Operational collaboration between rivals: The impact of cost reduction
Bibliographic record
Abstract
Business rivals often collaborate on specific aspects of their operations in order to achieve cost efficiency. To better understand and manage such an operational collaboration, we formulate a multi‐stage duopoly competition model to study the strategic and welfare implications of a cost‐reducing cooperation between competing firms. Without any additional agreement beyond the collaborative effort in deterministic cost reduction, we characterize intuitive conditions under which there exists a unique equilibrium for the operational collaboration, where the high‐cost firm inputs more effort. Furthermore, the equilibrium cost reduction would benefit both firms when they have similar costs and/or their products have small substitutability. Moreover, such a pure operational collaboration never hurts consumer surplus. We then consider the effect of facilitating agreements and find that, 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. Finally, we assume that firms could receive signals of some random shock on the cost reduction process and examine the resulting Bayesian game. If the random shock is on the cost reduction fraction, then firms' equilibrium efforts could be independent of the random signal. If, however, the random shock is on the effort, then we apply simplifying assumptions and use special stochastic orders to capture the impact of signal variability on firms' effort levels. Our findings provide useful managerial insights into the underlying drivers of an operational collaboration between rivals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".