Regulatory framework on governing equity crowdfunding: a systematic literature review and future directions
Bibliographic record
Abstract
Purpose The purpose of this study is to comprehensively analyse and compare equity crowdfunding (ECF) regulations across 26 countries, shedding light on the diverse regulatory frameworks, investor and issuer limits and the evolution of ECF globally. By addressing this research gap and providing consolidated insights, the study aims to inform policymakers, researchers and entrepreneurs about the regulatory landscape of ECF, fostering a deeper understanding of its potential and challenges in various economies. Ultimately, the study contributes to the advancement of ECF as an alternative financing method for small and medium enterprises (SMEs) and startups, empowering them to access much-needed capital for growth. Design/methodology/approach The study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model for a systematic literature review on global ECF regulations. Starting with 74 initial articles from Web of Sciences and Scopus databases, duplicates were removed and language criteria applied, leaving 42 articles. After a thorough full-text screening, 20 articles were excluded, resulting in the review of 22 papers from 2016 to 2022. PRISMA’s structured framework enhances the quality of systematic reviews, ensuring transparency and accessibility of findings for various stakeholders, including researchers, practitioners and policymakers, in the field of ECF regulations. Findings This study examines ECF regulations across various countries. Notably, the UK has advanced regulations, while the USA adopted them later through the Jumpstart Our Business Startups Act. Canada regulates at the provincial level. Malaysia and China were early adopters in Asia, but Hong Kong, Japan, Israel and India have bans. Turkey introduced regulations in 2019. New Zealand and Australia enacted laws, with Australia referring to it as “crowd-sourced equity funding”. Italy, Austria, France, Germany and Belgium have established regulations in Europe. These regulations vary in investor and issuer limits, disclosure requirements and anti-corruption measures, impacting the growth of ECF markets. Research limitations/implications This study’s findings underscore the diverse regulatory landscape governing ECF worldwide. It reveals that regulatory approaches vary from liberal to protectionist, reflecting each country’s unique economic and political context. The implications of this research highlight the need for cross-country analysis to inform practical implementation and the effectiveness of emerging ECF ecosystems. This knowledge can inspire regulatory adjustments, support startups and foster entrepreneurial growth in emerging economies, ultimately reshaping early-stage funding for new-age startups and SMEs on a global scale. Originality/value This study’s originality lies in its comprehensive analysis of ECF regulations across 26 diverse countries, shedding light on the intricate interplay between regulatory frameworks and a nation’s political-economic landscape. By delving into the nuanced variations in investor limits, investment types and regulatory strategies, it unveils the multifaceted nature of ECF regulation globally. Furthermore, this research adds value by comparing divergent perspectives on investment constraints and offering an understanding of their impact on ECF efficacy. Ultimately, the study’s unique contribution lies in its potential to inform practical implementation, shape legislative frameworks and catalyse entrepreneurial ecosystems in emerging economies, propelling the evolution of early-stage funding practices.
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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 | 0.080 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".