The Role of Angels in Technology SMEs: A Link to Venture Capital
Bibliographic record
Abstract
The presence of angels among early-stage financiers of new technology-based firms should improve chances of eventual venture capital financing. Reasons to expect that firms with private investment would have easier access to venture capital are discussed. This study presents findings that support this expectation. A total of 57% of the firms that had received private investor financing had also received financing from institutional venture capitalists; only 10% of firms that had not received angel financing obtained venture capital. Angel investor financing was a significant explanatory variable (among others) of differences between venture capital recipients and firms that had not received venture capital. It would appear that angels help firms to become more ready for future stages of investment by, among other contributions, being closely involved with the firms in which they invest. They usually provide advice and networking opportunities. They also serve on Boards of Directors and Advisors, and provide hands-on assistance and business intelligence. Angels also fulfill an important accreditation role. Overall, this study provides empirical support for the expectation that involvement of angels can substantially increase the attractiveness of firms to institutional venture capitalists.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".