Do Alliances Lead to Competition? An Empirical Analysis of the US Biopharmaceutical Industry
Bibliographic record
Abstract
This study extends the learning race perspective to examine whether familiarity between firms developed through R&D alliances will motivate them to engage in technological competitions. Specifically, we argue that the payoffs of an alliance, in terms of common and private benefits that accrue to individual firms, are updated over the course of alliances between two firms. Firms are likely to reduce competition in their initial alliance contacts for the prospect of larger common benefits over private benefits. However, the likelihood of competition is heightened at later stages of their repeated interactions due to increased payoffs in private benefits. We further contend that this U-shaped relationship between the number of R&D alliances and technological competition is moderated by partner firm’s reputation of trustworthiness and technological similarity with the focal firm. Analyses of US biopharmaceutical firms during 1985 and 2004 support our hypotheses. Our study contributes to an enriched understanding of the dynamics of learning races across multiple alliances between firms, and the interplay between collaboration and competition between firms.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".