Assessing the institutional readiness and capacity for AI adoption in public audit institutions in developing countries: evidence from Ghana

Abstract

• Develops a validated, multi-dimensional framework for assessing AI readiness in public audit institutions, grounded in the TOE framework and Institutional Theory. • Empirically examines the influence of technological capacity, organizational capability, and environmental context on AI adoption in developing countries. • Demonstrates that technological infrastructure is the strongest predictor of AI readiness, followed by organizational and environmental factors. • Establishes a significant link between institutional readiness and behavioral intention to adopt AI, integrating insights from UTAUT and Theory of Planned Behavior. • Provides actionable policy and institutional recommendations to guide AI integration in public sector audits, emphasizing infrastructure, human capital, and regulatory alignment. • Offers a scalable diagnostic tool for policymakers and audit leaders to benchmark readiness and tailor strategic interventions. • Contributes to the literature on digital governance and public financial management in developing economies, with specific relevance to Ghana and similar contexts. This study investigates the institutional readiness and capacity for adopting Artificial Intelligence (AI) in public audit institutions in developing countries, using Ghana as a case study. Anchored in the Technology-Organization-Environment (TOE) framework and Institutional Theory, the research employed a cross-sectional survey design with 332 respondents, including auditors, IT professionals, and public financial managers. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test relationships among e-Government readiness, institutional pressure, AI readiness, and behavioral intention to adopt AI. The results show that both e-Government readiness and institutional pressure significantly predict AI readiness, which in turn strongly influences behavioral intention. Notably, AI readiness mediates the effects of institutional and technological factors, affirming its role as a pivotal construct in the adoption process. The findings underscore that while external regulatory incentives are important, internal digital infrastructure and adaptive institutional capacity are more critical drivers of AI adoption. The study contributes a validated model of AI readiness for public sector audits and provides actionable insights for policymakers, including the need for digital infrastructure investments, human capital development, and cultural transformation within audit institutions. The research concludes with a call for future studies to explore the influence of organizational culture and trust on AI adoption in public governance.