The global cross-border mergers and acquisitions network between 1990 and 2021
Bibliographic record
Abstract
Abstract The literature is characterized by a lack of research analyzing cross-border mergers and acquisitions (CBM&A) as a network. This article aims to evaluate the topology properties (the geographical and sectoral structure) of the global CBM&A network in 1990–2021. A quantitative study is conducted by using the social network analysis (SNA) method. The countries’ structural power in this global system is measured by the centrality indicators. From a geographical perspective, the study shows that in 1990–2021, the United States, the United Kingdom, Germany, Canada, and France occupied the most central place in the network. From the beginning of the 21st century, there has also been a marked increase in the importance of Asian countries, with China and India receiving a large inflow of foreign capital. In turn, entities from Hong Kong, Singapore, Japan, and China invested heavily abroad through M&A. The Asian countries’ economies also played the role of important intermediaries in the global CBM&A network. From a sectoral perspective, it can be stated that in 1990–2021, mainly entities operated in the financial, industrial, basic materials, technology, and consumer cyclical sectors made transactions in the global CBM&A network. They were also the main investment targets within this network.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".