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Students’ Adoption of m-Learning in Higher Education: An Empirical Study

Year 2023, , 160 - 171, 15.06.2023
https://doi.org/10.5152/JSSI.2022.22041

Abstract

Universities have an important role in integrating technology into education; therefore, it is crucial
to increase technology use in higher education. Technological improvement, especially in mobile
technology, has a great impact on education, leading to the shift in educational activities from
the web environment to mobile platforms. Since mobile technology is important for education, it
is necessary to evaluate how students benefit from the adoption of the mobile learning concept
and its systems. Considering the importance of mobile technology in education, this study aimed
to reveal the factors affecting students’ intentions toward m-learning. An adoption model was
examined by taking the technology acceptance model as a base. A questionnaire was employed
on 417 undergraduate or postgraduate students to collect data. Model validation was performed
by structural equation modeling. The model revealed the factors that affect students’ acceptance
of m-learning as perceived usefulness, technical efficacy, social norm, system features, perceived
trust, and innovativeness. Examination of these factors will be useful for the design of m-learning
applications, understanding the main reasons behind the users’ attitude toward m-learning and
promoting the use of m-learning in education.

References

  • Abernathy, D. J. (2001). Get ready for M-learning. Training and Development, 55(2), 20.
  • Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distributed Learning, 14(5), 82–107. [CrossRef]
  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215. [CrossRef]
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. [CrossRef]
  • Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior, 56, 93–102. [CrossRef]
  • Alkis, N., Coskunçay, D. F., & Yildirim, S. Ö. (2014). A systematic review of technology acceptance model in e-learning context. ACM International Conference Proceeding Series, 1–5. [CrossRef]
  • Alkis, N., & Findik-Coskuncay, D. (2018). Mobile learning acceptance: A systematic literature review. Erzincan University Journal of Education Faculty, 20(2), 571–589.
  • Alrasheedi, M., & Capretz, L. F. (2015). Determination of critical success factors affecting mobile learning: A meta-analysis approach. Turkish Online Journal of Educational Technology, 14(2), 41–51.
  • Arpaci, I. (2016). Understanding and predicting students’ intention to use mobile cloud storage services. Computers in Human Behavior, 58, 150–157. [CrossRef]
  • Bere, A., & Rambe, P. (2016). An empirical analysis of the determinants of mobile instant messaging appropriation in university learning. Journal of Computing in Higher Education, 28(2), 172–198. [CrossRef]
Year 2023, , 160 - 171, 15.06.2023
https://doi.org/10.5152/JSSI.2022.22041

Abstract

References

  • Abernathy, D. J. (2001). Get ready for M-learning. Training and Development, 55(2), 20.
  • Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distributed Learning, 14(5), 82–107. [CrossRef]
  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215. [CrossRef]
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. [CrossRef]
  • Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior, 56, 93–102. [CrossRef]
  • Alkis, N., Coskunçay, D. F., & Yildirim, S. Ö. (2014). A systematic review of technology acceptance model in e-learning context. ACM International Conference Proceeding Series, 1–5. [CrossRef]
  • Alkis, N., & Findik-Coskuncay, D. (2018). Mobile learning acceptance: A systematic literature review. Erzincan University Journal of Education Faculty, 20(2), 571–589.
  • Alrasheedi, M., & Capretz, L. F. (2015). Determination of critical success factors affecting mobile learning: A meta-analysis approach. Turkish Online Journal of Educational Technology, 14(2), 41–51.
  • Arpaci, I. (2016). Understanding and predicting students’ intention to use mobile cloud storage services. Computers in Human Behavior, 58, 150–157. [CrossRef]
  • Bere, A., & Rambe, P. (2016). An empirical analysis of the determinants of mobile instant messaging appropriation in university learning. Journal of Computing in Higher Education, 28(2), 172–198. [CrossRef]
There are 10 citations in total.

Details

Primary Language Turkish
Subjects World Languages, Literature and Culture (Other)
Journal Section Research Articles
Authors

Nurcan Alkış Bayhan

Duygu Fındık Coşkunçay This is me

Publication Date June 15, 2023
Published in Issue Year 2023

Cite

APA Alkış Bayhan, N., & Fındık Coşkunçay, D. (2023). Students’ Adoption of m-Learning in Higher Education: An Empirical Study. Current Perspectives in Social Sciences, 27(2), 160-171. https://doi.org/10.5152/JSSI.2022.22041

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