Image Transfer in Sports Creative Sponsorship and Participation Sponsorship: What’s the Difference?
Bibliographic record
Abstract
Participation sponsorship and creative sponsorship of sports events can have strategic communication effects. In this study of the car brand “Peugeot” in Tunisia, the impact of participation sponsorship on image transfer was compared with that of creative support, considering the moderating effect of the involvement of spectators on the image transfer process. Structural equation modeling analysis was used to validate the measurement of images and structural models of image transfer. The results largely favored sports-related creative sponsorship. Furthermore, when moving from “low involvement” to “high involvement” groups, observers of the creative backing were perceived as more intense, indicating that participation sponsorship has a decreasing impact. Research results have shown that it can be a more effective communication tool than multi-sponsoring. We conclude that creative support presents a real image transfer opportunity for sponsors that is less costly but more persuasive than participation. This assumption deserves to be verified for the indirect sports audience and in other cases of events with high and low media coverage.
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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 | 0.003 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".