What’s Next for Green Human Resource Management: Insights and Trends for Sustainable Development
Bibliographic record
Abstract
The theme of green human resource management (GHRM) has got immense attention among researchers and professionals due to its potential to pacify environmental needs and simultaneously allowing firms to have win-win situation, hence achieving sustainable competitive edge over their rivals. In this context, a systematic review of 70 articles from the past 12 years (2008-2020) on green human resource management was conducted based on Scopus database in terms of (1) the reflections of green HRM, (2) execution of green HRM, (3) factors of green HRM, (4), Effects of green HRM. Results demonstrated that Green HRM is still in developing phase and a multidimensional paradigm with green training as an important element along with teamwork, management support, green organizational culture are the pioneer factors in ensuring sustainable development both at firm and individual level. Finally, this paper highlights the past, current and future endeavors in green HRM paradigm, sustainable development and serves as a guide for researchers who are new to this novel concept; it will also intensity their understanding about the productive journals, appropriate methodology, underpinning theories, sustainable development and substantial knowledge gaps.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".