Knowledge management and research motivation in higher education: Exploring the moderating role of AI proficiency

Abstract

In the context of higher education's growing emphasis on research productivity and digital transformation, this study investigates how knowledge management (KM) infrastructure and KM processes influence university lecturers’ research motivation, with research self-efficacy serving as a mediator and AI proficiency acting as a moderator. Drawing on Social Cognitive Theory and Task–Technology Fit Theory, a conceptual model was developed and empirically tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) with data collected from 295 academic staff members at public and private universities in Vietnam. The findings reveal that both KM infrastructure and KM processes significantly enhance research motivation, both directly and indirectly through research self-efficacy. Moreover, AI proficiency not only has a direct positive effect on research motivation and self-efficacy but also strengthens the relationship between research self-efficacy and research motivation. These results underscore the critical importance of fostering institutional KM systems, cultivating psychological capabilities, and equipping faculty with digital competencies—particularly AI literacy—to drive sustainable academic engagement. The study offers theoretical contributions to the literature on knowledge management and academic motivation, as well as practical implications for higher education institutions aiming to build research-capable environments in the digital era.