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Record W4379164847 · doi:10.32782/2413-9971/2023-47-3

PROSPECTS OF INNOVATIVE BUSINESS FINANCING IN CURRENT GLOBALIZED CHANGES

2023· article· en· W4379164847 on OpenAlexaboutno aff
Andrii Homotiuk

Bibliographic record

VenueHerald UNU International Economic Relations And World Economy · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsFinanceBankruptcyQuarter (Canadian coin)BusinessInnovative financingBusiness modelMarketing

Abstract

fetched live from OpenAlex

The article considers the formation and development of innovative models of business financing. Small and medium-sized businesses around the world have been severely affected by the COVID-19 pandemic. Many businesses declared bankruptcy, and many business owners lacked funding to resume operations after the lockdown was lifted. A large number of enterprises are rejected by banks, so they are looking for opportunities to attract funds from sources of innovative alternative financing. The purpose of this article is to reveal the essence of innovative financing; analysis of the functioning of the innovative financing market in the world and determination of its development prospects. The article examines the formation and development of innovative business financing models. The theoretical foundations of alternative business financing, motives for its use by enterprises are disclosed. The main characteristic features of the world market of alternative financing are analyzed, including by categories of crowdfunding models. It is possible to state a slowdown in the development of this market, since the volume of financing decreased in the IV quarter of 2022. Attention is focused on the functioning of the European market, its main elements are characterized, factors affecting its growth are identified. The growth of the P2P market has been found to be driven by the growing demand for alternative financing options, the ease of access to funds for small businesses, and the low interest rates offered by lending platforms. The development of P2P is influenced by the use of the latest technologies. Continued adoption of artificial intelligence (AI) among peer-to-peer lending platforms could bring additional funds to the market. In Ukraine, alternative financing is only at the initial stage of development, but there is a tendency for rapid growth. This process is hampered by the lack of a legal framework. The influence of the war in Ukraine on the development of the alternative lending market is analyzed. The prospects of the crowdfunding market and P2P financing in the near term have been determined, given the impact of the war in Ukraine.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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