Organizational Citizenship Behavior in Sport: Relationships with Leadership, Team Cohesion, and Athlete Satisfaction
Bibliographic record
Abstract
The purpose of this study was to introduce the construct of organizational citizenship behavior (OCB; Organ, 1988 Organ, D. W. 1988. Organizational citizenship behavior: The good soldier syndrome, Lexington, MA: Lexington Books. [Crossref] , [Google Scholar]) into the sport psychology literature and examine its utility in sport. Based upon OCB research in the organizational literature, the Multidimensional Model of Leadership (MML; Chelladurai, 1978 Chelladurai, P. 1978. “A contingency model of leadership in athletics”. In Unpublished doctoral dissertation, Waterloo: University of Waterloo, Canada. [Google Scholar]), the conceptual framework of team cohesion (CFC; Carron & Hausenblas, 1998 Carron, A. V. and Hausenblas, H. A. 1998. Group dynamics in sport, 2nd., Morgantown, WV: Fitness Information Technology. [Google Scholar]), and a model of athlete satisfaction (MAS; Chelladurai & Riemer, 1997 Chellardurai, P. and Riemer, H. A. 1997. A classification of facets of athlete satisfaction. Journal of Sport Management, 11: 133–159. [Crossref], [Web of Science ®] , [Google Scholar]) were selected as theoretically sound antecedents to be associated with OCB in sport. A total of 193 student-athletes from a large Division I university and a smaller Division III university representing a variety of sports participated in the study. Results of the study provide preliminary evidence for OCB as a unique and meaningful construct in sport and support many of the predictions hypothesized in the MML, CFC, and MAS. Results are discussed in the context of previous literature as well as theoretical, research, and practical implications.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".