Universal Access in the Information Society

, Volume 18, Issue 3, pp 659–673 | Cite as

Extending UTAUT2 toward acceptance of mobile learning in the context of higher education

  • Aijaz Ahmed ArainEmail author
  • Zahid Hussain
  • Wajid H. Rizvi
  • Muhammad Saleem Vighio
Long Paper


The use of smartphones as a learning tool in education is on the rise, causing a rapidly developing use of mobile learning (m-learning) in both developed and developing countries. The key features of smartphones, i.e., mobility, ubiquity, lightweight, low-cost and connectivity from anywhere and anytime, enhance their usage in a variety of ways. M-learning is an innovative idea that provides enormous opportunities by connecting humans and technology, such as better learning experiences and technology acceptance. The use of m-learning is growing at a higher pace worldwide, yet sufficient understanding of the factors that influence its acceptance in society is still lacking, particularly in developing countries. A number of models related to m-learning acceptance do exist, for instance, the extended unified theory of acceptance and use of technology (UTAUT2); however, the use of UTAUT2 to study m-learning acceptance is scant in the context of higher education institutes and it does not cover specific features of mobile devices. Therefore, this study not only uses UTAUT2 as a base theoretical framework but also extends it using five other constructs: ubiquity, information quality, system quality, appearance quality and satisfaction. A cross-sectional survey was conducted in two engineering universities in Pakistan. The questionnaire was administered among 900 students, out of which 730 usable responses were selected for further analysis. The data were analyzed using structural equation modeling. The findings revealed that the model fits data well; the model fit indices were within the recommended thresholds. The performance expectancy, hedonic motivation, habit, ubiquity and satisfaction have statistically significant impact on the behavioral intention and the information quality, system quality and appearance quality also have statistically significant impact on the mediator satisfaction toward m-learning acceptance. This study contributes to the body of literature related to technology acceptance models for m-learning by making a tailored extension in UTAUT2 that provides valuable insights into assess m-learning acceptance in the context of higher education institutes of developing countries, specifically in Pakistan.


M-learning acceptance UTAUT2 Theoretical framework Ubiquity Structural equation modeling 



This research work is based on the Ph.D. thesis of the first author carried out at Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan [127].


  1. 1.
    Horng, S.M., Chao, C.L.: How Risk Tolerance Constrains Perceived Risk on Smartphone Users’ Risk Behavior, pp. 67–72. Information Technology-New Generations. Springer, Cham (2018)Google Scholar
  2. 2.
    Sung, Y.T., Chang, K.E., Liu, T.C.: The effects of integrating mobile devices with teaching and learning on students’ learning performance: a meta-analysis and research synthesis. Comput. Educ. 94(1), 252–275 (2016)Google Scholar
  3. 3.
    Kumar, B.A., Mohite, P.: Usability Study of Mobile Learning Application in Higher Education Context: An Example from Fiji National University. Mobile Learning in Higher Education in the Asia-Pacific Region, pp. 607–622. Springer, Singapore (2017)Google Scholar
  4. 4.
    Pakistan Telecommunication Authority. Annual Report 2017. (2018). Accessed 25 Sept 2018
  5. 5.
    Klopfer, E., Squire, K., Jenkins, H.: Environmental detectives: PDAs as a window into a virtual simulated world. In: Wireless and Mobile Technologies in Education, Proceedings. IEEE International Workshop, pp. 95–98 (2002)Google Scholar
  6. 6.
    BenMoussa, C.: Workers on the move: new opportunities through mobile commerce. In: Stockholm Mobility Roundtable, UKAIS Conference, pp. 22–23 (2003)Google Scholar
  7. 7.
    Churchill, D., Churchill, N.: Educational affordances of PDAs: a study of a teacher’s exploration of this technology. Comput. Educ. 50(4), 1439–1450 (2008)Google Scholar
  8. 8.
    Shaikh, A.A., Karjaluoto, H.: Mobile banking adoption: a literature review. Telemat. Inform. 32(1), 129–142 (2015)Google Scholar
  9. 9.
    Chung, H.H., Chen, S.C., Kuo, M.H.: A study of EFL college students’ acceptance of mobile learning. Procedia Soc. Behav. Sci. 176, 333–339 (2015)Google Scholar
  10. 10.
    Al-Emran, M., Elsherif, H.M., Shaalan, K.: Investigating attitudes towards the use of mobile learning in higher education. Comput. Hum. Behav. 56, 93–102 (2016)Google Scholar
  11. 11.
    Cheon, J., Lee, S., Crooks, S.M., Song, J.: An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Comput. Educ. 59(3), 1054–1064 (2012)Google Scholar
  12. 12.
    Althunibat, A.: Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Comput. Hum. Behav. 52, 65–71 (2015)Google Scholar
  13. 13.
    Mohammadi, H.: Social and individual antecedents of m-learning adoption in Iran. Comput. Hum. Behav. 49, 191–207 (2015)Google Scholar
  14. 14.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)Google Scholar
  15. 15.
    Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46(2), 186–204 (2000)Google Scholar
  16. 16.
    Ajzen, I., Fishbein, M.: Understanding Attitudes and Predicting Social Behaviour. Prentice-Hall, Englewood Cliffs (1980)Google Scholar
  17. 17.
    Fishbein, M., Ajzen, I.: Belief, attitude, intention and behavior: an introduction to theory and research. ISBN 0201020890 (1975). Accessed 25 Sept 2018
  18. 18.
    Rogers, E.: Diffusion of innovations. The Free Press, New York. (1983). Accessed 25 Sept 2018
  19. 19.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)Google Scholar
  20. 20.
    Venkatesh, V., Thong, J.Y., Xu, X.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012)Google Scholar
  21. 21.
    Hao, S., Dennen, V.P., Mei, L.: Influential factors for mobile learning acceptance among Chinese users. Educ. Technol. Res. Dev. 65(1), 101–123 (2017)Google Scholar
  22. 22.
    Iqbal, S., Bhatti, Z.A.: An investigation of university student readiness towards m-learning using technology acceptance model. Int. Rev. Res. Open Distrib. Learn. 16(4), 83–102 (2015)MathSciNetGoogle Scholar
  23. 23.
    Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J.A., García-Peñalvo, F.J.: Learning with mobile technologies–Students’ behavior. Comput. Hum. Behav. 72, 612–620 (2017)Google Scholar
  24. 24.
    Huan, Y., Li, X., Aydeniz, M., Wyatt, T.: Mobile learning adoption: an empirical investigation for engineering education. Int. J. Eng. Educ. 31(4), 1081–1091 (2015)Google Scholar
  25. 25.
    Hassan, W.U., Nawaz, M.T., Syed, T.H., Arfeen, M.I., Naseem, A., Noor, S.: Investigating students’ behavioral intention towards adoption of mobile learning in higher education institutions of Pakistan. University of Engineering and Technology Taxila. Tech. J. 20(3), 34 (2015)Google Scholar
  26. 26.
    Al-Adwan, A.S., Al-Madadha, A., Zvirzdinaite, Z.: Modeling students’ readiness to adopt mobile learning in higher education: an empirical study. Int. Rev. Res. Open Distrib. Learn. 19(1), 221–241 (2018)Google Scholar
  27. 27.
    Badwelan, A., Drew, S., Bahaddad, A.A.: Towards acceptance m-learning approach in higher education in Saudi Arabia. Int. J. Bus. Manag. 11(8), 12 (2016)Google Scholar
  28. 28.
    El-Masri, M., Tarhini, A.: Factors affecting the adoption of e-learning systems in Qatar and USA: extending the unified theory of acceptance and use of technology 2 (UTAUT2). Educ. Tech. Res. Dev. 65(3), 743–763 (2017)Google Scholar
  29. 29.
    Bharati, V.J., Srikanth, R.: Modified UTAUT2 model for m-learning among students in India. Int. J. Learn. Change 10(1), 5–20 (2018)Google Scholar
  30. 30.
    Yang, S.: Understanding undergraduate students’ adoption of mobile learning model: a perspective of the extended UTAUT2. J. Converg. Inf. Technol. 8(10), 969 (2013)Google Scholar
  31. 31.
    Mohammadi, H.: Investigating users’ perspectives on e-learning: an integration of TAM and IS success model. Comput. Hum. Behav. 45, 359–374 (2015)Google Scholar
  32. 32.
    Cheng, D., Liu, G., Qian, C., Song, Y.F.: Customer acceptance of Internet banking: integrating trust and quality with UTAUT model. In: IEEE International Conference, Service Operations and Logistics, and Informatics (SOLI), vol. 1, pp. 383–388 (2008)Google Scholar
  33. 33.
    Wang, Y.S., Liao, Y.W.: The conceptualization and measurement of m-commerce user satisfaction. Comput. Hum. Behav. 23(1), 381–398 (2007)MathSciNetGoogle Scholar
  34. 34.
    Almarashdeh, I.: Sharing instructors experience of learning management system: a technology perspective of user satisfaction in distance learning course. Comput. Hum. Behav. 63, 249–255 (2016)Google Scholar
  35. 35.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: Extrinsic and intrinsic motivation to use computers in the workplace. J. Appl. Soc. Psychol. 22(14), 1111–1132 (1992)Google Scholar
  36. 36.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989)Google Scholar
  37. 37.
    Taylor, S., Todd, P.: Assessing IT usage: the role of prior experience. MIS Q. 19(4), 561–570 (1995)Google Scholar
  38. 38.
    Bandura, A.: Social Foundations of Thought and Action: A Social-Cognitive View. Englewood Cliffs, New York (1986)Google Scholar
  39. 39.
    Thompson, R.L., Higgins, C.A., Howell, J.M.: Personal computing: toward a conceptual model of utilization. MIS Q. 15(1), 125–143 (1991)Google Scholar
  40. 40.
    Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2010)Google Scholar
  41. 41.
    Wang, Y.S., Wu, M.C., Wang, H.Y.: Investigating the determinants and age and gender differences in the acceptance of mobile learning. Br. J. Educ. Technol. 40(1), 92–118 (2009)Google Scholar
  42. 42.
    Wu, J.H., Wang, S.C., Lin, L.M.: Mobile computing acceptance factors in the healthcare industry: a structural equation model. Int. J. Med. Inform. 76(1), 66–77 (2007)Google Scholar
  43. 43.
    Al-Gahtani, S.S.: Empirical investigation of e-learning acceptance and assimilation: a structural equation model. Appl. Comput. Inform. 12(1), 27–50 (2016)MathSciNetGoogle Scholar
  44. 44.
    Chu, T.H., Chen, Y.Y.: With good we become good: understanding e-learning adoption by theory of planned behavior and group influences. Comput. Educ. 92, 37–52 (2016)Google Scholar
  45. 45.
    Merhi, M.I.: Factors influencing higher education students to adopt podcast: an empirical study. Comput. Educ. 83, 32–43 (2015)Google Scholar
  46. 46.
    Marchewka, J.T., Kostiwa, K.: An application of the UTAUT model for understanding student perceptions using course management software. Commun. IIMA 7(2), 10 (2007)Google Scholar
  47. 47.
    Oye, N.D., Iahad, N.A., Rahim, N.A.: The history of UTAUT model and its impact on ICT acceptance and usage by academicians. Educ. Inf. Technol. 19(1), 251–270 (2014)Google Scholar
  48. 48.
    Chiu, C.M., Wang, E.T.: Understanding Web-based learning continuance intention: the role of subjective task value. Inf. Manag. 45(3), 194–201 (2008)MathSciNetGoogle Scholar
  49. 49.
    Wu, Y.L., Tao, Y.H., Yang, P.C.: The use of unified theory of acceptance and use of technology to confer the behavioral model of 3G mobile telecommunication users. J. Stat. Manag. Syst. 11(5), 919–949 (2008)zbMATHGoogle Scholar
  50. 50.
    Sheppard, B.H., Hartwick, J., Warshaw, P.R.: The theory of reasoned action: a meta-analysis of past research with recommendations for modifications and future research. J. Consum. Res. 15(3), 325–343 (1988)Google Scholar
  51. 51.
    Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)Google Scholar
  52. 52.
    Yuan, Y., Fulk, J., Shumate, M., Monge, P.R., Bryant, J.A., Matsaganis, M.: Individual participation in organizational information commons: the impact of team level social influence and technology-specific competence. Hum. Commun. Res. 31(2), 212–240 (2005)Google Scholar
  53. 53.
    Rice, R.E., Grant, A.E., Schmitz, J., Torobin, J.: Individual and network influences on the adoption and perceived outcomes of electronic messaging. Soc. Netw. 12(1), 27–55 (1990)Google Scholar
  54. 54.
    Kraut, R.E., Rice, R.E., Cool, C., Fish, R.S.: Varieties of social influence: the role of utility and norms in the success of a new communication medium. Organ. Sci. 9(4), 437–453 (1998)Google Scholar
  55. 55.
    Brown, S.A., Venkatesh, V.: A model of adoption of technology in the household: a baseline model test and extension incorporating household life cycle. Manag. Inf. Syst. Q. 29(3), 11 (2005)Google Scholar
  56. 56.
    Lee, M.C.: Understanding the behavioural intention to play online games: an extension of the theory of planned behaviour. Online Inf. Rev. 33(5), 849–872 (2009)Google Scholar
  57. 57.
    Leong, L.Y., Ooi, K.B., Chong, A.Y., Lin, B.: Modeling the stimulators of the behavioral intention to use mobile entertainment: does gender really matter? Comput. Hum. Behav. 29(5), 2109–2121 (2013)Google Scholar
  58. 58.
    Limayem, M., Hirt, S.G., Cheung, C.M.: How habit limits the predictive power of intention: the case of information systems continuance. MIS Q. 31(4), 705–737 (2007)Google Scholar
  59. 59.
    Crabbe, M., Standing, C., Standing, S., Karjaluoto, H.: An adoption model for mobile banking in Ghana. Int. J. Mobile Commun. 7(5), 515–543 (2009)Google Scholar
  60. 60.
    Chuang, Y.F.: Pull-and-suck effects in Taiwan mobile phone subscribers switching intentions. Telecommun. Policy 35(2), 128–140 (2011)Google Scholar
  61. 61.
    Nikou, S., Bouwman, H.: Ubiquitous use of mobile social network services. Telemat. Inform. 31(3), 422–433 (2014)Google Scholar
  62. 62.
    Parsons, D, Ryu, H.: A framework for assessing the quality of mobile learning. In: Proceedings of the International Conference for Process Improvement, Research and Education, pp. 17–27 (2006)Google Scholar
  63. 63.
    Hwang, G.J., Tsai, C.C., Yang, S.J.: Criteria, strategies and research issues of context-aware ubiquitous learning. J. Educ. Technol. Soc. 11(2), 81–91 (2008)Google Scholar
  64. 64.
    Wang, W.T., Li, H.M.: Factors influencing mobile services adoption: a brand-equity perspective. Internet Res. 22(2), 142–179 (2012)Google Scholar
  65. 65.
    Liao, C., Palvia, P., Lin, H.N.: The roles of habit and web site quality in e-commerce. Int. J. Inf. Manag. 26(6), 469–483 (2006)Google Scholar
  66. 66.
    Noorman Masrek, M., Jamaludin, A., Awang, M.S.: Evaluating academic library portal effectiveness: a Malaysian case study. Libr. Rev. 59(3), 198–212 (2010)Google Scholar
  67. 67.
    Tella, A., Bashorun, M.T.: Undergraduate students’ satisfaction with the use of web portals. Int. J. Web Portals 4(2), 56–73 (2012)Google Scholar
  68. 68.
    Zhou, T.: Understanding users’ initial trust in mobile banking: an elaboration likelihood perspective. Comput. Hum. Behav. 28(4), 1518–1525 (2012)Google Scholar
  69. 69.
    Saba, T.: Implications of E-learning systems and self-efficiency on students outcomes: a model approach. Hum. Cent. Comput. Inf. Sci. 2(1), 6 (2012)Google Scholar
  70. 70.
    Kim, K., Trimi, S., Park, H., Rhee, S.: The impact of CMS quality on the outcomes of e-learning systems in higher education: an empirical study. Decis. Sci. J. Innov. Educ. 10(4), 575–587 (2012)Google Scholar
  71. 71.
    Wang, H.C., Chiu, Y.F.: Assessing e-learning 2.0 system success. Comput. Educ. 57(2), 1790–1800 (2011)Google Scholar
  72. 72.
    Alam Napitupulu, T.O., Patria, J., Hasoloan, S.: Factors that determine electronic medical records users satisfaction: a case of Indonesia. J. Theor. Appl. Inf. Technol. 58(3), 499–505 (2013)Google Scholar
  73. 73.
    Delone, W.H., McLean, E.R.: The DeLone and McLean model of information systems success: a ten-year update. J. Manag. Inf. Syst. 19(4), 9–30 (2003)Google Scholar
  74. 74.
    Mohammadi, H.: Factors affecting the e-learning outcomes: an integration of TAM and IS success model. Telemat. Inform. 32(4), 701–719 (2015)Google Scholar
  75. 75.
    DeLone, W.H., McLean, E.R.: Information systems success: the quest for the dependent variable. Inf. Syst. Res. 3(1), 60–95 (1992)Google Scholar
  76. 76.
    Roca, J.C., Chiu, C.M., Martínez, F.J.: Understanding e-learning continuance intention: an extension of the technology acceptance model. Int. J. Hum Comput. Stud. 64(8), 683–696 (2006)Google Scholar
  77. 77.
    Tajuddin, R.A., Baharudin, M., Hoon, T.S.: System quality and its influence on students’ learning satisfaction in UiTM Shah Alam. Procedia Soc. Behav. Sci. 90, 677–685 (2013)Google Scholar
  78. 78.
    Petter, S., McLean, E.R.: A meta-analytic assessment of the DeLone and McLean IS success model: an examination of IS success at the individual level. Inf. Manag. 46(3), 159–166 (2009)Google Scholar
  79. 79.
    Urbach, N., Müller, B.: The updated DeLone and McLean model of information systems success. Inf. Syst. Theory. 1, 1–18 (2012)Google Scholar
  80. 80.
    Chang, C.C.: Exploring the determinants of e-learning systems continuance intention in academic libraries. Libr. Manag. 34(1/2), 40–55 (2013)Google Scholar
  81. 81.
    Tandi, L.E.: Measuring the success of library 2.0 technologies in the African context: the suitability of the DeLone and McLean’s model. Campus Wide Inf. Syst. 30(4), 288–307 (2013)Google Scholar
  82. 82.
    Aladwani, A.M.: An empirical test of the link between web site quality and forward enterprise integration with web consumers. Bus. Process Manag. J. 12(2), 178–190 (2006)Google Scholar
  83. 83.
    Zhou, T.: An empirical examination of initial trust in mobile banking. Internet Res. 21(5), 527–540 (2011)Google Scholar
  84. 84.
    Trakulmaykee, N., Trakulmaykee, Y., Hnuchek, K.: Statistical analysis: improvement of technology acceptance model in mobile tourist guide context. J. Adv. Manag. Sci. 4(3), 181–186 (2016)Google Scholar
  85. 85.
    Sanchez-Franco, M.J.: The moderating effects of involvement on the relationships between satisfaction, trust and commitment in e-banking. J. Interact. Market. 23(3), 247–258 (2009)Google Scholar
  86. 86.
    Hassanzadeh, A., Kanaani, F., Elahi, S.: A model for measuring e-learning systems success in universities. Expert Syst. Appl. 39(12), 10959–10966 (2012)Google Scholar
  87. 87.
    Islam, A.K.: The role of perceived system quality as educators’ motivation to continue e-learning system use. AIS Trans. Hum. Comput. Interact. 4(1), 25–43 (2012)Google Scholar
  88. 88.
    Udo, G.J., Bagchi, K.K., Kirs, P.J.: Using SERVQUAL to assess the quality of e-learning experience. Comput. Hum. Behav. 27(3), 1272–1283 (2011)Google Scholar
  89. 89.
    Hsia, J.W.: The effects of locus of control on university students’ mobile learning adoption. J. Comput. High. Educ. 28(1), 1–7 (2016)Google Scholar
  90. 90.
    Kadilar, C., Cingi, H.: Ratio estimators in stratified random sampling. Biometr. J. J. Math. Methods Biosci. 45(2), 218–225 (2003)MathSciNetzbMATHGoogle Scholar
  91. 91.
    Kim, H.W., Xu, Y., Koh, J.: A comparison of online trust building factors between potential customers and repeat customers. J. Assoc. Inf. Syst. 5(10), 13 (2004)Google Scholar
  92. 92.
    Kim, H.Y., Kim, J.W.: An empirical research on important factors of mobile internet usage. Asia Pac. J. Inf. Syst. 12(3), 89–113 (2002)Google Scholar
  93. 93.
    Cordeiro, C., Machás, A., Neves, M.M.: A Case Study of a Customer Satisfaction Problem: Bootstrap and Imputation Techniques. Handbook of Partial Least Squares, pp. 279–287. Springer, Berlin, Heidelberg (2010)Google Scholar
  94. 94.
    Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L.: Multivariate Data Analysis. Pearson Prentice Hall, Upper Saddle River (2006)Google Scholar
  95. 95.
    Tabachnik, B.G., Fidell, L.S.: Using multivariate statistics, 5th edn. Allyn & Bacon, Boston (2007)Google Scholar
  96. 96.
    Mahalanobis, P.C.: On the Generalized Distance in Statistics, pp. 49–55. National Institute of Science of India, Bengaluru (1936)zbMATHGoogle Scholar
  97. 97.
    Abbasi, M.S.: Culture, demography and individuals’ technology acceptance behaviour: a PLS based structural evaluation of an extended model of technology acceptance in South-Asian country context. Brunel University Brunel Business School Ph.d. theses (2011)Google Scholar
  98. 98.
    Baron, R.M., Kenny, D.A.: The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. 51(6), 1173 (1986)Google Scholar
  99. 99.
    Henseler, J., Ringle, C.M., Sinkovics, R.R.: The use of partial least squares path modeling in international marketing. New Challenges to International Marketing. In: Advances in International Marketing. vol 20, pp. 277–319. Emerald Group Publishing Limited, UK (2009)Google Scholar
  100. 100.
    Anderson, J.C., Gerbing, D.W.: Structural equation modeling in practice: a review and recommended two-step approach. Psychol. Bull. 103(3), 411 (1988)Google Scholar
  101. 101.
    Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res. 18(1), 39–50 (1981)Google Scholar
  102. 102.
    Churchill Jr., G.A.: A paradigm for developing better measures of marketing constructs. J. Market. Res. 16(1), 64–73 (1979)Google Scholar
  103. 103.
    Pallant, J.: SPSS survival manual: a step by step guide to data analysis using SPSS for Windows (versions 10 and 11): SPSS student version 11.0 for Windows. Open University Press, Milton Keynes (2001)Google Scholar
  104. 104.
    Cronbach, L.J.: Coefficient alpha and the internal structure of tests. Psychometrika 16(3), 297–334 (1951)zbMATHGoogle Scholar
  105. 105.
    Werts, C.E., Linn, R.L., Jöreskog, K.G.: Intraclass reliability estimates: testing structural assumptions. Educ. Psychol. Meas. 34(1), 25–33 (1974)Google Scholar
  106. 106.
    Nunnally, J.C., Bernstein, I.H.: Psychometric Theory. McGraw-Hill, New York (1967)Google Scholar
  107. 107.
    Gefen, D., Straub, D., Boudreau, M.C.: Structural Equation Modeling and Regression: Guidelines for Research Practice. Commun. Assoc. Inf. Syst. 4(1), 7 (2000)Google Scholar
  108. 108.
    Chin, W.W.: The partial least squares approach to structural equation modeling. Modern Methods for Business Research. pp. 295–336. Lawrence Erlbaum Associates Publishers, Mahwah, NJ, US (1998)Google Scholar
  109. 109.
    Byrne, B.M.: Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. Routledge, London (2016)Google Scholar
  110. 110.
    Chen, Y.C., Wu, J.H., Peng, L., Yeh, R.C.: Consumer benefit creation in online group buying: the social capital and platform synergy effect and the mediating role of participation. Electron. Commer. Res. Appl. 14(6), 499–513 (2015)Google Scholar
  111. 111.
    Arain, A.A., Hussain, Z., Rizvi, W.H., Vighio, M.S.: An analysis of the influence of a mobile learning application on the learning outcomes of higher education students. Univ. Access Inf. Soc. 17(2), 325–334 (2018)Google Scholar
  112. 112.
    Al-alak, B.A., Alnawas, I.A.: Measuring the acceptance and adoption of e-learning by academic staff. Knowl. Manag. E-Learn. 3(2), 201 (2011)Google Scholar
  113. 113.
    Arpaci, I.: Understanding and predicting students’ intention to use mobile cloud storage services. Comput. Hum. Behav. 58, 150–157 (2016)Google Scholar
  114. 114.
    Lee, K.C., Chung, N.: Understanding factors affecting trust in and satisfaction with mobile banking in Korea: a modified DeLone and McLean’s model perspective. Interact. Comput. 21(5–6), 385–392 (2009)Google Scholar
  115. 115.
    Gupta, K.P., Manrai, R.: Prioritizing Factors Affecting the Adoption of Mobile Financial Services in Emerging Markets—A Fuzzy AHP Approach. Performance Prediction and Analytics of Fuzzy, Reliability and Queuing Models, pp. 55–81. Springer, Singapore (2019)Google Scholar
  116. 116.
    Sair, S.A., Danish, R.Q.: Effect of performance expectancy and effort expectancy on the mobile commerce adoption intention through personal innovativeness among Pakistani consumers. Pak. J. Commer. Soc. Sci. 12(2), 501–520 (2018)Google Scholar
  117. 117.
    Madan, K., Yadav, R.: Understanding and predicting antecedents of mobile shopping adoption: a developing country perspective. Asia Pac. J. Mark. Logist. 30(1), 139–162 (2018)Google Scholar
  118. 118.
    Marriott, H.R., Williams, M.D.: Exploring consumers perceived risk and trust for mobile shopping: a theoretical framework and empirical study. J. Retail. Consum. Serv. 42, 133–146 (2018)Google Scholar
  119. 119.
    Kumar, A., Adlakaha, A., Mukherjee, K.: The effect of perceived security and grievance redressal on continuance intention to use M-wallets in a developing country. Int. J. Bank Market. 36(7), 1170–1189 (2018)Google Scholar
  120. 120.
    Megadewandanu, S.: Exploring mobile wallet adoption in Indonesia using UTAUT2: an approach from consumer perspective. In: International Conference IEEE, Science and Technology-Computer (ICST), pp. 11–16 (2016)Google Scholar
  121. 121.
    Baabdullah, A.M., Alalwan, A.A., Rana, N.P., Kizgin, H., Patil, P.: Consumer use of mobile banking (M-Banking) in Saudi Arabia: towards an integrated model. Int. J. Inf. Manag. 44, 38–52 (2019)Google Scholar
  122. 122.
    Zhang, T., Lu, C., Kizildag, M.: Banking “on-the-go”: examining consumers’ adoption of mobile banking services. Int. J. Qual. Serv. Sci. 10(3), 279–295 (2018)Google Scholar
  123. 123.
    Sari, H., Othman, M., Al-Ghaili, A.M.: A proposed conceptual framework for mobile health technology adoption among employees at workplaces in Malaysia. In: International Conference of Reliable Information and Communication Technology, pp. 736–748. Springer, Cham (2018)Google Scholar
  124. 124.
    Nisha, N., Iqbal, M., Rifat, A.: service quality and knowledge as determinants of mobile health services: empirical investigation and further considerations. In: Entrepreneurship, Collaboration, and Innovation in the Modern Business Era, pp. 151–177. IGI Global (2018)Google Scholar
  125. 125.
    Musa, A., Khan, H.U., AlShare, K.A.: Factors influence consumers’ adoption of mobile payment devices in Qatar. Int. J. Mobile Commun. 3(6), 670–689 (2015)Google Scholar
  126. 126.
    Lwoga, E.T., Lwoga, N.B.: User acceptance of mobile payment: the effects of user-centric security, system characteristics and gender. Electron. J. Inf. Syst. Dev. Ctries. 81(1), 1–24 (2017)Google Scholar
  127. 127.
    Arain, A.A.: Empirical analysis of m-learning acceptance in the context of higher education institutes, Ph.D. thesis. Department of Information Technology, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan (2018)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Aijaz Ahmed Arain
    • 1
    Email author
  • Zahid Hussain
    • 1
  • Wajid H. Rizvi
    • 2
  • Muhammad Saleem Vighio
    • 1
  1. 1.Quaid-e-Awam University of Engineering, Science and TechnologyNawabshahPakistan
  2. 2.Institute of Business AdministrationKarachiPakistan

Personalised recommendations