Bibliographic record
Abstract
Venture capital has a significant positive impact on corporate innovation. However, innovation has great risks. Investors often lack sufficient confidence in innovation, which often leads to investors stopping their investment or to inadequate support for innovation behavior. Therefore, enhancing investor confidence is crucial. Monitoring is considered to be the most direct and common way to promote investor confidence. This paper mainly focus on the effect of venture capitalist monitoring on corporate innovation. We use companies listed in the Shenzhen and Shanghai stock exchanges from 2009 to 2017 as samples. We performed a metrological test and a series of robustness tests and found that venture capitalists on boards play a significant role in promoting corporate innovation. When dividing the sample according to the experience of venture capital or the governance level of the corporation, we found that venture capitalists on boards with high experience or in corporations with high-quality governance have a greater impact on corporate innovation. Furthermore, we studied the monitoring mechanism of venture capital on boards and found that the monitoring of venture capital can alleviate managers’ anxiety about being dismissed due to innovation failure.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".