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Record W4388043728 · doi:10.1080/15378020.2023.2265794

Label design, packaging, and the Canadian Millennial/Gen Z wine consumer

2023· article· en· W4388043728 on OpenAlexafffundabout
Julie Kellershohn, Natalia Lumby

Bibliographic record

VenueJournal of Foodservice Business Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsToronto Metropolitan University
FundersMitacs
KeywordsWineMarketingCasualPurchasingAdvertisingQuality (philosophy)PreferenceConsumption (sociology)BusinessAppealFocus groupConsumer behaviourSociologyEconomicsPolitical scienceFood science

Abstract

fetched live from OpenAlex

The purpose of this study was to examine how Canadian Millennial/Gen Z wine consumers use packaging cues when purchasing wine. Focus groups (ages 19–33) were used to qualitatively evaluate package design preferences. A simulated shopping experience was combined with a series of led discussion questions and sorting exercises. The results showed that when considering wine packaging, Millennials/Gen Z show a preference for simple and modern packaging. They were more likely to experiment with products with non-traditional designs when consuming wine at home or casually with friends. They are concerned with value over quality and expect and perceive modern designs to be well-priced. When the aspirational nature of being a wine drinker and the importance of family and professional recommendations are combined, it was observed that traditional wine labels are still important in this market. Designs that appeal to this age group for personal/casual consumption are simple and streamlined. As this consumer group matures, observing and discussing the shopping behaviors of wine in real time can provide unique insights into their potential shift from traditional to more modern packaging design in the Canadian wine industry.

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 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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.544
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.128
GPT teacher head0.331
Teacher spread0.203 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes3
Has abstractyes

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