Applying a Hybrid Multiple Attribute Decision-Making Model to Evaluate the Improvement Strategy for Effective Technology Transfer Modes
Bibliographic record
Abstract
With the unprecedented growth of technological advancement, effective technological transfer has become increasingly important in all dimensions of human lives.Technological transfer is a multi-level and complex ecosystem network with complicated inter-relational elements and effective factors.This complexity raises the question of how to rearrange the elements of the technology transfer to improve its positive performance.To address this issue, this study aims to compare the performance and gaps of the three modes of technology transfer, which are technology entrepreneurship, technology licensing, and technology shareholding, by evaluating the three participants, which are universities/research institutes, corporations, and intermediary agencies, using related attributes.This study applies a hybrid multiple attribute decision-making (HMADM) model including the DEMATEL for constructing the INRM, DANP for computing influence weights, modified VIKOR for evaluating the performances and gaps among the three technology transfer modes so that to develop sustainable and systemic improvement strategies.At the macro level, the results show that, the technology transfers modes receive an overall positive effect, especially universities/research institutions.At the micro level, the technology licensing has not only the highest performance but also the largest gap.According to this finding, technology licensing is the most feasible way to cater to technology transfer at the macro level from the micro level.The findings suggest decision makers pay attention to the role of universities/research institutes as the main factor influencing technology transfer effectiveness.Also, they should focus on influential attributes such as researcher participation and technical collaboration ability for reducing the gap.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".