Characteristics and factors for the innovation performance of New R&D Institutes at start‐up stages: an exploratory study from China
Bibliographic record
Abstract
To improve independent innovation ability, China has explored a unique form of R&D organization called the New R&D Institute (NRDI). The spillover effect of NRDIs in the region arouses curiosity about what exactly drives its innovation performance. After clarifying the NRDI concept and its characteristics, this study studies Nanjing, a typical city with the rapid development of NRDIs in China, to empirically explore the impact mechanism of NRDI development in their start‐up period. The study uses panel data from 103 NRDIs spanning 10 quarters from the third quarter of 2018 to the fourth quarter of 2020. Our analysis reveals that R&D investment, government support, research infrastructure, and angel investment have mixed impacts on the revenue, innovation, and enterprise incubation of NRDIs. Specifically, resource inputs such as R&D staff, R&D service platforms, and R&D expenditures boost the revenue growth of NRDIs. In contrast, only a few inputs play an important role in NRDIs’ innovation and enterprise incubation, including service platforms, capital investment from high‐tech parks, and angel funding. The early development of NRDIs has four features. (1) It is driven more by material capital (R&D expenditure) than by human capital (R&D staff). (2) It relies more on government support rather than institution investment. (3) Research infrastructure has specific significant effects on the innovative output of NRDIs. (4) Angel investment is critical to promote technological innovation and business incubation. Most of the input elements have not yet been very effective in the innovation and incubation of NRDIs. Our research offers essential insights for understanding the innovation mechanism in NRDIs and promoting their healthy development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".