Regional-level coopetition strategies and company performance: evidence from the Canadian wine industry
Bibliographic record
Abstract
Regional-level coopetition (collaboration among competitors in rural communities) has been linked to company performance. That said, there could be conditions (moderators) that help or hinder these networks from fulfilling such outcomes. This investigation examines the nature of the relationship between regional-level coopetition and company performance under key moderating effects. A resource-based theoretical lens is utilized to underpin the study. Following field interviews to shape the operationalizations and survey instructions, a quantitative study was undertaken in the Canadian wine industry to test the elements of the conceptual framework. The findings revealed that while regional-level coopetition drives company performance, regional-level rivalry negatively impacts this association. Surprisingly, industry experience intensified the potential dark-sides of these activities. As such, improved evidence has emerged on how coopetition strategies can be implemented in rural communities through the underlying mechanisms that can assist decision-makers of small enterprises to enhance their performance. Additionally, stronger insights are offered regarding a relational, stakeholder perspective of resource-based theory, in terms of how decision-makers may need to work with complementary and trustworthy rivals that can assist them to increase their company performance in competitively intensive environmental-level conditions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".