Bibliographic record
Abstract
This paper examines the role of advice in early firm development and growth, drawing on detailed data from a global program where angel investors and venture capitalists (VCs) mentored founders over several months. Leveraging variation in mentors’ availability to support start-ups because of personal scheduling conflicts, I find that advice significantly improves start-ups’ future market performance. To explore how advice shapes early firm development, I develop a novel typology of start-up activities, finding that a defining element of mentors’ advice is to do less and learn more. Although angels and VCs are consistent in this message, they differ significantly in when they choose to advise start-ups in achieving their business objectives. Angels are more likely than VCs to help founders design and execute product market experiments, whereas VCs provide more mentoring support on business analysis and planning tasks. I find evidence consistent with the hypothesis that experimentation is a skill developed via learning-by-doing, and angels have a skill advantage in that domain because of having more operational experience. This paper was accepted by Alfonso Gambardella, business strategy. Funding: The author acknowledges support from the Government of Canada’s Strategic Innovation Fund [Grant 811949] and RBC Borealis Graduate Fellowship [Grant 2018-0439]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05115 .
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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 | 0.011 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".