Influence of Artificial Intelligence Techniques as Predictors on the Digital Transformation of Accounting Systems in Niger State Public Sector Institutions

This study examined the influence of Artificial Intelligence (AI) techniques as predictors on the digital transformation of accounting systems in Niger State public sector institutions. Three purposes, three research questions and three corresponding null hypotheses guided the study. A correlational research design was adopted. The population comprised 420 respondents drawn from selected public sector institutions in Niger State, consisting of accounting officers, auditors, Information and Communication Technology personnel, and finance administrators. A sample of 205 respondents was determined using the Taro Yamane formula at a 5% level of precision. The sample comprised 51 accounting officers, 51 auditors, 51 Information and Communication Technology personnel, and 52 finance administrators. Data were collected using a structured questionnaire titled Artificial Intelligence Techniques and Digital Transformation Questionnaire (AITDTQ), validated by five experts in Business and Entrepreneurship Education. Cronbach’s Alpha reliability coefficients of 0.85, 0.83, and 0.87 were obtained for Sections 1, 2 and 3 respectively, with an overall reliability coefficient of 0.86. Linear regression was used to answer the research questions, while regression t-test was employed to test the null hypotheses at 0.05 level of significance. The study was anchored on the Technology Acceptance Model. Findings revealed that machine learning significantly predicted digital transformation of accounting systems, with R = .563 and R² = .316; robotic process automation also significantly predicted digital transformation, with R = .430 and R² = .185; while data analytics significantly predicted digital transformation, with R = .386 and R² = .149. The corresponding t-values of 16.014, 11.198, and 9.836, respectively, were significant at p < .05, leading to the rejection of all three null hypotheses. The study concluded that machine learning, robotic process automation and data analytics are significant predictors of digital transformation of accounting systems in Niger State public sector institutions. The study recommends that public-sector institutions strengthen AI adoption through appropriate policies, ICT infrastructure, continuous professional development, data governance, cybersecurity and institutional support to enhance efficient and sustainable digital transformation of accounting systems.