Artificial Intelligence Adoption, Administrative Innovation and Organisational Efficiency in Service-Based Industry, Imo State

This study examined the relationship among artificial intelligence (AI) adoption, administrative innovation and organisational efficiency in the service-based industry in Imo State, Nigeria. The study examined the effect of AI adoption on organisational efficiency, its effect on administrative innovation, and the mediating role of administrative innovation in the relationship between AI adoption and organisational efficiency. The study was anchored on the Technology-Organisation-Environment (TOE) framework and Resource-Based View (RBV). A cross-sectional survey design was adopted. The population comprised management and operational staff of service firms in banking, telecommunications, hospitality and health-service sub-sectors. Using the Taro Yamane formula, 320 respondents were selected through stratified random sampling, while 300 valid responses were obtained, representing a 93.75 per cent response rate. Data were collected using a structured questionnaire with Cronbach’s alpha coefficients of 0.84, 0.81 and 0.86 for AI adoption, administrative innovation and organisational efficiency, respectively. Data were analysed using descriptive statistics, Pearson correlation, regression and mediation analysis. Findings indicated high levels of AI adoption (mean = 3.94), administrative innovation (mean = 3.81) and organisational efficiency (mean = 3.88). AI adoption significantly affected organisational efficiency (β = 0.71, t = 12.84, p < 0.05) and administrative innovation (β = 0.68, t = 11.02, p < 0.05). Administrative innovation significantly mediated the relationship between AI adoption and organisational efficiency (β = 0.24, t = 5.16, p < 0.05). The study concluded that AI generates greater efficiency when supported by administrative restructuring and innovation. Service firms should therefore integrate AI adoption with workflow redesign, employee training and strategic administrative innovation, while government and regulators should provide supportive incentives and capacity-building programmes. Future studies should examine other sectors and states using longitudinal designs.