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Factors propelling the adoption of m-learning among students in higher education

Author

Listed:
  • Jasmine A. L. Yeap

    (Universiti Sains Malaysia)

  • T. Ramayah

    (Universiti Sains Malaysia)

  • Pedro Soto-Acosta

    (University of Murcia)

Abstract

University students seem primed for mobile learning (m-learning) given their affinity with technology and the ubiquity of mobile computing devices on campuses. However such conditions do not necessarily guarantee their readiness for m-learning. For m-learning to thrive in higher education, it is crucial to understand the factors propelling its adoption. Accordingly this study uncovers factors that drive the adoption of m-learning among university students. Using a mobile learning readiness model based on the Theory of Planned Behavior, data was collected from 900 undergraduates in a local, public university in Malaysia. Partial least squares analysis revealed that all three constructs of attitude, subjective norm and perceived behavioral control significantly influenced students’ intention to adopt m-learning. These three constructs were significantly predetermined by their respective external beliefs components. In fostering m-learning adoption among students, more emphasis should be expended to capitalize on subjective norm and improve perceived behavioral control.

Suggested Citation

  • Jasmine A. L. Yeap & T. Ramayah & Pedro Soto-Acosta, 2016. "Factors propelling the adoption of m-learning among students in higher education," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(4), pages 323-338, November.
    • Handle: RePEc:spr:elmark:v:26:y:2016:i:4:d:10.1007_s12525-015-0214-x
    DOI: 10.1007/s12525-015-0214-x
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    References listed on IDEAS

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    Cited by:

    1. Rainer Alt & Hans-Dieter Zimmermann, 2016. "Electronic Markets on electronic markets in education," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(4), pages 311-314, November.
    2. Eun-Jung Kim & Jinkyung Jenny Kim & Sang-Ho Han, 2021. "Understanding Student Acceptance of Online Learning Systems in Higher Education: Application of Social Psychology Theories with Consideration of User Innovativeness," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    3. Nurul Aisyah Kamrozzaman & Jamaludin Badusah & Wan Muna Ruzanna, 2019. "Coggle: SWOT Analysis in Lifelong Learning Education Using Online Collaborative Mind-Mapping," International Journal of Asian Social Science, Asian Economic and Social Society, vol. 9(1), pages 139-147, January.
    4. Farida Veerankutty & Thurasamy Ramayah & Noor Azman Ali, 2018. "Information Technology Governance on Audit Technology Performance among Malaysian Public Sector Auditors," Social Sciences, MDPI, vol. 7(8), pages 1-19, July.
    5. Aleksandra Kobicheva & Elena Tokareva & Tatiana Baranova, 2022. "Students’ Affective Learning Outcomes and Academic Performance in the Blended Environment at University: Comparative Study," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    6. Elaheh Yadegaridehkordi & Mehrbakhsh Nilashi & Liyana Shuib & Shahla Asadi & Othman Ibrahim, 2019. "Development of a SaaS Adoption Decision-Making Model Using a New Hybrid MCDM Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(06), pages 1845-1874, November.
    7. Xiu-Kin Loh & Voon-Hsien Lee & Xiu-Ming Loh & Garry Wei-Han Tan & Keng-Boon Ooi & Yogesh K. Dwivedi, 2022. "The Dark Side of Mobile Learning via Social Media: How Bad Can It Get?," Information Systems Frontiers, Springer, vol. 24(6), pages 1887-1904, December.

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