The Sponsor-Event Geographical Match as a Dimension of Event-Sponsor Fit: An Investigation in Europe and North America
Bibliographic record
Abstract
The study presented in this article investigates a new basis for the fit construct in sponsorship, namely the sponsor-event geographical (SEG) match. In light of the fast growing internationalization of events and of the increased globalization of sponsoring brands, many event-sponsor relationships are bound to lack fit regarding a SEG match (e.g., a brand strongly associated with the European culture sponsoring an event in Australia). First, the conceptual distinction between the known bases of the fit construct and the SEG match is developed. This is followed by an experiment carried out in two different countries. Results indicate that event-sponsor relationships with a strong SEG match yield more favorable responses than non-SEG match relationships. In addition, when the SEG match is strong, event-sponsor fit is critical for sponsorship success due to its intervening role in the attitude formation process. Managerial recommendations and further research avenues are also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".