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Record W4405320430 · doi:10.1287/mnsc.2022.00994

The Crowdfunding Effects on Venture Capital Investment

2024· article· en· W4405320430 on OpenAlexaffabout
Ming Hu, Yannan Jin, Jussi Keppo

Bibliographic record

VenueManagement Science · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVenture capitalEquity crowdfundingSeed moneyBusinessEquity (law)Outcome (game theory)Investment (military)FinanceEntrepreneurial financeMonetary economicsEconomicsMicroeconomics

Abstract

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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 .

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.010
GPT teacher head0.232
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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