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Record W3016811227 · doi:10.1108/ijwbr-07-2019-0043

Wine consumers in British Columbia, Canada

2020· article· en· W3016811227 on OpenAlexaffabout
Svan Lembke, Lee Cartier

Bibliographic record

VenueInternational Journal of Wine Business Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsOkanagan College
Fundersnot available
KeywordsWineWine tastingMarketingPurchasingBusinessConsumption (sociology)OriginalityAdvertisingCompetition (biology)Value (mathematics)Consumer behaviourQualitative research

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to redirect wine producer marketing strategies in British Columbia (BC) to better market their wine to the next generation of local consumers and compete against foreign imports. Design/methodology/approach This study was conducted using representative data collected from BC wine consumers through a survey of 500 participants and subsequent focus groups to better understand and interpret the findings. Findings The findings confirm that the growth of wine sales in BC is driven by the Millennial generation. This generation shows some different wine purchasing and consumption behaviours than previous generations. BC wine producers compete against foreign imports by using their direct-to-consumer sales channel (s) and could also use their superior understanding for the next generation of wine consumers to better sell their local wines across multiple channels. Practical implications To onboard the next generation of Millennial consumers to BC wines, BC wine producers are advised to use the tasting room environment to learn more about their local consumers and also sell via other channels. Some gaps in consumer needs across generations are identified and BC producers are advised to further target this new consumer and meet the needs of the local Millennials better than the competition. Originality/value The study is unique in its location. BC wine producers have often used US research or anecdotal data from their own tasting rooms to inform marketing decisions. The researchers argue that this carries significant risk, especially as the next generation of BC wine consumers displays different purchasing behaviours than those reported in US research.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.292
Teacher spread0.243 · 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.

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

Citations7
Published2020
Admission routes2
Has abstractyes

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