A study on effects of the best human resource management methods on employee performance based on Guest model: A case study of Charmahal-Bakhtiari Gas distribution firm
Bibliographic record
Abstract
Human resource management plays an essential role on the success of any business units such as utility firms.In this paper, we present a study to investigate the effects of different human resource management on employee performance.The proposed study is applied on one of gas distribution units in province of Charmahal-Bakhtiari, which is located west part of Iran.There were 161 people working for this firm where 75 employees were working in center of province and 86 employees were working in other sides of province.Cronbach alpha is calculated as 0.83, which is well above the minimum desirable limit.The study uses Pearson correlation test to investigate the effects of Hiring system, Training system, Job design, Organizational relationship and Share ownership programs on employee performance.The results of our survey indicate that job design is the most important technique for employee management followed by training system, organizational relationship and share ownership programs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".