Adoption of mobile learning among 3g-enabled handheld users using extended technology acceptance model

This paper examines various constructs of an extended TAM, Technology Acceptance Model, that are theoretically influencing the adoption and acceptability of mobile learning among 3G enabled mobile users. Mobile learning activity-based, used for this study were drawn from behaviorist and “learning and teaching support” educational paradigms. An online and manual survey instruments were used to gather data. The structural equation modeling techniques were then employed to explain the adoption processes of hypothesized research model. A theoretical model ETAM is developed based on TAM. Our result proved that psychometric constructs of TAM can be extended and that ETAM is well suited, and of good pedagogical tool in understanding mobile learning among 3G enabled handheld devices in southwest part of Nigeria. Cognitive constructs, attitude toward m-learning, self efficacy play significant roles in influencing behavioral intention for mobile learning, of which self-efficacy is the most importance construct. Implications of results and directions for future research are discussed.

[1]  Daniel J. Brass,et al.  Changing patterns or patterns of change: the effects of a change in technology on social network str , 1990 .

[2]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[3]  Patricia Thornton,et al.  Using mobile phones in education , 2004, The 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education, 2004. Proceedings..

[4]  John Ingham,et al.  Why do people use information technology? A critical review of the technology acceptance model , 2003, Inf. Manag..

[5]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[6]  Sung Youl Park,et al.  An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning , 2009, J. Educ. Technol. Soc..

[7]  Mike Sharples,et al.  Disruptive devices: mobile technology for conversational learning , 2002 .

[8]  J. Hair Multivariate data analysis , 1972 .

[9]  Malu Roldan,et al.  Toward Third Generation Threaded Discussions for Mobile Learning: Opportunities and Challenges for Ubiquitous Collaborative Environments , 2005, Inf. Syst. Frontiers.

[10]  Richard P. Bagozzi,et al.  Causal Modeling: a General Method For Developing and Testing Theories in Consumer Research , 1981 .

[11]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[12]  Margaret Tan,et al.  Factors Influencing the Adoption of Internet Banking , 2000, J. Assoc. Inf. Syst..

[13]  Dennis F. Galletta,et al.  Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[14]  E. Wagner Enabling Mobile Learning. , 2005 .

[15]  Nelson Oly Ndubisi,et al.  Factors of Online Learning Adoption: A Comparative Juxtaposition of the Theory of Planned Behaviour and the Technology Acceptance Model. , 2006 .

[16]  Robert J. Dufresne,et al.  Classtalk: A classroom communication system for active learning , 1996, J. Comput. High. Educ..

[17]  Karl G. Jöreskog,et al.  LISREL 7: A guide to the program and applications , 1988 .

[18]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[19]  Michael Stefanone,et al.  The Effects of Wireless Computing in Collaborative Learning Environments , 2001, Int. J. Hum. Comput. Interact..

[20]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .