Leveraging Digital Technologies for Financial Inclusion: An Empirical Analysis of SME Financing in Emerging Economies (Enterprise Development and Microfinance)

Summary

• The study evaluates the interaction between AI-driven credit assessment tools, FinTech adoption barrier reductions, and regulatory policy frameworks using data from 150 finance professionals.p.1
• The measurement model established high construct reliability and validity with Cronbach's Alpha ranging from 0.843 to 0.901 and AVE between 0.564 and 0.713.p.1
• The path from AI-driven credit assessment (R1) to FinTech barrier reduction (R2) was estimated at β = -0.196 with an R² of 0.038.p.1
• The path from FinTech barrier reduction (R2) to regulatory policy framework (R3) yielded β = -0.071 with an R² of 0.005.p.1
• Combining the negative direct paths produced a positive indirect path from R1 to R3 of β = 0.014.p.1
• A post-hoc power analysis indicated that the R2 to R3 pathway had an achieved power of 22.0%, implying a minimum of 1,220 respondents are needed for adequate statistical power.p.1

Key insights

1. HTMT ratios were consistently below 0.20 across constructs, demonstrating high discriminant validity.p.1
2. Random Forest Tree modeling highlights that SME digitization stands at 57% while access to alternative finance is at 38%.p.3
3. PESTEL analysis identifies 27 implementation challenges for AI within SMEs, including elevated finances and digital inadequacy.p.3
4. A linear regression analysis using data from 385 SMEs demonstrates that digital FinTech tools improve service quality and accessibility.p.3
5. Item 12 exhibited a low outer factor loading of 0.516, falling below the standard 0.70 threshold.p.8
6. The structural model generated fit indices of SRMR = 0.064 and NFI = 0.866, both falling within acceptable limits.p.12
7. Future studies need a minimum sample size of 1,220 respondents across 3 to 5 emerging economies to reliably detect structural pathways.p.13