Gut Liking for the Ordinary: How Product Design Features Help Predict Car Sales
Bibliographic record
Abstract
Abstract In many markets, design is one of the key factors in determining a product’s success. The present research offers insights into the role of design for the success of cars, and offers procedures to measure the quality of the designs objectively. The authors show that visual design plays a major role in a product’s success in the automobile market. In the study, two visual design aspects were already sufficient to significantly improve traditional sales forecasting models for cars. Visual prototypicality and visual complexity both had a positive impact on sales, and designs that were perceived as both prototypical and complex were the ones that displayed the best results. Most design evaluation used to be based on subjective measures, but the researcher applied a new, objective procedure to measure prototypicality and complexity. While the latter was detected by the disk space needed by the compressed image file, the new approach for measuring prototypicality was even more sophisticated. It relied on the technique of image morphing. Morphing is a technique that allows the construction of a visual synthesis – or average picture – from a number of individual pictures. Once a car morph is developed, one can determine the visual similarity of different car models to the morph in order to obtain its prototypicality. In principle, this procedure can be automated completely, and including a large number of versions is possible. These measures therefore seem suitable for supporting design decision processes in practice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".