The Crowdfunding Effects on Venture Capital Investment
Bibliographic record
Abstract
We examine the impact of crowdfunding on venture capital (VC) investments in the presence of competition among VC firms. Our economic model comprises a startup, a crowdfunding platform, and two VC firms, each with its own perception of the startup’s potential. The startup seeks equity funding from the VC firms and decides on the size of its equity offer. If the VC firms decline to invest, the startup pivots to crowdfunding. Following the crowdfunding campaign, all firms update their beliefs about the startup’s likelihood of success based on the crowdfunding outcome, prompting the VC firms to revisit their investment decisions, now with a reduced equity offer if the crowdfunding outcome was positive. This study provides theoretical underpinnings at the firm level for the observed aggregate-level empirical relationships, both positive and negative, between crowdfunding and VC investments. Specifically, based on our model, the positive relationship, where increased crowdfunding activity leads to a rise in subsequent VC investments, is attributed to startups that fail to secure VC funding in the absence of a crowdfunding platform but succeed in attracting VC attention after a successful crowdfunding campaign. In contrast, the negative relationship is attributed to highly valued startups that could have secured VC funding without crowdfunding, but with crowdfunding, VC firms choose to defer their investment decision until after the crowdfunding campaign’s result is known; on the one hand, a successful crowdfunding outcome lowers their post-crowdfunding VC investment demand, and on the other hand, an unsuccessful outcome deters potential VC investors. Besides these relationships, we also identify another dynamic, where the option of accessing crowdfunding raises VC investment, even if the startup does not actually launch a crowdfunding campaign. This paper was accepted by Sridhar Tayur, entrepreneurship and innovation. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295] and the National Natural Science Foundation of China [Grant 71901135]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.00994 .
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".