Consonants in brand names influence brand gender perceptions
Bibliographic record
Abstract
Purpose – This paper examines to what extent consonants in brand names influence consumers’ perceptions of feminine and masculine brand personality. Design/methodology/approach – Four experiments empirically test the influence of consonants on feminine and masculine brand personality. The experiments involve different sets of new brand names, variations regarding the consonants tested (the stops k and t, the fricatives f and s), as well as different locations of the focal consonant in the brand name. Findings – Consonants influence consumers’ brand perceptions: brand masculinity is enhanced by stops (rather than fricatives), and brand femininity is enhanced by fricatives (rather than stops). Consonants specifically affect feminine and masculine brand personality, but not other brand personality dimensions. Consumers’ responses to brand names and resulting brand gender perceptions (i.e. likelihood to recommend) were moderated by salience of masculinity or femininity as a desirable brand attribute. Practical implications – This research has implications for brand name selection: consonants are effective in creating a specifically masculine or a feminine brand personality. Originality/value – This research is the first to specifically link consonants and feminine/masculine brand personality. By specifically examining consonants, this research extends the marketing literature on sound symbolism that is characterized by a focus on vowels effects. This research is also the first to address whether the position of the focal phoneme in the brand name matters.
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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".