The Effect of Serif and San Serif Typeface of Luxury Fashion Logotype on Chinese Consumers’ Brand Perception
Bibliographic record
Abstract
An appropriate and well-designed logotype is essential to create brand awareness and positive brand perception. The effect of different typefaces has not been well researched in the luxury fashion sector. This paper expands on previous findings on typeface applications, in which two studies test the impact of Serif and San Serif typefaces, and three experiments test the effects of San and Serif typefaces on brand perception. Study 1 (N = 102) tests the visual complexity of Serif and San Serif typefaces; study 2 (N = 134) further investigates the visual simplicity and perceived luxury; and study 3 (N = 92) studies the brand gender of the two typefaces. The results of these three studies suggest that Serif typeface is more complex in structure than San Serif typeface. However, it does not have too much impact on the perceived luxury. Male consumers have greater gender cognitive differences than female consumers, and the San Serif typefaces are considered to be more masculine than Serif typefaces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".