The Design Factors of Cosmetic Packaging Textures for Conveying Feelings
Bibliographic record
Abstract
There have been a lot of studies on the relationship between visual appearances of packaging—such as color, font, and illustration—and consumers’ feelings, but very few focused on touch sensation. Well-designed touch texture can attract consumers to cosmetic products and can be considered as a rarely-explored way of sensory marketing. The objectives of this study was to seek for design factors (design elements that can be associated with feeling words). Thirty-six different 3-D texture models were constructed. Their designs were produced from established 2-D visual design elements. Those models were tested by a group of participants to see whether they could clearly convey different feelings. Only 6 models were deemed valid in this sense. These 6 models were then sought for distinctive design factors. The 5 design factors that were obtained were the following: 1) structure of lines, 2) distance between lines, 3) small and large empty spaces, 4) line uniformity, and 5) number of lines. These design factors were able to elicit 16 feeling words: 1. Busy, 2. Tense, 3. Strong, 4. Confident, 5. Manful, 6. Delicate, 7. Friendly, 8. Gentle, 9. Sensitive, 10. Enjoyable, 11. Independent, 12. Natural, 13. Simple, 14. Comfortable, 15. Easy, and 16. Flexible. These design factors can be directly used by designers for constructing textured surface components of packages or products that can affect consumers’ feelings by touch.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".