Facing Dominance: Anthropomorphism and the Effect of Product Face Ratio on Consumer Preference
Bibliographic record
Abstract
Abstract A product’s front face (e.g., a watch face or car front) is typically the first point of contact and a key determinant of a consumer’s initial impression about the product. Drawing on evolutionary accounts of human face perception suggesting that the face width-to-height ratio (fWHR: bizygomatic width divided by upper-face height) can signal dominance and affect its overall evaluation, this research is based on the premise that product faces are perceived in much the same way as human faces. Five experiments tested this premise. Results suggest that like human faces, product faces with high (vs. low) fWHR are perceived as more dominant. However, while human faces with high fWHR are liked less, product faces with high fWHR are liked more as revealed by consumer preference and willingness-to-pay scores. The greater preference for the high fWHR product faces is motivated by the consumers’ desire to enhance and signal their own dominant status as evidenced by the moderating effects of type of goal and of usage context. Brand managers and product designers may be particularly interested in these findings since a simple design feature can have potentially significant marketplace impact, as was also confirmed by the field data obtained from secondary sources.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".