Red Dragons and Blue Jeans: Branding Colors across Cultures
Bibliographic record
Abstract
In long-term social practice, world civilizations have formed contrasting understandings and emotional resonance to colors and endowed them with symbolic meanings. The differences between Chinese and Western cultures lead to widely contrasting perceptions of the meaning of certain colors. Therefore, color research based on western society may not be applicable to Chinese society. Color has a pivotal impact on whether a brand’s logo, advertisements, and products can attract consumers. Two experiments were conducted to explore the relationship between color, brand personality, and purchase intention. Study 1 explores the relationship between color and brand personality within the context of Chinese culture, tests the Chinese brand personality scale developed by Chu and Sung (2011), and shows that the association between color and brand personality is influenced by cultural factors. Study 2 is a cross-cultural study. Taking North America (the US and Canada) and China as examples, Study 2 explores the influence of people’s perception of brand personality on their purchasing decisions in different cultural backgrounds. The results suggest that consumers’ purchase intention is positively correlated with the consistency between the brand personality they perceived and the personality they preferred, and that the purchase intention of consumers in different cultures (collectivism/individualism) is affected by the product category (personal/nonpersonal products) and varies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".