Is Sport Sponsorship Global? Evidence from the United States, the United Kingdom, and India
Bibliographic record
Abstract
This study sought to develop and test a cross-national sport sponsorship model. Sponsorship and Hofstede’s cultural dimensions theories were utilized for the theoretical framework for this study. A survey was conducted with 522 Chelsea FC soccer club’s fans from the United States, the United Kingdom, and India in the area of sponsorship through a jersey sponsorship. Single and multiple-group confirmatory factor analysis and structural equation modeling were used to analyze the global sport sponsorship model. The results acknowledged the measurement and structural invariance of a global model for five sport sponsorship outcomes (i.e., sponsorship awareness, sponsorship fit, attitude toward the sponsor, gratitude, and purchase intentions), controlling for age, gender, education, household income and the household’s decision maker. The statistical analyses indicated that structural relationships among the analyzed sponsorship outcomes were invariant among all three countries. The effect of sponsorship fit predicted the presence of purchase intentions, while the attitude toward the sponsor was the strongest predictor of purchase intentions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".