Factors Affecting Repeat Visitation and Flow-on Tourism as Sources of Event Strategy Sustainability
Bibliographic record
Abstract
The sustainability of including medium sized one-time sport events in an event portfolio is examined with reference to the capacity of one such event to stimulate flow-on tourism (i.e. tourism activities beyond the event but around the time of the event), a desire to return to the destination, and positive word-of-mouth. Relationships among four motives (socialising, escape, learning about the destination, and learning about athletics), identification with the event (self and social identity), previous visitation to the host destination, information search, tourism activities, and likelihood of recommending and/or returning to the host destination were examined for four categories of attendees at the Pan American Junior Athletics Championships: primary purpose spectators, casual spectators, athletes, and non-athlete participants. All four categories of attendee engaged in some information search and participated in flow-on tourism, but to a low degree. Information search fostered flow-on tourism. Classic tourism activities (e.g. sightseeing, visiting museums) were motivated by a desire to learn about the destination, and encouraged future visitation and likelihood of recommendation. It is concluded that medium-sized one-time sport events can play a sustainable role in event portfolios, but their efficacy requires greater integration of destination experiences with the event. It is suggested that future work should examine the means to cultivate that integration, including creation of more effective alliances between destination marketers and event organizers.
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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 | 0.002 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".