International Diversification and MNE Innovativeness: A Contingency Perspective of Foreign Subsidiary Portfolio Characteristics
Bibliographic record
Abstract
Abstract We advance research on how international diversification affects MNEs’ innovativeness by reconciling contradictory views on the role of international diversification for innovation. We do so by developing a portfolio perspective of MNE innovation that moves beyond foreign R&D subsidiaries to consider firms’ entire international footprints and by theorizing that MNE innovativeness depends on the interplay of geographical (i.e., regional diversification and institutional distance) and organizational (i.e., asset diversification and functional mandate breadth) characteristics of the foreign subsidiary portfolio. We test our proposed relationships on a unique multi-source panel dataset of Japanese listed electronics firms (266 firms and their 4505 subsidiaries between 2007 and 2015 resulting in 1936 firm-year observations and 28,350 subsidiary-year observations). We find that the institutional distance and asset diversification of the foreign subsidiary portfolio constrain the extent to which geographical (regional) diversification can enhance MNEs innovativeness. We also find that, at high levels of geographical diversification, MNEs with low levels of institutional distance and asset diversification in the foreign subsidiary portfolio tend to achieve higher innovativeness. Lastly, we did not find empirical support for functional mandate breadth as affecting how geographical diversification influences MNE innovativeness. Overall, the study highlights that, for a nuanced understanding of MNE innovativeness, managers need an encompassing and deliberate portfolio-level strategy that explicitly considers the interrelatedness of geographical and organizational characteristics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".