Subnational institutions and outward FDI by Chinese firms
Bibliographic record
Abstract
Purpose – The purpose of this study is to extend the classic country-specific advantage (CSA) – firm-specific advantage (FSA) framework by integrating an institution-based view of CSAs into the discussion of FSAs. In his classic CSA – FSA framework, Rugman suggests that successful multi-national enterprises (MNEs) are often built on the interaction between strong FSAs and strong CSAs at home. In the case of emerging market multi-nationals (EMNEs), he argued that strong CSAs were of particular importance in allowing EMNEs to develop FSAs. In particular, we examine CSAs at the sub-national level. Design/methodology/approach – The authors suggest that sub-national heterogeneity in market-supporting institutions is an important feature of emerging market economies, and that consideration of such heterogeneity contributes to our understanding of firm capabilities and overseas investment behavior of emerging market firms. The authors also identify explicitly the mechanisms through which sub-national institutions at home affect FSAs and, subsequently, the ability of emerging market firms’ entry into developed markets. Specifically, the authors argue that strong local institutions that support effective and well-functioning markets create the conditions that induce firms in that location to develop market-related capabilities in R & D and marketing, which, in turn, enable them to expand into developed countries. Findings – Using a unique data set on overseas investment by Chinese firms and causal mediation analysis, the authors find strong evidence in support of the view that strong sub-national institutions help emerging market firms develop the capabilities to enter developed country markets. Originality/value – This study extends the classic CSA–FSA framework by integrating an institution-based view of CSAs into the discussion of FSAs. In particular, the authors examine CSAs at the sub-national level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".