Performance Appraisal and Training and Development of Human Resource Management Practices (HRM) on Organizational Commitment and Turnover Intention
Bibliographic record
Abstract
It is widely agreed that the impact of human resource management (HRM) practices can create comparative advantage for the organizational performance when organizational commitment matters. On the contrary, turnover has become a trend and it is at rise in the current working environment. The main intention of this study is to demonstrate a relationship between HRM practices and organizational commitment and its impact on turnover intention. Data of 75 employees from several different industries were collected throughout Klang Valley in Malaysia. The outcome reflects a correlation among Performance Appraisal and Training and Development (HRM practices) with organizational commitment which in turn contributed an inverse relationship with employee turnover intention. The greater commitment developed among employees will improve the organizational effectiveness through maintained skilled and experienced employees thus reducing turnover intentions. Therefore, this study dedicates to the knowledge on the impact of HRM practices on organizational commitment and turnover intention. The data results can serve as a reference or guideline when conducting relevant studies in the future.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".