Green Human Resource Management and Organizational Sustainability: A Systematic Literature Review and Bibliometric Analysis
Bibliographic record
Abstract
Adopting Green Human Resource Management (GHRM) practices is rapidly seen as the modern industrial revolution.The development of strategies for achieving organizational sustainability depends heavily on GHRM.Hence, organizations are continuously adopting such practices to gain a competitive edge and achieve sustainability.This article offers a comprehensive overview of current developments in the field of GHRM and organizational sustainability by using a systematic literature review and bibliometric analysis.As bibliometric study also develops the linkage between various concepts.The data analysis was conducted using a software package developed [1], version 1.6.16.The results demonstrated through a systematic literature review that GHRM considerably affects sustainable performance, with employee behavior serving as the primary mediator.Though the study was the first of its kind, it focused on identifying key GHRM indicators and exposed some imperative relationships between GHRM and organization sustainability that may provide support in developing a strong conceptual foundation.This study shows that by combining the key elements of these two concepts into a single idea, a new research topic can be developed, introducing new research opportunities in both the broad field of GHRM and the relatively emerging and contentious area of sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.125 | 0.096 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".