Green HRM practices, green commitment, and green innovative work behavior in UAE higher education institutes
Bibliographic record
Abstract
This study examines the relationships among green human resource management (GHRM), green commitment, green innovative work behavior (GIWB), and the moderating effect of environmentally specific servant leadership (ESSL) in UAE higher education institutes of the United Arab Emirates (UAE). Using a sample of employees, data were collected through a survey from 243 employees working in different universities across the UAE and analyzed using Structural Equation Modeling (SEM). The SEM analysis confirms robust relationships between GHRM, environmentally specific servant leadership, green commitment, and green innovative workplace behavior. GHRM has a positive impact on GHRM. ESSL fosters the relationship between GHRM and green commitment, while green commitment positively impacts green innovative workplace behavior. Females were found to be more environmentally aware of the needed adjustments compared to male workers at the UAE campuses. The study suggests that higher education institutes in the UAE should adopt ESSL to support eco-conscious behaviors and green practices on their campuses and contribute to the achievement of national sustainability goals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".