Global Outward Foreign Direct Investment Network: Perspectives for Knowledge Transfer from Host Countries to Home Countries by MNEs
Bibliographic record
Abstract
The possession of diverse knowledge is vital for countries to maintain competitive advantages as new technologies and other disruptive changes emerge. Foreign Direct Investment (FDI) has become an important instrument to access knowledge and innovation available in other countries with the increasing trend of globalization. However, little is known about how the knowledge base of a country can be enriched in the context of knowledge sourcing through FDI, drawing upon general contexts of knowledge and innovation in countries. By adopting the social network analysis methodology to model and analyze the global outward FDI network for the period 2009–2016 and conducting longitudinal regression analyses, this study reveals that the global outward FDI network has a core–periphery structure, the network centrality position of a country is positively and significantly associated with the knowledge base of the country, and the absorptive capacity of a country positively moderates the association. Equipped with empirical evidence to support our theory, we develop the K-NACK framework to understand the context of knowledge sourcing through FDI. This empirical research article offering novel analysis and a theoretical and conceptual framework will be useful for policymakers at firms and in individual countries to find advanced as well as complementary knowledge from host countries to improve the knowledge base in home countries.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".