The effect of perceived entitativity on implicit image transfer in multiple sponsorships
Bibliographic record
Abstract
This dissertation proposes that in the case of multiple sponsorships (i.e., brands sponsoring concomitantly the same event), the group constituted by the sponsoring brands and the sponsored event will be perceived as an entity; a phenomenon that Campbell (1958) called entitativity. The extent to which a group of brands and a sponsored event is seen as being entitative will result in stereotypic processing of the group members (Brewer and Harasty 1996). Information about an entitative group is abstracted and used to form judgments about every group member (McConnell, Sherman, and Hamilton 1997). Characteristics tied to one brand or to the event will become associated to the other brands due to category-based information processing (Fiske and Neuberg 1990). As a result, images associated with a brand or an event that belongs to an entitative group will be transferred to other brands of that group due to stereotyping. Image transfer effects were investigated through an experiment.\nImage transfer in sponsorship occurs primarily at an implicit level because sponsorship messages are subtle (Pham and Vanhuele 1997). As a consequence, the savings in relearning paradigm (Ebbinghaus 1885/1964) was the methodology used. It allows investigating implicit memory by comparing the recall of paired-associations between brands and image-traits across a multiple sponsorship and a no sponsorship condition. The findings confirmed that the event and the concomitant sponsoring brands were perceived as an entitative group, which resulted in an implicit transfer of image among the brands (Brand Image Transfer, BIT) as well as from the event to the brands (Event Image Transfer, EIT). These transfer effects were moderated by the brand-concept associated with the sponsors considered. BIT was only found for sponsors with a similar brand-concept (i.e., “sport”) whereas EIT was only found for sponsors with a dissimilar brand-concept (i.e., “no sport”). Further analyses confirmed that these phenomena of implicit transfer of image were due to a category-based as opposed to an individuating processing of information. Due to high entitativity, perceivers relied on a group impression to process information. As a result, the group of brands and the event were seen as interchangeable.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".