Accessing Alternative Finance in Europe: The Role of SMEs, Innovation, and Digital Platforms
Bibliographic record
Abstract
Access to business financing in Europe has historically been a challenge for small and medium-sized enterprises (SMEs), which represent a significant share of economic activity and employment in Europe. This issue has been significantly intensified since the global financial crisis, disproportionately affecting this segment. This study analyzes firm-level determinants influencing access to alternative financing sources, including crowdfunding, venture capital, and other non-bank channels, using data from the 2023 SAFE covering 15,855 firms across Europe. Results indicate that firm size significantly affects access, with larger, established firms more likely to secure such funding. However, younger, innovation-driven firms demonstrate a higher propensity to pursue equity and crowdfunding options, driven by their need for flexible and early-stage capital. Sectoral patterns also emerge: industrial firms more often obtain public grants, while service-sector firms lead in adopting equity-based and crowdfunding models. The findings highlight the critical role of innovation capacity and international orientation in broadening financial access. Digital platforms are identified as key enablers in democratizing funding, particularly for SMEs. This research advances understanding of SME financing dynamics within evolving financial landscapes and provides actionable insights for policymakers and practitioners aiming to promote inclusive and sustainable access to finance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".