ARTIFICIAL INTELLIGENCE ADOPTION AND EMPLOYEE PRODUCTIVITY IN PUBLIC INSTITUTIONS AND HIGHER EDUCATION ORGANISATIONS IN NIGERIA: THE MEDIATING ROLE OF ORGANISATIONAL READINESS
DOI:
https://doi.org/10.61421/IJSSMER.2026.4501Keywords:
Artificial intelligence adoption, AI-assisted decision making, AI-based automation, generative AI utilisation, organisational readiness, employee productivityAbstract
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.
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References
Agbarakwe, H. A., & Chibueze, O. O. (2024). Leveraging artificial intelligence for enhanced assessment and feedback mechanisms in Nigeria higher education system. International Journal of Research and Innovation in Social Science, 8(9), 142–151.
Amin, S. (2024). The adoption of Industry 4.0 technologies by using the technology organizational environment framework: The mediating role to manufacturing performance in a developing country. Business Strategy & Development, 7(4).
Audu, Y. P., & Aziwe, N. I. (2025). Artificial intelligence and the performance of manufacturing firms in North-Central Nigeria: Reward system as the moderator. International Journal of Research and Innovation in Social Science, 9(1), 1738–1755.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
Chondough, S. M., & Chondough, J. T. (2022). The moderating role of intellectual capital on the relationship between artificial intelligence and employee performance of the commercial banks in Nigeria. Cross-Cultural Management Journal, 24(2), 115–119.
Chukwuka, E. J., & Onokero, I. I. (2025). Impact of artificial intelligence on employee's productivity in technological organizations in Delta State, Nigeria. Jalingo Journal of Social and Management Sciences, 6(3), 239–251.
Cochran, W. G. (1977). Sampling techniques (3rd ed.). Wiley.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data: Evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
Nwosu, C. C., Obalum, D. C., & Ananti, M. O. (2024). Artificial intelligence in public service and governance in Nigeria. Journal of Governance and Accountability Studies, 4(2), 109–120.
Obi, A. V. (2024). Management of artificial intelligence and the performance of manufacturing firms in Enugu State, Nigeria. Tec Empresarial, 19(1).
Ogbaga, I. (2026). Effect of mentorship training on generative AI adoption among academic staff in a Nigerian health sciences university: A quasi-experimental study. Direct Research Journal of Engineering and Information Technology.
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
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