Impact of Green Human Resources Practices on Green Work Engagement in the Renewable Energy Departments
Bibliographic record
Abstract
In recent years, the sense of responsibility among companies and organizations has increased manifold because of external pressure towards environmental sustainability. There are several measures taken by the organizations to work in a green or eco-friendly manner, and among these measures, green human resources management has become an important practice of an organization. This study explores the role of Green human resources management (green HRM) in predicting the green work engagement (GWE) of employees. The study surveyed employees from three big energy companies that operate in Hungary. The research focused on four main practices of Green HRM and aimed to find out whether they can predict green work engagement. In this study, self-administered questionnaires were used as a tool for collecting the data through online channels, and around 238 employees responded to fill out the questionnaire. After collecting the data, hypotheses were tested by using SEM analysis to fulfill the study's objectives. The results indicated that only green rewards, green training, and green performance management significantly predicted GWE. In contrast, green performance management was not a significant predictor of GWE. The study tries to bring better understanding for the managers, policymakers, and future researchers to identify the role of these practices in an organization. 
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".