The Impact of Talent Management Strategies on Bank Performance in Jordanian Commercial Banks
Bibliographic record
Abstract
Talent management is very significant to the survive of firms in highly competitive business environment today. it refers to the application of needed employees for a firm and the programs to fit those needs and it combines employee knowledge, skills, attitude, values, competencies and work preferences. The explosive growth of Jordanian banking sector has led to an urgent need to develop talent management strategies as a means of boosting bank performance. The purpose of this study was to investigate the impact of talent management strategies on bank performance in Jordanian commercial banks. The design of the study has quantitative approach. Primary data was obtained by questionnaire instrument. The respondents in this study were line managers and HR managers in head admistrations. The number of population was 120 respondents. Random sampling was used in the study. 101completed questionnaires were analyzed as a final sample. Three hypotheses have been developed through literature review and tested using descriptive analysis and independent t-sample test performed by SPSS. The results indicate a positive association of attracting, developing and retaining talents with bank performance. bank management is advised to keep developing the attracting mechanism they have applied in order to cope with the changes in the business environment and stay competitive. Also, its advised to maintain developing the motivation system according to the labor market conditions and competitivnes in order to retain talented staff and to avoid labor turnover. As it should concentrate on the rewards mechanizim as a main key to retain talents. future studies recruiting larger sample sizes are needed. Furthermore, prospective studies should effectively compare Jordanian bank performance with other banks in the Middle East based on these variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".