Region of origin and product knowledge. A cross-national analysis of the purchasing decisions of Chianti classico wine
Bibliographic record
Abstract
The paper explores the country of origin (COO) effect in the wine sector with particular emphasis on the region of origin (ROO) as a factor when evaluating alternatives in the purchasing decision-making process. The ROO identifies key information about the product. It is now generally recognized that the terroir is a crucial attribute for the quality of a wine. We have focused on case study of the appellation of Chianti Classico on the German, British, USA and Canadian markets. The choice is motivated by the long history and international reputation of the Chianti Classico. Specifically, the paper aims to answer the following main research questions: what is the importance of the country of origin/region of origin assigned by consumers in an evaluation of Italian wines and the evaluation of Chianti Classico? Is there a relationship between the region of origin and knowledge of wine in the choice processes of the buyers? The image of the Chianti Classico influences the willingness to pay a premium in the analyzed markets included in the current study? Is there a difference in product perception and buying behavior among consumers of “Old” and “New World”? The analysis was conducted on a total sample of 2.380 consumers. The results confirm the importance of the COO and especially the ROO in the process of purchase of wine products. Specifically, it reveals that COO and ROO influence on the purchasing process in different ways, and that knowledge and a familiarity with a brand might have a moderating effect on purchase decisions. It was also found that in cases where consumers were not familiar with the product choices the ROO effect might be positive factor leading to acceptance of higher prices for wines originating in well recognized regions
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".