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<p>The contemporary research aims to execute a systematized assessment of AI-driven Green Human Resource Management (GHRM) practices by employing a novel mixed-methodology framework, integrating bibliometric, content, thematic, altmetric, and Fuzzy ISM–MICMAC analyses, owing to be one of the pioneering extensive applications of such an integrated approach in this field. Following PRISMA framework, a final set of (N = 371) documents has been reviewed by merging documents from Scopus and Web of Science for bibliometric analysis and barrier identification. Content and altmetric analysis were conducted on the top global cited documents (N = 10). Further, Fuzzy ISM–MICMAC analysis was conducted using expert opinion (59.09% from industry and 40.91% from academics) to evaluate interrelationship among eight critical barriers identified. Fuzzy ISM–MICMAC analysis classified the critical barriers according to their driving (DRP) and dependence powers (DEP). Findings highlighted that B8 (DRP = 8, DEP = 1) appears to be the strongest driver (Quadrant-IV, Level-5) as it significantly affects other barriers, whereas B4 (DRP = 1, DEP = 8) emerges to be most dependent barrier (Quadrant-II, Level-1). The linkage barriers (B1, B2, B3, B5) falling under Quadrant-III, Level-2, mediates the interactions between high-driving and high-dependence factors. This structured approach got validated through a 3-round Delphi process by incorporating fuzzy logic for ensuring robustness of results. Overall, the study provides actionable insights for organizations, practitioners, and policymakers seeking to strengthen sustainability initiatives through effective implementation of AI-enabled GHRM practices. By addressing the identified barriers, organizations can better align their sustainability strategies with global development priorities, contributing to the achievement of SDGs 9, 10, 11 and 12.</p>

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From knowledge mapping to structural modelling of green human resource management in the artificial intelligence era: an integrated review with fuzzy ISM–MICMAC analysis

  • 期刊论文

  • 发表日期：

    2026年4月16日

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  • Abinash Jena,
  • Nisrutha Dulla,
  • Sugyanta Priyadarshini,
  • Sukanta Chandra Swain

摘要

The contemporary research aims to execute a systematized assessment of AI-driven Green Human Resource Management (GHRM) practices by employing a novel mixed-methodology framework, integrating bibliometric, content, thematic, altmetric, and Fuzzy ISM–MICMAC analyses, owing to be one of the pioneering extensive applications of such an integrated approach in this field. Following PRISMA framework, a final set of (N = 371) documents has been reviewed by merging documents from Scopus and Web of Science for bibliometric analysis and barrier identification. Content and altmetric analysis were conducted on the top global cited documents (N = 10). Further, Fuzzy ISM–MICMAC analysis was conducted using expert opinion (59.09% from industry and 40.91% from academics) to evaluate interrelationship among eight critical barriers identified. Fuzzy ISM–MICMAC analysis classified the critical barriers according to their driving (DRP) and dependence powers (DEP). Findings highlighted that B8 (DRP = 8, DEP = 1) appears to be the strongest driver (Quadrant-IV, Level-5) as it significantly affects other barriers, whereas B4 (DRP = 1, DEP = 8) emerges to be most dependent barrier (Quadrant-II, Level-1). The linkage barriers (B1, B2, B3, B5) falling under Quadrant-III, Level-2, mediates the interactions between high-driving and high-dependence factors. This structured approach got validated through a 3-round Delphi process by incorporating fuzzy logic for ensuring robustness of results. Overall, the study provides actionable insights for organizations, practitioners, and policymakers seeking to strengthen sustainability initiatives through effective implementation of AI-enabled GHRM practices. By addressing the identified barriers, organizations can better align their sustainability strategies with global development priorities, contributing to the achievement of SDGs 9, 10, 11 and 12.