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


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.

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Correspondence to Pedro Soto-Acosta.

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Responsible Editor: Ricardo Colomo-Palacios



Attitude towards M-Learning.

I would like my coursework more if I used m-learning (ATT1).

Using m-learning in my coursework would be a pleasant experience (ATT2).

Using m-learning in my coursework would be a wise idea (ATT3).

Intention to Adopt M-Learning.

I predict I would use a mobile device for my courses (INT1).

I plan to use a mobile device if a course has mobile learning functions (INT2).

I intend to adopt a mobile device for university courses (INT3).

Instructor Readiness.

I think instructors (i.e. lecturers, tutors) would approve of utilizing m-learning for their courses (IR1).

I think instructors (i.e. lecturers, tutors) would believe that a mobile device could be a useful educational tool in their courses (IR2).

I think instructors (i.e. lecturers, tutors) would have adequate technical skills to use a mobile device in their teaching (IR3).

Learning Autonomy.

I would be able to actively access coursework material with a mobile device (LA1).

I would have more opportunities to create knowledge in my coursework with a mobile device (LA2).

I would be able to control the pace (speed) of learning in my classes with a mobile device (LA3).

Perceived Behavioral Control.

I have sufficient extent of knowledge to use m-learning (PBC1).

I have sufficient extent of control to make a decision to adopt m-learning (PBC2).

I have sufficient extent of self-confidence to make a decision to adopt m-learning (PBC3).

Perceived Ease of Use.

I believe that mobile devices would be easy to use (PEU1).

I believe it would be easy to access course material with my mobile device (PEU2).

I believe that mobile devices would be easy to operate (PEU3).

Perceived Self-Efficacy.

I am confident about using a mobile device for my courses (PSE1).

Using a mobile device for my courses would not be a challenge for me (PSE2).

I would feel comfortable using a mobile device in my courses (PSE3).

Perceived Usefulness.

I believe that using mobile devices would improve my ability to learn (PU1).

I believe that mobile devices would allow me to get my work done more quickly (PU2).

I believe that mobile devices would be useful for my learning (PU3).

Subjective Norm.

Most people who are important to me think that it would be fine to use a mobile device for university courses (SN1).

I think other students in my classes would be willing to adopt a mobile device for learning (SN2).

Most people who are important to me would approve of using a mobile device for university courses (SN3).

Student Readiness.

I think other students would approve of utilizing m-learning in their coursework (SR1).

I think other students would believe that a mobile device could be a useful educational tool in their coursework (SR2).

I think other students would have adequate technical skills to use a mobile device in their coursework (SR3).

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Yeap, J.A.L., Ramayah, T. & Soto-Acosta, P. Factors propelling the adoption of m-learning among students in higher education. Electron Markets 26, 323–338 (2016).

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  • Mobile learning readiness
  • Mobile learning acceptance
  • Mobile devices
  • Theory of planned behavior
  • Universities
  • Undergraduates