The mutual construction of knowledge transfer and shared context in capability development within the networked MNC
Bibliographic record
Abstract
Consistent with the knowledge-based view of the firm, capability augmentation in the multinational corporation (MNC) entails the adaptation and diffusion of knowledge within a network of globally dispersed subsidiaries. We used case study methodology to examine the transfer of knowledge practices within four MNCs in order to identify specific mechanisms through which social context impacts efficiency and effectiveness of transfer, as well as the resulting development of associated capabilities. The primary findings of this study are the identification of mechanisms that headquarters can use to create mutual interdependencies among subsidiaries, which in turn hasten capability development and enhance knowledge flow; and the mutually constructive roles of knowledge transfer and shared context, through promotion of organizational knowledge use. The manner in which these coordination and control mechanisms are implemented facilitates the integration of different units within the networked MNC.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 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".