This study used the Technology Acceptance Model (TAM) to predict students' acceptance of Learning Management Systems (LMS) through Behavioural Intention in the English Department of Ibn Zohr University. A quantitative approach with a cross-sectional survey design was adopted. Convenience sampling as a non-probability sampling technique was used. Data were collected from 126 respondents through a questionnaire developed. The structural model developed was based on previous works into TAM and included Social Influence (SI), computer self-efficacy (CSE), Perceived Usefulness (PU), Perceived Ease of Use (PEU), Attitude (ATT), and Behavioural Intention (BI). The proposed model was tested and evaluated using the partial least squares structural equation modelling (PLS-SEM) data analysis technique. There were eight supported hypotheses. Albeit, there were six rejected hypotheses. SI had a significant strong effect on both PU and ATT. PEU had a significant strong effect on PU. CSE had a significant strong effect on both PEU and BI. PU had a significant strong effect on both ATT and BI. Nonetheless, SI had an insignificant effect on both BI and PEU. CSE had an insignificant negative effect on PU and an insignificant effect on ATT. PEU had an insignificant negative effect on BI and an insignificant effect on ATT. Of all the proposed variables, ATT had the greatest effect on BI. This study discovered that the resulting model was able to predict and explain BI amongst students at Ibn Zohr University. Furthermore, the model showed an ability to explain 66.2% of the variance in BI, 44.2% in PU, 59.9% in PEU, and 63.5% in ATT. These findings have important implications for developing and improving LMS that can be accepted by students. The findings of this study are pertinent to the higher education management administrations, LMS developers, researchers, and stakeholders for improving and promoting the use of Learning Management Systems amongst university students.Show Less
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