Perception of assistant teaching staff of their leaders’ transformational leadership behaviors and its relation to their organizational citizenship behaviors in Suez Canal University
Bibliographic record
Abstract
Background and objective: Heads of academic departments are the first line leaders who directly influence the quality of their departments, transformational leadership has been demonstrated as an effective leadership style in leading organizations and motivating the subordinates to do more than what is being expected. This study aimed to assess the perception of assistant teaching staff of their leaders’ transformational leadership behaviors and its relation to their organizational citizenship behaviors (OCBs) in Suez Canal University.Methods: The studied sample was 456 of assistant teaching staff working at 13 faculties in Suez Canal University. Data collected using self-administrated questionnaire consists of two tools: transformational leadership scale, and organizational citizenship behaviors (OCBs) scale, both likert types rating scale.Results: More than two thirds (65.2%) of the participated assistant teaching staff perceived their leaders as having unsatisfactory transformational leadership behaviors, two thirds (66.4%) of them were satisfied with their organizational citizenship behaviors, and there was significant positive correlation between transformational leadership behaviors and OCBs.Conclusions: There was significant positive correlation between transformational leadership behaviors and OCBs.
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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 | 0.002 |
| 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 | 0.000 |
| 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".