The Impact of Corporate Culture, Efficiency and Geographic Distance on M&A Results: the European Case
Bibliographic record
Abstract
The volume of merger and acquisition (M&A) transactions has soared over the last few years. According to the Thomson Financial (2007), the volume of worldwide M&As declared during 2007 reached US$ 4.5 trillion in announced deals and US$ 3.8 trillion in completed deals, that is, a 24 per cent increase over the previous record set in 2006. Since 2000, the volume of M&A deals has increased by 32 per cent, despite the fall off during the third quarter of 2007 caused by concerns in the credit markets. The M&A phenomenon concerns all countries worldwide (see Table 8.1); in 2007, M&A deals increased by 25 per cent in North America (reaching a volume of almost US$2 trillion over 2007, that is, 52 per cent of the value of M&A deals worldwide), by 18 per cent in Europe (reaching a volume of almost US$1.3 trillion over 2007, that is, 34 per cent of M&A deals by value worldwide) and also strongly increased in the Asia-Pacific area — by 61 per cent (reaching a volume of almost US$0.4 trillion over 2007, that is, 10 per cent of M&A deals by value worldwide). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".