The Effectiveness of Model’s Body Size in Digital and Print Advertisements
Bibliographic record
Abstract
Advertisings usually display thin bodies creating and endorsing the beauty standards of the society. Large body size models are sometimes featured in advertisings to show a more inclusive marketing communication. Previous researches have investigated consumer responses to more diverse body sizes in Beauty and Fashion industries ads. This paper aims at investigating advertisement effectiveness, for both print and digital advertisings, through consumer responses (Memorization, Aad, Ab, and Purchase Intention) to body sizes in food advertisements. We used a mixed design study with a between group factor (Media type: Print or Digital) and a within-subject variable (body shape: large, thin or no model), via a folder test procedure. Participants were exposed to a fictive magazine to measure their responses toward advertisements featuring large size model versus thin one. The findings reveal that “large model” advertisements are less effective compared to “thin model” advertisements for Memorization, Attitude towards Ad, and Purchasing Intention. However, participants expressed the same Attitude towards the Brand for both conditions. Moreover, hardly any significant influence of the means of exposure to ads (printed or digital) was found. Despite the latest consumer pertinacity trends on companies to adopt diversity for social reasons; consumers, of the food industry, are still better influenced by thin models when it comes to Memorization, Aad, and PI. Furthermore, this study offers practical and societal implications not only for the experimental design, but also for practitioners to comprehend and utilize the match‐up hypothesis of body size condition needed for their marketing and advertising objectives.
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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 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".