The Role of Cultural Factors on Intra-Firm Technology Transfer Performance and Corporate Sustainability: A Conceptual Study
Bibliographic record
Abstract
Technological innovations have emerged as crucially significant factor for sustaining market competition and achieving sustainable competitive advantage in the 21st century. The Multinational Corporations (MNCs) as celebrities of innovation play significant role in diffusing technological knowledge throughout firms both nationally and internationally. Although numerous studies exist on technology transfer the majority of existing literature addresses the issues related to inter-firm transfer of technology only while the area related to intra-firm transfer of technology has been largely underexposed; study of which is believed to be ideal for fruitful exploration of profitability in technology transfer projects. By exploring the existing relevant literature, the current study would attempt to posit a new model in regards to the effect of host-country cultural environment on the performance of technology transferred by the MNCs to their subsidiaries in Malaysia and its subsequent impact on the corporate sustainability of the firm. In the present study the relative influence of two cultural environment factors, namely national cultural distance and organizational cultural distance have been addressed and the study is expected to contribute both theoretically in the body of knowledge and also in terms of practical implication for policy makers of the host-country and the involved MNCs and hence enriching the existing intra-firm technology transfer literature simultaneously.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".