Prior Alliances with Targets and Acquisition Performance in Knowledge-Intensive Industries
Bibliographic record
Abstract
An important focus of the research on mergers and acquisitions is the conditions under which acquisitions create value for the acquiring firm's shareholders. Given that the acquisition process is plagued by serious issues of information asymmetry, which are exacerbated in the context of knowledge acquisitions, we examine whether prior alliances with potential targets reduce the information asymmetry enough to create “partner-specific absorptive capacity” and yield superior stock returns on acquisition, compared with acquisitions not preceded by alliances. We test our hypotheses on a sample of high-technology acquisitions by U.S. firms during 1990–1998 using an event study methodology to assess abnormal stock returns. We find, unexpectedly, that no significant general effect emerges for acquisitions with prior alliances. However, international acquisitions following alliances show significantly better returns relative to both acquisitions without prior alliances and domestic acquisitions. Additionally, stronger forms of prior alliances lead to better acquisition performance than weaker forms of alliances. Together, the results broadly support our thesis that partner-specific absorptive capacity may be at work and suggest that under certain prior alliance conditions, acquisitions can indeed create value for acquirers.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".