Consumer wine perceptions in the Brand Origin framework: the role of product market value
Bibliographic record
Abstract
Despite many studies in the literature have shown that the Brand of Origin (BO) is a well known \nwine choice factor significantly affecting the consumer purchasing process, further investigations \nare still necessary. In particular it is not clear how the components that make up part of the \nconsumer’s wine perception can affect the level of importance a consumer associates to the wine’s \nBO in the purchasing decision process. The paper aims to bridge this gap investigating the buying \nbehavior of Brunello di Montalcino, an “high value wine”, compared to the buying behavior of \nChianti Classico, “a medium value wine”. We hypothesized that, in such a case, the consumer’s \nwine perception components can be important and can moderate (reinforce) the BO effect on \npurchasing behavior. \nThe analysis was conducted on a total sample of 5,173 consumers originating from USA, Canada; \nAustralia, Germany; UK; Sweden; Belgium; Italy. The results of the ordered logistic regression \nconfirms the relevancy and importance of the BO framework in the process of purchasing wine \nproducts. In particular, the results show a different role that the wine consumer perception \ncomponents such as brand knowledge, brand attitude and brand image can have. Brand knowledge \nmoderate the BO effect in the case of “high value wine” while reinforces it in the case of “medium \nvalue wine”; brand attitude reinforce the BO effect only for “medium level wines” while brand \nimage has a general reinforcing role of BO effect both for high and medium value wines. \nFrom these findings we derive some managerial implications concerning a different strategic use of \nBO for wine having a different market value.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 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".