Recent Trends in UK Cross-Border Mergers and Acquisitions
Bibliographic record
Abstract
This paper seeks to outline the driving forces behind the acceleration of cross-border mergers and acquisitions (CBMAs) and to review the recent trends involving United Kingdom (UK) companies. The paper draws on data available from Thomson One Banker and the Office of National Statistics, to examine the trends in CBMAs between 1996 and 2005. The driving forces underlying the trend of CBMAs are complex and vary by sector. One of the most significant driving forces is technological change. In addition, changes to government policies influence CBMAs by opening up opportunities and increasing the availability of favourable targets for mergers and acquisitions (M&As). Other forces are market drivers, industry-level drivers and firm-level drivers. The scale of CBMAs involving UK companies has increased rapidly in recent years. The area analysis shows that European Union (EU) companies are the most significant target for UK companies followed by the USA and Canada. In terms of distribution within sectors, UK companies tend to acquire more manufacturing companies in the EU, the USA and Canada than in the Asia-Pacific region. In contrast, UK companies tend to acquire more service sector companies in the Asia-Pacific region than in the EU, the USA and Canada. The paper provides an accessible account of drivers of CBMAs and considers in detail the value and scale of activity relating to UK CBMAs. The paper will be of value to academics and practitioners interested in CBMAs as an important element of firm strategy.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".