Mega-Special-Event Promotions and Intent to Purchase: A Longitudinal Analysis of the Super Bowl
Bibliographic record
Abstract
Mega-special-event properties (sponsees) have the ability to attain significant resources through sponsorship by offering exclusive promotional opportunities that target sizeable consumer markets and attract sponsors. The Super Bowl, one of the most watched television programs in the world, was selected as the mega-special-event for this study as it provides a rare environment where a portion of the television audience tunes in specifically for the purpose of watching new and entertaining commercials. A longitudinal analysis of consumer opinion related to the 1998, 2000, 2002, 2004, and 2006 Super Bowls provides empirical evidence that questions the ability of Super Bowl sponsorship to influence the sales of sponsor offerings. Results pertaining to consumers’ intent to purchase sponsors’ products—one of the most sought after metrics in relating sponsorship effectiveness to sales—demonstrate that levels of intent-to-purchase inspired by sponsorship of the Super Bowl is relatively low and, most importantly, that increases are not being achieved over time. These findings have implications for both mega-sponsees and their sponsors as well as media enterprise diffusing mega-special-events.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".