The Design Factors of Cosmetic Packaging Textures for Conveying Feelings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it