The Influence of Host-Country’s Enviroments on the FDI Entry Mode Choice of Chinese Companies
Bibliographic record
Abstract
Based on a sample of 280 China’s listed companies with foreign direct investment (FDI) during 2005-2009, this paper examines how the country-specific factors influence these firms’ FDI entry mode choice between mergers and acquisitions (M&As) and greenfield investment. Our results show that in the presence of higher country risk or more rapid economic growth, the Chinese enterprises prefer the greenfield investment. When the host country has stronger national innovation ability or a higher level of human capital, the enterprises tend to choose the entry mode of cross-border M&As. An increase in the cultural distance, excluding the effect of other variables, appears to induce the enterprises to select M&A entry mode. However, when factor endowments and other institutional environments are taken into consideration, the cultural distance produces no significant effect on FDI entry mode choice of these Chinese firms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".