Green Human Resource Management: Analyzing sustainable practices and organizational impact through a Word2Vec approach

Abstract

Green Human Resource Management (GHRM) integrates environmental sustainability into HR practices, aligning corporate strategies with ecological responsibility. The organization may achieve sustainability by educating and implementing green practices to employees, emphasizing their value, and showing them how their actions affect the environment. In this study, the authors analyzed 3,233 articles extracted from the Scopus database from 1996 to 2024. This study explores GHRM trends using a bibliometric analysis and Word2Vec-based natural language processing (NLP) approach to analyze keyword relationships. This study highlights how the Word2Vec model effectively maps the semantic relationships within a textual corpus with its sophisticated configuration of twenty hidden layers and a batch size of 1000. The input discusses the importance of adopting eco-friendly policies and procedures, specifically through Green Human Resource Management (GHRM), to address the environmental consequences of technological advancement. The study underscores organizations’ need to embed GHRM into corporate culture, linking it to employee training, performance evaluation, and strategic decision-making. It suggests that companies leverage AI-driven environmental monitoring systems for real-time sustainability assessments. Future research should examine the impact of emerging technologies on GHRM adoption and its long-term influence on corporate sustainability and employee engagement. Policymakers and businesses must collaborate to develop adaptable, region-specific GHRM frameworks, ensuring global environmental goals are met through human-centric sustainability initiatives. Additionally, it emphasizes the need for policy development and global adoption of GHRM and other sustainable corporate practices to achieve sustainable development. The analysis provides a novel insight into the significant keywords Related to GHRM by applying advanced natural language processing techniques to a substantial dataset spanning multiple decades.

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