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Extending the TAM to examine the effects of quality features on mobile learning acceptance

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Abstract

The purpose of this study is to examine the effects of quality features on students’ beliefs towards acceptance of mobile learning based on extending technology acceptance model (TAM) and the updated DeLone and McLean information system success model (DL&ML). This study gathered sample data from five public universities in Jordan. A total of 400 questionnaires were randomly distributed, and 392 usable questionnaires were analyzed, with a usable response rate of 81.6 %. The research results revealed that learning content quality, content design quality, interactivity, functionality, user-interface design, accessibility, personalization, and responsiveness, as the primary antecedents of mobile learning acceptance which had positive effects on students’ perception with regard to their beliefs (i.e., perceived usefulness and perceived ease of use) and this situation can lead to enhance students’ behavioral intention to use of mobile learning application.

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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, 491–496.

    Article  Google Scholar 

  • Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. The International Review of Research in Open and Distributed Learning, 14(5)

  • Ahn, T., Ryu, S., & Han, I. (2007). The impact of web quality and playfulness on user acceptance of online retailing. Information & Management, 44(3), 263–275.

    Article  Google Scholar 

  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Al-Debei, M. M. (2014). The quality and acceptance of websites: an empirical investigation in the context of higher education. International Journal of Business Information Systems, 15(2), 170–188.

    Article  Google Scholar 

  • Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior, 56, 93–102.

    Article  Google Scholar 

  • Ali, A., Alrasheedi, M., Ouda, A., & Capretz, L. F. (2015). A study of the interface usability issues of mobile learning applications for smart phones from the users perspective. arXiv preprint arXiv:1501.01875.

  • Almaiah, M. A., & Man, M. (2016). Empirical investigation to explore factors that achieve high quality of mobile learning system based on students’ perspectives. Engineering Science and Technology: An International Journal.

  • Almarashdeh, I. A., Sahari, N., Zin, N. A. M., & Alsmadi, M. (2010). The success of learning management system among distance learners in Malaysian universities. Journal of Theoretical & Applied Information Technology, 21(2)

  • Almasri, A. K. M. (2014). The influence on mobile learning based on technology acceptance model (TAM), mobile readiness (MR) and perceived interaction (PI) for higher education students.

  • Al-Mushasha, N. F., & Nassuora, A. B. (2012). Factors determining e-learning service quality in Jordanian higher education environment. Journal of Applied Sciences, 12(14), 1474.

    Article  Google Scholar 

  • Al-Shboul, M., Rababah, O., Al-Sayyed, R., Sweis, G., & Aldreabi, H. (2013). Roadmap to advance e-Learning management system at The University of Jordan. Journal of American Science, 9(1), 531–545.

    Google Scholar 

  • Althunibat, A. (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Computers in Human Behavior, 52, 65–71.

    Article  Google Scholar 

  • Ariffin, S. A. (2011). Mobile learning in the institution of higher learning for Malaysia students: Culture perspectives. International Journal on Advanced Science, Engineering and Information Technology, 1(3), 283–288.

    Article  Google Scholar 

  • Bidin, S., & Ziden, A. A. (2013). Adoption and application of mobile learning in the education industry. Procedia-Social and Behavioral Sciences, 90, 720–729.

    Article  Google Scholar 

  • Bontis, N. (1998). Intellectual capital: An exploratory study that develops measures and models. Management Decision, 36(2), 63–76.

    Article  Google Scholar 

  • Campbell, D., & Fiske, D. (1998). Convergent and discriminant validation by the multitrait-multimethods matrix. Personality, 56, 162.

    Google Scholar 

  • Chang, C. C., Yan, C. F., & Tseng, J. S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5), 809–826.

    Article  Google Scholar 

  • Chen, H. R., & Tseng, H. F. (2012). Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan. Evaluation and Program Planning, 35(3), 398–406.

    Article  Google Scholar 

  • Cheng, Y. M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361–390.

    Article  Google Scholar 

  • Cho, V., Cheng, T. E., & Lai, W. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53(2), 216–227.

    Article  Google Scholar 

  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19, 189–211.

    Article  Google Scholar 

  • Dahlstrom, E., Walker, J. D., & Dziuban, C. (2013). ECAR study of undergraduate students and information technology. 2013.

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.

    Article  Google Scholar 

  • Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30.

    Google Scholar 

  • Field, A. (2009). Discovering statistics using SPSS. Thousand Oaks: Sage Publications.

    Google Scholar 

  • Franklin, T. (2011). Mobile learning: At the tipping point. TOJET: The Turkish Online Journal of Educational Technology, 10(4), 261.

    Google Scholar 

  • Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.

    Google Scholar 

  • Gikas, J., & 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, 18–26.

    Article  Google Scholar 

  • Glackin, B. C., Rodenhiser, R. W., & Herzog, B. (2014). A library and the disciplines: A collaborative project assessing the impact of eBooks and mobile devices on student learning. The Journal of Academic Librarianship, 40(3), 299–306.

    Article  Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.

    Google Scholar 

  • Hassan, M. H., Alhosban, F., & Hourani, M. A. (2016). Using mobile technologies for enhancing student academic experience: University of Jordan case study. International Journal of Interactive Mobile Technologies (iJIM), 10(1), 13–18.

    Article  Google Scholar 

  • Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39(12), 10959–10966.

    Article  Google Scholar 

  • Holsapple, C. W., & Lee-Post, A. (2010). Behavior-based analysis of knowledge dissemination channels in operations management. Omega, 38(3), 167–178.

    Article  Google Scholar 

  • Hunt, S. D., Sparkman Jr, R. D., & Wilcox, J. B. (1982). The pretest in survey research: Issues and preliminary findings. Journal of Marketing Research, 19(2), 269–273.

    Article  Google Scholar 

  • Jaradat, M. I. R. M. (2014). Understanding individuals’ perceptions, determinants and the moderating effects of age and gender on the adoption of mobile learning: Developing country perspective. International Journal of Mobile Learning and Organisation, 8(3–4), 253–275.

    Article  Google Scholar 

  • Kanthawongs, P., & Kanthawongs, P. (2013). Individual and social factors affecting student’s usage intention in using Learning Management System. Procedia-Social and Behavioral Sciences, 88, 89–95.

    Article  Google Scholar 

  • Kellerer, W., Wagner, M., & Balke, W. T. (2003). Preference-based session management for personalized services. In Proceedings of the MOMUC

  • Khan, A. I., Al-Shihi, H., Al-Khanjari, Z. A., & Sarrab, M. (2015). Mobile learning (M-Learning) adoption in the Middle East: Lessons learned from the educationally advanced countries. Telematics and Informatics, 32(4), 909–920.

    Article  Google Scholar 

  • Lan, Y. F., & Sie, Y. S. (2010). Using RSS to support mobile learning based on media richness theory. Computers & Education, 55(2), 723–732.

    Article  Google Scholar 

  • Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interacting with Computers, 21(5–6), 385–392.

    Article  Google Scholar 

  • Lee, Y., & Kozar, K. A. (2006). Investigating the effect of website quality on e-business success: An analytic hierarchy process (AHP) approach. Decision Support Systems, 42(3), 1383–1401.

    Article  Google Scholar 

  • Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320–1329.

    Article  Google Scholar 

  • Liang, T. P. (1987). User interface design for decision support systems: A self-adaptive approach. Information & Management, 12(4), 181–193.

    Article  Google Scholar 

  • Lin, J. C. C., & Lu, H. (2000). Towards an understanding of the behavioural intention to use a web site. International Journal of Information Management, 20(3), 197–208.

    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 

  • Lwoga, E. T. (2014). Critical success factors for adoption of web-based learning management systems in Tanzania. International Journal of Education and Development using Information and Communication Technology, 10(1), 4.

    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 

  • Molenet.org. (2009). The Mobile Learning Network (MoLeNET). Retrieved Apr 28, 2016 from http://www.molenet.org.uk/

  • O’bannon, B. W., & Thomas, K. (2014). Teacher perceptions of using mobile phones in the classroom: Age matters! Computers & Education, 74, 15–25.

    Article  Google Scholar 

  • Ozdamli, F., & Cavus, N. (2011). Basic elements and characteristics of mobile learning. Procedia-Social and Behavioral Sciences, 28, 937–942.

    Article  Google Scholar 

  • Özer, A., Argan, M. T., & Argan, M. (2013). The effect of mobile service quality dimensions on customer satisfaction. Procedia-Social and Behavioral Sciences, 99, 428–438.

    Article  Google Scholar 

  • Paechter, M., Maier, B., & Macher, D. (2010). Students’ expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction. Computers & Education, 54(1), 222–229.

    Article  Google Scholar 

  • Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-Learning. Educational technology & society, 12(3), 150–162.

    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 

  • Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244.

    Article  Google Scholar 

  • Pollara, P., & Kee Broussard, K. (2011, March). Student perceptions of mobile learning: A review of current research. In Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 1643–1650).

  • Prieto, J. C. S., Migueláñez, S. O., & García-Peñalvo, F. J. (2014, October). Mobile learning adoption from informal into formal: An extended TAM model to measure mobile acceptance among teachers. In Proceedings of the Second International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 595–602). ACM.

  • Rahman, S., Haque, A., & Ahmad, M. I. S. (2010). Exploring influencing factors for the selection of mobile phone service providers: A structural equational modeling (SEM) approach on Malaysian consumers. African Journal of Business Management, 4(13), 2885.

    Google Scholar 

  • Rieh, S. Y. (2002). Judgment of information quality and cognitive authority in the Web. Journal of the American Society for Information Science and Technology, 53(2), 145–161.

    Article  Google Scholar 

  • Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683–696.

    Article  Google Scholar 

  • Rogers, R. W. (1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. Social Psychophysiology, 153–176.

  • Rovai, A. P. (2004). A constructivist approach to online college learning. The Internet and Higher Education, 7(2), 79–93.

    Article  Google Scholar 

  • Sarrab, M., Alzahrani, A., Alwan, N. A., & Alfarraj, O. (2014). From traditional learning into mobile learning in education at the university level: Undergraduate students perspective. International Journal of Mobile Learning and Organisation, 8(3–4), 167–186.

    Article  Google Scholar 

  • Sarrab, M., Elbasir, M., & Alnaeli, S. (2016). Towards a quality model of technical aspects for mobile learning services: An empirical investigation. Computers in Human Behavior, 55, 100–112.

    Article  Google Scholar 

  • Sekaran, U., & Bougie, M. (2009). Research methods for business: A skill building approach. Chichester: Wiley.

    Google Scholar 

  • Su, C. H., & Cheng, C. H. (2015). A mobile gamification learning system for improving the learning motivation and achievements. Journal of Computer Assisted learning, 31(3), 268–286.

    Article  Google Scholar 

  • Prieto, J. C. S., Migueláñez, S. O., & García-Peñalvo, F. J. Intención de Uso de Tecnologías Mobiles Entre los Profesores en Formación.

  • The Jordan Times. (2014). 95 % of Jordanians own mobiles; 47% use the Internet. Retrieved 21 April, 2016 from http://jordantimes.com/95-of-jordanians-own-mobiles-47-use-the-internet.

  • 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., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.

    Google Scholar 

  • Viberg, O., & Grönlund, Å. (2013). Cross-cultural analysis of users’ attitudes 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 

  • Voss, C. (2000). Developing an eService strategy. Business Strategy Review, 11(1), 21–34.

    Article  Google Scholar 

  • Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761–774.

    Article  Google Scholar 

  • Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of Internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501–519.

    Article  Google Scholar 

  • Wingkvist, A. (2009). Understanding scalability and sustainability in mobile learning: A systems development framework.

  • Yamakawa, P., Delgado, C., Díaz, E., Garayar, E., & Laguna, H. (2013). Factors influencing the use of mobile technologies in a university environment: A case from Latin America. International Journal of Information and Communication Technology Education (IJICTE), 9(2), 24–38.

    Article  Google Scholar 

  • Yong Wee, S., Siong Hoe, L., Kung Keat, T., Check Yee, L., & Parumo, S. (2011). Prediction of user acceptance and adoption of smart phone for learning with technology acceptance model. Journal of Applied Sciences, 10(20), 2395–2402.

    Google Scholar 

  • Zarmpou, T., Saprikis, V., & Vlachopoulou, M. (2012). Examining behavioral intention toward mobile services: An empirical investigation in Greece. In Mobile opportunities and applications for E-service innovations (Vol. 37).

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Almaiah, M.A., Jalil, M.A. & Man, M. Extending the TAM to examine the effects of quality features on mobile learning acceptance. J. Comput. Educ. 3, 453–485 (2016). https://doi.org/10.1007/s40692-016-0074-1

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