The antecedents and consequence of brand coolness: A case of millennial consumers toward fashion clothing brands
Bibliographic record
Abstract
This research aimed to study the antecedents and consequences of brand coolness for fashion clothing brands in the millennial consumer context. The data was collected through an online questionnaire on 380 consumers who used to buy brand-name fashion clothing. The data were analyzed using the structural equation model. The results showed that the antecedents of brand coolness consisted of brand experience and brand identification, both of which positively influence brand coolness. Brand coolness (i.e., reference, singular, personal, esthetic, functional, energetic, and high status) was the key driver that creates brand equity. The research results were able to explain 94% of the variance in brand coolness and 81% of the variance in brand equity. This research is empirical support that helps expand the perspective on brand coolness and presents a dimension to measure brand coolness in a more transparent and complete method. The research result also complements the marketing knowledge that can guide academics and practitioners in creating substantial brand equity in the customers' hearts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".