Bringing corporate governance into internalization theory: State ownership and foreign entry strategies
Bibliographic record
Abstract
We use internalization theory to analyze the establishment and entry mode decisions of state-owned (SOE) and privately owned (POE) enterprises. We enrich internalization theory by building on insights from economic theory of corporate governance and taking into account particular characteristics of SOEs such as non-economic motivations, long-term orientation, and different risk preferences. We examine foreign entries over a 10-year period in the Canadian oil and gas industry. This single-country and single-industry context features foreign SOEs and POEs from a wide range of home countries, allowing a focused study of the combined influence of state ownership and home-country factors. Compared to POEs, SOEs tend to prefer acquiring stand-alone assets rather than firms, and to take lower ownership shares. We also find that differences between SOEs and POEs diminish when home countries are characterized by high government quality and market orientation and identify differences between types of SOEs, with partially owned SOEs exhibiting behaviors more similar to POEs than fully owned SOEs. We demonstrate how our enrichment of internalization theory strengthens its predictive and explanatory capacity. Our results also show that SOEs from strong and market-oriented institutional environments are similar to POEs and can be studied using the traditional internalization theory.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".