Reputation and relative price positioning of small wineries in Québec, Canada
Bibliographic record
Abstract
Purpose In a highly competitive market, the price of wine is a variable controlled by suppliers to suggest a level of quality. An index of relative firm position in the market based on relative prices is calculated for a sample of wine producers. The purpose of the paper is to analyze some of the factors related to the characteristics of a firm and quality that may explain the price strategy of wine producers in a new and small wine region, i.e. Québec province in Canada. Design/methodology/approach Data on types of wines and prices are collected from a sample of 40 small wine producers in Québec, Canada for the selected years 2008, 2010 and 2015. Findings The authors demonstrate that a high price strategy is significantly related to the reputation of the vineyard rather than the age of the domain, the size or the number of wines produced. Research limitations/implications The analysis has been carried out based on a data set of only 40 firms for which the price-position index could be calculated. Unfortunately, only limited information is available on producers and production volumes. Practical implications This analysis is of particular relevance for small or new wine-producing regions, which lack an established reputation. Because wine quality and taste differ by geographic origin and variety, new wine-producing regions may have opportunities to define a wine’s image (or a winery image) and the producer must inform the market on quality of the wine by reflecting it on the final selling price. Originality/value Prior works on the analysis of the price-quality relationship give rise to various and sometimes contradictory results. This analysis is of particular relevance to explain the price strategy of small wine producers in a strongly competitive market where the price remains an obvious commercial argument to signal the quality of a wine.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".