Playing the Business Angel: The Impact of Well-Known Business Angels on Venture Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People well known to the general public are increasingly acting as business angels (BAs) for young and innovative ventures worldwide. These BAs are less known for their venture evaluation skills and often do not have a professional reputation as investors. The signaling function of these well-known investors could therefore be less relevant for founders because of a limited quality assurance function. Nonetheless, a venture’s affiliation with a well-known BA may still positively alter the quality perceptions of various stakeholders because the BAs can put their reputation in other areas of life at risk, provide an easy-to-interpret and fluent cue to the general public, and improve the observability of the signal. Using a sample of more than 2,900 early-stage ventures that made a venture pitch during the Canadian, German, U.K., and U.S. versions of the reality TV show Dragons’ Den, we find that BAs’ degree of being known has a positive impact on target firm survival, web traffic, and sales. The impact of BAs’ general degree of being known is particularly strong if the congruency between the investors and the target ventures is high. These effects exist over and above potential selection effects, the professional reputation of the BA, and the greater financial resources of a funded venture. The empirical findings indicate that well-known BAs can have a positive effect on venture performance and that founders should consider not only the professional reputation of BAs but also the degree to which they are known to a general audience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it