Reverse knowledge transfer from subsidiaries to multinational companies: Focusing on factors affecting market knowledge transfer
Bibliographic record
Abstract
Abstract Despite increasing research on reverse knowledge transfer (RKT) from subsidiaries to headquarters (HQs), there is no academic consensus on the primary determinants influencing RKT. By incorporating four different facets (i.e., absorption, sharing, implanting, and application of market knowledge) of the phenomenon, we draw new insights into RKT. Through empirically testing the phenomenon in the Korean context, we reveal that market knowledge absorption by subsidiaries is a critical component that influences the knowledge integration mechanisms (KIMs) within MNC networks. Furthermore, KIMs within MNC networks are primary keys for absorptive capacity (AC) of HQs and knowledge relevance between HQs and subsidiaries. Our results extend our understanding of RKT, while also offering useful implications for MNCs that intend to establish subsidiaries in foreign markets. Copyright © 2016 ASAC. Published by John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".