Characteristics of Business Models, Business Diversification and Price Segmentation Strategies of Wineries in the Wine Route of Baja California, Mexico
Bibliographic record
Abstract
This study examined the characteristics of business models used by wineries in Baja California, Mexico wine route. It also identified strategies being used regarding price segmentation and business diversification, and how business diversification relates to production performance. The results showed a limited presence in the supermarket channel, vineyard/land and wine-making facilities/machinery being considered as the most valuable resources, a growing tendency of companies having lodging facilities, a low differentiation in key activities performed, high differentiation regarding revenue generation structure, three revenue generation clusters containing the majority of the companies, companies with a diversification strategy outperformed those with single business strategies in regards to case production during a five year period, and the price segments from $251 to $600 Mexican pesos for 750ml bottles of wine being the most popular ones. This study used a non-random sampling technique to collect primary data in the form of surveys and face-to-face structured in-depth interviews. A total of 65 companies, accounting for approximately 55% of the total wine producers in the area, were interviewed during the data 1-month data collection period (July 2018). Out of these 65 companies, 50 provided complete useable data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".