Title sponsorship of cause-related sport events
Bibliographic record
Abstract
Purpose Corporations often benefit from associating their brand(s) with a sports property; in some cases, the property is owned or supported by a not-for-profit organization (NFP) championing a cause. Title sponsorship of such a sport event has received limited research attention but is important to a NFP for raising funds and in-kind contributions to support their cause. The purpose of this paper is to investigate title sponsorship of cause-related sport events. Design/methodology/approach This research examines the title sponsorship of a cause-related sport event and its effectiveness in relation to the event, the organization, the cause and other sponsors of the NFP. Specifically, this study examines these questions in the context of a specific annual event, Sports Day in Canada organized by ParticipACTION, a national Canadian NFP and whose title sponsor is Royal Bank of Canada (RBC). Findings Results show that title sponsorship has significant potential value for the sponsor and the cause, perhaps to the detriment of other (lower tier) sponsors of the event and the NFP. Originality/value This research has value to sponsors and cause-related sport events alike. In the case of sponsors, it provides insight into the value of title sponsorship vs other categories of sponsorship, for a brand considering sponsorship of cause-related sport property. For cause-related sport events, the research informs about the importance and possible revenue generation opportunity linked to the title sponsor category.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".