A meta-analysis of team reflexivity: Antecedents, outcomes, and boundary conditions
Bibliographic record
Abstract
In this meta-analysis, we assess the performance benefits of team reflexivity. Drawing on the teams-as-information-processors perspective, we provide evidence that team reflexivity facilitates team performance, yet we also find that these benefits depend on key team design contingencies, namely team size and team tenure. In addition, we examine how team leaders can make their team more reflexive. Our study shows that leaders who support team members' active participation in group discussion and decision-making set the stage for greater reflexivity and then greater performance by fostering the emergence of team psychological safety. We also provide a comprehensive review of the research on team reflexivity by examining the strength of relationships in its nomological network. Overall, this meta-analysis challenges certain assumptions about reflexivity and opens new avenues for research to further understand its role in the effectiveness of teams. • Our study plays a central role in resolving the debate about the value of team reflexivity. • The performance benefits of team reflexivity vary with team size and team tenure. • Supporting members' participation in discussions and decisions increases team reflexivity. • Psychological safety is key to understanding leaders' influence on team reflexivity.
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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 | 0.057 | 0.159 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.043 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".