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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".