Corporate Venture Capital as a Window on New Technologies: Implications for the Performance of Corporate Investors When Acquiring Startups
Bibliographic record
Abstract
Gaining a “window” on new technologies is a prominent motive for corporate venture capital (CVC) investing. Recent studies suggest that information gained through CVC-related activities can improve the internal R&D productivity of established firms. This study investigates an alternative means by which information gained through CVC investing could improve firm performance — by increasing the returns to corporate investors when acquiring startups. We provide new insights based on an event study of the returns to 34 corporate investors from acquiring 242 technology startups. Consistent with predictions drawn from the absorptive capacity literature, we find that the effect of CVC investing on acquisition performance hinges critically on the strength of the acquirer’s internal knowledge base: as CVC investments increase relative to an acquirer’s total R&D expenditures, acquisition performance improves at a diminishing rate. We also find that firms consistently engaged in venture financing earn greater returns when acquiring startups than do firms with more sporadic patterns of investing, even controlling for firm profitability, size, and acquisition experience. These findings suggest that corporate investors systematically differ in their abilities to derive added benefits from external venturing as acquirers of entrepreneurial firms.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".