Operational Collaboration Between Rivals: Strategic and Welfare Implications
Bibliographic record
Abstract
Business rivals often collaborate on specific aspects of their operations. The collaboration can benefit firms operationally, but may also intensify competition. To better understand and manage this interaction, this paper studies the strategic and welfare implications of a cost-reducing operational collaboration between competing firms. We formulate a duopoly competition model to capture important aspects of the problem, which includes the possible use of facilitating agreements and randomness in cost reduction. Our analysis starts with operational collaboration without any additional agreement beyond the collaborative effort in deterministic cost reduction. While the high-cost firm always benefits from it, the low-cost firm needs to consider the products substitutability and the firms' asymmetry in cost. Moreover, such a pure operational collaboration never hurts consumer surplus. We then consider the effect of centrally decided facilitating agreements. Specifically, with a properly designed unit transfer payment, the competition may be softened so that both firms are willing to collaborate. However, consumer surplus may decrease as a consequence. We further discuss the situation in which the facilitating agreement results from a negotiation process. Finally, we examine the impact of the randomness in cost reduction. We find that, for risk-neutral firms, the uncertainty in the potential cost savings may either increase or decrease the likelihood of collaboration; and the stochastic orders between the two firms' cost reduction may have non-intuitive implications on their willingness to collaborate. Our findings provide useful managerial insights into the underlying drivers of an operational collaboration between rivals.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".