Examining the role of transformational leadership and entrepreneurial orientation on employee retention with moderating role of competitive
Bibliographic record
Abstract
This study aims to investigate employee motivational and environmental factors and their effects on employee retention and organizational performance. Employee retention is predicted by organizational environment, intrinsic motivation, organizational learning, knowledge management, entrepreneurial orientation, external connect and explained R2=76.3% of the variance in employee retention. Therefore, organizational performance is predicted by competitive advantage and employee retention and explained R2=19.9% of the variance in organ-izational performance. Effect size analysis indicates that intrinsic motivation had substantial effect on size when compared with other exogenous variables. The predictive relevance of the model was found substantial revealed Q2=40.5% relevance to predict employee retention. The moderating role of the competitive advantage was confirmed and directs that the positive relationship between employee retention and organizational performance will be stronger when competitive advantage is higher. Finally, results showed that intrinsic motivation had the highest importance level when compared with other constructs. Therefore, manager and policy makers should take into consideration intrinsic motivation and transformational leadership in order to boost employee retention and organizational performance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".