An internalization theory rationale for MNE regional strategy
Bibliographic record
Abstract
Purpose This paper seeks to demonstrate that internalization theory, as a “complete” theory of the firm, is particularly well equipped to analyze multinational enterprise (MNE) regional strategies, thanks to its joint transaction cost economics and resource‐based foundations. Design/methodology/approach This paper builds on recent work by Wolf, Egelhoff, and Dunemann to show that internalization theory's predictions on MNE regional strategy are superior to those suggested by several other conceptual frameworks. For each of the 11 hypotheses formulated by Wolf and his co‐authors, an alternative is proposed here that is consistent with internalization theory predictions. Findings MNE regional strategy is an important empirical phenomenon. Internalization theory, as a powerful conceptual framework with general applicability, simplicity and accuracy, allows in‐depth analysis of MNE regional strategies. Research limitations/implications Internalization theory scholars need to find new ways of operationalizing MNE firm‐specific advantages (FSAs), as well as MNE resource recombination trajectories, to predict accurately when and how MNEs will pursue regional versus global strategies. Practical implications MNE senior management should rethink international expansion strategies and realize that most large MNEs actually pursue regional, not global strategies. Social implications Even the world's largest MNEs have great difficulty engaging in novel resource recombination across the globe, and their alleged market power should therefore not be overestimated. Originality/value International business scholars should embrace internalization theory as the general theory of the MNE, rather than looking for insight from theories not intended – nor properly equipped – to study strategies of the world's most complex entrepreneurial organizations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".