Exploring the Major Determinants of Mobile Learning Adoption

Year 2011, Volume: 28 Issue: 1, 31 - 46, 03.09.2015

Nuri Başoğlu Kubilay Özdoğan

Abstract

From notebook computers to mobile phones, wireless devices have become affordable and popular. With rapidly improving Internet capabilities, the demand for mobility has spread to education. Mobile learning (m-learning) combines individualized learning with anytime and anywhere learning. MLARG is an application designed to support the learning of a foreign language. It provides course content and examinations in various formats. The purpose of this research was to identify independent and intermediary factors that could contribute to the adoption and success of MLARG. A list of likely factors influencing adoption was developed by modifying and extending the Technology Adoption Model (TAM). Feedback concerning the application was gathered from 9th grade students in a tourism vocational high school in Istanbul, the students for whom the application was intended

Keywords

Mobile learning, Technology Adoption Model (TAM)

Exploring the Major Determinants of Mobile Learning Adoption

Abstract

From notebook computers to mobile phones, wireless devices have become affordable and popular. With

rapidly improving Internet capabilities, the demand for mobility has spread to education. Mobile learning (mlearning) combines individualized learning with anytime and anywhere learning. MLARG is an application

designed to support the learning of a foreign language. It provides course content and examinations in

various formats. The purpose of this research was to identify independent and intermediary factors that could

contribute to the adoption and success of MLARG. A list of likely factors influencing adoption was developed

by modifying and extending the Technology Adoption Model (TAM). Feedback concerning the application

was gathered from 9th grade students in a tourism vocational high school in Istanbul, the students for whom

the application was intended. 

Keywords

Mobile learning, Technology Adoption Model (TAM)

References

• Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
• Bandura, A. (1977). Self-Efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. doi: 10.1037/0033-295X.84.2.191.
• Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147. doi: 10.1037/0003-066X.37.2.122
• Bonk, C. J., & Graham, C. R. (2006). Handbook of blended learning: Global perspectives, local designs. San Francisco, CA: Pfeiffer Publishing.
• Cavus, N., & Ibrahim, D. (2009). M-Learning: An experiment in using SMS to support learning new English language words. British Journal of Educational Technology, 40(1), 78–91. doi: 10.1111/j.1467-8535.2007.00801.x
• Çavuş, N., & Uzunboylu, H. (2009). Improving critical thinking skills in mobile learning. Procedia - Social and Behavioral Sciences, 1(1), 434–438. doi:10.1016/j.sbspro.2009. 01.078
• Chang, C., Chen, Y., & Kao, D. (2008). From mobile learning to pervasive learning. The XVIII Acme International Conference on Pacific Rim Management, Toronto, Canada.
• Cohen, A. (2010). Characteristics of effective mobile learning. Retrieved from http://www.brainscape.com/Blog/2010/09/Characteristics-of-Effective-Mobile-Learning/.
• Condos, C., James, A., Ever,y P., & Simpson, T. (2002). Ten usability principles for the development of effective WAP and M-Commerce services. Aslib Proceedings, 54(6), 345–355. doi: 10.1108/00012530210452546
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
• Eteokleous. N., & Ktoridou, D. (2009). Investigating mobile devices integration in higher education in Cyprus: Faculty perspectives. International Journal of Interactive Mobile Technologies, 3(1), 38-48.
• Evans, C. (2008). The effectiveness of M-Learning in the form of podcast revision lectures in higher education. Computers & Education, 50(2), 491–498. doi:10.1016/j.compedu. 2007.09.016
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
• Hashemi, M., & Ghasemi, B. (2011). Using mobile phones in language learning/teaching. Procedia - Social and Behavioral Sciences, 15, 2947–2951. doi: 10.1016/j.sbspro.2011. 04.220
• Hashemi, M., Azizinezhad, M., Najafia, V., & Nesari, A. J. (2011). What is mobile learning? Challenges and capabilities. Procedia - Social and Behavioral Sciences, 30, 2477–2481. doi: 10.1016/j.sbspro.2011.10.483
• Homan, S., & Wood, K. (2003, October). Taming the mega-lecture: Wireless quizzing. Syllabus Magazine, 7–8.
• Hung, S-Y, Ku, C-Y, & Chang, C-M. (2003). Critical factors of WAP service adoption: an empirical study. Electronic Commerce Research and Applications, 2(1), 42–60. doi: 10.1016/S1567-4223(03)00008-5
• Information and Communication Technologies Authority, Republic of Turkey. (2011). Turkish Electronic Communication Sector: Report On First Quarter of 2011. Retrieved from http://www.Btk.Gov.Tr/Kutuphane_Ve_Veribankasi/Pazar _Verileri/Ucaylik11_ 1.pdf.
• ITU (International Telecommunication Union), OECD. (2011). M-Government Mobile Technologies For Responsive Governments and Connected Societies. OECD Publishing. http://dx.doi. org/10.1787/9789264118706-en.
• Julnes, P. L. & Holzer, M. (2001). Promoting the utilization of performance measures in public organizations: An empirical study of factors affecting adoption and implementation. Public Administration Review, 61(6), 693–708. doi: 10.1111/0033-3352.00140
• Karagiannidis, C., Koumpis, A., & Lekakos, G. (2009). M-learning and m-commerce in pervasive environments. The Electronic Journal for Emerging Tools and Applications. Retrieved from http://www.ejeta.org/specialOct09-issue/ejeta-special-09oct-1.pdf
• Kargın, B., Başoğlu, N., & Daim, T. (2009). Adoption factors of mobile services. International Journal of Information Systems In Service Sector, 1(1), 15–34. doi: 10.4018/jisss.2009010102
• Keil, M., Beranek, P. M., & Konsynski, B. R. (1995). Usefulness and ease of use: Field study evidence regarding task considerations. Decision Support Systems, 13(1), 75–91. doi: 10.1016/0167-9236(94)E0032-M
• Kelman, H. C. (1958). Compliance, identification, and internalization: Three processes of attitude change. Journal of Conflict Resolution, 2, 51–60.
• Kleinrock, L. (1996). Nomadicity: Anytime, anywhere in a disconnected world. Mobile Networks and Applications, 1(4), 351-357.
• Lan, Y., & Sie, Y. (2010). Using RSS to support mobile learning based on media richness theory. Computers & Education, 55(2), 723–732. doi: 10.1016/j.compedu.2010.03.005
• Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29, 269–282. doi: 10.1016/ S0167-9236(00)00076-2
• Lu, J., Yaob, J. E., & Yua, C. S. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268. doi: 10.1016/j.jsis.2005.07.003
• Lu, J., Yu, C. S., Liu, C., Yao, J. E. (2003). Technology acceptance model for wireless internet. Internet Research, 13(3), 206-223. doi: 10.1108/10662240310478222
• Mallat, N. (2007). Exploring consumer adoption of mobile payments–a qualitative study. Journal of Strategic Information Systems, 16(4), 413–432. doi: 10.1016/j.jsis.2007. 08.001
• Mallat, N., Rossi, M., Tuunainen, V. K., Öörni, A. (2006). The impact of use situation and mobility on the acceptance of mobile ticketing services. Proceedings of the 39th Hawaii International Conference on System Sciences, Hawaii.
• Ministry of National Education, Republic of Turkey (2011). National Education Statistics: Formal Education 2009–2010. Retrieved from http://sgb.meb.gov.tr/ istatistik/meb_istatistikleri_ orgun_ egitim_2009_2010.pdf.
• Nagella U. B., & Govindarajulu, P. (2008). Adaptive approaches to context aware mobile learning applications. International Journal of Computer Science and Security, 2(2), 15–27.
• Özdamlı, F., & Çavuş, N. (2011). Basic elements and characteristics of mobile learning. Procedia–Social and Behavioral Sciences, 28, 937–942. doi: 10.1016/j.sbspro.2011. 11.173
• Pagani, M. (2004). Determinants of adoption of third generation mobile multimedia services. Journal of Interactive Marketing, 18(3), 46-59. doi: I0.100il/dir.20011
• Rogers, E. (1995). Diffusion of innovations (4th ed.). New York: Free.
• Press, Seppälä, P. & Alamäki, H. (2003). Mobile learning in teacher training. Journal of Computer Assisted Learning, 19, 330–335. doi: 10.1046/j.0266-4909.2003.00034.x
• State Planning Organization, Republic of Turkey. (2011). Information Society Statistics For Turkey. Retrieved from http://www.bilgitoplumu.gov.tr/documents/1/ diger/ bilgi_ toplumu_istatistikleri_2011.pdf.
• Suo, Y., & Shi, Y. (2008). Towards blended learning environment based on pervasive computing technologies. Proceedings of the 1st International Conference On Hybrid Learning and Education, Hong Kong, 190–201.
• Teo, T. S. H., & Pok, S. H. (2003). Adoption of wap-enabled mobile phones among internet users. Omega, 31(6), 483-498. doi: 10.1016/j.omega.2003.08.005
• Troshani, I & Rao, S. (2007). A conceptual framework and propositions for the acceptance of mobile services. Journal of Theoretical and Applied Electronic Commerce Research, 2(2), 61–73.
• Uzunboylu, H., Çavuş, N. & Erçağ, E. (2009). Using mobile learning to increase environmental awareness. Computers & Education, 52(2), 381–389. doi: 10.1016/j. compedu.2008.09.008
• Vavoula, G., & Sharples, M. (2009). Lifelong learning organizers: Requirements for tools for supporting episodic and semantic learning. Educational Technology & Society, 12(3), 82.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
• Venkatesh V., & Davis, F. D. (2000), A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
• Venkatesh, V., & Morris, M.G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139.
• Venkatesh, V. (1998). User acceptance of information technology: A unified view. (Unpublished doctoral dissertation). University of Minnesota, USA.
• Venkatesh, V., & Davis F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27, 451–481.
• Venkatesh, V., & Davis F. D. (1994). Modeling the determinants of perceived ease of use. Paper presented at the International Conference On Information Systems, Vancouver, Canada.
• Vinu, P. V., & Sherimon P. C. (2011). Towards pervasive mobile learning–the vision of 21st century. Procedia - Social and Behavioral Sciences, 15, 3067–3073. doi: 10.1016/ j.sbspro.2011.04.247
• Virvou, M. & Alepis, E. (2005). Mobile educational features in authoring tools for personalized tutoring. Computers & Education, 44, 53–68.
• Yi, W., Ming, W., & Hsiu, W. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92. doi: 10.1111/j.1467-8535.2007.00809.x
• Warshaw, P. R. (1980). A new model for predicting behavioral intentions: An alternative to Fishbein. Journal of Marketing Research, 17(2), 153–172.
• Zhang, D. (2003). Delivery of personalized and adaptive content to mobile devices: A framework and enabling technology. Communications of the Association for Information Systems, 12, 183–202.