Predicting Intention to Volunteer for Mega-Sport Events in China: The Case of Universiade Event Volunteers
Bibliographic record
Abstract
Attracting and retaining a loyal base of volunteers is critical to the success of mega-sport events (MSEs). The purpose of this study was to examine the antecedents of MSE volunteering in a Chinese context. Drawing upon self-determination theory, the study establishes a valid structural equation model of antecedents of Chinese volunteers' satisfaction and their intention to volunteer in future MSEs. The XXVI Summer Universiade provides a case-specific context. After a pilot study to validate questionnaire items, location-based convenience sampling was employed to collect data from Universiade volunteers. A total of 1,015 questionnaires were completed and analyzed. Results from the covariance-based structural equation modeling analysis showed that all of the three exogenous factors—external attractiveness, altruism, and intrinsic motivation—emerged as significant predictors of volunteer satisfaction. In turn, volunteers' perceived level of satisfaction predicted future MSE volunteer intention. Our findings reveal unique differences between Chinese sport event volunteers and their Western counterparts. Implications for event planning and volunteer program design are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".