Does brand market value affect consumer perception of brand origin in the purchasing process? The case of Tuscan wines
Bibliographic record
Abstract
A plethora of studies have demonstrated that brand of origin is a significant factor \nin the purchasing process. It is not clear however how the various components of \nquality perception can affect the level of importance that a consumer might associate \nwith a brand of origin. This paper aims to bridge this gap by investigating the buying \nbehaviour of a luxury wine compared to that of a super-premium wine. We believe that \nthe components driving consumer quality perception play an important role in \nreducing or reinforcing the brand of origin effect, but this role can vary in intensity \naccording to the wine market price segment. \nThe analysis was conducted on a sample of 5,173 consumers from the USA, \nCanada, Australia, Germany, UK, Sweden, Belgium and Italy. The results reflect that \n“brand knowledge” and “brand attitude” act differently on the brand of origin effect \ndepending on the market price segment. On the other hand, results regarding “brand \nimage” showed an overall reinforcing role of the brand of origin effect not affected by \nwine price. Some managerial implications arise from the strategic use of the brand of \norigin.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".