The effect of HRM practices and employees’ job satisfaction on employee performance
Bibliographic record
Abstract
Saudi Ports, is one of the busiest seaports in the world and the biggest ports in the Middle East, which is facing challenges from different ports such as Salalah and Dubai ports especially in the cargo section, which can release their cargoes and goods in a short time without any congestion of stock. The current challenges of Saudi Ports Authority are the overstock of cargo which is the result of low performance of workers at cargo field. This paper investigated the root of this problem based on the role of the human resource practices such as training and development, reward, job analysis, social support, recruitment and selection, employee relationship and empowerment, employee satisfaction into employee performance. In other words, the current study explored to know whether HRM practices offer direct impact on the employee performance or through employees' job satisfaction on employee performance to solve the said problem. The study analysed the 367 Saudi port authority employees' data and found a positive significant relationship between HRM practices and employee performance. Furthermore, the current study revealed a positive relationship between employee job satisfaction and employee performance. Moreover, the present study found the insignificant relationship between HRM practices and employee job satisfaction but reported no significant mediating role of employee job satisfaction between HRM practices and employee performance.
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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 | 0.002 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".