Abstract
Teaching and learning in higher education institutions have been transformed due to the integration of technology, shaping institutions to adopt Learning Management Systems (LMS). The University of Johannesburg migrated from Blackboard LMS to Moodle LMS in 2024. This implementation influences both teaching methods and technology use, affecting student learning outcomes. Lecturers may need to redesign how they structure and deliver content, which can increase workload and sometimes lead to resistance, while students may also struggle to adapt to new teaching approaches and navigate the new system. The study aimed at investigating factors influencing students’ acceptance and use of Moodle LMS at a South African University, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) 3 framework. Online surveys were used to collect data, and 301 students at one university participated in the study. The survey consisted of items that were adopted from the UTAUT3 questionnaire and adapted to fit the South African context. The Structural Equation Modelling (SEM) was used to test the hypothesis and the relationship between constructs. The results indicated that social influence ( =0,160; p < 0,05), personal innovativeness ( = 0,437; p < 0,001), facilitating conditions ( = 0,236; p < 0,001), and habit ( = 0,182; p < 0,001) had a statistically significant effect on the behavioural intention to accept the Moodle learning management system. Lastly, habit ( = 0,197; p < 0,001) and behavioural intention ( = 0,557; p < 0,001) had a statistically significant effect on behavioural usage of the Moodle LMS. These findings also provide valuable insights for LMS developers and administrators, instructional designers, and learning developers regarding the factors that influence students’ acceptance and use of LMSs. Such insights can guide lecturers in designing and improving modules and in making more effective use of the LMS for teaching and learning. Future research can build on exploring moderating variables such as age, gender, and study level that influence technology adoption and use.