The effect of green human resource management on environmental performance: The mediating role of employee eco-friendly behavior
Bibliographic record
Abstract
The current study examines the change in environment performance through green human resource management in a developing country’s higher education institutes. The data were collected by survey using a reliable and valid instrument adopted from the literature. The unit of analysis in the current study is an individual consisting of employees working in higher educational institutions of Pakistan. Three hundred questionnaires were distributed while 220 questionnaires were found completely filled for statistical analysis. The current study utilizes the multiple regression techniques through structural equation modelling using second-generation software SmartPLSv3.0. The results indicate the positive influence of green human resource policies on environmental performance and provide significant insights on the partial mediating effect of employee eco-friendly behavior between green human resource management and environmental performance. The present study provides numerous theoretical and practical implications through the extension of Ability-Motivation-Opportunity theory by constituting the employee behaviors for the implementation of environmental strategies in the organization context. The findings of the present study suggest guidelines for human resource managers and management of educational institutes to implement green human resource policies that are likely to improve institutes environmental performance.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".