Does industry experience positively moderate the quadratic relationship between coopetition and financial performance Evidence from the New Zealand wine sector
Bibliographic record
Abstract
Earlier research has established that a positive relationship exists between coopetition (the interplay between cooperation and competition) and financial performance. However, certain studies have investigated this link as being linear and/or without potential moderating factors. Consequently, under resource-based theory (and its association with the relational view), this current study evaluates the nonlinear (quadratic - inverted U-shaped) relationship between coopetition and financial performance under different degrees of industry experience. Survey data collection took place via a sample of 101 wine producers in New Zealand (passing all major assessments of reliability and validity, including common method variance and endogeneity bias). Additionally, 20 semi-structured interviews explored the in-depth meanings behind the statistical results. Specifically, the findings indicated that coopetition exhibited a quadratic relationship with financial performance. Furthermore, industry experience positively moderated this association, as it helps decision-makers to yield mutually beneficial performance outcomes. Collectively, this study contributes to knowledge by evaluating the complexities of coopetition strategies and their impact on financial performance. This investigation ends with some practitioner implications, alongside a series of limitations and avenues for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".