The impact of national culture on the effectiveness of interorganizational knowledge transfer
Bibliographic record
Abstract
In today's business climate, organizational knowledge has been widely accepted as a principle organizational source for sustainable advantages for global competitiveness. Inter-organizational knowledge transfer lays a significant foundation for obtaining new organizational knowledge. The role of middle managers in inter-organizational knowledge transfer is getting more and more attentions nowadays, although it cannot be more than enough. In addition, considerable evidence supports the importance of culture in the success or failure of knowledge transfer within organizations. The main purpose of this research is to identify what roles of middle managers play in each stage of inter-organizational knowledge transfer as well as the impact of national culture on such roles. The research used a case study methodology and was conducted among Canadian, American and Chinese middle managers in two well-known multinational organizations. The findings suggest that first, middle managers play the roles of Radar, Filter and Champion in the initiation stage, the role of Coordinator in the interrelation stage and the role of Problem solver in the implementation stage; second, Chinese middle managers are involved less than those from North America in the activity 'Suggesting and prioritizing the different courses of action to acquire new knowledge', 'Defining and justifying the importance of new knowledge transfer proposals to upper-level managers' and 'Embedding the newly acquired knowledge in organizational processes and routines'. This study opens new insights of research in knowledge transfer that link up the roles of middle managers, national culture, and the effectiveness of knowledge transfer.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".