Wine label design and personality preferences of millennials
Bibliographic record
Abstract
Purpose To better understand the unique preferences of the newest segment of wine consumers, the purpose of this paper is to explore the design and brand personality of wine labels, and their appeal to the millennial market. Design/methodology/approach The study methodology comprised two components: an experimental design of wine label creations by millennial students of a university beverage management course; and a survey of over 400 millennial consumers to assess wine label design and brand personality preferences. Findings Wine labels created by millennials tend to be very non‐traditional in terms of the image selected, name of wine, color choice and overall label design. New wine consumers in the 19 to 22 year‐old category are much more likely to select wine based on package features, such as name and image, than based on product features, such as producer and country‐of‐origin. Spirited, up‐to‐date brand personalities appeal to this generation. Originality/value The millennial market is a large, important segment new to wine consumption. The experimental creation of wine label designs by millennials themselves provides a unique insight in terms of the new, and somewhat hedonistic, images that appeal specifically to this growing market.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".