Envisioning the Invisible: Unleashing the Interplay Between Green Supply Chain Management and Green Human Resource Management: An Ability-Motivation-Opportunity Theory Perspective Towards Environmental Sustainability
DOI:
https://doi.org/10.47852/bonviewJCBAR42022030Keywords:
green human resource management, green supply chain management, environmental strategy, ability-motivation-opportunity theory, firm performanceAbstract
The topics of green human resource management (GHRM) and green supply chain management (GSCM) have gained significant popularity within the fields of HRM and operations management, respectively. Scholars in various fields have been making progress in exploring the contributions of GSCM and GHRM towards the development of sustainable firms. However, it is worth noting that integrating these two cutting-edge topics has been significantly delayed, mostly due to a substantial gap in the integration of SCM and HRM. The objective of this study is to present a comprehensive framework that combines and enhances the interaction between these two elements, as well as to suggest a research agenda for further exploration of this integration. This study employs the ability-motivation-opportunity theory framework to examine the influence of GHRM, GSCM, and environmental strategy on firm performance. The researchers employed a structural equation model to examine the hypotheses, utilizing a sample of 640 survey questionnaires obtained from manufacturing firms. The findings indicate that there is a positive relationship between GHRM and firm performance. Additionally, the findings indicate that GSCM serves as a positive and significant mediator in the association between GHRM and firm performance. Ultimately, the study’s results and ramifications are revealed to serve as valuable policy tools for various entities such as manufacturing firms, administrations, and other relevant parties of supply chain.
Received: 18 November 2023| Revised: 20 March 2024 | Accepted: 16 April 2024
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
The data that support this work are available upon reasonable request to the corresponding author.
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