The influence of cultural diversity on organizational citizenship behaviors in professional sport teams: The moderating role of intercultural competence
Bibliographic record
Abstract
Professional sports teams provide a relevant setting for the study of multicultural work groups. Engaging in additional tasks or voluntary efforts—broadly referred to as organizational citizenship behaviors (OCBs)—is key to the effective functioning of such teams. Unfortunately, cultural diversity has been shown to decrease team cohesion and could therefore be detrimental to OCBs. However, intercultural competence (IC) should help team members understand and adapt to the cultural diversity in their teams. Because these aspects remain poorly understood, this study examines the influence of cultural diversity on OCB (i.e., sportspersonship, civic virtue, helping behavior), and the moderating role of IC. A vignette survey study was conducted with 219 professional athletes from different sports (i.e., football, basketball, and volleyball), each exposed to one of three scenarios representing different levels of cultural diversity. Data were analyzed using hierarchical linear modeling. Results suggest an inverted curvilinear relationship between cultural diversity and OCB, with OCBs being significantly lower in the moderate cultural diversity condition compared to the low and high conditions. In addition, the results suggest that athletes’ level of IC acted as a positive moderator between the level of cultural diversity and perceptions of OCB. These findings are discussed in relation to the literature on cultural diversity and (sport) team dynamics.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".