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Enregistrement W2974620343 · doi:10.1177/1465750319877017

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

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

Notice bibliographique

RevueThe International Journal of Entrepreneurship and Innovation · 2019
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueFinTech, Crowdfunding, Digital Finance
Établissements canadiensUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésFinanceMoral hazardAdverse selectionEconomicsStructured financeFinancial crisisPrivate equityInvestment bankingPrivate finance initiativeEquity (law)Financial systemBusinessMarket economyPrivate sectorIncentiveEconomic growth

Résumé

récupéré en direct d'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. [...]

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,473
Score d'incertitude au seuil0,283

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,008
Tête enseignante GPT0,223
Écart entre enseignants0,214 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2019
Routes d'admission1
Résumé présentoui

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