Drivers of Green Supply Chain Management Practices and their Impact on Firm Performance: A Developing Country Perspective

Authors

  • Haris Aslam Department of Operations and Supply Chain, University of Management and Technology(UMT), Pakistan
  • Kamran Rashid University of Management and Technology (UMT), Pakistan
  • Asadur Rahman Wahla University of Management and Technology (UMT), Pakistan
  • Uzma Tahira University of Management and Technology (UMT), Pakistan

DOI:

https://doi.org/10.29145/2018/jqm/020104

Keywords:

green supply chain management (GSCM), developing country, environmental performance, structural equation modeling, partial least square (PLS)

Abstract

The purpose of this paper is to identify the drivers of Green Supply Chain Management (GSCM) practices among the manufacturing firms of a developing country, and to examine the impact of GSCM practices on firms’ economic and environmental performance. A structural equation model is developed to study the hypothesized relationships between three drivers and GSCM practices. Furthermore, the relationship between GSCM practices and firm’s economic and environmental performance is also investigated. A sample of manufacturing firms is taken from the companies listed in the local stock exchange. Cross-sectional data of 80 responses from these manufacturing firms is collected. The developed model is tested through Partial Least Square Structural Equation Modeling (PLS-SEM) technique. Results show that customer’s pressure and firm’s internal drive (enviropreneurship) positively impacts the adoption of GSCM practices whereas the relationship between governmental legislation GSCM practices adoption is not significant. GSCM practices positively impact the supply chain buying firm’s economic and environmental performance.

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Published

2019-03-13

How to Cite

Aslam, H., Rashid, K., Wahla, A. R., & Tahira, U. (2019). Drivers of Green Supply Chain Management Practices and their Impact on Firm Performance: A Developing Country Perspective. Journal of Quantitative Methods, 2(1), 87–113. https://doi.org/10.29145/2018/jqm/020104

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