Assessing environments of commercialization of innovation for SMEs in the global wine industry: A market dynamics approach
Bibliographic record
Abstract
Small and medium enterprises (SMEs) can play an important role in the diffusion of wine innovation. Employing a market dynamics approach where the interaction of producers (supply) and buyers (demand) are seen to influence innovation creation, a conceptual framework is applied to the global wine industry to identify commercialization strategies for SMEs. The framework identifies four commercialization environments or clusters; Innovation Nirvana, Innovation Push, Innovation Pull and Innovation Wasteland as determined by the principle market dimensions of wine supply (innovation-push) and wine demand (market-pull). A k-means cluster analysis is undertaken on twenty-two wine-producing member countries of the OECD to determine which jurisdictions occupy each of the four clusters. The study results is a diverse distribution of old world and new world wine producing countries across all of the identified commercialization environments. Conclusions about national commercialization environments and related strategies for wine industry entrepreneurs are presented. These findings have implications for wine industry SMEs, investors and agri-policy makers.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".