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Record W4408459345 · doi:10.3390/jrfm18030154

Crowdfunding Amidst FinTech Winter: Complement or Substitute?

2025· article· en· W4408459345 on OpenAlexvenueno aff
Shadi Al Shebli, Ahmet Faruk Aysan, Ruslan Nagayev

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsComplement (music)BusinessBiologyGenetics

Abstract

fetched live from OpenAlex

This study examines the transformative potential of FinTech, particularly crowdfunding, in the context of traditional financial systems amidst the FinTech market downturn (‘Winter’) of 2022. We address a fundamental question: Does crowdfunding represent a viable alternative to conventional finance, or does it merely function as an extension of existing financial infrastructure? To investigate this relationship empirically, we developed a proprietary crowdfunding index and employed Interconnectedness Index methodology to compare the spillover effects of crowdfunding and traditional financial systems with common economic factors such as interest rates, USD index, economic uncertainty, cryptocurrency (Bitcoin), as well as commodities (gold and oil). Despite crowdfunding’s remarkable growth trajectory over the past decade, our empirical evidence indicates that it has not yet emerged as a viable substitute for conventional financial mechanisms. These findings carry substantial implications for regulatory frameworks, industry stakeholders, and academic discourse, contributing to the broader understanding of FinTech’s disruptive capacity within the financial ecosystem.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.007
Scholarly communication0.0090.008
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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