An Empirical Investigation on the Impacts of the Adoption of Green Hrm in the Agricultural Industry
Bibliographic record
Abstract
The green human resources management (Green HRM) developed from companies engaging in practices that are concerned about environmental conservation and maintaining sustainable ecological balance. It includes all the activities that are geared towards helping an organization carry out its goals for environmental management to reduce carbon footprints in areas that concern the employment of employees, their training and compensation. Green HRM plays a useful role in supporting environment and agricultural related issues by following and adopting green HR practices and policies. There is a great deal of increase in the adoption of sustainable agricultural systems by the agricultural industry. Literature has highlighted the importance of the adoption of the sustainable agricultural systems as a key objective of the agricultural sector thus making it very significant to identify with the support of green HRM practices. In most parts of the world today, there are ongoing debates and uncertaintiesthat are associated with how green management principles can be effectively implemented in a workforce in an organization. Research methodology is based on quantitative research and primary data was collected. The results was calculated by SPSS 24 , different tests were applied to measure reliability and validity, to analyze the variables simple linear regression, one way repeated measures ANVOA and Paired- Samples t Test were applied. This research identified the various ways that green HRM practices are helping in improving agriculture now and in the future. The key finding of this research was that there is very little understanding of GHRM in Pakistan, therefore more effect manners should be applied to collect appropriate data and learning employee behavior towards change can make a major difference in this field.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".