Green human resource management, energy saving behavior and environmental performance: a systematic literature review
Bibliographic record
Abstract
Purpose This study aims to conduct a comprehensive assessment of the existing literature on green human resource management (GHRM) and its correlation with environmental performance (EP) and energy saving behavior (ESB) by using the PRISMA methodology. Design/methodology/approach A thorough examination was undertaken involving a total of 25 articles which included a diverse range of years and geographic areas. Findings The findings suggest a growing emphasis on the intersection of GHRM, EP and ESB, supported by a substantial increase in research in recent times. The literature in question was mostly contributed by Malaysia, Pakistan, Canada and Thailand. Majority of research endeavors were carried out within the context of manufacturing companies. The studies under scrutiny mostly used quantitative research methodologies and often applied the resource-based view (RBV) and theory of planned behavior (TPB) frameworks to investigate the relationships between GHRM, ESB and EP. In addition, structural equation modeling (SEM) has garnered significant attention as a commonly used analytical methodology. This analysis emphasizes the growing importance of GHRM strategies such as green recruiting, green performance management and green remuneration, in fostering sustainable organizational results. Originality/value This work offers significant contributions to the existing body of research in this particular sector; shedding light on its present condition and pinpointing prospective avenues for future inquiries.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".