Modification of MNE Strategies in the Face of Financing Constraints
Bibliographic record
Abstract
Abstract Cross-border mergers and acquisitions (CBM&As) are a significant component of foreign direct investment (FDI), which in turn is at the heart of the international business strategy of multinational enterprises (MNEs). Given that CBM&As involve both a scale and an immediacy to raising financing that greenfield investments often lack, financially constrained firms are inhibited from undertaking CBM&As. The authors go further and show that these financing constraints impact the markets where MNEs target firms for CBM&A transactions. In the presence of such financing constraints, the determination of which markets are targeted by MNEs is directly related to the supply of capital in the target market and the institutional distance between the home and host markets. Evidence in support of the hypotheses is documented using firm-level data on CBM&As from Organization for Economic Co-operation and Development (OECD) countries as well as Brazil, Russia, India, China, South Africa (BRICS) into every country globally for which data exist. The main implication of the research is that not only are financial constraints at the firm level important, but also that these constraints have a significant impact on the location of an acquisition target.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".