An exploration of consumer perceptions of sustainable wine
Bibliographic record
Abstract
Despite increasing interest in the category, there has been limited consumer-focused research to date around sustainable wines, especially in the Canadian context, despite the importance of consumer perceptions in driving wine purchase behaviour. In this mixed methods online survey of Canadian wine consumers (n = 725), we sought to determine how important sustainability-related cues are in purchase decisions, what beliefs and knowledge consumers possess around sustainable wines, and what are the characteristics of consumers with low and high sustainable wine involvement. Results show that sustainability-related cues are somewhat valued by consumers when making purchase decisions, but have low importance relative to the other cues examined. Environmental dimensions of sustainability have high saliency, in contrast with social and economic dimensions, and a significant minority of respondents report no or very limited knowledge of sustainable wines. Additionally, consumers with high involvement in sustainable wines tend to be younger, better educated, more involved in wine in general, and spend more per bottle than those with low involvement. Our findings support initiatives to develop global frameworks around sustainable wine and educate consumers on sustainable wine practises, as well as offer insights into marketing opportunities to promote this important product category.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".