Organizational Embeddedness and Reverse Knowledge Transfer in Emerging Multinational Corporations
Bibliographic record
Abstract
A central question for international business researchers and practitioners is if and how organizational embeddedness in multinational corporations (MNCs) can help subsidiaries transfer knowledge to their parent firms. It has been suggested that different types of embeddedness provide different benefits for knowledge transfer. This paper aims to investigate the relationship between the level of subsidiaries’ relational and structural embeddedness and the degree of reverse knowledge transfer. To contextualize this relationship, drawing from social network theory and institutional theory, we develop a theoretical model showing how firms’ home country contexts (industry knowledge intensity and ownership) facilitate the importance of structural and relational embeddedness to reverse knowledge transfer. Using survey data from 197 Chinese MNCs, and by applying structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), our results show that relational embeddedness, and the combination of industry knowledge intensity and structural embeddedness, facilitates reverse knowledge transfer. Moreover, private firms can achieve high levels of reverse knowledge transfer in the absence of structural embeddedness. By considering the interplay of country and organizational level variables, and using both SEM and fsQCA, our study provides new insights on reverse knowledge transfer in emerging MNCs and contributes to growing methodological considerations regarding complexity theory.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.004 |
| 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".