Knowledge Seeking and Outward FDI of Chinese MNEs: The Moderating Effect of Inward FDI
Bibliographic record
Abstract
An important motivation for emerging market multinational enterprises (EM MNEs) to invest overseas is to access and acquire advanced technological knowledge in the host countries. Viewing outward FDI by EM MNEs as an important “catch-up” strategy, we propose that EM MNEs are attracted to host countries with comparative technology advantages in the industries relevant to the investor. We further propose that inward FDI in emerging markets, by generating knowledge spillovers in the relevant industries, also provides important sources of technological knowledge for EM MNEs to acquire and can slow the rate of entry of EM MNEs into foreign countries for knowledge seeking purposes. Using a dataset of the overseas investment activities of over 400 Chinese manufacturing firms in the last two decades, we find strong support for our hypotheses. Our results also suggest that despite some overlap between inward and outward FDI in providing learning opportunities to emerging market firms, outward FDI provides additional knowledge benefits that may not be readily available in domestic markets. We discuss the implications of our study for research and practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".