Sponsorship antecedents and outcomes in participant sport settings
Bibliographic record
Abstract
Purpose The purpose of this paper is to measure participants’ sponsorship awareness, and assess a model designed to predict participants’ behavioral intentions toward the sponsors of the Fayetteville Race Series. Design/methodology/approach The study is based on non-experimental survey research design using path analysis. Findings Perceived sponsor goodwill had a positive direct effect on participants’ sponsor behavioral intentions, as well as a positive indirect effect partially mediated by sponsor image. Sponsor image and future event participation also had positive direct effects on behavioral intentions. Overall, participants had very positive perceptions of the sponsors’ goodwill and image, and indicated positive future intentions. Participants’ ability to identify event sponsors through aided recall was inconsistent between the two events studied. Practical implications The positive outcomes for sponsors observed in this study should make small, regional, participant-based sport events appealing marketing channels, especially for generating goodwill in the community. Further, even small sponsorship spends can have a significant impact on these smaller events, since traditional funding sources continue to be cut. Originality/value Existing literature on sponsorship of participant sport-based events has generally focused on large events (i.e. marathons that draw participants nationally), despite the prevalence of smaller scale, regional events around the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".