Examination of factors influencing students and faculty behavior towards m-learning acceptance: An empirical study

International Journal of Information and Learning Technology

Volumn 34, Issue 3, 2017, Pages 166-188

  Bakhsh, Muhammad ^a   Mahmood, Amjad ^b   Sangi, Nazir A ^a  

^a ALLAMA IQBAL OPEN UNIVERSITY   (Pakistan)

^b UNIVERSITY OF BAHRAIN   (Bahrain)

Author keywords

Factors influence technology adoption; M learning; Technology adoption behaviour

Indexed keywords

EID: 85019252035     PISSN: 20564880     EISSN: 20564899     Source Type: Journal    
DOI: 10.1108/IJILT-08-2016-0028     Document Type: Article

References (101)

1. Abas, Z.W., Peng, C.L. and Mansor, N. (2009), “A study on learner readiness for mobile learning at Open University Malaysia”, IADIS International Conference Mobile Learning, pp. 151-157.
2. Adams, D.A., Nelson, R.R. and Todd, P.A. (1992), “Perceived usefulness, ease of use and usage of information technology: a replication”, MIS Quarterly, Vol. 16 No. 2, pp. 227-247.
3. Adjin-Tettey, T.D. (2014), “Adoption and utilisation of learning/course management systems: the study of university of professional studies”, International Journal of ICT and Management, Vol. 2 No. 1, pp. 48-65.
4. Alrasheedi, M., Capretz, L.F. and Raza, A. (2015), “A systematic review of the critical factors for success of mobile learning in higher education (university student perspective)”, Journal of Educational Computing Research, Vol. 52 No. 2, pp. 257-276.
5. Andaleeb, A.A., Idrus, R.M., Ismail, I. and Mokaram, A.K. (2010), “Technology readiness index (TRI) among USM distance education students according to age”, International Journal of Human and Social Sciences, Vol. 5 No. 3, pp. 189-192.
6. Arbuckle, J.A. (1995), “AMOS users guide”, available at: http://amosdevelopment.com (accessed 26 April 2016).
7. Avciyucel, U. and Gulbahar, Y. (2013), “Technology acceptance model: a review of the prior predictors”, Journal of Faculty of Educational Sciences, Vol. 46 No. 1, pp. 89-109.
8. Bakhsh, M., Mahmood, A. and Sangi, N.A. (2015), “An assessment of students’ readiness towards mobile learning at AIOU, Pakistan”, Information and Communication Technologies (ICICT), 2015 International Conference on IEEE, pp. 1-6.
9. Bakhsh, M., Mahmood, A. and Sangi, N.A. (2016), “Developing m-readiness assessment model for open and distance education”, presented at 3rd International Conference on Open and Distance Education, Lahore, 14-15 March.
10. Bakhsh, M., Mahmood, A. and Sangi, N.A (2017), “M-readiness model development to investigate readiness index and demographic differences in readiness”, International Journal of Distance Education Technologies, Vol. 16 No. 1.
11. Barclay, D., Higgins, C. and Thompson, R. (1995), “The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration”, Technology Studies, Vol. 2 No. 2, pp. 285-309.
12. Bentler, P.M. (1990), “Comparative fit indexes in structural models”, Psychological Bulletin, Vol. 107 No. 2, pp. 238-46.
13. Bentler, P.M. and Bonnet, D.C. (1980), “Significance tests and goodness of fit in the analysis of covariance structures”, Psychological Bulletin, Vol. 88 No. 3, pp. 588-606.
14. Bhatti, T. (2007), “Exploring factors influencing the adoption of mobile commerce”, Journal of Internet Banking and Commerce, Vol. 12 No. 3, pp. 1-13.
15. Brown, M.W. and Cudeck, R. (1993), “Alternative ways of assessing model fit”, Testing Structural Equation Models, Vol. 154, pp. 136-162.
16. Byrne, D.S. (1998), Complexity Theory and the Social Sciences: an Introduction, Psychology Press.
17. Carmines, E.G. and Zeller, R.A. (1979), Reliability and Validity Assessment, Vol. 17, Sage Publications.
18. Chang, C.-Y., Sheu, J.-P. and Chan, T.-W. (2003), “Concept and design of ad-hoc and mobile classrooms”, Journal of Computer Assisted Learning, Vol. 19 No. 3, pp. 336-346.
19. Chen, L., Gillenson, M.L. and Sherrell, D.L. (2002), “Enticing online consumers: an extended technology acceptance perspective”, Information & Management, Vol. 39 No. 8, pp. 705-719.
20. Cheon, J., Sangno, L., Steven, M.C. and Jaeki, S. (2012), “An investigation of mobile learning readiness in higher education based on the theory of planned behavior”, Computer & Education, Vol. 59 No. 3, pp. 1054-1064, available at: http://doi.org/10.1016/j.compedu.2012.04.015
21. Chin, W.W. (1998), Commentary: issues and opinion on structural equation modeling.
22. Cooper, R.B. and Zmud, R.W. (1990), “Information technology implementation research: a technological diffusion approach”, Management Science, Vol. 36 No. 2, pp. 123-139.
23. Chung, H.H., Chen, S.C. and Kuo, M.H. (2015), “A study of EFL college students’ acceptance of mobile learning”, Procedia – Social and Behavioral Sciences, Vol. 176, pp. 333-339, available at: https://doi.org/10.1016/j.sbspro.2015.01.479
24. Churchill, D. and Churchill, N. (2008), “Educational affordances of PDAs: a study of a teacher’s exploration of this technology”, Computer and Education, Vol. 50 No. 4, pp. 1439-1450.
25. Davis, F.D. (1989), “Perceived usefulness, perceived ease of use and user acceptance of information technology”, MIS Quarterly, Vol. 13 No. 3, pp. 318-339.
26. Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer technology: a comparison of two theoretical models”, Management Science, Vol. 35 No. 8, pp. 982-1003.
27. Deloitte (2013), “The state of global mobile consumers: divergence deepens”, available at: www2.deloitte.com (accessed 11 January 2016).
28. Dishaw, M.T. and Strong, D.M. (1999), “Extending the technology acceptance model with task-technology fit constructs”, Information & Management, Vol. 36 No. 1, pp. 9-21.
29. Fishbein, M. and Ajzen, I. (1975), Belief, Attitude, Intentions and Behavior: An Introduction to Theory and Research, Addison-Wesley, Boston, MA.
30. Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
31. Gartner (2014), “Gartner says worldwide traditional PC, tablet, ultra mobile and mobile phone shipments to grow 4.2 percent in 2014”, available at: www.gartner.com/newsroom/id/2791017 (accessed 14 January 2016).
32. Gefen, D., Karahanna, E. and Straub, D.W. (2003), “Trust and TAM in online shopping: an integrated model”, MIS Quarterly, Vol. 27 No. 1, pp. 51-90.
33. Gong, M., Xu, Y. and Yu, Y. (2004), “An enhanced technology acceptance model for web-based learning”, Journal of Information Systems Education, Vol. 15 No. 4, pp. 365-374.
34. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis with Readings.
35. Hair, J.F. Jr, Anderson, R.E., Tatham, R.L. and Black, W.C. (1995), Multivariate Data Analysis with Readings, Prentice Hall, New Jersy, NJ.
36. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (1998), Multivariate data analysis, Multivariate Data Analysis, 5th ed, Upper Saddle River.
37. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006), Multivariate Data Analysis, Upper Saddle River, Auflage.
38. Hopper, H.U., Joiner, R., Milrad, M. and Sharples, M. (2003), “Guest editorial: wireless and mobile technologies in education”, Journal of Computer Assisted Learning, Vol. 19 No. 3, pp. 255-259, doi: 10.1046/j.0266-4909.2003.00027.x.
39. Horton, W. (2006), E-Learning by Design, Pfeiffer, San Francisco, CA.
40. Horzum, M.B., Öztürk, E., Bektaş, M., Güngören, Ö.C. and Çakır, Ö. (2014), “Secondary school students tablet computer acceptance and readiness: a structural equation modeling”, Education and Science, Vol. 39 No. 176, pp. 81-93.
41. Hoyle, R.H. (1995), Structural Equation Modeling: Concepts, Issues, and Applications, Sage.
42. Hu, L.T. and Bentler, P.M. (1999), “Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives”, Structural Equation Modeling, Vol. 6 No. 1, pp. 1-55.
43. Hussain, S., Manap, M., Amir, Z. and Krish, P. (2012), “Mobile learning readiness among Malaysian students at higher learning institutes”, Asian Social Science, Vol. 8 No. 12, p. 276.
44. Iqbal, S. and Bhatti, Z.A. (2015), “An investigation of university student readiness towards m-learning using TAM”, International Review of Research in Open and Distributed Learning, Vol. 16 No. 4, pp. 83-103.
45. Kazeime, F. and Bayat, S. (2012), “The effect of integrating technology in teaching as part of teachers’ PCK on students’ attitude, misconceptions, and mathematical problem solving performance”, International Journal of Education And Information Technologies, Vol. 6 No. 1, pp. 1-8.
46. Karahanna, E., Straub, D. and Chervany, N. (1999), “Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs”, MIS Quarterly, Vol. 23 No. 2.
47. Kelloway, E.K. (1998), Using LISREL for Structural Equation Modeling: A Researcher's Guide, Sage.
48. Khaddage, F. and Knezek, G. (2013), “iLearn via mobile technology: a comparison of mobile learning attitudes among university students in two nations”, Proceedings of the IEEE 13th International Conference on Advanced Learning Technologies (ICALT), 15-18 July, Beijing Normal University, Beijing.
49. Kim, H.W., Chan, H.C. and Gupta, S. (2007), “Value-based adoption of mobile internet: an empirical investigation”, Decision Support Systems, Vol. 43 No. 1, pp. 111-126.
50. Klem, L. (2000), “Structural equation modeling”, in Grimm, L.G. and Yarnold, P.R. (Eds), Reading and Understanding More Multivariate Statistics, American Psychology Association, Washington, DC, pp. 227-260.
51. Kline, R.B. (2005), Principles and Practice of Structural Equation Modeling, 2nd ed., Guilford Press, New York, NY.
52. Klopfer, E., Squire, K. and Jenkins, H. (2002), “Environmental detectives: PDAs as a window into a virtual simulated world”, Proceedings of IEEE International Workshop on Wireless and Mobile Technologies in Education: IEEE Computer Society, Vaxjo, Sweden, pp. 95-98.
53. Koufaries, M. (2002), “Applying the technology acceptance model and flow theory to online consumer behaviour”, Information Systems Research, Vol. 13 No. 2, pp. 205-223.
54. Kuo, Y.F. and Yen, S.N. (2009), “Towards an understanding of the behavioral intention to use 3G mobile value-added services”, Computers in Human Behavior, Vol. 25 No. 1, pp. 103-110.
55. Legris, P., Ingham, J. and Collerette, P. (2003), “Why do people use information technology? A critical review of the technology acceptance model”, Information & Management, Vol. 40 No. 3, pp. 191-204.
56. Liu, Y., Li, H. and Carlsson, C. (2010), “Factors driving the adoption of m-learning: an empirical study”, Computers & Education, Vol. 55 No. 3, pp. 1211-1219.
57. McDonald, R.P. and Ho, M.H.R. (2002), “Principles and practice in reporting structural equation analyses”, Psychological Methods, Vol. 7 No. 1, pp. 64-82.
58. Ma, W. and Yeun, A. (2011), “E-learning system acceptance and usage pattern”, in Teo, T. (Ed.), Technology Acceptance in Education: Research and Issues, Sense Publishers, Boston, MA, pp. 201-215.
59. Mahat, J., Ayub, A.F.M. and Luan, S. (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, Vol. 64, pp. 284-290.
60. Mathieson, K. (1991), “Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior”, Information Systems Research, Vol. 2 No. 3, pp. 173-191.
61. Moon, J.W. and Kim, Y.G. (2001), “Extending the TAM for a World Wide Web context”, Information & Management, Vol. 384 No. 4, pp. 217-230, available at: http://doi.org/10.1016/S0378-7206(00)00061-6
62. Moussa, C. (2003), “Workers on the move: new opportunities through mobile commerce”, paper presented at the Stockholm Mobility Roundtable, Stockholm.
63. Nunnally, J. (1978), Psychometric Theory, McGraw-Hill, New York, NY.
64. Osang, F.B., Ngole, J. and Tsuma, C (2013), “Prospects and challenges of mobile learning implementation in Nigeria. Case study national open university of Nigeria NOUN”, International Conference on ICT for Africa 2013, 20-23 February, Harare.
65. Park, S.Y. (2009), “An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning”, Educational Technology & Society, Vol. 12 No. 3, pp. 150-162.
66. Park, S.Y., Nam, M.W. and Cha, S.B. (2012), “University students’ behavioral intention to use mobile learning: evaluating the technology acceptance model”, British Journal of Educational Technology, Vol. 43 No. 4, pp. 592-605.
67. Park, Y. (2011), “A pedagogical framework for mobile learning: categorizing educational applications of mobile technologies into four types”, International Review of Research in Open and Distance Learning, Vol. 12 No. 2, pp. 78-102.
68. Petrova, K. and Sutedjo, Y. (2004), “Just in-time learning: ready for SMS?”, Cognition and Exploratory Learning in Digital Age (CELDA’04), Proceedings of the IADIS International Conference, 15-17 December, Lisbon, pp. 495-498.
69. Riemenschneider, C.K., Harrison, D.A. and Mykytn, P.P. (2003), “Understanding IT adoption decisions in small business: integrating current theories”, Information & Management, Vol. 40 No. 4, pp. 269-285.
70. Robinson, L., Marshall, G.W. and Stamps, M. B. (2005), “An empirical investigation of technology acceptance in a field sales force setting”, Industrial Marketing Management, Vol. 34 No. 4, pp. 407-415.
71. Saunders, S., Lewis, P. and Thornhill, A. (2003), Research Methods for Business Students, Pearson Education, Essex.
72. Shaikh, A.A. and Karjaluoto, H. (2015), “Mobile banking adoption: a literature review”, Telematics and Informatics, Vol. 32 No. 1, pp. 129-142.
73. Shih, Y.-Y. and Fang, K. (2004), “The use of a decomposed theory of planned behavior to study internet banking in Taiwan”, Internet Research, Vol. 14 No. 3, pp. 213-219.
74. Shih, K.P., Chen, H.C., Chang, C.E. and Kao, T.C. (2010), “The development and implementation of scaffolding-based self-regulated learning system for e/m-learning”, Educational Technology and Society, Vol. 13 No. 1, pp. 80-93.
75. Suki, N.M. (2013), “Students’ demand for smartphones: structural relationships of product features, brand name, product price and social influence”, Campus – Wide Information Systems, Vol. 30 No. 4, pp. 236-248, doi: 10.1108/CWIS-03-2013-0013.
76. Skui, N.M. and Suki, N.M. (2007), “Mobile phone usage for m-learning: comparing heavy and light mobile phone users”, Campus – Wide Information Systems, Vol. 24 No. 5, pp. 355-365.
77. Taylor, S. and Todd, P. (1995b), “Assessing IT usage: the role of prior experience”, MIS Quarterly, Vol. 19 No. 4, pp. 561-570.
78. Taylor, S. and Todd, P.A. (1995a), “Understanding information technology usage: a test of competing models”, Information Systems Research, Vol. 6 No. 2, pp. 144-176.
79. Traxler, J. (2007), “Current state of mobile learning”, International Review of Research in Open and Distance Learning, Vol. 8 No. 2, pp. 9-24.
80. Traxler, J. (2008), “Learning in a mobile age”, International Journal of Mobile and Blended Learning, Vol. 1 No. 1, pp. 1-12.
81. Vanschaik, P. (2011), “Unified theory of acceptance and use for web sites used by students in higher education”, in Teo, T. (Ed.), Technology Acceptance in Education: Research and Issues, Sense Publishers, Boston, MA, pp. 159-181.
82. Venkatesh, V. (2000), “Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model”, Information Systems Research, Vol. 11 No. 4, pp. 342-365.
83. Venkatesh, V. and Davis, F.D. (1996), “A model of the antecedents of perceived ease of use: development and test”, Decision Sciences, Vol. 27 No. 3, pp. 451-481.
84. Venkatesh, V. and 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, Vol. 24 No. 1, pp. 115-139.
85. Wang, S. and Higgins, M. (2006), “Limitations of mobile phone learning”, The JALT CALL Journal, Vol. 2 No. 1, pp. 3-14.
86. Winer, B.J., Brown, D.R. and Michels, K.M. (1971), Statistical Principles in Experimental Design, Vol. 2, McGraw-Hill, New York, NY.
87. Wu, J.H. and Wang, S.C. (2005), “What drives mobile commerce? An empirical evaluation of the revised technology acceptance model”, Information & Management, Vol. 42 No. 5, pp. 719-729.
88. Yuen, A.H. and Ma, W.W. (2002), “Gender differences in teacher computer acceptance”, Journal of Technology and Teacher Education, Vol. 10 No. 3, pp. 365-382.
89. Yuen, S.C. and Yuen, P.K. (2008), “Mobile learning”, in Tomei, L.A. (Ed.), Encyclopedia of Information Technology Curriculum Integration, Idea Group, Hershey, PA.
90. Bakhsh, M., Mahmood, A. and Sangi, N.A. (2015, December), “An assessment of students’ readiness towards mobile learning at AIOU, Pakistan”, Information and Communication Technologies (ICICT), 2015 International Conference on. IEEE, pp. 1-6.
91. Bakhsh, M., Mahmood, A. and Sangi, N.A. (2016), “Developing m-readiness assessment model for open and distance education”, presented at 3rd International Conference on Open and Distance Education, 14-15 March, Lahore.
92. Boateng, R., Heeks, R., Molla, A. and Hinson, R. (2011), “Advancing E-commerce beyond readiness in a developing country: experiences of Ghanaian firms”, Journal of Electronic Commerce in Organizations, Vol. 9 No. 1, pp. 1-16.
93. Dempere-Marco, L., Hu, X.P., MacDonald, S.L., Ellis, S.M., Hansell, D.M. and Yang, G.Z. (2002), “The use of visual search for knowledge gathering in image decision support”, IEEE Transactions on Medical Imaging, Vol. 21 No. 7, pp. 741-754.
94. Dempere-Marco, N. (2013), “Students’ dependence on smart phones: the influence of social needs, social influences and convenience”, Campus-Wide Information Systems, Vol. 30 No. 2, pp. 124-134.
95. Hooper, D., Coughlan, J. and Mullen, M. (2008), “Structural equation modelling: guidelines for determining model fit”, Articles, 2.
96. Jöreskog, K. and Long, J.S. (1993), “Introduction”, in Kenneth, A.B. and Scott Long, J. (Eds), Testing Structural Equation Models, Sage, Newbury Park, CA.
97. Koole, M.L. (2009), “A model for framing mobile learning”, Mobile Learning: Transforming the Delivery of Education and Training, Vol. 1 No. 2, pp. 25-47.
98. Lu, J., Liu, C., Yu, C.-S. and Wang, K. (2008), “Determinantes of accepting wireless mobile data services in China”, Information and Management, Vol. 45 No. 1, pp. 52-64, available at: http://doi.org/10.1016/j.im.2007.11.002
99. Lu, J., Yao, J.E. and C-sheng (2005), “Personal innovativeness, social influences and adoption of wireless internet services via mobile technology”, The Journal of Strategic Information Systems, Vol. 14 No. 3, pp. 245-268.
100. Lule, I., Omwansa, T.K. and Waema, T.M. (2012), “Application of technology acceptance model (TAM) in M-banking adoption in Kenya”, International Journal of Computing and ICT Research, Vol. 6 No. 1, pp. 31-43.
101. Sangi, N. (2007), “E-assessment models and methods for student evaluation”, 21st AAOU Conference, 29-31 October, Malaysia, Kuala Lumpur.