An Investigation of Innovation Capability in Small and Medium-Sized Enterprises of China
Bibliographic record
Abstract
Over the last decades, small and medium-sized enterprises (SMEs) have grown exponentially and become a key element in China’s economy. The purpose of this paper is to conduct a compararive evaluations of innovation capabilities status of SMEs in China across different sectors. Six dimensions of innovation capability that influence mostly NPD performance of firms are identified, such as technology, organization, strategy, organizational climate, manufacturing and marketing. A practical survey was carried out in the manufacturing industry of Zhejiang Province. We find that in different firm sizes and development stages SMEs have significant disparities in intensity of different capability dimensions respectively. Medium firms has higher scores in all of innovation capability dimensions than small firms. The bottleneck of capabilities in small firms are found in Org_Process, M_C, and MKT_S. All of firms in three stages are consistently adept in MKT_L, Climate_L, and lack in Org_Process; Mature firms exhibit the greatest level in all of innovation capability dimensions, and growing firms and startup firms are lower in sequence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".