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Record W2974620343 · doi:10.1177/1465750319877017

The evolution of entrepreneurial finance – 10 years after the global financial crisis

2019· article· en· W2974620343 on OpenAlexaff
Ciarán Mac an Bhaird, Robyn Owen, Mark Freel

Bibliographic record

VenueThe International Journal of Entrepreneurship and Innovation · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFinanceMoral hazardAdverse selectionEconomicsStructured financeFinancial crisisPrivate equityInvestment bankingPrivate finance initiativeEquity (law)Financial systemBusinessMarket economyPrivate sectorIncentiveEconomic growth

Abstract

fetched live from OpenAlex

In the period following the global financial crisis, as banks and private equity investors withdrew from early stage entrepreneurial finance markets in the United Kingdom and developed economies (Mac an Bhaird, 2014; Wilson and Silver, 2013), there was a profusion in supply of alternative sources of early stage entrepreneurial finance (World Bank, 2013). These new financing options for firms partly alleviated the adverse effects of pro-cyclical provision of entrepreneurial finance (Mac an Bhaird et al., 2019). The large increase in provision of nontraditional sources of finance for the real economy was viewed as revolutionary (Harrison, 2013) and potentially transformative (Bruton et al., 2015), and its sustained use over more than a decade suggests that it is more than a passing fad.
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\nThe amount of finance procured from these sources has grown significantly in a very short time period and is estimated to surpass investment from traditional sources of funding in the near future (Barnett, 2015). These developments have significant implications in relation to the supply of, and demand for, entrepreneurial finance, including well-established issues which primarily stem from information asymmetries, such as agency, signalling, moral hazard and adverse selection. The emergence of new sources of alternative finance introduces additional concerns in relation to regulation, investor protection, ownership and governance, among other issues (Bruton et al., 2015). The significant increase in the supply and use of alternative sources of finance has been facilitated to a large extent by the expansion of the Internet and use of social media. The increase in supply of, and demand for, alternative sources of finance has been accompanied by a burgeoning literature on the subject, due primarily to the availability of data that are accessible from the online platforms and websites.
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\nOver a decade has passed since the increased provision and use of alternative finance in its various forms and amounts, providing us with an opportunity to assess and analyse its adoption and to appraise how its provision may be improved for the benefit of investors and borrowers. At this juncture, we should have adequate evidence to increase the efficiency of provision from alternative sources, in order to improve the supply of finance in private debt and equity markets and to provide greater diversification and depth in financial markets.
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\nThe International Journal of Entrepreneurship and Innovation has been to the forefront in publishing innovative studies on topical issues at the nexus of entrepreneurship and innovation (e.g. Volume 19, Issue 1, ‘Green innovation – connecting governance, practices and outcomes’). This special issue continues in that tradition, publishing state-of-the-art studies on a variety of issues related to innovations in entrepreneurial finance. This special issue is significantly different from other journal special issues on this subject (e.g. Baldock and Mason, 2015; Harrison, 2015; Owen et al., 2019) in the range and breadth of issues investigated and analysed. The studies represent a broad geographic spread, including New Zealand, the United Kingdom, France, and the United States. A broad range of financing innovations are also considered, including blockchain, peer-to-peer (P2P) lending, equity-based crowdfunding and mobile payment systems. Each article provides a unique contribution to our knowledge of entrepreneurial finance, and a brief summary is provided in the following section. [...]

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.473
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.008
GPT teacher head0.223
Teacher spread0.214 · 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 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

Citations11
Published2019
Admission routes1
Has abstractyes

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