Willingness to Pay for a Regional Wine Brand
Bibliographic record
Abstract
The paper explores the "country of origin" (Coo) and "region of origin" (Roo) effects as related to the wine sector. We have focused on case study of the appellation of Brunello di Montalcino and its perceived familiarity for consumers originating from the Usa, Canadian, Australian, German, British, Belgian, Swedish and Italian country markets. In particular, the paper aims to answer the following research questions: what is the importance of the country/region of origin assigned by consumers when evaluating Brunello wine? What is the role of wine knowledge within the decision-making process of the consumer? Does the image of Brunello influence the willingness to pay a premium price in the analyzed markets? Is there a difference in product perceptions and buying behavior that could be identified between consumers originating from «Old vs. New World»? The analysis was conducted on a total sample of 4,190 consumers. The results confirm the relevancy and importance of the Coo framework and especially the Roo frame of reference in the process of purchasing wine products. In particular, the results shows an evident New/Old World consumer different behaviour in terms of the willingness to pay a premium price for Brunello di Montalcino.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".