Multinational enterprises' R&D commitments in Chinese provinces: A configurational approach
Bibliographic record
Abstract
Multinational enterprises (MNEs) are increasingly off-shoring some of their R&D to emerging markets, including China. Much of the extant literature on MNEs' investments in R&D facilities abroad analyses technological and institutional factors at the national level, typically using regressions to examine how host-country institutions influence foreign MNEs' outlays. It, therefore, tends to downplay the importance of sub-national and non-technology-related institutions, and how configurations of home- and host-country institutions interact to influence R&D commitments abroad. Drawing on the global factory model and the Varieties of Capitalism approach, we identify five causal conditions that may influence MNEs' R&D commitments abroad. Conducting an abductive fuzzy-set qualitative comparative analysis, we find four combinations of causal conditions are sufficient to explain substantial R&D commitments in different Chinese provinces. The combination of local corruption and provincial R&D intensity is important, as are the MNE's home-country stock-market capitalization to GDP ratio and minority investor protection. We contribute to the literature on MNEs' investments abroad by extending the importance of sub-national institutions to include those not directly related to technology. We also reveal how combinations of institutions (rather than individual ones acting independently) from the MNE's home and host contexts explain MNEs' R&D commitments in Chinese provinces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".