Are size-zero female models always more effective than average-sized ones?
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate if brand might affect consumers’ response to replacing size-zero models (SM) with average-sized models (AM) in advertising and how individuals’ psychological states might underlie consumers’ reactions. Design/methodology/approach – Three studies manipulating brand and model body size were conducted and advertising images to female individuals differing in self-esteem were exposed. Findings – This research finds that brand moderates consumers’ model evaluation. Participants evaluated AM as being more attractive than SM for new brands, whereas for well-established brands associated with SM, participants rated both AM and SM as being equally attractive. Self-esteem shapes participants’ evaluation of AM and SM. For new brands, low self-esteem individuals evaluated AM as being more attractive than SM, whereas high self-esteem individuals evaluate AM and SM as being equally attractive. The results are consistent, regardless of whether it is a luxury and a generic brand. These results emerged for both model attractiveness rating and product evaluations. Practical implications – A better understanding of the relative consequences of the use of AM versus SM is essential for more effective policy initiatives and better targeted marketing campaigns. Originality/value – Limited research has documented the possible effects of brand on individuals’ responses to AM as opposed to SM. How individuals of different psychological characteristics may react distinctively to advertisements containing AM versus stereotype SM has not yet been explored until this study. This research takes the first step to bridge these knowledge gaps by looking into how brand and perceiver psychological characteristics jointly work with model features to determine how consumers perceive the AM as opposed to SM. This study provides empirical and comparative evidence of the advantages of using AM and SM in print media.
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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 | 0.003 | 0.015 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".