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View corpus contextFirms reporting stronger AI capabilities in Jordan's FinTech ecosystem also report more financial innovation, largely because AI fuels digital entrepreneurial activity; the direct effect of AI on innovation is smaller once entrepreneurship is accounted for.
From Artificial Intelligence Capabilities to Financial Innovation in FinTech-Oriented Digital Ecosystems: The Mediating Role of Digital Entrepreneurship
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In a cross-sectional survey of 159 Jordanian FinTech actors, reported AI capabilities are positively associated with digital entrepreneurship and financial innovation, with digital entrepreneurship partially mediating the AI–innovation relationship.
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View corpus contextArtificial intelligence (AI) has become a strategic capability for enhancing innovation within FinTech-oriented digital ecosystems. However, the mechanisms through which AI capabilities are associated with financial innovation remain insufficiently understood. This study examines the relationship between AI capabilities and financial innovation by investigating the mediating role of digital entrepreneurship. Rather than if AI adoption automatically leads to financial innovation, the study proposes that digital entrepreneurship represents an important organizational mechanism through which AI-enabled analytics, automation, prediction, and intelligent decision-support capabilities are translated into innovation outcomes. A quantitative cross-sectional research design was employed, and data were collected from 159 respondents involved in technology-enabled business, entrepreneurial, and financial activities in Jordan. Partial least squares structural equation modeling (PLS-SEM) was used to evaluate both the measurement and structural models. The findings indicate that AI capabilities are positively associated with digital entrepreneurship, while digital entrepreneurship is positively associated with financial innovation. AI capabilities also demonstrate a smaller but statistically significant direct association with financial innovation. Furthermore, digital entrepreneurship partially mediates this relationship, highlighting its role in strengthening the translation of AI-enabled capabilities into innovation outcomes. These findings suggest that organizations achieve stronger financial innovation not merely through AI adoption but through their ability to combine AI capabilities with entrepreneurial opportunity recognition, digital experimentation, and commercialization activities. The study contributes to FinTech, artificial intelligence, and digital entrepreneurship literature by providing empirical evidence from an emerging digital economy and by demonstrating how entrepreneurial processes help explain the relationship between AI capabilities and financial innovation.
Summary
Main Finding
AI capabilities (analytics, automation, prediction, decision-support) are positively associated with financial innovation in FinTech-oriented digital ecosystems, but much of that effect is transmitted through digital entrepreneurship. Digital entrepreneurship (opportunity recognition, digital experimentation, commercialization) both mediates and strengthens the translation of AI capabilities into financial-innovation outcomes. A smaller, but still statistically significant, direct effect from AI capabilities to financial innovation remains (partial mediation).
Key Points
- Theoretical framing: resource-based view (RBV) and dynamic capabilities — AI is a valuable resource whose innovation impact depends on organizational processes that mobilize and reconfigure it.
- Hypotheses tested:
- H1: AI capabilities → Digital entrepreneurship (supported; positive).
- H2: Digital entrepreneurship → Financial innovation (supported; positive).
- H3: AI capabilities → Financial innovation (supported; positive direct effect, smaller magnitude).
- Mediation: Digital entrepreneurship partially mediates the AI → Financial innovation link (supported).
- Constructs:
- AI capabilities: analytics, automation, prediction, intelligent decision support applied in financial services.
- Digital entrepreneurship: entrepreneurial processes leveraging digital tech — opportunity recognition, experimentation, business-model development, commercialization.
- Financial innovation: new/modified financial products, services, channels, processes, and business models.
- Context: empirical evidence from Jordan (an emerging digital economy) — relevance for MENA and similar markets.
Data & Methods
- Design: Cross-sectional quantitative survey.
- Sample: 159 respondents engaged in technology-enabled business, entrepreneurial, or financial activities in Jordan.
- Analysis: Partial least squares structural equation modeling (PLS-SEM) to assess measurement and structural models and test mediation.
- Main results: Significant positive paths for AI → digital entrepreneurship and digital entrepreneurship → financial innovation; AI → financial innovation direct path smaller but significant; mediation of digital entrepreneurship is partial.
- Notes (methodological): Paper emphasizes organizational-process mechanisms rather than simple adoption; no detailed effect sizes or robustness tests are reported in the excerpt.
Implications for AI Economics
- For valuation and impact assessment: AI investments alone do not guarantee innovation-driven economic value. Models predicting economic returns from AI should incorporate complementary organizational capabilities (entrepreneurial processes, commercialization capacity).
- For firms and investors: Maximize ROI on AI by investing simultaneously in entrepreneurial capabilities — mechanisms for opportunity discovery, rapid digital experimentation, customer testing, and commercialization pathways.
- For policymakers and ecosystem builders: Support regulatory sandboxes, entrepreneurship training, startup finance, and platform infrastructure that enable firms to convert AI capability into marketable financial innovations; this is especially crucial in emerging digital economies where institutional supports lag technology adoption.
- For empirical research in AI economics: Future studies should quantify effect sizes across contexts, use longitudinal designs to assess causality and dynamic capability development, and investigate moderating factors (firm size, sector, regulatory environment) that influence the capability-conversion process.
- Limitations to consider when using these results: cross-sectional design (limits causal claims), single-country sample (Jordan) which may limit external generalizability, and reliance on survey measures (potential common-method bias).
Overall takeaway: In FinTech ecosystems, the economic impact of AI on innovation is substantially determined by entrepreneurial processes that convert AI’s technical potential into commercially valuable financial innovations.
Assessment
Paper Typecorrelational
Evidence Strengthlow — Findings are based on a single cross-sectional self-report survey (n=159) analyzed with PLS-SEM, so associations may reflect common-method bias, reverse causality, or unobserved confounding; mediation is statistical rather than causal.
Methods Rigorlow — Uses appropriate structural equation tools for survey data, but the design lacks temporal ordering or exogenous variation, sample size is modest, sampling approach and measure validation details are not provided in the excerpt, and common-method and selection biases are likely.
SampleCross-sectional survey of 159 respondents 'involved in technology-enabled business, entrepreneurial, and financial activities' in Jordan (emerging digital economy context); likely convenience or non-random sampling; self-reported measures of AI capabilities, digital entrepreneurship, and financial innovation.
Themesinnovation org_design
IdentificationCross-sectional survey analyzed with PLS-SEM to test associations and mediation; no experimental or quasi-experimental identification (no instruments, no longitudinal ordering, no exogenous variation).
GeneralizabilitySingle-country (Jordan) context limits transferability to developed markets or other regulatory environments, Small, likely non-representative sample of individuals/organizations within FinTech ecosystems, Cross-sectional self-reports limit applicability to causal or temporal claims and to firm-level outcomes beyond perceptions, Sectoral and firm-size heterogeneity not described, constraining extrapolation across types of financial firms
Claims (5)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| AI capabilities are positively associated with digital entrepreneurship. Innovation Output | positive | Digital entrepreneurship |
Reading fidelity
high
Study strength
low
|
n=159
|
| Digital entrepreneurship is positively associated with financial innovation. Innovation Output | positive | Financial innovation, including new financial products, services, delivery channels, processes, and business models |
Reading fidelity
high
Study strength
low
|
n=159
|
| AI capabilities have a smaller but statistically significant direct association with financial innovation. Innovation Output | positive | Financial innovation |
Reading fidelity
high
Study strength
low
|
n=159
smaller but statistically significant direct association
|
| Digital entrepreneurship partially mediates the relationship between AI capabilities and financial innovation. Innovation Output | positive | Financial innovation as explained by AI capabilities through digital entrepreneurship |
Reading fidelity
high
Study strength
low
|
n=159
partial mediation
|
| The study argues that stronger financial innovation results from combining AI capabilities with entrepreneurial opportunity recognition, digital experimentation, and commercialization activities, rather than from AI adoption alone. Innovation Output | positive | Financial innovation |
Reading fidelity
high
Study strength
low
|
n=159
|