Does Technology Matter When Selecting a Target Firm in an M&A? Some Evidence on a European M&A Sample
Bibliographic record
Abstract
Using a sample of European M&As from 2009 to 2017, this work assesses whether there is a linkage between a firm's innovation orientation and its participation in an M&A. This main aim has been divided into three aims: defining the target firms’ characteristics in M&A deals with a special focus on innovation orientation, performance, financial structure and size; understanding what kind of firm is generally acquired from institutional investors; describing the effects of M&A deals on the targets. With reference to the analysis method, aims were pursued through logistic regressions on the cross-sectional sample and by comparing pre-deal and post-deal average balance values. The result is twofold. First, a high probability of being targeted is associated with high portfolio patents and low research and development costs of a firm, but only in cases of technological overlap; otherwise the R&D intensity, performance and size of firms are relevant. Second, when analysing the effects of M&As, comparing the same variables in post vs. pre deal period, they are only significant for patent and R&D costs. These results confirm that the M&A transactions produce useful synergy in terms of innovation capability.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".