The organisation of innovation in the wine industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to contribute to the debate on the spatial organisation of the open innovation model in the wine industry in Canada. Design/methodology/approach The paper employs a micro‐firm level survey among 146 wine firms in Canada. Descriptive and non‐parametric tests are used in the analysis. Findings The results on the occurrence of collaborations depict modest collaborative activities with external sources. Most of the collaborations and information are sourced locally because the local climate and growing conditions are so specific that alternative sources and collaborations are less relevant. The results also show that the firm's openness strategy has a weak influence on innovation capacity but firms that introduce more innovations are those that embrace an open innovation strategy to a greater extent than the less innovative. Research limitations/implications The number of respondents is still limited (i.e. about 150). Moreover, only the relationship between some firm‐specific factors related to innovation and the degree of openness is studied. Practical implications The paper provides managerial implications because it suggests that firms adopting an open innovation strategy through collaborations have a higher impact on innovation development by means of introducing new types of innovation and on R&D activities. Originality/value The paper introduces the spatial dimension of the open innovation strategy in the wine industry in order to understand the link between the geographically‐dispersed open innovation networks and their impacts on innovation capacities and innovation development of winery firms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 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".