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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".