Assessing the eco-environmental performance: an PLS-SEM approach with practice-based view

ABSTRACT

Awareness of environmental issues is increasing, which is also putting pressure on firms, requiring their supply chain operations to be green. The objective of this article is to examine the impact of green supply chain practices (GSCPs) on competitive advantage, economic, environmental, and organisational performance under the influence of internal environment management and green information systems. The data of 415 manufacturing firms are gathered and employed PLS-SEM modelling to test the hypotheses. The findings show that Internal Environmental Management (IEM) and Green Information Systems (GISs) strongly and positively support the execution of green supply chain practices. The outcomes also revealed that green supply chain management (GSCM) practices significantly improves firms’ competitiveness, economic, and environmental performance, which finally translates into organisational performance. The study presents an experimental assessment of the influence of IEM and GISs on GSCPs, which leads towards competitiveness, economic, environmental, and organisational performance. The findings provide an advantageous understanding, and the results also offer a policy-framework for manufacturers, managers, legislators to further promote GSCPs for better socio-environmental sustainability.

KEYWORDS:

• Internal environmental management
• GSCM practices
• Green information systems
• Competitive advantage
• PLS-SEM

Acknowledgement

This study is an extended version of the first author’s PhD thesis. Further, This work supported by the China Postdoctoral Science Foundation (No. 2019M660700); the Beijing Key Laboratory of Megaregions Sustainable Development Modelling, Capital University of Economics and Business (No. MCR2019QN09).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work supported by the China Postdoctoral Science Foundation [grant number 2019M660700]; the Beijing Key Laboratory of Megaregions Sustainable Development Modelling, Capital University of Economics and Business [grant number MCR2019QN09].