Quality and Intention Signaling: A Meta-Analysis of How Sponsorship Relates to Consumer Responses According to Content, Observability, Credibility, and National Culture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Organizations use sponsorships to inform consumers about their quality and positive intentions. Prior research has explained how these sponsorships signal quality to reduce selection challenges and prosocial intentions to reduce moral hazard concerns. Yet, previous meta-analyses do not assess and compare the relationships that sponsorship signaling has with consumer responses across samples of treatments (i.e., using sponsorships vs. not using sponsorships) that convey primarily quality or intention content. Thus, our meta-analysis focused on how sponsorship treatments relate to consumer responses according to samples conveying generalized content (quality and intention content combined) and distinct quality or intention content. The results suggest that sponsorship treatments conveying generalized content positively related to consumers’ cognitive, affective, and behavioral outcomes. They also suggest that signaling quality content has more positive relationships with consumers’ cognitive and affective outcomes than signaling intention content, and that the relationships quality and intention signaling content have with consumers’ affective responses are moderated by different conditions. Theoretically, quality and intention signaling processes appear to operate in distinct ways. Managerial takeaways are that sponsorships can positively relate to consumer outcomes, these relationships can be accentuated or diminished under various moderating conditions, and sponsorships for cause marketing in particular could require clearer and more credible messaging.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it