An investigation on effective factors influencing employee performance: A case study
Bibliographic record
Abstract
Human resources are considered as one of the key components of any organization to reach its objectives. Human resources help organization performance doing organizational duties and making employees' improvement. Because of this, employee performance appraisal has changed to one of the most important issues for top managers. Performance appraisal is necessary to select useful strategies for increasing productivity of human resource management along with productivity of employee to get strategic targets. In this study, we extract effective factors on increasing of employee performance and subsequently, present some suggestions to managers of academic organizations. The study was performed on some employees who worked for Islamic Azad University in 2012. Cronbach alpha was equal to 99.4% for employee performance appraisal, which confirmed the overall survey. To recognize key factors we used path analysis technique too. The results of the study revealed that in this school, employee performance in practical field was higher than expected, but in terms of operational and behavioral fields, they were in the middle stage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".