The role of positive affectivity in team effectiveness during crises
Bibliographic record
Abstract
Summary Organizational efforts to improve team effectiveness in crisis situations primarily have focused on team training initiatives and, to a lesser degree, on staffing teams with respect to members' ability, experience, and functional backgrounds. Largely neglected in these efforts is the emotional component of crises and, correspondingly, the notion of staffing teams with consideration for their affective makeup. To address this void, we examined the impact of team member dispositional positive affect (PA) on team crisis effectiveness and the role of felt negative emotion in transmitting that influence. A study of 21 nuclear power plant crews engaged in crisis training simulations revealed that homogeneity in PA, but not mean‐level PA, was associated with greater team effectiveness. Mediation analysis suggested that homogeneity in PA leads to greater team effectiveness by reducing the amount of negative emotions that team members experience during crises. Furthermore, homogeneity in PA compensated for lower mean‐level PA in predicting effectiveness. Discussion focuses on the implications of these findings for understanding and further exploring the importance of affective factors and especially team affective composition in team crisis performance. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".