Toward an understanding of industry cluster development among New World wineries: A comparative study of the Okanagan Valley, Canada and the Waipara region, New Zealand
Bibliographic record
Abstract
There is growing consensus that clusters are beneficial for regional economic prosperity. Furthermore the maturity of an industry cluster and its regional competitive advantage is often critical to the prosperity and long-term success of rural businesses. Clusters enable operational scale, enhance resilience to unexpected events or threats, and deliver innovation opportunities particularly for small and mid-size businesses on the basis of their synergies. The wine industry – and other agricultural activities that create value beyond primary production – appears to be drawn to the cluster model on the basis of shared land base, physical infrastructure, and often perishable nature of their goods that need to be processed quickly. While clusters are a function of the unity of firms and institutions, no specific activities have yet been identified that can be readily duplicated to achieve effective cluster behaviours. An understanding of the dynamics between stakeholders, and ways to encourage and/or discourage competition and cooperation, can deliver valuable insights to new and embryonic clusters, while enabling existing clusters to navigate novel risks and emerging challenges. While Michael Porter’s cluster model has enabled researchers to identify and map clusters (Porter, 1990; Porter, 2003), it provides limited insight into the factors influencing cluster development. As a result, the catalysts for cluster maturity remain undefined. Drawing on comparative case study analysis, this paper proposes a model of cluster development. Two wine regions – the Okanagan Valley, British Columbia (BC), Canada and Waipara, Canterbury, New Zealand – provide the basis for this study. Both regions have been studied previously using Porter’s (1990, 2003) diamond framework, allowing a structured comparison across the determinants of the clusters (Dana & Winstone, 2008; Dana, Granata, Lascha, & Carnaby, 2013; Cartier, 2014; Cartier, 2017; Lembke & Cartier, in press). The wine clusters in these regions are examined to assess strategic alignment, and determine the balance of strategic activities that nurture cooperation and competition. Gap analysis and comparison within and between each wine region seeks to identify some of the building blocks for cluster maturity. Future research directions with the potential to refine our understanding of cluster development and lifecycles are also identified.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".