INTELLIGENT DECISION SUPPORT IN SMART GOVERNANCE: LEVERAGING AI AND BIG DATA ANALYTICS FOR PUBLIC SECTOR EFFICIENCY AND TRANSPARENCY

Abstract

Smart governance represents a transformational shift in public administration, characterized by the integration of artificial intelligence (AI) and big data analytics (BDA) to optimize decision-making, improve operational efficiency, and enhance transparency. However, existing research has not sufficiently established whether these technologies directly translate into measurable public value outcomes, nor how data governance influences this translation. This study empirically examines the extent to which AI adoption and big data analytics capability (BDAC) contribute to administrative efficiency and organizational transparency across public-sector agencies, and whether data governance acts as a moderating mechanism that conditions these effects. A quantitative, cross-sectional, multi-case research design was implemented across five public agencies utilizing cloud-enabled infrastructures and analytics-driven decision environments. Using purposive sampling, 268 respondents who were actively engaged in data, IT, or management functions completed a validated Likert-scale survey measuring AI adoption, BDAC, data governance strength, administrative efficiency, and transparency. Hierarchical ordinary least squares (OLS) regression with agency-clustered robust standard errors was employed to estimate main effects, while moderation analysis tested data governance as a structural amplifier. Control variables included agency size, budget band, IT maturity, service domain, and fixed effects to isolate contextual variation. The results demonstrate that BDAC is the strongest predictor of both administrative efficiency and transparency, indicating that analytic capability—not mere technology deployment—is the key determinant of performance outcomes in the public sector. AI adoption is positively associated with both outcomes, though to a lesser extent. Crucially, data governance significantly moderates the impact of both AI adoption and BDAC on transparency, suggesting that governance structures such as auditability, stewardship, documentation standards, and data lineage are essential for converting internal analytics into externally verifiable public value. The moderating effect on efficiency is present but less pronounced. This study advances smart governance theory by validating a capability-dominant model and positioning data governance as the enabling mechanism that transforms technical assets into accountable governance outcomes. Practically, it provides a strategic implementation roadmap emphasizing capability maturation, governance integration, and the intentional design of transparency as a measurable performance output.