CORPORATE VENTURE CAPITAL DIVERSIFICATION, PARENT COMPANY VALUE SPILLOVERS AND VALUE CREATION OF START-UPS
Bibliographic record
Abstract
Corporate venture capital (CVC) not only promotes value creation for CVC parent companies but also brings rich entrepreneurial resources to invested start-ups. This paper explores the mechanisms through which CVC portfolio diversification promotes value creation for both parent companies as well as the invested start-ups. Focusing on 142 start-ups in China from 2003 to 2015, invested in by 49 companies listed on the Shanghai and Shenzhen Main Boards, we find that CVC portfolio diversification has a positive impact on the value of invested start-ups and CVC parent company value spillovers play a mediating role in this effect. In addition, CVC portfolio diversification has a nonlinear U-shaped relationship with the value of parent companies. Finally, geographical proximity between parent companies and their invested start-ups renders a significant reciprocal positive moderating effect on the relationship between CVC diversification and the value of parent companies, as well as the relationship between the value of parent companies and the value of invested start-ups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".