Leadership for the management of change: Academic staff's perspectives on the status and determinants of leadership in the public universities of Ethiopia
Bibliographic record
Abstract
This study examined the status of leadership regarding the implementation of academic-related change schemes and the views about leadership for change in public universities in Ethiopia. Using a concurrent embedded mixed-methods design, data were collected from 372 faculty members, 216 managers of Organizational Academic Units (OAUs) through questionnaires, and 22 experienced managerial academic leaders via interview. The findings indicated that the degree of Change Leadership Behaviors (CLBs) practice was at the average position, but not to the level expected. Besides, one-sample t-test results indicated that the academic leaders' level of resilience was significantly lower than the expected value. Results from a one-way ANOVA on academic leaders' use of CLBs showed significant differences across the three generations of universities with a p-value of less than .001. The results imply a pressing need to consider the moderating variables for change leadership effectiveness and establish performance rewards for the HEI leadership. The findings also suggest that effective change management depends on transformational leadership that focuses on the human dimensions and trustworthiness (role-modeling and fairness). Understanding the leadership approaches and competencies to align with the variety of situations that arise during change is crucial.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".