Exploring the role that attendance at a special event plays in the formation of attitudes towards the host destination: An empirical analysis
Bibliographic record
Abstract
Special events have emerged as an important component of many destination marketing strategies. Marketers of both special events and their host destinations appear to acknowledge that there are synergies between the products, however, very little research has explored this phenomenon. This research explored the role of attendance at a special event in the formation of attitudes towards the host destination. Data were collected using a random sampling frame and involved telephone interviews with a sample of attendees of a theatre-event held in Melbourne, Australia. The resulting sample was 788 that included 321 respondents from outside the host destination who provided information on whether their attitude towards the host destination had changed as a result of their attendance at the special event. Respondents were also asked to provide the reasons for the changes in their attitudes towards the host destination. The results indicate that for almost a quarter of the respondents their attitudes towards the host destination had changed and for over 90% of these respondents, their attitude change was in a positive direction. Key reasons provided by respondents for their changes in attitudes included Access, the Special Event itself and Attractions in the destination. Recommendations were made in regard to how marketers of special events and their host destinations can capitalise on the synergies between the two tourism products.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".