Determinants of artificial intelligence adoption in student support information systems in Vietnamese higher education

Artificial intelligence (AI) is increasingly viewed as a promising tool for improving student support services in higher education. However, AI adoption remains uneven, particularly in contexts where information systems are fragmented, data quality is inconsistent, and institutional readiness is limited. This study develops and tests an integrated model linking management information systems (MIS), data quality, AI readiness, perceived benefits, and acceptance of AI-enabled student support systems. Survey data were collected from 450 respondents in Vietnamese higher education, including administrators, faculty members, and students. Because the study focused on the exploratory examination of relationships among constructs rather than confirmatory model testing, the analysis was conducted using construct-level composite scores and regression-based path analysis. The findings provide preliminary evidence of a possible sequential pattern in which MIS is positively associated with data quality, which in turn is associated with AI readiness. AI readiness is positively associated with perceived benefits and shows a tentative positive association with acceptance. Additional direct effects of data quality and MIS on downstream outcomes suggest that upstream system and data conditions may play an important role alongside user perceptions in shaping AI adoption outcomes. Notably, perceived benefits do not significantly predict acceptance in the final model. The study contributes to the literature by extending user-centered technology adoption perspectives with system- and data-level conditions. It also offers practical implications for higher education institutions seeking to strengthen data governance, system integration, and organizational preparedness for responsible AI implementation.