Leadership Practices and Talent Turnover: Study on Yemeni Organizations
Bibliographic record
Abstract
To date, many studies have been conducted in order to identify factors that have a great impact on employees’ turnover. A number of studies have shown that leadership style influences employees’ job satisfaction and their turnover intention. However, very few studies have dealt with the relationship between Kouzes and Posner (1987)’s leadership practices and employees’ turnover. This study aims to identify the role that Leadership practices play in enhancing loyalty and at the same time reducing turnover intention among the most valuable employees - talent employees - in telecommunication sector in Yemen. In addition, current study also aims at examine whether the relationship between leadership practices and turnover intention is mediated by the talent employees’ job satisfaction. Using the response of 280 employees working in the five Yemeni telecommunication organizations, the results indicated that there is a negative relationship between overall leadership practices and turnover intentions among talent employees, four out of five practices were negatively correlated with talent employees’ turnover intention. Moreover, job satisfaction founded to be a mediator of the relationship between leadership practices and turnover intentions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".