The Leading Wine Cooperatives in Argentina and Europe: What Are the Strategic Choices to Penetrate the Distribution Channels in the United States and Canada?
Bibliographic record
Abstract
This work focuses on understanding the different strategies adopted by wine cooperatives located in Argentina and the main E.U. wine countries for penetrating international distribution networks in the U.S. and Canadian markets. This study adopts a contingency framework integrating a stakeholder approach for the understanding of the logic behind a cooperative’s strategies to penetrate distribution networks (wholesalers, importers, and alcohol monopolies). This empirical study is based on the analysis of still and sparkling wine exports to the U.S. and Canadian markets, covering a period of approximately 54 months (2017–2021). The sample includes the analysis of more than 7000 containers shipped by the leading wine cooperatives in each individual country. The findings suggest the existence of heterogeneous choices in the distribution networks among wine cooperatives but also uniqueness related to the nature of the type of products marketed (still wines, sparkling wines, etc.) as well as the nature of the geographic origin of wine cooperatives. More precisely, the distribution of wine cooperatives in the U.S. and Canada shows different patterns. This investigation contributes to a better understanding of the behavior of wine cooperatives in marketing channels and to the literature on buyer-driven chains. It provides insights into the strategic choices of wine cooperatives and contributes to wine policies by providing insights on the modalities for the financing of the promotion of cooperative wines in non-E.U. countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".