Collaborative Market Driving: How Peer Firms Can Develop Markets Through Collective Action
Bibliographic record
Abstract
Firms often aim to develop markets as part of their long-term strategies. Conventionally, research in marketing has explained this complex process by stressing firms’ efforts to outdo their peers. While this emphasis is valuable, it overlooks the role of another major force in market evolution: collective action among peer firms. To address this oversight, this article conceptualizes “collaborative market driving,” defining it as the collective strategy in which peer firms consistently cooperate among themselves and with other actors to develop markets in ways that increase their overall competitiveness. This conceptualization includes the triggers that lead peer firms to mobilize for collective action and coalesce with other market actors; it also identifies how this coalition converts collective resources into market-driving power. These theoretical contributions, based on a multimethod analysis of the rise of U.S. craft breweries, offer an alternative course of action for firms interested in driving new markets when they lack adequate resources to do so individually.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".