Understanding Sparkling Wine Consumers and Purchase Cues: A Wine Involvement Perspective
Bibliographic record
Abstract
Research on sparkling wine (SW) consumers, their market segmentation, and how they use purchase cues is relatively sparse compared to that for table wine, despite the substantial growth in sparkling wine in recent years. We address these gaps and particularly how the importance of SW purchase cues varies with wine involvement in an online survey of SW consumers from Ontario, Canada (n = 1011). Thirty intrinsic and extrinsic purchase cues were rated for importance (n = 609), and wine involvement was determined using the shortened version of the wine involvement scale. Overall, consumers rated (in descending order) price, flavour, quality, country, and sweetness level as the most important purchase cues, whereas several extrinsic factors, including bottle colour and shape, awards won, and vintage were of low importance. Females were 1.4 times more likely than males to cite target end use as the most important purchase cue. We further show that SW consumers can be segmented into three wine involvement categories (low, medium, high) which vary across multiple demographic, consumption, knowledge, and preference measures (n = 1003). Notably, the importance of six purchase cue categories (manufacture, price, endorsements, parentage, prestige/reputation, and place) varied with wine involvement (n = 609). These findings provide timely guidance for marketers and retailers seeking to align their products and communications with the needs and perceptions of SW consumers.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".