Market-oriented activities and communal wine consumption events: does coopetition make a difference?
Bibliographic record
Abstract
Earlier work has indicated that communal wine consumption events (e.g. wine tourism) are driven through employing a market orientation, namely, the firm-wide implementation of the marketing concept. Although market-oriented activities are intended to create value for customers, many vineyards and wineries are small and lack the resources and capabilities that are needed to achieve these outcomes. Consequently, there could be merits in owner-managers employing a collaborative (rather than individualistic) business model to overcome their limited tangible and intangible assets. In practice, this could be undertaken via cooperating with their competitors (coopetition) to help them to host or participate in communal wine consumption events. Therefore, grounded in resource-based theory, this current investigation reviews the literature surrounding these issues (focusing on the wine industry) to develop a conceptual framework examining the relationship between market-oriented activities and communal wine consumption events under the moderating role of coopetition. This provides the wider alcohol-focused community of scholars with new evidence on how a market orientation can be enhanced by wine producers collaborating with rival businesses to create positive experiences for their chosen customer segments. This includes drawing upon ‘best practices’ from several wine-producing nations about how decision-makers can navigate these organisation-wide activities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".