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How does university students' academic major (STEM vs. non-STEM) affect their acceptance of e-learning: a multi-group analysis.

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Title: How does university students' academic major (STEM vs. non-STEM) affect their acceptance of e-learning: a multi-group analysis.
Authors: Han, Feifei1 (AUTHOR) feifei.han@griffith.edu.au, Guo, Jiesi2 (AUTHOR)
Source: International Journal of Educational Technology in Higher Education. 7/7/2025, Vol. 22 Issue 1, p1-22. 22p.
Subject Terms: *College majors, *Digital learning, *College students, Structural equation modeling, Technology Acceptance Model
Abstract: This study investigated the relations between the five important external factors outlined in the General Extended Technology Acceptance Model for E-Learning and the four core components in Technology Acceptance Model (Perceived Usefulness: PU, Perceived Ease of Use: PEOU, Behavioural Intention of Use: BIU, and Actual Use: AU). It also examined the extent to which these relations differed by academic majors (STEM versus non-STEM). A total of 745 Chinese undergraduates answered a Likert-scale questionnaire. The data were analysed using structural equation modeling, measurement invariance, and multi-group analysis. Except Computer Anxiety, the other four external factors were significantly and positively associated with either PU or PEOU. Although there were no significant differences in the total effects of the five external factors on PU, differences were found in the effects from Computer Self-efficacy, Subjective Norm, and Prior Experience on PEOU between STEM and non-STEM students. Furthermore, the effects of PEOU on PU and BIU were stronger for non-STEM undergraduates; whereas the path from PU to BIU was stronger for STEM undergraduates. The results suggested that different strategies should be implemented according to students' academic majors to encourage Chinese undergraduates to actively adopt e-learning. For Chinese STEM undergraduates, emphasis should be placed to improve their perceptions of the usefulness aspect in e-learning via strategies such as incorporating gamified elements and engaging features into the learning activities. It is important to enable non-STEM students to operate and navigate the e-learning systems free of effort through essential orientation programs and quality technical support. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: How does university students' academic major (STEM vs. non-STEM) affect their acceptance of e-learning: a multi-group analysis.
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  Data: <searchLink fieldCode="AR" term="%22Han%2C+Feifei%22">Han, Feifei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> feifei.han@griffith.edu.au</i><br /><searchLink fieldCode="AR" term="%22Guo%2C+Jiesi%22">Guo, Jiesi</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Educational+Technology+in+Higher+Education%22">International Journal of Educational Technology in Higher Education</searchLink>. 7/7/2025, Vol. 22 Issue 1, p1-22. 22p.
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  Data: *<searchLink fieldCode="DE" term="%22College+majors%22">College majors</searchLink><br />*<searchLink fieldCode="DE" term="%22Digital+learning%22">Digital learning</searchLink><br />*<searchLink fieldCode="DE" term="%22College+students%22">College students</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Acceptance+Model%22">Technology Acceptance Model</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study investigated the relations between the five important external factors outlined in the General Extended Technology Acceptance Model for E-Learning and the four core components in Technology Acceptance Model (Perceived Usefulness: PU, Perceived Ease of Use: PEOU, Behavioural Intention of Use: BIU, and Actual Use: AU). It also examined the extent to which these relations differed by academic majors (STEM versus non-STEM). A total of 745 Chinese undergraduates answered a Likert-scale questionnaire. The data were analysed using structural equation modeling, measurement invariance, and multi-group analysis. Except Computer Anxiety, the other four external factors were significantly and positively associated with either PU or PEOU. Although there were no significant differences in the total effects of the five external factors on PU, differences were found in the effects from Computer Self-efficacy, Subjective Norm, and Prior Experience on PEOU between STEM and non-STEM students. Furthermore, the effects of PEOU on PU and BIU were stronger for non-STEM undergraduates; whereas the path from PU to BIU was stronger for STEM undergraduates. The results suggested that different strategies should be implemented according to students' academic majors to encourage Chinese undergraduates to actively adopt e-learning. For Chinese STEM undergraduates, emphasis should be placed to improve their perceptions of the usefulness aspect in e-learning via strategies such as incorporating gamified elements and engaging features into the learning activities. It is important to enable non-STEM students to operate and navigate the e-learning systems free of effort through essential orientation programs and quality technical support. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 7/7/2025
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