Emotional Effects of Product Form in Individualist and Collectivist Cultures
Bibliographic record
Abstract
This research investigates differences in visual form perception of products in individualist and collectivist societies to help designers and market analysts design products according to their users’ formal preferences. This study was conducted in two phases. First, five visual form types were selected out of 500 samples by design expertise for the test: geometric, organic, complex, simple, and symbolic forms. Second, three groups of American (highly individualist), German (individualist), and Iranian (collectivist) were selected based on their cultural dimensions. We developed Exploratory Factor Analysis (EFA) with the emotions to focus solely on limiting emotions. Since the emotions were in two opposite spectrums, four emotions were considered as positive and the next four emotions as negative. ANOVA was conducted for normally distributed data and Kruskal–Wallis test for non-normal data. The results of this research showed that people with different cultural dimensions have different reactions, feelings, and preferences to products with different formal features. While individualists embrace geometric, simple, and symbolic forms, collectivists prefer organic forms most. The complex form is rejected by all three groups. This research extends our knowledge about cultural differences and provides a very critical tool for designers to design their products according to their users’ emotions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".