An Exploratory Study on How Talent Management Affects Employee Retention and Job Satisfaction for Personnel Administration in Ain Shams University Egypt
Bibliographic record
Abstract
Our study examines how talent management affects both job satisfaction and employee retention at a public university in Egypt. The sample for the field study consists of a 105 administrative employees who work at Ain Shams University (a public university). The study instrument is a questionnaire that consists of four parts: talent management, job satisfaction, employee retention and the sample’s demographic variables. The study uses Cronbach’s Alpha, Ordinary Least Squares Regressions and the Kruskal-Wallis test. We find that the components of talent management (motivating outstanding performance, training and development, job enrichment) have a significant impact on job satisfaction and on employee retention but have no significant impact on the sample’s demographic variables (gender, age, education and experience). The contribution of the study is to examine how talent management affects job satisfaction and employee retention in a higher educational institution in Egypt, an Arab, Muslim, Middle Eastern country. Talent management research in Arab/Muslim countries, such as Egypt, remains mostly unexamined. By researching new countries and regions, we can help provide further insight for organizations on how to adapt their talent management practices to fit different national and cultural contexts.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".