Do environmental regulations drive MNEs’ equity ownership? Considering the impact of exogenous shocks on MNEs’ cross-border acquisitions
Bibliographic record
Abstract
Extant studies of how firms respond to environmental regulations in devising foreign expansion strategies often fail to consider how multinational enterprises’ (MNEs) equity ownership decision-making might depend on the host country's environmental regulations. To advance a conceptual framework based on institutional theory, the current study tests whether the stringency of host country environmental regulations influences MNEs' decisions about equity ownership. Novel, recent data pertaining to 3,679 cross-border acquisition (CBA) deals by 1,135 MNEs from 30 countries also provide insights into whether environmental capabilities and environmental regulation distance affect the relationships of MNEs' equity ownership decision-making and the stringency of host countries' environmental regulations. Because exogenous shocks, such as COVID-19, create notable disruptions, this study also considers how exogenous shocks have influenced MNEs' strategic decisions in international markets. The results reveal that MNEs choose higher equity ownership in host countries with more stringent environmental regulations; environmental capabilities and environmental regulation distance positively moderate the relationship between the degree of environmental regulation stringency and the level of equity ownership. Finally, the links between environment regulation stringency and equity ownership grow stronger when MNEs experience an exogenous shock such as COVID-19.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".