Leveraging the Dominant Pole: How Champions of an Industry-Wide Environmental Alliance Navigate Coopetition Paradoxes
Bibliographic record
Abstract
Companies increasingly collaborate with competitors to innovate, minimize risks, and address sustainability crises. However, these alliances often falter or fail due to challenges arising from coopetition paradoxes—contradictory yet interdependent tensions between competition and cooperation. Extant research predominantly focuses on addressing these paradoxes through seeking a stable balance between competition and cooperation; however, we lack in-depth processual understandings of how to navigate these paradoxes as they shift over time. To address this gap in the literature, we analyze longitudinal data over the 3 years it took to establish Canada’s Oil Sands Innovation Alliance (COSIA), the unlikely alliance across 13 competitive Canadian oil sands companies to improve their industry’s environmental performance. We noted the role of competition, which we label as the dominant pole—the more powerful of two paradoxical poles—and identify leveraging the dominant pole as a core mechanism for navigating intensifying coopetition paradoxes. Rather than diminishing the dominant competition pole, alliance champions leveraged competition to enable cooperation aided by a paradox mindset. These findings reorient coopetition scholarship away from seeking stability between the two forces, toward a processual understanding of how to navigate the shifting coopetition paradoxes in alliances over time.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".