The impact of human resource practices on job satisfaction and intention to stay in emerging economies: Model development and empirical investigation among high caliber governmental employees in Qatar
Bibliographic record
Abstract
High rates in turnover of skilled employees can turn to be a very significant concern in organizations world-wide. Turnover affects productivity, product or service quality and of course profitability. As Human Resources (HR) activities can be considered as a primary source of sustainable competitive advantage in organisations, it would also play a major role in employees' intention to stay or to leave. This paper examines the influence of different human resource practices on employees' job satisfaction and organizational commitment and eventually on job turnover with a focus on the governmental sector in the State of Qatar. In order to develop such investigation, a model was suggested and an online questionnaire was developed and distributed. Hypotheses were tested on a sample of more than 250 highly ranked employees in Qatar. Statistical correlations were conducted to examine the relationships among variables under investigation. The results show that positive relationships were observed between the HR practices under investigation and job satisfaction and organizational commitment which indicate intention to stay.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".