The Effects of Talent Management on Employees Performance in Oil Jam Petrochemical Complex (Oil JPC): The Mediating Role of Job Satisfaction
Bibliographic record
Abstract
Talent management science uses strategic human resource planning in order to improve business value and to help firms and organizations to achieve their purposes. Efforts have been done to attract, retain, develop, reward people in order to make them a part of talent management and strategic workforce planning. Talent management not only can hire, reinforce and evaluate the talent, but also it can lead to Personal growth, satisfaction in employees’. The aim in this study is to investigate the relationship between talent management and Job satisfaction among the Oil Jam Petrochemical Complex. This is a descriptive survey research. The questionnaire with 31 questions was distributed among the Oil JPC employees’. The study adopted a descriptive research design in which the target population of 2,500 employees of Oil Jam Petrochemical Complex. The study used stratified sampling method to select 83 employees according to their job cadres. Reliability was assessed by Cronbach's Alpha 0.836. The collected data were analyzed by Descriptive and inferential statistics in descriptive and correlations test format for analyzing the SPSS data. The findings indicated that, from audiences' perspective, there is a significant effect between factors such as attracting the talents, Alignment, talents maintenance, developing the talents and job satisfaction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".