Artificial Intelligence Adoption And Employee Productivity In Public Institutions And Higher Education Organisations In Nigeria: The Mediating Role Of Organisational Readiness

This study examined the effect of artificial intelligence (AI) adoption on employee productivity in public institutions and higher education organisations in Nigeria, disaggregating AI adoption into AI-assisted decision making, AI-based automation, and generative AI utilisation, and testing organisational readiness—comprising management support, digital infrastructure, employee AI knowledge and skills, and institutional policy—as a mediating mechanism linking adoption to productivity outcomes. A descriptive cross-sectional survey design was employed, targeting lecturers, academic administrators, registry staff, ICT personnel, departmental administrators, and management staff drawn from federal and state universities, polytechnics, colleges of education, and government agencies. Data were collected using a structured, validated five-point Likert-scale questionnaire adapted from established scales, and analysed using hierarchical multiple regression together with bootstrapped mediation analysis (Hayes' PROCESS macro, Model 4) at the 0.05 significance level, preceded by reliability, multicollinearity, and normality diagnostics. Findings revealed that AI-assisted decision making (β = 0.472, p < 0.001), AI-based automation (β = 0.357, p < 0.001), and generative AI utilisation (β = 0.167, p < 0.001) each exerted a positive and significant effect on employee productivity. Furthermore, organisational readiness significantly mediated the relationships between AI-based automation and employee productivity (indirect effect = 0.261, 95% CI: 0.189–0.331) and between generative AI utilisation and employee productivity (indirect effect = 0.221, 95% CI: 0.099–0.362). However, organisational readiness did not significantly mediate the relationship between AI-assisted decision making and employee productivity (indirect effect = -0.038, 95% CI: -0.121–0.041). The study extends the limited Nigeria-centric evidence base on AI adoption within public-sector and higher-education settings and informs institutional policy on infrastructure investment, staff training, and governance frameworks required to convert AI adoption into measurable productivity gains.