The Application of Artificial Intelligence in Data-Driven Governance and Sustainable Development of Emerging Economies: From Challenges to Solutions
الموضوعات : Artificial Intelligence Tools in Software and Data Engineering
Sadra Rahmati
1
,
shima ghobadi
2
1 - Islamic Azad University, Shahreza, Iran
2 - Department of Foreign Languages Teaching, Shah. C., Islamic Azad University, Shahreza, Iran
الکلمات المفتاحية: Artificial Intelligence, Data-Driven Governance, Four-Dimensional Model, Emerging Economies, Structural Equation Modelling,
ملخص المقالة :
AI has shifted from a technical innovation to a strategic foundation for data-driven governance and sustainable growth.. This study seeks to fill theoretical and methodological voids by creating and empirically validating a Four-Dimensional Data Governance Model (FDGM) that encompasses four essential dimensions: data infrastructure, intelligent policymaking, technological human capital, and institutional trust, alongside data transparency. Employing a convergent mixed-method design, the research combines cross-national quantitative indicators—sourced from Oxford Insights (2024), the World Intellectual Property Organization’s Global Innovation Index (2025), the World Bank’s Digital Development Indicators (2025), and UNESCO’s AI Ethics and Governance Index (2025)—with a qualitative thematic content analysis of national AI policy documents from India, Brazil, Indonesia, and Iran. Structural Equation Modelling (PLS-SEM) results demonstrate that data infrastructure exerts a significant direct effect on the efficiency of smart policymaking (β = 0.67, p < 0.01); smart governance, mediated by technological human capital, positively influences technology-driven sustainable development (β = 0.58, p < 0.05); and institutional trust and data transparency play a moderating role in strengthening the relationship between policymaking and innovation outcomes (β = 0.41, p < 0.01). Comparative results reveal that India exhibits a high maturity level across all four dimensions, particularly in data trust and policy coherence. At the same time, Iran and Indonesia demonstrate lower maturity due to institutional fragmentation and limited transparency. Brazil, on the other hand, lies in a transitional phase, leveraging human capital and agricultural innovation as strategic advantages. The novelty of this study lies in proposing and empirically validating an actionable, data-driven analytical framework that quantifies causal and moderating relationships among the four pillars of AI governance. The FDGM offers a replicable framework for national AI strategies, enabling policymakers to identify barriers, enact reforms, and monitor progress toward trustworthy AI governance.
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