ROLE OF MANAGEMENT SUPPORT ON ARTIFICIAL INTELLIGENCE ADOPTION AND DETECTION OF PAYROLL FRAUD IN THE NIGERIAN PUBLIC SECTOR

This study investigated the moderating effects of management support on the relationship between artificial intelligence (AI) adoption and payroll fraud in the public sector of Nigeria. The persistence of payroll fraud in public sector institutions has raised concerns about inefficiencies in traditional control systems, thereby necessitating the adoption of advanced technologies such as AI to improve fraud detection mechanisms. However, the effectiveness of these technologies may depend on the extent of managerial commitment and support within organisations. The study is grounded in innovation diffusion theory and adopts a quantitative research design complemented by explanatory correlational analysis. A population of 820 participants from the Bayelsa State Ministry of Finance were involved in accounting and auditing functions across public sector institutions, while a sample of 380 participants was drawn using a stratified random sampling technique. Primary and secondary data were employed with a questionnaire as the major source of data collection based on a 5-point Likert scale. The questionnaire was tested using content and face validity, while reliability was determined by the Cronbach's Alpha coefficient. The responses from the administered questionnaire were tested using univariate, bivariate and multivariate analysis. The multipe regression analysis revealed a positive and significant association between MAL, NLP, DTA, DIM, and EXS on the detection of financial statement fraud in the public sector. The results further revealed that MAS positively and significantly moderates the relationship between AI adoption and the detection of financial statement fraud in the public sector. The study concludes that while AI tools are critical in improving payroll fraud detection in the public sector, their success is significantly influenced by the level of management support. It recommends that public institutions should prioritise not only the adoption of AI technologies but also the provision of adequate managerial backing through training, resource allocation, and policy implementation to maximise effectiveness and improve accountability