Replication Data for: "Unlocking the Power of ‘Us’: Longitudinal Evidence that Identity Leadership Predicts Team Functioning and Athlete Well-Being"
Bibliographic record
Abstract
The social identity approach has become an important framework for understanding effective leadership in sport contexts. More specifically, leaders’ identity leadership (i.e., their capacity to cultivate shared social identity — a shared sense of ‘we’ — in their teams) is argued to be an important basis for effective leadership. The present study is the first to longitudinally examine the relative impact of coaches’ and athlete leaders’ identity leadership on athletes’ identification with their team. In addition, we tested whether athletes’ team identification mediated the relationships with key team and individual outcomes. To investigate these research questions, 18 sport teams (N = 279; 54% female athletes) completed a questionnaire early and late in their regular season competition. To analyse these data, we conducted structural equation modelling and controlled both for baseline values and the nested structure of our data. Results revealed that it was mainly the identity leadership of athlete leaders (and not of the coach) early in the season that predicted athletes’ team identification later in the season. This increased team identification in turn fed into both team outcomes (i.e., task climate, team resilience, team performance) and individual outcomes (i.e., well-being, burnout, and individual performance). The mediating role of team identification suggests that by building a shared sense of ‘we’, athlete leaders can improve their team’s effectiveness and enhance athletes’ well-being. Accordingly, we can conclude that empowering athlete leaders and strengthening their identity leadership skills is an important way to unlock sport teams’ full potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.020 |
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".