The paradox of social capital in China: venture capitalists and entrepreneurs’ social ties and public listed firms’ technological innovation performance
Bibliographic record
Abstract
Contemporary research on entrepreneurship has showed that social capital can improve the success rate of entrepreneurial activities, but focusing too much on social capital can also divert entrepreneurs’ attention to certain firm activities such as technological innovation. The purpose of this study is to explore such a paradox in order to better understand the impact of social capital on firms’ technological innovation performance in China. The current study explores this paradox with the data from 249 public listed Chinese firms that have received venture capital investment. The results show that both venture capitalists’ social ties and entrepreneurs’ social ties negatively affect these firms’ technological innovation performance, including total patents granted, R&D expenditure, and total factor productivity. In other words, venture capitalists’ social ties as well as entrepreneurs’ social ties actually impede, rather than facilitate their firms’ technological innovation. The results also show that entrepreneurs’ social capital mediates the impact of venture capitalists’ social capital on technological innovation performance. Managerial and policy implications for entrepreneurship research and technological innovation are then discussed for future research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".