Perspectives of event leveraging by restaurants and city officials
Bibliographic record
Abstract
Purpose Hosting events can attract visitors to an area and provide an opportunity for local businesses in the host community to benefit economically. Restaurants, in particular, have an opportunity to benefit as food is a necessary expenditure. However, previous research suggests that the intentional attraction of event visitors by local businesses has been minimal. The purpose of this paper is to explore perspectives of event leveraging held by restaurant owners/managers and a destination marketing organization (DMO). Design/methodology/approach Data were collected through semi-structured interviews with owners/managers of 16 local restaurants and from three DMO executives in one medium-sized city in Ontario, Canada. Data were analyzed using initial and axial coding. Findings Findings indicate that restaurants did not engage in event leveraging. Three common reasons emerged to explain their lack of engagement in leveraging, including: a lack of a belief in benefits from leveraging, inconvenient proximity to event venue, and not being prepared for event leveraging opportunities. The DMO had a desire to assist local business in leveraging, but their ability to do so was negatively impacted by a lack of awareness of events being hosted, disengagement by local businesses, and limited resources. Originality/value Findings suggest that there is a need for DMOs and local businesses to create stronger and more supportive working relationships that address financial and human resources constraints preventing the adoption and success of event leveraging. As part of this approach there is a need for cities to make stronger financial investments in supportive agencies such as a DMO.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".