Developing Wine Appreciation for New Generations of Consumers
Bibliographic record
Abstract
Wine education poses a paradox to the industry: consumers both say they desire to know more about wine, yet they also report being overwhelmed and confused. In addition, the traditional analytic approach to wine education has involved teaching consumers a “grid” with rules for analysis that rely heavily on language which younger consumers in particular report disliking. A holistic approach to learning about wine was shown to be effective for more expert consumers, but those researchers did not consider how the learning approach affected their overall liking of the wine. Our first study considers teaching young consumers (Gen Z and millennials) through a holistic technique (involving drawing the wine’s taste) to a more verbal analytic approach (writing decompositional tasting notes) with consumers having some prior wine education and those without any. We find that the holistic approach led to greater liking for the wine, particularly with consumers having no prior wine education. We also found that consumers with prior wine education desired a more eudaimonic approach to their learning than new wine consumers. Both levels of prior experience desired a hedonic learning experience. Although we and other research groups have found younger consumers receptive to wine education, some have suggested that the wine industry is losing touch with younger consumers. We partnered with a digitized wine tasting platform, QUINI, to determine generational differences in their consumers’ engagement. In preparation for Study 2 we mined 3 years of data, and then conducted an online survey of three generations of their wine consumers in terms of their interest and education in wine. We discuss our results and implications for how managers might seek to engage new wine consumers, particularly in the virtual world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".