Team Political Skill Composition as a Determinant of Team Cohesiveness and Performance
Bibliographic record
Abstract
This study examines the role of team political skill in predicting team effectiveness. Extending the current paradigm of individual political skill and contributing to the team effectiveness literature, we offer a theoretical framework for team political skill composition and test a model whereby task and social cohesion mediate the relationship between team political skill and team performance. On the basis of the results obtained from 189 student project teams and 28 business work teams, we demonstrate that team political skill benefits extend to groups. In both samples, team political skill directly related to subjective and objective team performance. Among several team political skill composition models, the interaction between the group skill mean and standard deviation (“skill strength”) was found to be the best predictor of team emergent states and outcomes. Team political skill was related to objective team performance via social and task cohesion in the student teams and via task cohesion in the work teams. Finally, we investigated the potential dark side of high team political skill but failed to support the too-much-of-a-good-thing hypothesis. Given the social focus of the construct, an aim for future research is to further understand how the composition of individual political skill influences team dynamics and outcomes. Multiple organizational implications extend to recruitment, training, development, and team building.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".