Knowledge Transfer in Offshore Outsourcing and International Joint Ventures (IJVS): A Critical Literature Review from Cross-Cultural Context
Bibliographic record
Abstract
“Knowledge transfer” or “Knowledge sharing” is considered an important strategic issue among the partner firms in today’s globalized business ventures. In a trans-border or transcontinental business relation, cultural dimensions are important factors for success or failure of these ventures. Culture plays an important role in various management issues including “Knowledge transfer” among the business partners of international ventures such as offshore outsourcing or International joint ventures. However, there is very scarce literature dealing with the organizational learning & knowledge transfer from the international and cross-cultural point of view. With the critical literature review method, this paper tries to shed light on cultural effects on knowledge transfer process, challenges and strategies of knowledge transfer in cross-cultural context.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".