Organizational structure and knowledge-practice diffusion in the MNC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it