Arms Race or Détente? How Interfirm Alliance Announcements Change the Stock Market Valuation of Rivals
Bibliographic record
Abstract
Most prior event studies find that the announcement of a new alliance is accompanied by a positive stock market response for the partners. This result has usually been interpreted as evidence for the prevailing view that alliances are effective vehicles for partners to acquire or access new skills and thus become stronger competitors. However, partners should also earn positive abnormal returns if alliances are used to shape competitive interactions, attenuating competitive intensity industry-wide. In this study, we disentangle these different mechanisms by examining how alliance announcements affect the stock market's evaluation of allying firms' rivals: if an alliance is expected to make partner firms more competitive, this should lead to negative abnormal returns for partners' rivals; if an alliance is expected to facilitate a reduction in competitive intensity, this should lead to positive abnormal returns for rivals. Results from an event study analysis of research and development alliances in the telecommunications and electronics industries during 1996–2004 provide evidence consistent with competition attenuation in some alliances. Our research thus challenges the increasingly narrow focus on learning and resource accumulation through alliances, and calls for broader consideration of the roles and effects of collaboration, both for individual firms and for industry structure.
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 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".