How a coopetition-oriented mindset and competitive intensity drive coopetition behaviour to support export scale-up activities in a post-crisis environment
Bibliographic record
Abstract
This study unpacks the complexity of the relationship between a coopetition-oriented mindset, coopetition activities (collaboration with competitors), and competitive intensity. The research setting features passive exporting firms seeking to scale-up sales abroad in an immediate post-crisis period (after COVID-19). Following 20 field interviews, a survey of 306 under-resourced wine producers in the USA was utilised in the model testing stage. Findings evidence respective significant positive relationships between first, a coopetition-oriented mindset; second, competitive intensity, and engaging in coopetition activities. A non-significant moderation effect existed regarding competitive intensity on the coopetition-oriented mindset - coopetition activities relationship. The field interviews offer unique insights highlighting that pivoted coopetition practices in the move from a passive to active exporting involvement following a crisis can take time to achieve benefits. Not least, because decision-makers face new levels of competitive intensity across product-market strategies, affecting the nature of their coopetition partners.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".