Don’t stop believing: The manifestations of coopetition in export markets
Bibliographic record
Abstract
Although a coopetition-oriented mind-set (belief about the importance of cooperating with competitors) is likely to drive coopetition strategies, the nuances of this relationship remain under-researched. Furthermore, coopetition has typically been investigated in domestic settings, rather than in export markets, where different opportunities and challenges are likely to exist. Thus, underpinned by resource-based theory, and focusing on smaller-sized companies, this study examines the association between an export coopetition-oriented mind-set and export coopetition strategies under key moderating effects. Survey responses were collected from 107 small wine producers in South Africa (passing all major robustness checks). As hypothesized, the results showed that an export coopetition-oriented mind-set drives export coopetition strategies. However, surprisingly, this link was positively moderated by export competitive aggressiveness, but was not significantly impacted by export competitive intensity. Consequently, unique insights emerge for academics and practitioners regarding what factors help or hinder the facilitation of coopetition activities in export markets.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".