Brand Stereotyping and Image Transfer in Concurrent Sponsorships
Bibliographic record
Abstract
François A. Carrillata*, Paul J. Solomonb & Alain d'Astousca University of Technology Sydney, Ultimo, Australiab University of South Florida, Tampa, Florida, USAc HEC Montreal, Montreal, Quebec, CanadaAddress correspondence to François A. Carrillat, University of Technology Sydney, Business School, Marketing Discipline Group, 14-28 Ultimo Road, Ultimo 2007, Australia. E-mail: francois.carrillat@uts.edu.auFrançois A. Carrillat (PhD, University of South Florida) is an associate professor of marketing, University of Technology Sydney.Paul J. Solomon (PhD, Arizona State University), is a professor of marketing, College of Business, University of South Florida.Alain d'Astous (PhD, University of Florida) is a professor of marketing, HEC Montreal.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ujoa.Concurrent sponsorship, that is, when several brands simultaneously sponsor the same event, is a common yet understudied marketing communication situation. The research presented in this article explores the transfer of image that takes place among the sponsoring brands in this situation. The results of two experiments reveal that this image transfer is due to stereotypic processing. Additional analyses delineate more specifically the stereotyping process at work. They show first that the stereotype is ad hoc, rather than based on some a priori developed mental schema, and therefore that it is construed from the images associated with the concurrent sponsoring brands. And second, that brand stereotyping serves a cognitive rather than an evaluative function, thus suggesting a valence-neutral process whose outcomes can be beneficial or detrimental to a focal sponsor, depending on the images initially associated with the other sponsors. The implications of these findings for sponsorship research and practice are discussed, along with research limitations and future research avenues.
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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 | 0.002 | 0.023 |
| 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) | 0.006 | 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".