The Problems with and Promise of Entrepreneurial Finance
Bibliographic record
Abstract
Research summary This article provides a review of the entrepreneurial finance literature in the surprisingly not very well integrated entrepreneurship and finance journals. Entrepreneurial finance encompasses venture capital, private equity, private debt, trade credit, IPOs, angel finance, and crowdfunding, among other forms of finance. We analyze trends in citation activity to these topic areas across 16 journals that publish at least somewhat regularly on these topics, and we show there has been a rise in citations on venture capital, private equity, and IPOs post‐2006. We highlight an unfortunate degree of segmentation in the literature, as well as topics that have been the subject of scholarly focus, and identify promising topics for future research. Managerial summary Who does research in entrepreneurial finance—entrepreneurship or finance scholars? And which types of journals are more likely to publish research in entrepreneurial finance? In this article, we provide an overview of the literature on topics that include venture capital, private equity, private debt, trade credit, IPOs, angel finance, and crowdfunding. Our review of the literature shows some elements of segmentation by the specific topic, which we explain is partly due to the fact that datasets on entrepreneurial finance themselves are often segmented and do not include information on more than one form of entrepreneurial finance at a time. Further, we show citation patterns are segmented by the type of journal, with finance journals being much less likely to refer to entrepreneurship journals. Copyright © 2017 Strategic Management Society
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".