Is identity leadership provided by coaches and athlete leaders associated with performance? A cross‐cultural study in football teams
Bibliographic record
Abstract
Abstract The social identity approach to leadership contends that the most effective leaders represent, advance, create, and embed a shared social identity (i.e., a sense of ‘we’ and ‘us’) within the groups they lead. Building on previous research, our study examines whether the perceived identity leadership of coaches and athlete leaders is associated with a range of key performance indicators (notably team and individual performance and effort) through team identification and team cohesion. We also examine if these relationships are generalisable across WEIRD (Westernised, Educated, Industrialised, Rich, and Democratic) and non‐WEIRD countries while looking at whether they vary as a function of national culture (i.e., ingroup collectivism). To this end, we collected data from 3,135 football players across 211 teams in nine countries who engaged in an average of 4.02 sessions per week ( SD = 2.03). Data were analysed using multilevel (multigroup) regressions and indicated that coaches' and athlete leaders' perceived identity leadership was associated with all performance indicators via both team identification and cohesion. For the most part, these relationships held across WEIRD and non‐WEIRD countries. However, we also found some evidence that the relationships between identity leadership and performance varied cross‐culturally and were generally stronger in countries high on ingroup collectivism. Together, these data suggest that identity leaders—across geographical and cultural borders—can make teams more effective and that they achieve this by leveraging ‘our’ strength in ways that make ‘us’ more cohesive.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".