Optimization and Application of the Wine Neophobia Scale
Bibliographic record
Abstract
Wine consumers’ willingness (wine neophilia) or reluctance (wine neophobia) to try new wines represent, respectively, an opportunity or barrier for product innovation and market development in the wine industry. Here, we first sought to validate and optimize the Wine Neophobia Scale (WNS) in a large sample of 1269 Canadian wine consumers. Both exploratory and confirmatory factor analyses showed that a seven-item scale was optimal. This modified WNS (mWNS) was then used to investigate demographic and behavioral correlates of wine neophobia. Using lower and upper quartile values, 316 neophiles and 326 neophobes were identified. Wine neophiles and neophobes did not differ with respect to gender or age; however, neophobes had lower household income, education, and wine involvement, and reported consuming fewer wine styles than neophiles. Interestingly, while neophiles drank wine considerably more frequently than neophobes—a finding that is mediated by wine involvement—total annual wine intake did not differ between the groups. Importantly, the price typically paid per bottle of wine also varied with wine neophobia. We recommend adoption of the modified mWNS as a useful tool for more fully understanding the drivers of wine behavior and providing guidance to wine marketers.
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 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".