Exploring the Effect of Emotion Leadership on Individual and Team Level Outcomes
Bibliographic record
Abstract
The role of team leaders in shaping group affect has gained increased attention in modern organizations. Nonetheless, we are still missing integrative studies on leadership behaviors that aim to influence team members’ affective processes in service of collective goals. In Study 1, building on previous research on group affect, leaders’ emotional intelligence, and emotional labor, we propose a conceptualization of emotion leadership. We then develop and validate a measure of emotion leadership. Integrating the concept of emotion leadership and the conservation of resource (COR) theory, we advance a theoretical model that links emotion leadership to individual and team-level outcomes mediated by team affective processes. The model is tested in Study 2, where we found support for the indirect effects of emotion leadership on employees taking charge and turnover intention through employee burnout. Team creativity and team viability are indirectly affected by emotion leadership through team psychological capital. Furthermore, we examined the moderating effect of team functional diversity and found that the impact of emotion leadership was stronger in teams with greater functional diversity.
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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 | 0.004 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".