Survey of Cross-Cultural Technology Transfer Research
Bibliographic record
Abstract
This study aims at reviewing the technology/knowledge transfer literature and identifying which research areas on cross-cultural technology transfer field which should explore to obtain the new insights. With it in mind, the intersection of research fields concerning cross-cultural technology/knowledge transfer, the national culture difference and the extended literature of hybridization in the broad field of cross-cultural management is focused.As a result, this study identifies the five research areas meriting the further research on cross-cultural technology transfer: (1) the impact of cultural differences on technology transfer; (2) management practice factors for achieving efficient technology transfer; (3) the evaluation of current management practices at Japanese manufacturing subsidiaries; (4) the relationship between efficient technology transfer and business performance; and (5) research approach in cross-cultural technology transfer, such as research methodology, viewpoint and theoretical foundation. Accordingly, this study suggests the dimensions for further qualitative and quantitative investigations and the integration of fundamental theories-Hofstede’s national culture, Adler’s hybridization perspective, Abo’s management practice framework and organizational learning view-to underpin the investigating models. Consequently, this study draws the significant ways to answer the prevailing problem of how to implement cross-cultural technology transfer efficiently for achieving the successful business performance.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".