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

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Abstract

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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References

  • Abachi, H. R., & Muhammad, G. (2014). The impact of m-learning technology on students and educators. Computers in Human Behavior, 30(January), 491–496.

    Article  Google Scholar 

  • Abas, Z.W., Chng, L.P., & Mansor, N. (2009). A study on learner readiness for mobile learning at Open University Malaysia. IADIS International Conference Mobile Learning 2009 (pp. 151–157).

  • Ajjan, H., & Hartshorne, R. (2008). Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. The Internet and Higher Education, 11(2), 71–80.

    Article  Google Scholar 

  • Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11–39). Berlin, Heidelberg, New York: Springer-Verlag.

    Chapter  Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52, 27–58.

    Article  Google Scholar 

  • Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683.

    Article  Google Scholar 

  • Ajzen, I., & Driver, B. L. (1991). Prediction of Leisure Participation from Behavioral, Normative, and Control Beliefs: An Application of the Theory of Planned Behavior. Leisure Sciences, 13(3), 185–204.

    Article  Google Scholar 

  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, New Jersey: Prentice-Hall.

    Google Scholar 

  • Ajzen, I., & Madden, T. J. (1986). Prediction of Goal-Directed Behavior: Attitudes, Intentions, and Perceived Behavioral Control. Journal of Experimental Social Psychology, 22(5), 453–474.

    Article  Google Scholar 

  • Ally, M., & Samaka, M. (2013). Open Education Resources and Mobile Technology to Narrow the Learning Divide. The International Review of Research in Open and Distance Learning, 14(2), 14–27.

    Google Scholar 

  • Alzaza, N. S., & Yaakub, A. R. (2010). Students’ awareness and requirements of mobile learning services among Malaysian students in the higher education environment. In Proceedings of the knowledge management 5th international conference. Malaysia: Universiti Utara.

    Google Scholar 

  • Andaleeb, A. A., Idrus, R. M., Ismail, I., & Mokaram, A. K. (2010). Technology Readiness Index (TRI) Among USM Distance Education Students According to age. International Journal of Human and Social Sciences, 5(3), 189–192.

    Google Scholar 

  • Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended two-Step Approach. Psychological Bulletin, 103(May), 411–423.

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. In Englewood cliffs. NJ: Prentice-Hall.

    Google Scholar 

  • Barbosa, D., & Geyer, C. (2005). Pervasive personal pedagogical agent: A mobile agent shall always be with a learner. Proceedings of the IADIS International Conference Mobile Learning 2005 (pp. 281–285).

  • Baylor, A. L., & Ritchie, D. (2002). What Factors Facilitate Teacher Skill, Teacher Morale, and Perceived Student Learning in Technology-Using Classrooms? Computers & Education, 39(4), 395–414.

    Article  Google Scholar 

  • Beckmann, E. (2010). Learners on the Move: Mobile Modalities in Development Studies. Distance Education, 31(2), 159–173.

    Article  Google Scholar 

  • Brown, D. J., McHugh, D., Standen, P., Evett, L., Shopland, N., & Battersby, S. (2011). Designing Location-Based Learning Experiences for People with Intellectual Disabilities and Additional Sensory Impairments. Computers & Education, 56(1), 11–20.

    Article  Google Scholar 

  • Burman, L. E., Reed, W. R., & Alm, J. (2010). A Call for Replication Studies. Public Finance Review, 38(6), 787–779.

    Article  Google Scholar 

  • Chang, K. C., & Chang, C. C. (2009). Library Self-Service: Predicting User Intentions Related to Self-Issue and Return Systems. Electronic Library, 27(6), 938–949.

    Article  Google Scholar 

  • Chen, H., & Huang, H. (2010). User Acceptance of Mobile Knowledge Management Learning System: Design and Analysis. Educational Technology & Society, 13(3), 70–77.

    Google Scholar 

  • Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An Investigation of Mobile Learning Readiness in Higher Education Based on the Theory of Planned Behavior. Computers & Education, 59(3), 1054–1064.

    Article  Google Scholar 

  • Cheung, C. M. K., Chiu, P., & Lee, M. K. O. (2011). Online Social Networks: Why do Students use Facebook? Computers in Human Behavior, 27(4), 1337–1343.

    Article  Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–358). Mahwah, New Jersey: Lawrence Erlbaum.

    Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Mahwah, New Jersey: Lawrence Erlbaum.

    Google Scholar 

  • Compeau, D. R., & Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189–211.

    Article  Google Scholar 

  • Corbeil, J. R., & Valdes-Corbeil, M. E. (2007). Are you Ready for Mobile Learning? Educause Quarterly, 30(2), 51–58.

    Google Scholar 

  • Corrin, L., Lockyer, L., & Bennett, S. (2010). Technological diversity: An investigation of students’ technology use in everyday life and academic study. Learning, Media and Technology, 35(4), 387–401.

  • Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of two Theoretical Models. Management Science, 35(8), 982–1003.

    Article  Google Scholar 

  • Djamasbi, S., Siegel, M., & Tullis, T. (2010). Generation Y, web Design, and eye Tracking. International Journal of Human-Computer Studies, 68(5), 307–323.

    Article  Google Scholar 

  • Donnelly, K. (2009). Learning on the Move: How m-Learning Could Transform Training and Development. Development and Learning in Organisations, 23(4), 8–11.

    Google Scholar 

  • Easley, R. W., Madden, C. S., & Dunn, M. G. (2000). Conducting Marketing Science: The Role of Replication in the Research Process. Journal of Business Research, 48, 83–92.

    Article  Google Scholar 

  • Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher Beliefs and Technology Integration Practices: A Critical Relationship. Computers & Education, 59(2), 423–435.

    Article  Google Scholar 

  • Evans, C. (2008). The Effectiveness of m-Learning in the Form of Podcast Revision Lecturers in High Education. Computers & Education, 50(2), 491–498.

    Article  Google Scholar 

  • Ferreira, F., Dias, F., Braz, J., Santos, R., Nascimento, R., Ferreira, C., & Martinho, R. (2013). Protege: a mobile health application for the elder-caregiver monitoring paradigm. Procedia Technology, 9, 1361–1371.

  • Flaherty, C. (2015). Why replication matters. Inside Higher Ed. Retrieved from https://www.insidehighered.com/news/2015/08/28/landmark-study-suggests -most-psychology-studies-dont-yield-reproducible-results

  • Fornell, C., & Cha, J. (1994). Partial least squares. In R. P. Bagozzi (Ed.), Advanced methods in marketing research (pp. 52–78). Cambridge: Blackwell.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Gedik, N., Hanci-Karademirci, A., Kursun, E., & Cagiltay, K. (2012). Key Instructional Design Issues in a Cellular Phone-Based Mobile Learning Project. Computers & Education, 58(4), 1149–1159.

    Article  Google Scholar 

  • Gholami, R., Sulaiman, A. B., Ramayah, T., & Molla, A. (2013). Senior Managers’ Perception on Green Information Systems (IS) Adoption and Environmental Performance: Results from a Field Survey. Information Management, 50(7), 431–438.

    Article  Google Scholar 

  • Gikas, J. G., & Grant, M. M. (2013). Mobile Computing Devices in Higher Education: Student Perspectives on Learning with Cellphones, Smartphones & Social Media. The Internet and Higher Education, 19(October), 18–26.

    Article  Google Scholar 

  • Gist, M. E., Schwoerer, C., & Rosen, B. (1989). Effects of Alternative Training Methods on Self-Efficacy and Performance in Computer Software Training. Journal of Applied Psychology, 74(6), 884–891.

    Article  Google Scholar 

  • Goh, T., Seet, B., & Chen, N. (2011). The Impact of Persuasive SMS on Students’ Self-Regulated Learning. British Journal of Educational Technology, 43, 624–640.

    Article  Google Scholar 

  • Grönlund, Ǻ., & Islam, Y. M. (2010). A Mobile e-Learning Environment for Developing Countries: The Bangladesh Virtual Interactive Classroom. Information Technology for Development, 16(4), 244–259.

    Article  Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Los Angeles: Sage Publication.

    Google Scholar 

  • Hamat, A., Embi, M. A., & Hassan, H. A. (2012). Mobile Learning Readiness Among UKM Lecturers. Procedia - Social and Behavioral Sciences, 59, 406–410.

    Article  Google Scholar 

  • Hayati, A., Jalilifar, A., & Mashhadi, A. (2012). Using short message service (SMS) to teach English idioms to EFL students. British Journal of Educational Technology, 44(1), 66–81.

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of Partial Least Squares Path Modeling in International Marketing. Advances in International Marketing, 20, 277–320.

    Google Scholar 

  • Herrington, A., & Herrington, J. (2007). Authentic mobile learning in higher education. Retrieved from http://www.aare.edu.au/data/publications/2007/her07131.pdf

    Google Scholar 

  • Hrubes, D., Ajzen, I., & Daigle, J. (2001). Predicting Hunting Intentions and Behavior: An Application of the Theory of Planned Behavior. Leisure Sciences, 23(3), 165–178.

    Article  Google Scholar 

  • Hsu, C.,& Lu, H. (2004). Why do People Play on-Line Games? An Extended TAM with Social Influences and Flow Experience. Information Management, 41, 853–868.

  • Ibrahim, N. A., Salisu, M., Popoola, A. A., & Ibrahim, T. I. (2014). Use of Smartphones Among Medical Students in the Clinical Years at a Medical School in Sub-Sahara Africa: A Pilot Study. Journal of Mobile Technology in Medicine, 3(2), 28–34.

    Article  Google Scholar 

  • Izwan, M. (2014). Gender Imbalance in Universities and Colleges Worrying, Says Pak Lah. Malaysian Insider. Retrieved from http://www.themalaysianinsider.com/malaysia/article/gender-imbalance-in-universities-and-colleges-worrying-says-pak-lah

  • Jairak, K., Praneetpolgrang, P., & Mekhabunchakij, K. (2009, December). An acceptance of mobile learning for higher education students in Thailand. In Sixth International Conference on eLearning for Knowledge-Based Society, Thailand, 17,(18), 361–368.

  • Jones, C., Ramanau, R., Cross, S., & Healing, G. (2010). Net Generation or Digital Natives: Is There a Distinct new Generation Entering University? Computers & Education, 54(3), 722–732.

    Article  Google Scholar 

  • Junco, R., & Mastrodicasa, J. M. (2007). Connecting to the Net generation: What higher education professionals need to know about today’s students. Washington, DC: NASPA.

    Google Scholar 

  • Karahanna, E., & Straub, D. (1999). The Psychological Origins of Perceived Usefulness and Ease-of-use. Information Management, 35, 237–250.

    Article  Google Scholar 

  • Keegan, D. (2003). The future of learning: From e-learning to m-learning. Institute for research into distance education. Hagen, Germany: Fern Universitat Retrieved from http://files.eric.ed.gov/fulltext/ED472435.pdf.

    Google Scholar 

  • Kukulska-Hulme, A., Sharples, M., Milrad, M., Arnedillo-Sanchez, I., & Vavoula, G. (2011). The genesis and development of mobile learning in Europe. In D. Parsons (Ed.), Combining e-learning and m-learning: New applications of blended educational resources (pp. 151–177). Hershey, PA: IGI Global.

    Chapter  Google Scholar 

  • Lai, C., Wang, Q., & Lei, J. (2012). What Factors Predict Undergraduate Students’ use of Technology for Learning? Computers & Education, 59(2), 569–579.

    Article  Google Scholar 

  • Lascu, D., & Zinkhan, G. (1999). Consumer Conformity: Review and Applications for Marketing Theory and Practice. Journal of Marketing Theory and Practice, 7, 1–12.

    Article  Google Scholar 

  • Lee, Y., & Kozar, K. A. (2005). Investigating Factors Affecting the Adoption of Anti-Spyware Systems. Communications of the ACM, 48(8), 72–77.

    Article  Google Scholar 

  • Liaw, S.-S., Huang, H.-M., & Chen, G.-D. (2007). Surveying Instructor and Learner Attitudes Toward e-Learning. Computers & Education, 49(4), 1066–1080.

    Article  Google Scholar 

  • Liker, J., & Sindi, A. (1997). User Acceptance of Expert Systems: A Test of the Theory of Reasoned Action. Journal of Engineering and Technology Management, 14, 147–173.

    Article  Google Scholar 

  • Lim, C. K. (2001). Computer Self-Efficacy, Academic Self-Concept, and Other Predictors of Satisfaction and Future Participation of Adult Distance Learners. American Journal of Distance Education, 15(2), 41–51.

    Article  Google Scholar 

  • Lim, H., & Dubinsky, A. J. (2005). The Theory of Planned Behavior in e-Commerce: Making a Case for Interdependencies Between Salient Beliefs. Psychology and Marketing, 22(10), 833–855.

    Article  Google Scholar 

  • Liu, Y., Li, H., & Carlsson, C. (2010). Factors Driving the Adoption of m-Learning: An Empirical Study. Computers & Education, 55(3), 1211–1219.

    Article  Google Scholar 

  • Madden, T. J., Ellen, P. S., & Ajzen, I. (1992). A Comparison of the Theory of Planned Behavior and the Theory of Reasoned Action. Personality and Social Psychology Bulletin, 18(1), 3–9.

    Article  Google Scholar 

  • Mahat, J., Ayub, A. F. M., & Wong, S. L. (2012). An assessment of students’ mobile self-efficacy, readiness and personal innovativeness towards mobile learning in higher education in Malaysia. Procedia – Social and Behavioral Sciences, 64, 284–290.

    Article  Google Scholar 

  • Malaysian Communications and Multimedia Commission (2015a). Communications and Multimedia: Pocket Book of Statistics Q2 2015. Retrieved from http://www.skmm.gov.my/skmmgovmy/media/General/pdf/C-M-Q2-2015.pdf

  • Malaysian Communications and Multimedia Commission (2015b). Internet Users Survey 2014. Retrieved from http://www.skmm.gov.my/skmmgovmy/media/General/pdf/Internet-Users-Survey-2014.pdf

  • Malikhao, P., & Servaes, J. (2010). The Media use of American Youngsters in the age of Narcissism: Surviving in a 24/7 Media Shock and awe - Distracted by Everything. Telematics and Informatics, 28(2), 66–76.

    Article  Google Scholar 

  • Margaryan, A., Littlejohn, A., & Vojt, G. (2011). Are Digital Natives a Myth or Reality? University Students’ use of Digital Technologies. Computers & Education, 56(2), 429–440.

    Article  Google Scholar 

  • Marler, J. H., & Dulebohn, J. H. (2005). A model of employee self-service technology acceptance. In J. J. Martocchio (Ed.), Research in personnel and human resources management, volume 24 (pp. 137–180). Emerald Group Publishing Limited.

  • Miesen, H. W. J. M. (2003). Predicting and Explaining Literary Reading: An Application of the Theory of Planned Behavior. Poetics, 31(3–4), 189–212.

    Article  Google Scholar 

  • Milošević, I., Živković, D., Manasijević, D., & Nikolić, D. (2015). The effects of the intended behaviour of students in the use of m-learning. Computers in Human Behavior, 51(Part A), 207–215.

  • Mohammad, N. M. N., Mamat, M. N., & Isa, P. M. (2012). M-learning in Malaysia: Challenges and strategies. Procedia – Social and Behavioral Sciences, 67, 393–401.

    Article  Google Scholar 

  • Mohammadi, H. (2015). Social and Individual Antecedents of m-Learning Adoption in Iran. Computers in Human Behavior, 49, 191–207.

    Article  Google Scholar 

  • Motiwalla, L. (2007). Mobile Learning: A Framework and Evaluation. Computers & Education, 49(3), 581–596.

    Article  Google Scholar 

  • Ng, W., Howard, N., Loke, S., & Torabi, T. (2010). Designing effective pedagogical systems for teaching and learning with mobile and ubiquitous devices. Multiplatform e-learning systems and technologies: Mobile devices for uniquitous ICT-based education (pp. 42–56), Hershey: PA: IGI Global Publishing.

  • Nordin, N., Embi, M. A., & Yunus, M. M. (2010). Mobile Learning Framework for Lifelong Learning. Procedia -Social and Behavioral Sciences, 7, 130–138.

    Article  Google Scholar 

  • Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University Students’ Behavioral Intention to use Mobile Learning: Evaluating the Technology Acceptance Model. British Journal of Educational Technology, 43(4), 592–605.

    Article  Google Scholar 

  • Palacios-Marqués, D., Merigó, J. M., & Soto-Acosta, P. (2015a). Online social networks as an enabler of innovation in organizations. Management Decision, 53(9), 1906–1920.

  • Palacios-Marqués, D., Soto-Acosta, P., & Merigó, J. M. (2015b). Analyzing the effects of technological, organizational and competition factors on Web knowledge exchange in SMEs. Telematics and Informatics, 32(1), 23–32.

  • Parkes, M., Stein, S., & Reading, C. (2015). Student Preparedness for University e-Learning Environments. The Internet and Higher Education, 25(April), 1–10.

    Article  Google Scholar 

  • Patten, B., Sanchez, I. A., & Tangney, B. (2006). Designing Collaborative, Constructionist and Contextual Applications for Handheld Devices. Computers in Education, 46, 294–308.

    Article  Google Scholar 

  • Pozzi, A. (2007). The Impact of m-Learning in School Contexts: An “Inclusive” Perspective. Lecture Notes in Computer Science, 4556, 748–755.

    Article  Google Scholar 

  • Ramayah, T., Lee, J. W. C., & Boey, J. C. I. (2011). Network Collaboration and Performance in the Tourism Sector. Service Business, 5(4), 411–428.

    Article  Google Scholar 

  • Ramayah, T., Yeap, J. A. L., & Ignatius, J. (2013). An Empirical Inquiry on Knowledge Sharing Among Academicians in Higher Learning Institutions. Minerva: A Review of Science, Learning and Policy, 51(2), 131–154.

    Article  Google Scholar 

  • Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Hamburg: SmartPLS Retrieved from http://www.smartpls.com.

    Google Scholar 

  • Riquelme, H. E., & Rios, R. E. (2010). The Moderating Effect of Gender in the Adoption of Mobile Banking. The International Journal of Bank Marketing, 28(5), 328–341.

    Article  Google Scholar 

  • Rodriguez, J.E. (2011). Social media use in higher education: Key areas to consider for educators. MERLOT Journal of Online Learning and Teaching, 7(4). Retrieved from http://jolt.merlot.org/vol7no4/rodriguez_ 1211.htm

  • Saccol, A., Barbosa, J., Schlemmer, E., & Reinhard, N. (2011). Mobile Learning in Organizations: Lessons Learned from two Case Studies. International Journal of Information and Communication Technology Education, 7, 11–24.

    Article  Google Scholar 

  • Sharma, S. K., & Kitchens, F. L. (2004). Web Services Architecture for m-Learning. Journal of e-Learning, 2(1), 203–216.

    Google Scholar 

  • Sheppard, B. H., Hatrwick, J., & Warshaw, P. R. (1988). The Theory of Reasoned Action: A Meta-Analysis of past Research with Recommendations for Modifications and Future Research. Journal of Consumer Research, 15(3), 325–343.

    Article  Google Scholar 

  • Sledgianowski, D., & Kulviwat, S. (2009). Using Social Network Sites: The Effects of Playfulness, Critical Mass and Trust in a Hedonic Context. Journal of Computer Information Systems, Summer, 74-83.

  • Soto-Acosta, P., Popa, S., & Palacios-Marqués, D. (2015). E-business, organizational innovation and firm performance in manufacturing SMEs: an empirical study in Spain. Technological and Economic Development of Economy, doi:10.3846/20294913.2015.1074126.

  • Sullivan, G. M., & Feinn, R. (2012). Using Effect Size - or why the p Value is not Enough. Journal of Graduate Medical Education, 4(3), 279–282.

    Article  Google Scholar 

  • Taleb, Z., & Sohrabi, A. (2012). Learning on the move: The use of mobile technology to support learning for university students. Procedia – Social and Behavioral Sciences, 69, 1102–1109.

    Article  Google Scholar 

  • Tan, G. W.-H., Ooi, K.-B., Leong, L.-Y., & Lin, B. (2014). Predicting the Drivers of Behavioural Intention to use Mobile Learning: A Hybrid SEM-Neural Networks Approach. Computers in Human Behavior, 36(July), 198–213.

    Article  Google Scholar 

  • Tapscott, D. (1998). Growing up digital: The rise of the net generation. New York: McGraw-Hill.

    Google Scholar 

  • Taylor, S., & Todd, P. (1995). Decomposition and Crossover Effects in the Theory of Planned Behavior: A Study of Consumer Adoption Intentions. International Journal of Research in Marketing, 12(2), 137–155.

    Article  Google Scholar 

  • Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS Path Modeling. Computational Statistics and Data Analysis, 48, 159–205.

    Article  Google Scholar 

  • Torkzadeh, G., & Van Dyke, T. P. (2001). Development and Validation of an Internet Self-Efficacy Scale. Behaviour & Information Technology, 20(4), 275–280.

    Article  Google Scholar 

  • Torkzadeh, G., & Van Dyke, T. P. (2002). Effects of Training on Internet Self-Efficacy and Computer User Attitudes. Computers in Human Behavior, 18(5), 479–494.

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes. Organizational Behavior and Human Decision Processes, 83(1), 33–60.

    Article  Google Scholar 

  • Viberg, O., & Gronlung, A. (2013). Cross-Cultural Analysis of Users’ Attitude Toward the use of Mobile Devices in Second and Foreign Language Learning in Higher Education: A Case from Sweden and China. Computers & Education, 69, 169–180.

    Article  Google Scholar 

  • Völckner, F., Sattler, H., Hennig-Thurau, T., & Ringle, C. M. (2010). The Role of Parent Brand Quality for Service Brand Extension Success. Journal of Service Research, 13(4), 379–396.

    Article  Google Scholar 

  • Winters, N. (2006). What is mobile learning? In M. Sharples (Ed.), Big issues in mobile learning. Retrieved from http://matchsz.inf.elte.hu/tt/docs/Sharples-20062.pdf

  • Yang, H. C., Liu, H., & Zhou, L. (2012). Predicting Young Chinese Consumers’ Mobile Viral Attitudes, Intents and Behavior. Asia Pacific Journal of Marketing and Logistics, 24(1), 59–77.

    Article  Google Scholar 

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Correspondence to Pedro Soto-Acosta.

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Appendix

Appendix

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). https://doi.org/10.1007/s12525-015-0214-x

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  • DOI: https://doi.org/10.1007/s12525-015-0214-x

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