Effective Leadership Characteristics and Behaviours for Female Department Chairs in Higher Education in Saudi Arabia
Bibliographic record
Abstract
The academic department is a fundamental unit for transforming the university's visions and goals into reality. In contrast, higher education undervalues administrative positions in general and department chairs in particular, believing that an administrative role is a temporary task. Little investigation has been conducted into effective leadership approaches in departmental leadership in higher education in general and in higher education in Saudi Arabia in particular. Therefore, the overarching purpose of this study was to identify effective leadership practices, characteristics and behaviors that contribute to the effectiveness of female academic department chairs and the challenges that they face. A qualitative approach informed with grounded theory techniques was used in this study. Semi-structured interviews were conducted with former department chairs, current department chairs and faculty members. Vignettes were the basis of the faculty members' interviews to avoid any ethical concerns and to allay any fears of repercussion from their department chairs. The findings of the study indicate that effective chairs are distinguished by a combination of skills, knowledge, behaviors and attitudes. Although leadership in Saudi Arabia is based on a centralized system, the findings demonstrate the tendency toward more collaborative leadership that promotes collegiality and collective interest. Specific recommendations were made to better prepare department chairs for this crucial position in institutions of higher education. The study came at a time when the country is taking significant reforms in women’s issues. Article visualizations:
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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".