Examining Leadership Style Influence on Engagement in a National Change Process: Implications for Leadership Education
Bibliographic record
Abstract
Individuals expected to offer leadership are often chosen based on their power position within the field of interest and specialization in the context area being addressed and not on their leadership style. Leadership education curriculum often focuses on change as a product of leadership and leadership styles but places little emphasis on how the leadership styles of those chosen to lead change can influence the change process. In order to inform the development of curriculum targeting this aspect of leadership, research needs to be done to determine if leadership style impacts level of engagement in change. This research examined how transformational and transactional leadership styles impacted engagement in a national change process when 39 department chairs of universities across the United States were selected by the National Science Foundation to lead science, technology, engineering and math (STEM) educational reform at the undergraduate level. The findings revealed transformational leadership style positively predicted engagement in change and transactional leadership style negatively predicted engagement in change. While the small sample size makes the findings exploratory in nature and should be used with caution, they imply leadership education curriculum should include lessons on the impact these two styles have on engagement in change since there were statistically significant differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".