The Level of Digital Development in the Host Country and M&A Performance
Bibliographic record
Abstract
Economic globalization is increasing, and an aspect of this trend is the increasing frequency of cross‐border mergers and acquisitions (M&As). As the world has entered the era of digital development, the efficiency of information communication has doubled. However, whether digital development can help improve the performance of cross‐border M&A remains to be verified. Based on the entropy method, we constructed an index system for national digital development and analyzed the cross‐border M&A events of Chinese listed companies from 2010 to 2019. Through empirical analysis, the study finds that digital development in the host country improves the performance of cross‐border M&A. This effect is especially pronounced when the knowledge complexity of the M&A is inferior, or the M&A occurs in the same industry. We then analyze the mechanism of the impact of the host country’s level of digital development on improving firms’ M&A performance under Dunning’s OLI framework and the M&A transaction cost perspective, respectively. These findings enrich the theory of international investment and offer practical value to enterprises engaged in cross‐border M&A.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".