The face of the brand: Spokesperson facial width‐to‐height ratio predicts brand personality judgments
Bibliographic record
Abstract
Abstract Brands often employ spokespersons to serve as the face of their organization and spokespersons characteristics can influence consumer behavior. We examined whether a subtle, appearance‐based aspect—facial width‐to‐height ratio (fWHR)—affects brand judgments. Specifically, we demonstrate that high (low) fWHR spokespersons are more effective for rugged (sincere) brands leading to more positive ad evaluations, greater brand liking, and higher purchase intensions. Across four experiments, we used across‐target and within‐individual manipulations of spokesperson fWHR to test our hypotheses and investigate the downstream implications for consumer preferences and purchasing intentions. We find that spokesperson fWHR influenced judgments of spokesperson effectiveness for different kinds of brands (Study 1); spokesperson fWHR impacts a brand's perceived personality (Study 2); and that congruency between spokespersons’ faces and brands’ personalities influence how much consumers like brands, their advertisements, and how willing they are to purchase advertised products (Studies 3–4). This study has implications for marketers and contributes to the brand personality and person perception literatures by demonstrating how subtle variations in spokespersons’ face structure can influence consumer judgments.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".