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Factors Affecting the Intention to Adopt M-Learning

Author

Listed:
  • Sanduni I. Senaratne
  • Samantha M. Samarasinghe

Abstract

Over the recent years, emerging technological applications have been used for making student learning more effective and interactive. M-Learning has been one such technological initiative which has shown promising benefits in the higher education context. Even though the importance of mobile learning has been researched by many, the factors influencing mobile learning adoption intention has not been addressed sufficiently, particularly in the Sri Lankan context. Hence, the purpose of this paper was to present a conceptual model to examine the factors influencing the intention to adopt mobile learning by the students engaged in higher education. Based on a comprehensive literature review, this study extended the Technology Acceptance Model (TAM) (Davis, 1989) with mobile self-efficacy, intrinsic motivation to use mobile devices and the system quality of the m-Learning system. The model describes how the aforementioned factors influence the higher education students’ intention to adopt m-Learning via survey data collected from 151 postgraduate students. The findings suggest that the model explained the factors influencing the intention to adopt m-Learning among students in higher education. In detail, the mobile self -efficacy, system quality and intrinsic motivation significantly influenced the intention to adopt m-Learning. The results could be utilized for increasing the adoption of m-Learning practices and developing mobile applications useful for teaching and learning purposes. This study has incorporated three independent constructs in extending the TAM model; namely, system quality, mobile self-efficacy and intrinsic motivation. These were derived from the IS Success theory, Self-efficacy theory and Self-determination theory respectively. Accordingly, this study intends to address the theoretical gap in the higher education context pertaining to the adoption of mobile learning. Since Mobile Self-Efficacy and System Quality were the most significant factors that affect the perceived ease of use and perceived usefulness, these factors should be given prominence when developing mobile enabled Learning Management Systems within institutions.

Suggested Citation

  • Sanduni I. Senaratne & Samantha M. Samarasinghe, 2019. "Factors Affecting the Intention to Adopt M-Learning," International Business Research, Canadian Center of Science and Education, vol. 12(2), pages 150-164, February.
  • Handle: RePEc:ibn:ibrjnl:v:12:y:2019:i:2:p:150-164
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    References listed on IDEAS

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    1. Teo, Thompson S. H. & Lim, Vivien K. G. & Lai, Raye Y. C., 1999. "Intrinsic and extrinsic motivation in Internet usage," Omega, Elsevier, vol. 27(1), pages 25-37, February.
    2. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    3. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    4. Peter B. Seddon, 1997. "A Respecification and Extension of the DeLone and McLean Model of IS Success," Information Systems Research, INFORMS, vol. 8(3), pages 240-253, September.
    5. James E. Bailey & Sammy W. Pearson, 1983. "Development of a Tool for Measuring and Analyzing Computer User Satisfaction," Management Science, INFORMS, vol. 29(5), pages 530-545, May.
    6. Priyanka Surendran, 2012. "Technology Acceptance Model: A Survey of Literature," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 2(4), pages 175-178, August.
    7. Priyanka Surendran, 2012. "Technology Acceptance Model: A Survey of Literature," International Journal of Business and Social Research, LAR Center Press, vol. 2(4), pages 175-178, August.
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    Cited by:

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    2. Mihaela Moca & Alina Badulescu, 2023. "Determinants of Economical High School Students’ Attitudes toward Mobile Devices Use," Sustainability, MDPI, vol. 15(12), pages 1-21, June.

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    More about this item

    Keywords

    mobile learning; higher education; information and communication technology; technology acceptance model;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics

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