Principals’ influence tactics and turnover: the role of readiness for change
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.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".