Effects of Green Human Resource Management on Participation of Farmer Group Members in Sleman Yogyakarta: Organizational Commitment as Mediation Variable
Bibliographic record
Abstract
Green Human Resource Management (GHRM) reflects the aspect of human resource management in environmental management, and it focuses on the role of human resources in preventing pollution through the operational processes of a business.GHRM plays a vital role in environmental management as the human resource function also plays an important role in achieving the company's goals of a green company.Therefore, this study aims to identify the direct effect of the Green Human Resource Management variable on the participation of farmer group members and the indirect effect of Organizational Commitment as a mediating variable.The population of this study was all farmer group members in Tirtomartani Village with a total of 510 members.The determination of the sample used the cluster random sampling technique because the groups have similar characteristics such as farming behavior, level of education, farming patterns, plants planted, size of fields, to the organizational structure of each farmer group.This study used a five-point Likert scale with 5 for strongly agree and 1 for strongly disagree.Data were collected from 110 respondents.The analysis was performed with the help of SmartPLS with the Path Analysis Method.The results showed that Green Recruitment and Selection, Green Training, Green Performance Management, and Green Payment and Reward have a positive and significant effect on the participation of farmer group members, while Green Involvement did not.Moreover, organizational commitment cannot be used as a mediating variable in this research model.Farmer groups had programs to develop skills, knowledge and attitudes related to good environmental management so that the participation of their members' increased.A successful organization means that each member pays more attention to green performance targets, and indicators of green performance success, evaluate green performance and reduce errors that affect the decline in the predetermined green performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".