Environmental Regulation and MNE’s Resource Commitment: Considering Impacts of COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic is an unprecedented exogenous shock in the global economy and has caused noticeable disruptions to international business activities. In this study, we attempt to investigate how COVID-19 influences multinational enterprises' (MNE) strategic decisions in global markets. More specifically, we examine how MNE’s resource commitment is linked to a target market’s environmental regulation. We find that MNEs commit more resources in host countries with more stringent environmental regulations. This paper’s findings also reveal that MNE’s environmental capability and multiple dimensions of institutional distance, including environmental regulation distance and cultural distance, positively moderate the relationship between MNE’s resource commitment and the degree of environmental regulation in foreign markets. Finally, we underscore a significant impact of COVID-19 on MNE’s responses to cross-border activities. An analysis of 3,679 by 1,135 MNEs from 30 countries offers support for the hypotheses.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".