Complements or Substitutes? The Role of Integration Mechanisms in Knowledge Transfer in the MNE
Bibliographic record
Abstract
While research on integration mechanism in multinational enterprises (MNEs) has increased considerably since 1980s, empirical studies on the integration outcomes remain inconclusive. In this study, we use meta-analytic techniques to quantitatively synthesize and evaluate the relationships between different integration mechanisms (centralization, formalization and socialization mechanisms) and knowledge transfer. Based on meta-analyses from 35 independent samples and a total of 6,000 subsidiaries, we find that formalization and socialization both have moderately strong positive associations with knowledge transfer. These two integration mechanisms are also strongly inter-related, seemingly complementing each other to enhance knowledge transfer. Centralization is negatively associated with knowledge transfer and its non-significant inter-relatedness with formalization and socialization implies neither substitution nor complementarity for the other two integration mechanisms. When examining the simultaneous implementation of different mechanisms, socialization stands out as the most important integration mechanism to for knowledge transfer. Socialization even mitigates the impact from other mechanisms, especially the formalization mechanism, which suggests a substitution effect between formalization and socialization. Our study contributes to our understanding of MNE integration by illustrating the importance of socialization as well as the interrelatedness of integration mechanisms.
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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 | 0.061 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".