Linking place, mega‐event and sponsorship evaluations
Bibliographic record
Abstract
Purpose Little research has examined sponsorship within the context of mega‐event and related host images. This paper seeks to explore the relationships among country, destination, mega‐event and sponsor images through the evaluations formed of each entity. Design/methodology/approach Based on data collected from 291 Canadian consumers two months after the Beijing Olympics, a SEM model examines the relationships among consumer evaluations of the host country, the country as a destination, the mega‐event itself and sponsors. Findings Results support the hypothesized model and present a paradoxical situation for the Olympics hosted by China. While the overall country evaluation was found to have a strong and positive effect on its evaluation as a tourist destination and the destination evaluation has a subsequent positive relationship with Olympic evaluations, a direct and negative relationship between the evaluation of the country and of the Olympic Games was also supported. Research limitations/implications Future research should examine the relationship among country, destination, mega‐event and sponsor images in other mega‐event and country contexts. In addition, the pattern of these relationships should be assessed longitudinally. Practical implications This study provides evidence to show that the Olympic Games image is resilient and can thrive in challenging contexts. Further, sponsors can be assured that they are receiving value from Olympic sponsorships. Originality/value These results extend previous literature on sponsorship evaluation into the large, global sponsor context. In addition, this study examines the role of the host country in understanding the influence of the mega‐event on sponsor images.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".