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Record W4313533175 · doi:10.5430/jbar.v11n2p9

The Effect of Serif and San Serif Typeface of Luxury Fashion Logotype on Chinese Consumers’ Brand Perception

2022· article· en· W4313533175 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Business Administration Research · 2022
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsnot available
Fundersnot available
KeywordsTypefacePerceptionPsychologyAdvertisingTest (biology)ArtVisual artsBusiness

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.048
GPT teacher head0.412
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it