Striving to Implement Green Human Resource Management (GHRM) Policies and Practices: A Study from HR Managers Perspective (FMCG Sector)
Bibliographic record
Abstract
The purpose of this research is to explore the implementation of Green Human Resource Management practices and policies by the FMCG manufacturing companies of Pakistan. The researchers have enlightened various Green HRM strategies, initiatives, and practices that HR managers have undertaken in their respective organizations. Also, this research highlights the significance of Green HR practices and policies in employee retention, organizational citizenship behavior, and overall organizational image. This research is exploring the perception of Green HR from the HR professionals associated with FMCG companies of Karachi. For this purpose, in-depth interviews were taken by the HR managers of targeted companies to explore the implementation of HR practices and policies in Pakistan. The interview was conducted with the help of an interview protocol, consisting of various open-ended questions based on research objectives and research questions. The findings of this research suggest that the concept of Green HR practices and its benefits that an organization can gain by implementing such practices is vague among the HR professionals of Pakistan. The research has identified the need to train the managers regarding the Green HR initiatives and develop awareness campaigns which guide the managers about the significance that green practices have on the overall organizational performance and its image in the industry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".