The Influence of Supervisors’ Perceived Green HRM on Job Satisfaction and Affective Commitment: The Mediating Role of Subordinates’ Perceived Green HRM and the Moderating Role of HRM System Strength
Bibliographic record
Abstract
Green Human Resource Management (GHRM) scholars have urged studies to explain why employees' perceptions of green HR practices vary.Strategic HRM researchers increasingly adopt an employee perspective to understand how green HR practices affect employee outcomes.This study investigates how perceived green HR practices from both supervisors and subordinates influence job satisfaction and affective commitment.Second, this study also explores the mediating role of subordinates' perceived green HR practices and the moderating role of HRM system strength.HRM system strength refers to three broader featuresconsensus, distinctiveness, and consistency.Using the data from 624 subordinates reporting to 217 supervisors at Pakistani textile firms and applying the Hierarchical Linear Model (HLM), we found that supervisors' and subordinates' perceptions of green HR practices are the significant sources of variation in employees' job satisfaction and affective commitment.Further, supervisors' perceptions of green HR practices significantly influence subordinates' perceptions of green HR practices.Also, the indirect relationship between supervisorperceived green HR practices and job outcomes (job satisfaction and affective commitment) is significantly mediated by subordinate perceptions of green HR practices.Finally, HRM system strength significantly moderates the relationship between supervisors' and subordinates' perceived green HR practices.In GHRM, this study contributes by revealing the mediating role of subordinates' perceived green HR practices and the moderating role of HRM system strength.For practitioners and academicians, these findings imply open communication, feedback mechanisms, training, and clear expectations to bridge supervisorsubordinate perceptions.These methods help both parties understand their roles, expectations, and performance standards, improving collaboration and productivity, and for which a strong HRM system is essential.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".