The sponsorship‐advertising interface: is less better for sponsors?
Bibliographic record
Abstract
Purpose The objective of this article is to explore the general idea that there is a limit to the extent to which consumers make goodwill assumptions when sponsorship is used in combination with advertising. Design/methodology/approach An experiment was conducted where the number of different sponsorship activities by the same sponsor (i.e. one or two) in a sport event was varied in the context of an ongoing advertising campaign. Findings The results show that when brand advertising is used during a sport event, it is more beneficial for the brand to either be the official sponsor of the event or to be the official provider of products that are integrated in the event than to apply these two sponsorship strategies at the same time. Research limitations/implications Future studies should be conducted with representative samples of consumers and a larger array of sponsored entities such as different sports events, art events, athletes, and cultural organizations. In addition, these studies should incorporate the measurement of consumers' inferences during exposure to marketing communication stimuli. Originality/value The study is the first to explore the sponsorship‐advertising interface in order to provide insights on the conditions under which the combination of these two forms of marketing communication will lead to optimal benefits in terms of brand equity.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".