Attacking your partners: Strategic alliances and competition between partners in product markets
Bibliographic record
Abstract
Research Summary: This study contributes to the literature on strategic alliances by examining the impact of collaboration on competition between partners in product markets. We integrate the alliance learning and social network perspectives to examine how different combinations of exploratory and exploitative alliances between a firm and its partner influence the firm’s competition against its partner in product markets. Using a longitudinal dataset collected in the U.S. pharmaceutical industry (1984–2003), we find an inverted U‐shaped relationship between relative exploration (i.e., the proportion of exploratory alliances in the collaborative portfolio between a firm and its partner) and the firm’s competition against its partner. This relationship is negatively moderated by firms’ relational and structural embeddedness, but positively moderated by their positional embeddedness. Managerial Summary: This study examines how different combinations of exploratory and exploitative alliances between two firms affect their competition in the product market. Using a 20‐year dataset collected in the U.S. pharmaceutical industry, we find that the proportion of exploratory alliances (i.e., joint development of critical innovations) in the alliance portfolio between a firm and its partner increases the firm’s competition against its partner, up to a tipping point at which such competition starts to decline. Given a certain combination of the two types of alliances, such competition is stronger if the firm has more alternative allies than its partner but weaker if the firm and its partner have previously collaborated or share common allies in their networks.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".