy Tate, 2022. "Testing the shoulders of giants—Replication research using registered reports," Journal of Supply Chain Management, Institute for Supply Management, vol. 58(3), pages 89-94, July.

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