Education and Information Technologies

, Volume 24, Issue 1, pp 885–909 | Cite as

Examination of factors influencing the use of mobile learning system: An empirical study

  • Mohammed Amin AlmaiahEmail author
  • Omar Abdulwahab Alismaiel


Past studies have placed little emphasis on quality factors as the detebile learning application provides me a promptrminants of mobile learning adoption. Thus, this study’s purpose is to integrate the Technology Acceptance Model (TAM) with the updated DeLone and McLean’s model (DL&ML) to examine whether quality factors (including system quality, information quality, and service quality) and individual beliefs (including perceived usefulness and perceived ease of use) are the antecedents to students’ satisfaction and their intention to use, leading to enhancing their actual usage of mobile learning system. A total of 400 questionnaires were distributed. The results showed that quality factors (including system quality, information quality, and service quality) had significant effects on students’ satisfaction and their intention to use mobile learning; besides, perceived usefulness has significantly strong impacts on intention to use mobile learning, and satisfaction and intention to use both have significant effects on actual use of mobile learning. This study opens future work for using the identified quality factors as guidelines for researchers and designers to design and develop mobile learning applications.


Mobile learning Quality factors DeLone and McLean’s model TAM model Satisfaction Jordan 



The authors acknowledges the Deanship of Scientific Research at King Faisal University for the financial support.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Abachi, H. R., & Muhammad, G. (2014). The impact of m-learning technology on students and educators. Computers in Human Behavior, 30, 491–496.Google Scholar
  2. 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).Google Scholar
  3. Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.Google Scholar
  4. 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.Google Scholar
  5. 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.Google Scholar
  6. 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.Google Scholar
  7. Almaiah, M. A. (2018). Acceptance and usage of a mobile information system services in University of Jordan. Education and Information Technologies, 1–23.Google Scholar
  8. Almaiah, M. A., & Jalil, A. (2014). Investigating Students' perceptions on Mobile learning services. International Journal of Interactive Mobile Technologies, 8(4).Google Scholar
  9. 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, 19(3), 1314–1320.Google Scholar
  10. Almaiah, M. A., Jalil, M. A., & Man, M. (2016a). Preliminary study for exploring the major problems and activities of mobile learning system: A case study of Jordan. Journal of Theoretical & Applied Information Technology, 93(2).Google Scholar
  11. Almaiah, M. A., Jalil, M. A., & Man, M. (2016b). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3(4), 453–485.Google Scholar
  12. Almarashdeh, I. A., Sahari, N., Zin, N. A. M., & Alsmadi, M. (2010). The success of learning management system among distance learners in Malasian universities. Journal of Theoretical & Applied Information Technology, 21(2).Google Scholar
  13. 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. International Journal of Technical Research and Applications, 2(1), 05–11.Google Scholar
  14. 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.Google Scholar
  15. 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
  16. Althunibat, A. (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Computers in Human Behavior, 52, 65–71.Google Scholar
  17. 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.Google Scholar
  18. Bidin, S., & Ziden, A. A. (2013). Adoption and application of mobile learning in the education industry. Procedia-Social and Behavioral Sciences, 90, 720–729.Google Scholar
  19. Byrne, B. M. (2013). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Hove: Psychology PressGoogle Scholar
  20. 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.Google Scholar
  21. 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.Google Scholar
  22. Cheng, Y. M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361–390.Google Scholar
  23. Cho, C., & Lai. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53(2), 216–227.Google Scholar
  24. Dahlstrom, E., Walker, J. D., & Dziuban, C. (2013). ECAR study of undergraduate students and information technology. 2013.Google Scholar
  25. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319–340.Google Scholar
  26. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95.Google Scholar
  27. 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
  28. Delone, W. H., & Mclean, E. R. (2004). Measuring e-commerce success: Applying the DeLone & McLean information systems success model. International Journal of Electronic Commerce, 9(1), 31–47.Google Scholar
  29. Etezadi-Amoli, J., & Farhoomand, A. F. (1996). A structural model of end user computing satisfaction and user performance. Information & Management, 30(2), 65–73.Google Scholar
  30. Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2).Google Scholar
  31. Field, A. (2009). Discovering statistics using SPSS. Sage publications.Google Scholar
  32. 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
  33. 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.Google Scholar
  34. 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.Google Scholar
  35. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19, 213–236.Google Scholar
  36. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Upper Saddle River: Pearson Prentice Hall.Google Scholar
  37. 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.Google Scholar
  38. 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.Google Scholar
  39. Holsapple, C. W., & Lee-Post, A. (2010). Behavior-based analysis of knowledge dissemination channels in operations management. Omega, 38(3), 167–178.Google Scholar
  40. 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, 269–273.Google Scholar
  41. Iqbal, S., & Qureshi, I. A. (2012). M-learning adoption: A perspective from a developing country. The International Review of Research in Open and Distributed Learning, 13(3), 147–164.Google Scholar
  42. 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.MathSciNetGoogle Scholar
  43. 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.Google Scholar
  44. 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.Google Scholar
  45. Kim, T. G., Lee, J. H., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29(3), 500–513.Google Scholar
  46. 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.Google Scholar
  47. 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.Google Scholar
  48. Lee, Y. C., Li, M. L., Yen, T. M., & Huang, T. H. (2010). Analysis of adopting an integrated decision making trial and evaluation laboratory on a technology acceptance model. Expert Systems with Applications, 37(2), 1745–1754.Google Scholar
  49. Lindsay, R., Jackson, T. W., & Cooke, L. (2011). Adapted technology acceptance model for mobile policing. Journal of Systems and Information Technology, 13(4), 389–407.Google Scholar
  50. Liu, Y., Li, H., & Carlsson, C. (2010). Factors driving the adoption of m-learning: An empirical study. Computers & Education, 55(3), 1211–1219.Google Scholar
  51. 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
  52. Maditinos, D., Chatzoudes, D., & Sarigiannidis, L. (2013). An examination of the critical factors affecting consumer acceptance of online banking: A focus on the dimensions of risk. Journal of Systems and Information Technology, 15(1), 97–116.Google Scholar
  53. Mohammadi, H. (2015). Social and individual antecedents of m-learning adoption in Iran. Computers in Human Behavior, 49, 191–207.Google Scholar
  54. (2009). The Mobile learning network (MoLeNET) retrieved APR. 28th, 2016, From
  55. Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world-wide-web context. Information & Management, 38(4), 217–230.MathSciNetGoogle Scholar
  56. O'bannon, B. W., & Thomas, K. (2014). Teacher perceptions of using mobile phones in the classroom: Age matters! Computers & Education, 74, 15–25.Google Scholar
  57. Ong, C. S., Day, M. Y., & Hsu, W. L. (2009). The measurement of user satisfaction with question answering systems. Information & Management, 46(7), 397–403.Google Scholar
  58. Ozdamli, F., & Cavus, N. (2011). Basic elements and characteristics of mobile learning. Procedia-Social and Behavioral Sciences, 28, 937–942.Google Scholar
  59. Park, N., Roman, R., Lee, S., & Chung, J. E. (2009). User acceptance of a digital library system in developing countries: An application of the technology acceptance model. International Journal of Information Management, 29(3), 196–209.Google Scholar
  60. 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.Google Scholar
  61. Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236–263.Google Scholar
  62. Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244.Google Scholar
  63. Prieto, J. C. S., Migueláñez, S. O., & García-Peñalvo, F. J. (2014). 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.Google Scholar
  64. 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.Google Scholar
  65. 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.Google Scholar
  66. Rocha, Á. (2012). Framework for a global quality evaluation of a website. Online Information Review, 36(3), 374–382.Google Scholar
  67. Sanchez-Franco, M. J. (2009). The moderating effects of involvement on the relationships between satisfaction, trust and commitment in e-banking. Journal of Interactive Marketing, 23(3), 247–258.Google Scholar
  68. Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519–528.Google Scholar
  69. Sekaran, U. (2009). Bougie. M,“research methods for business: A skill building approach”. UK: John Wiley & sons.Google Scholar
  70. The Jordan Times. (2014). 95% of Jordanians own mobiles; 47% use the Internet. Retrieved 21 April, 2016 from
  71. Urbach, N., Smolnik, S., & Riempp, G. (2009). The state of research on information systems success. Business & Information Systems Engineering, 1(4), 315–325.Google Scholar
  72. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186–204.Google Scholar
  73. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425–478.Google Scholar
  74. 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.Google Scholar
  75. Wang, Y. S., & Liao, Y. W. (2008). Assessing eGovernment systems success: A validation of the DeLone and McLean model of information systems success. Government Information Quarterly, 25(4), 717–733.Google Scholar
  76. Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761–774.Google Scholar
  77. 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.Google Scholar
  78. Williams, P. W. (2009). Assessing mobile learning effectiveness and acceptance. Doctoral dissertation, The George Washington University.Google Scholar
  79. 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.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Mohammed Amin Almaiah
    • 1
    Email author
  • Omar Abdulwahab Alismaiel
    • 1
  1. 1.King Faisal UniversityAlEhsaSaudi Arabia

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