Determinants of continuance intention in AI-enabled HRM: an application of the UTAUT model in the Egyptian higher education sector

Abstract The increased adoption of artificial intelligence (AI) in human resource management (HRM) has transformed the administrative and decision-making processes in institutions of higher education. Despite the growing research interest in AI-based HRM systems, it has primarily focused on the initial adoption, which has provided limited insights into the factors that maintain consistent use over time. To fill this gap, the current study examines the factors that influence the intention of users to continue using the AI-based HRM systems in the Egyptian institutions of higher learning in the private sector based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and post-adoption theory. The article has a quantitative, cross-sectional design. The collected data were gathered through a structured survey, which was completed by 346 individuals: academic employees, administrative staff, and users who had directly or indirectly interacted with AI-enabled HRM-related systems within the Egyptian private universities. The hypothesized conceptual model is that perceived organizational support, perceived ease of use, perceived risk, and performance expectancy are major antecedents of continuance intention, and that digital literacy is a moderating variable. Partial least squares structural equation modeling (PLS-SEM) was used to test hypothesized relationships. Findings indicate that performance expectancy and perceived organizational support have the strongest impact on continuance intention, then the perceived risk and perceived ease of use. Interestingly, perceived risk has a positive impact meaning risk normalization in post-adoption AI utilization settings. Moreover, digital literacy skills have a considerable moderation effect on the entire model relationships, thus increasing the impact of the defined determinants on the continuance intention of users. The work contributes to the body of literature on information systems and human resource management by recategorizing AI adoption as a capability-based post-adoption process, as well as by broadening the use of UTAUT to continuance intention in the context of HRM. Based on the statistics of an underserved emerging economy, it provides empirical evidence, which can be used by higher education leaders to achieve sustainable AI-based HRM change.