Embracing Green Human Resource Management (GHRM) practices are swiftly emerging as the contemporary equivalent of an industrial revolution. The formulation of strategies to attain environmental sustainability now heavily relies on the adoption of AI-driven GHRM practices. Consequently, organizations are actively integrating these practices to not only gain a competitive edge but also to secure a sustainable future. This article provides a comprehensive overview of the latest growth in GHRM and environmental sustainability via a systematic literature review and bibliometric analysis, establishing crucial linkages between various concepts. This pioneering study, the first of its type, focuses on discerning main AI-driven GHRM parameters, unveiling essential relationships between GHRM and environmental sustainability. These findings contribute to building a robust conceptual foundation and showcase the potential for merging the fundamental components of these two concepts into a cohesive idea. This integration opens up new avenues for research, presenting fresh opportunities in both the expansive realm of AI-driven GHRM and the emerging, and sometimes contentious, field of environmental sustainability.

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AI-Driven Green HRM Practices and Environmental Sustainability: A Bibliometric Analysis

  • Neema Gupta,
  • Ambuj Kumar Agarwal,
  • Rajat Bhardwaj,
  • Nilesh Shelke,
  • Kamal Preet

摘要

Embracing Green Human Resource Management (GHRM) practices are swiftly emerging as the contemporary equivalent of an industrial revolution. The formulation of strategies to attain environmental sustainability now heavily relies on the adoption of AI-driven GHRM practices. Consequently, organizations are actively integrating these practices to not only gain a competitive edge but also to secure a sustainable future. This article provides a comprehensive overview of the latest growth in GHRM and environmental sustainability via a systematic literature review and bibliometric analysis, establishing crucial linkages between various concepts. This pioneering study, the first of its type, focuses on discerning main AI-driven GHRM parameters, unveiling essential relationships between GHRM and environmental sustainability. These findings contribute to building a robust conceptual foundation and showcase the potential for merging the fundamental components of these two concepts into a cohesive idea. This integration opens up new avenues for research, presenting fresh opportunities in both the expansive realm of AI-driven GHRM and the emerging, and sometimes contentious, field of environmental sustainability.