The Influence of Transformational Leadership Behaviours on Oman Public Employees’ Work Performance
Bibliographic record
Abstract
The purpose of this paper is to investigate the influence of transformational leadership behaviors work performance in the context of Omani governmental organizations. The research emphasizes that transformational leadership is a crucial element to enhance employees' performance. Transformational leadership Inventory was used to measure transformational leadership behaviors. Contextual and task performance were used to measure work performance. Quantitative survey method was applied and a sample of 335 middle-level managers of Omani public civil service organizations was selected to answer the instrument. Analysis of Moment Structures (AMOS) was utilized to analyse the collected data and test the research questions, and hypotheses. The techniques of data analysis comprised descriptive statistics, a Confirmatory Factor Analysis (CFA), and a Structural Equation Modeling (SEM) analysis. The empirical results indicate that transformational leadership behaviours (core transformational leadership, providing individualized supports, intellectual stimulation, and setting high performance expectation) have a significant impact on contextual performance. Whereas, core transformational leadership and providing individualized supports have a significant impact on task performance. k'>. Cronbach's Alpha was 0.885. The finding revealed that nurses supply, mostly from Secondary School (84%). A critical demand for more nurses 30%-40% in certain units due to high work load. Most of the nurses were not satisfied about monitory compensation, participation in decision making and inadequate supplies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".