The Influence of Nation-Level Institutions on Acquisition Premiums: A Cross-Country Comparative Study
Bibliographic record
Abstract
We build on neo-institutional theory to examine the manner in which nation-level institutions systematically affect domestic acquisitions—that is, acquisitions involving acquirers and targets from the same country. Specifically, we study in what way premiums are influenced through a set of cognitive, normative, and regulatory forces. In terms of cognitive pressures, we theorize that prior premium decisions of industry peers in the same country influence focal acquisition premiums, since prior premium decisions serve as reference frames for firms. In addition, we posit that normative forces in the form of the national cultural values of uncertainty avoidance, future orientation, and in-group collectivism affect bid premiums, as these factors influence the manner in which firms deal with the uncertainty, payoff time, and merger of groups inherent to acquisitions. Furthermore, we propose that a country’s regulatory pressures through its disclosure requirements influence premiums, since they reduce information asymmetries and affect a firm’s confidence in assessing its potential gains from acquisitions. Using a sample of domestic acquisitions, we find support for several of the hypotheses. Our work offers a cross-country comparative study of how nation-level institutions affect domestic bid premiums and makes theoretical contributions to acquisition premium research and institutional theory.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".