The stress-inducing potential of inclusive leadership: Does resilience help?
Bibliographic record
Abstract
• Inclusive leadership positively predicts project managers’ stress. • Project managers’ role overload mediates the relationship between inclusive leadership and the project manager's stress. • Leader resilience reduces the effect of inclusive leadership on project managers’ stress via role overload. • While inclusive leadership practices are essential in project management, they should be exercised cautiously. This study leverages the Conservation of Resource theory to investigate how project managers’ inclusive leadership might contribute to their stress through role overload while also considering how their resilience might lessen this stress. Drawing on multi-wave data from 436 leader-member dyads across various project-based sectors, including IT, construction, NGOs, and the petroleum industry, our analysis indicates a positive link between project managers’ inclusive leadership and stress, mediated by role overload. Notably, the study highlights that higher levels of resilience can reduce the effect of project managers’ inclusive leadership on stress. Our study contributes to the understanding of inclusive leadership's effects on leaders themselves, which has been a less-focused area of research within project management. Additionally, the study shows that the pursuit of inclusivity takes a toll on the project manager through increased role overload if they lack the resilience to buffer the stress-inducing potential of inclusive leadership.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".