FACTORS INFLUENCING RESIDENTS’ PERCEPTIONS, ATTITUDES AND BEHAVIORAL INTENTION TOWARD FESTIVALS AND SPECIAL EVENTS: A PRE-EVENT PERSPECTIVE
Bibliographic record
Abstract
This article reports the results of research investigating residents’ perceptions, attitudes and behavioral intention (BI) toward sports festivals and special events (FSE) from a pre-event perspective. A structural equation modeling (SEM) was utilized. Two sets of theoretical frameworks have been employed for this study: Social Exchange Theory (SET) and Social Representation Theory (SRT). A quantitative analysis was utilized. Using structural equation modeling (SEM). The authors have identified a strong association between media influence and FSE image evaluation; FSE image evaluation and residents’ perceptions; residents’ perceptions and attitudes; and residents’ attitudes and behavioral intention. However, the study found that social interactions do not have a significant impact on FSE image evaluation. The practical application of this research is that event planners should use media to promote FSE to local residents. This article concludes with the management implications for FSE planners and organizers. Future studies can build on the findings of the paper to generalize this China model for adaption to other countries.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".