The Important of Training and its Impact on the Performance of Employees in Banking Sectors (Abu Dhabi – UAE) to Rise Efficiency
Bibliographic record
Abstract
The study aims at measuring the impact of human resource management practices on creativity and innovation with the presence of competencies as an intermediary variable. The study highlights the importance of human resource management practices for UAE banks and explores the role of human resource management practices in enhancing the creativity and innovation of employees. To achieve this goal, six UAE banks were selected as a study area. A questionnaire was designed and distributed to a random sample of 150 respondents. The analytical, descriptive method was used for analysis. Data analysis and testing were carried out using SPSS.Some of the most important outcomes of this study are: Human resource management practices such as compensation and benefits, employment, empowerment and human resources planning have a positive impact on innovation. Compensation, benefits, employment, training and development, also have a positive effect on creativity. Human resource management practices have a positive impact on training. The study recommends that giving the UAE banks the priority of human resource management practices is of great importance in their dimensions according to the scale of human resource management practices that are interested in achieving innovation and creativity for employees within the banks. The further studies are suggested related to human resources management practices and creativity and innovation because of their impact on achieving competitive advantage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".