The ripple effect of strain in times of change: how manager emotional exhaustion affects team psychological safety and readiness to change
Bibliographic record
Abstract
Introduction Managers assume a pivotal role during periods of organizational change, yet there exists a notable gap in our understanding of how their emotional exhaustion may impact their capacity to generate readiness to change within their teams. Grounded in the conservation of resources theory (COR), this study explores the crossover effect of managers’ emotional exhaustion on team readiness to change. We expect this to occur through higher levels of laissez-faire leadership, which impacts the teams’ psychological safety. Methodology Data was gathered within a Canadian governmental organization undergoing two significant changes—cultural change and digitalization—with a specific focus on leadership as a pivotal factor in preparing teams for change. Employing surveys from 372 team members and 62 managers affected by this change, we conducted path analysis to empirically test the proposed model across 74 teams and their respective managers. Results Managers’ emotional exhaustion has a negative indirect effect on team readiness to change. The double mediation pathway implies a positive relationship on laissez-faire leadership, which hinders psychological safety. In turn, psychological safety hampers team readiness to change. Conclusion Managers must invest significant resources to fulfill their roles and responsibilities during strategic change. Those who feel exhausted during change may look for ways to protect some of their resources by reducing the time and energy they invest leading their team. This self-preserving resource strategy has detrimental consequences on teams’ effectiveness during change due to an indirect crossover effect that affects the levels of psychological safety on the team.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".