Visitor composition and event-related spending
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the spending patterns of non-local participants and spectators at a medium-sized international sport event, to segment their spending patterns and consider implications for the quality of each segment's event experience. Design/methodology/approach Spending in nine sectors of the economy is measured via self-report, and respondents are segmented into five groups: spectators, athletes, coaches, officials, and other participants (e.g. media, medical staff). The daily and aggregate spend for each segment in each economic sector is calculated and compared. Regression analysis tests differences among segments for each economic sector. Findings Participants account for 39 per cent of aggregate spend; coaches are the biggest spenders; athletes spend relatively little. The segments spend differently on hospitality, private transportation, grocery, and retail, with spectators spending significantly more than the participant groups on hospitality and private transportation, and significantly less on groceries and merchandise. Spending in sectors normally associated with celebration and festivity accounts for only 8 per cent of total spend. Research limitations/implications Findings are derived from a single event, but are consistent with other work, suggesting that inadequate attention is given to opportunities for festive celebration, especially among athletes. Practical implications Coaches are a particularly useful target market for retailers, whereas hoteliers and service stations should target their marketing at spectators. Event organizers should do more to build festivals. Originality/value This paper identifies the ways that different segments organize their spending at an event, and demonstrates that greater attention to festivals could enhance a sport event's overall impact.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".