Three sets of mediators between transformational leadership and team performance
Bibliographic record
Abstract
How is transformational team leadership related to team performance?We answer this question by testing a model, guided by transformational leadership and social cognitive theory.In the model, the transformational style of a team leader is hypothesized to explain team performance directly and through the mediation of team cohesion and efficacy in a series.Study 1 reports the results of a survey administered to 1517 members of 177 teams within 9 Iraqi universities.The three-path mediation model was supported by Study 1.We replicated the support for this model with another Iraqi sample in the educational sector, involving: 2168 members of 138 teams of school teachers and their immediate leaders.Common-source/method bias was reduced in both studies, through the split-sample technique and by assessing two different sources.Practical implications of the results point especially to the team performance-enhancing effects of transformational team leaders through the two mediators which appeared equally relevant.Longitudinal studies are needed to examine the suggested causal effects, also given our alternative-model explorations.Such new research must look as well into the precise timing of the constituting micro-behaviors of team members, led by transformational leaders, that embody the two mediational mechanisms toward high team performance.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".