Bibliographic record
Abstract
This research examines whether type font used to display brand names affects consumers ’ recall and recognition of unfamiliar brand names. Results suggest that likelihood of brand recall is higher for compressed type fonts. Likelihood of brand recognition decreases with perceived weight of the type font, but increases if the brand name is presented in an elaborate type font. Type Font Characteristics and Their Impact on Consumer Responses Type font is a visual brand element that has received only limited attention in the marketing literature, despite its important role in brand communication. Type font is used extensively as a brand communication tool in print advertising, in-store displays, product packaging, coupons, and brand logos (Childers & Jass, 2002). Research also suggests that type font influences managerially relevant brand related consumer responses. These include brand perceptions (Childers & Jass, 2002), readability of advertisements (McCarthy & Mothersbough, 2002), memorability of advertising claims (Childers & Jass, 2002), and brand attitude if type font image and product image are congruent (Pan and Schmitt 1996). Some authors have even linked type font use to organizations ’ financial performance (Bloch
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.447 | 0.161 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".