MNE Knowledge Management and General Manager Staffing in Local Market Seeking Subsidiaries
Bibliographic record
Abstract
Multinational enterprises (MNEs) often deploy parent-country nationals (PCNs) to facilitate and safeguard knowledge transfer from headquarters (HQs) to foreign subsidiaries while using host-country nationals for local adaptation. Yet a simplistic understanding like this may lead to oversimplifications in the context of local-market-seeking subsidiaries. Given the critical boundary spanning role of subsidiary general managers (GMs), we theorize both the transaction cost minimization and value maximization implications of knowledge transfer, exploitation, and adaptation on subsidiary GM staffing decisions. Based on longitudinal data of 557 Japanese manufacturing MNEs between 1991 and 2020 operating across 47 countries, we found that when comparing a focal MNE with itself over time, there is a positive relationship between MNE R&D intensity and PCN GM deployment. This relationship is then moderated by the focal MNE’s international R&D experience. However, when comparing the focal MNE with its peer MNEs, technologically leading firms are less likely to deploy PCN GMs in their local-market-seeking subsidiaries compared to technologically lagging firms.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".