Impact of Green HRM Practices and Employee Green Perception on Sustainable Organizational Performance
Bibliographic record
Abstract
Green human resource management (GHRM) methods can assist firms in aligning their business strategy and achieving long-term organizational performance. HRM researchers largely agree that employees' opinions of HR practices substantially impact their efficacy. The study investigates the impact of GHRM practices in green recruitment and selection (GRS), green training and development (GTD), green compensation and benefits (GCB), and employee green perception (EGP) on sustainable organizational performance (SOP) in Ghana, taking into account the mediating role of employee green behavior (EGB). Data was collected from 350 HR specialists and employees working for various Ghanaian manufacturing and service organizations to meet the study's aims. The data is acquired through a self-administered questionnaire. The data is analyzed using the smart PLS 4 structural equation modeling technique. The findings indicate that green recruitment and selection (GRS), green training and development (GTD), and employee green perception (EGP) all influence employee green behavior (GEB). The data also suggest that green training and development (GTD) and green recruitment and selection (GRS) have an impact on sustainable organizational performance (SOP). However, it was discovered that green compensation and benefits (GCB) have no meaningful effect on employee green behavior or sustainable organizational performance. The study has theoretical, methodological, and practical consequences for stakeholders, including academics, practitioners, legislators, and HR specialists.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".