Investigating academic major differences in perception of computer self-efficacy and intention toward e-learning adoption in China

ABSTRACT

Recognizing the underlying relationship between e-learning practice and the institutional environments hosted in, the Chinese educational practice on branching high school students into science, technology, engineering, and mathematics (STEM) and non-STEM academic major groups before being admitted into universities or colleges is examined. By extending the well-established Technology Acceptance Model (TAM) with computer self-efficacy, this study aims to examine the difference in perceptions and behaviours on e-learning adoption from the STEM and non-STEM students. The results revealed that STEM’s score of computer self-efficacy, perceived ease of use and behavioural intention to use e-learning are all greater than non-STEM’s.

KEYWORDS:

• Academic major difference
• STEM
• e-learning
• TAM
• self-efficacy
• China

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71810107003].

Notes on contributors

Nattaporn Thongsri

Nattaporn Thongsri is a researcher. She holds a Ph.D. in management science and engineering from Huazhong University of Science and Technology. Her research interests include technology-enhanced learning and behavioural information systems. Her research has appeared in Behaviour & Information Technology, Journal of Systems and Information Technology and International Journal of Information and Learning Technology.

Liang Shen

Liang Shen is an associate professor at Center for Big Data Analytics, Jinagxi University of Engineering. His research interests include educational informatics, computational intelligence based analytics in education and business.

Yukun Bao

Yukun Bao is currently professor at the School of Management, Huazhong University of Science and Technology, China, and Deputy Director of Center for Modern Information Management at the same university. His research interests are in computational intelligence-based predictive analytics, educational informatics and information system management. He has published more than 80 papers, and has been Principal Investigator for four research projects funded by National Science Foundation of China. He received IBM’s Excellent Faculty Award in 2012. He is Senior Member of IEEE, and Associate Editor of Neurocomputing (Elsevier) and Journal of Systems and Information Technology (Emerald).