International Diversification and MNE Innovativeness: A Contingency Perspective of Foreign Subsidiary Portfolio Characteristics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it