Applying Quantile Regression to Assess the Relationship between R&D, Technology Import and Patent Performance in Taiwan
Bibliographic record
Abstract
Electronics companies are facing global economic and trade competition. As patents can form an endowment shield that protects the development of corporate capabilities, companies are actively increasing their number of patents and attaching importance to technological research and development and patent management to achieve differentiated strategic effects. As such, patent layout and research and development (R&D) investment have become important strategic weapons for Taiwanese manufacturers, with which to enter the international market or compete among enterprises. This study first utilized the principal components analysis method to define patents in terms of the number of patents and the times patents are cited, with R&D defined in terms of expenditure and intensity. Furthermore, this study used a quantile regression model to visualize the relationship between R&D, technological imports, and patent performance in Taiwanese listed electronics companies. The empirical results show that technological imports in the second time-lag period require patents, while the effect on patents varies alongside industry characteristics. In addition, the empirical results found that the total assets, number of employees, and number of patent inventors are also factors that significantly affect patents. This research proposes that Taiwan’s listed electronics companies should expand their scale, increase their economic efficiency, maximize their resources, increase their patents, enhance their corporate value, boost their investor confidence, and improve their industry competitiveness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".