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Record W3114958504 · doi:10.3390/jrfm13120326

Institutional Drivers of Crowdfunding Volumes

2020· article· en· W3114958504 on OpenAlexvenueno aff
Mari-Liis Kukk, Laivi Laidroo

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
FundersTallinna Tehnikaülikool
KeywordsRelevance (law)Per capitaDatabase transactionArgument (complex analysis)DemocracyBusinessTransaction costService (business)EconomicsMarketingPolitical scienceFinanceLawSociology

Abstract

fetched live from OpenAlex

Crowdfunding improves access to financing, yet cases of crowdfunding’s importance, besides traditional financing, are rare and notably localized. In explaining why global crowdfunding volumes are so heterogeneous, previous academic research has focused mainly on the existence of a legal system that is supportive of crowdfunding, but with conflicting results. We argue that a broader range of institutions must be considered to describe the spread of crowdfunding at its current early stage of development, and provide first empirical evidence on the matter. Using a dataset covering crowdfunding volumes of 122 countries over the years 2015–2016, we confirm that the existence of crowdfunding-specific regulations has a positive association with total crowdfunding volumes per capita. We also find that regulation targeted at a specific type of crowdfunding has an economically stronger association with corresponding transaction volumes. In line with our argument, we find that a significantly broader range of less crowdfunding-specific institutions exhibit strong ties to crowdfunding volumes, with strong e-service culture emerging as an especially robust determinant of all types of crowdfunding volumes. Stronger legal rights, greater financial freedom, and higher democracy levels are also associated with greater total crowdfunding volumes, but exhibit varying relevance across different types of crowdfunding.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.011
GPT teacher head0.192
Teacher spread0.181 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2020
Admission routes1
Has abstractyes

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