Principals’ influence tactics and turnover: the role of readiness for change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study aims to investigate the influence tactics used by school principals and their effect on teachers’ readiness for change and their intention to leave their positions. The research explored how different types of influence tactics affect teachers’ stability and adaptability within educational settings. Design/methodology/approach The study used cross-sectional data from a sample of 251 teachers from the Québec region. Three primary hypotheses were examined: (1) a positive correlation between principals’ use of soft influence tactics and teachers’ readiness for change; (2) a negative correlation between principals’ use of hard influence tactics and teachers’ readiness for change and (3) a negative predictive relationship between teachers’ readiness for change and their turnover intention. Structural equation modeling (SEM) was employed to test the mediation model and its individual elements. Findings Results indicate that principals’ use of soft influence tactics significantly enhanced teachers’ readiness for change, whereas the use of hard influence tactics negatively affected it. Additionally, teachers with greater readiness for change showed a lower likelihood of intending to leave their positions. These findings illustrate the pivotal role of influence tactics in shaping teachers’ attitudes toward change and their retention. Originality/value The study provides novel insights into the dynamics of leadership at educational settings by showing how given influence tactics can promote or hinder teachers’ stability and readiness for change. The research suggests practical strategies for school leaders to foster a supportive and change-oriented environment, contributing to the literature on educational leadership and teacher retention.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it