PROSPECTS OF INNOVATIVE BUSINESS FINANCING IN CURRENT GLOBALIZED CHANGES
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".