Multinational Firm Knowledge, Use of Expatriates, and Foreign Subsidiary Performance
Bibliographic record
Abstract
abstract The impact of knowledge transfer on foreign subsidiary performance has been a major focus of research on knowledge management in multinational enterprises (MNEs). By integrating the knowledge‐based view and the expatriation literature, this study examines the relationship between a multinational firm's knowledge (i.e. marketing and technological knowledge), its use of expatriates, and the performance of its foreign subsidiaries. We conceptualize that expatriates play a contingent role in facilitating the transfer and redeployment of a parent firm's knowledge to its subsidiary, depending on the location specificity of the organizational knowledge being transferred and the time of transfer. Our analysis of 1660 foreign subsidiaries of Japanese firms over a 15‐year period indicates that the number of expatriates relative to the total number of subsidiary employees (1) strengthened the effect of a parent firm's technological knowledge (with low location specificity) on subsidiary performance in the short term, but (2) weakened the impact of the parent firm's marketing knowledge (with high location specificity) on subsidiary performance in the long term. We also found that the expatriates' influence on knowledge transfer eventually disappeared. The implications for knowledge transfer research and the expatriate management literature are discussed.
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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 | 0.002 | 0.013 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".