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Record W4223448874 · doi:10.1002/mar.21666

The face of the brand: Spokesperson facial width‐to‐height ratio predicts brand personality judgments

2022· article· en· W4223448874 on OpenAlexaff
Jason C. Deska, Sean T. Hingston, Devon DelVecchio, Eric Stenstrom, Ryan J. Walker, Kurt Hugenberg

Bibliographic record

VenuePsychology and Marketing · 2022
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPsychologyPurchasingPersonalityAdvertisingPerceptionFace (sociological concept)Personality psychologySocial psychologyBig Five personality traitsMarketingBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.027
GPT teacher head0.320
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2022
Admission routes1
Has abstractyes

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