Organizational structure and knowledge-practice diffusion in the MNC
Bibliographic record
Abstract
Purpose – This study aims to examine the interaction of formal and informal cross-border knowledge-sharing practices of four large multinational corporations (MNCs) in aerospace, software, IT services and telecommunications industries. The goal was to determine the manner in which coordination and control mechanisms facilitated knowledge transfer. Design/methodology/approach – Case studies comprised secondary data and semi-structured interviews with corporate headquarters and subsidiary managers in large MNCs conducted in the USA, Canada, Mexico, China, India and Eastern Europe. Findings – The primary finding of this study is that knowledge transfer mechanisms arise as a result of both formal and informal structures of the MNC. Formal structures which create either mutual dependencies or occasions for knowledge exchange facilitate transfer. Formal structure which inhibits knowledge transfer can be overcome by knowledge brokers and evaluation metrics. Research limitations/implications – These findings suggest that knowledge transfer is more informal than formal, but that MNC headquarters does play a role, intended or not, through shaping the interdependencies among geographically distributed units. Managers should be mindful of both the manner in which tasks and the organization are structured, as these have an indirect impact on the development of knowledge channels. Originality/value – This paper investigates the role of organizational structure and its effect, both intended and unintended, on the transfer of knowledge-based practices. While knowledge transfer has been heavily researched, this study examines the phenomenon at a finer-grained level of analysis.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 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".