Green HRM: The Link Between Environmental and Employee Performance, Moderated by Green Work Climate Perception
Bibliographic record
Abstract
Sustainable organizations think about how their operating systems impact the environment.In this regard, organizations can prevent environmental pollution by adopting green human resource management practices resulting in the development of environmentally responsible behavior among employees.The aim of this study is to provide an understanding of how organizations transform human resource management practices into green human resource management practices that enhance environmental performance and further lead to green employee performance moderated by employee perceptions of a green work climate.This study has used a quantitative research approach.Data analysis uses an approach structural equation modeling-partial least squares supported by the Smart-PLS 3 computer software program.The selected sample is the hospitality sector in Indonesia.The results of the study show that green human resource management has a positive effect on environmental performance.Environmental performance has a positive effect on employee green performance.Employees' green work climate perceptions have a positive effect on employees' green performance.Employees' green work climate perceptions do not moderate the effect of environmental performance on employees' green performance.The implications of the magnitude of the results, opinions and responses of other sustainability stakeholders can add important findings for further research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".