Advertisement

Adoption of E-Book for University Students

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 845)

Abstract

This paper employs the Technology Acceptance Model (TAM) to study the adoption of E-book amongst higher-education students in a well-known academic institute in the UAE, where E-book was being implemented. Computer self-efficacy, confirmation, innovativeness, satisfaction, and subjective norm are the five factors that this model embarks on to realize the influence on the university students as a result of the adoption of the E-book. This study was conducted among 350 university students through a survey which has used the quantitative evaluation to gain the optimum advantage from the subjective methods. The hypotheses were analyzed, and the model was assessed with the help of the statistical package for Structural Equation Modeling (SEM). The main findings that can be derived from the existing study are the factors that have positive impact on students’ perceived ease of use and perceived usefulness of E-book. They are computer self-efficacy, confirmation, innovativeness, and subjective norm. As a result, it is imperative for legislators and managers of E-book applications to concentrate on the factors that are critical for encouraging learning and enhancing students’ efficiency in developing and executing successful E-book applications.

Keywords

E-book Technology Acceptance Model (TAM) Computer self-efficacy Confirmation Innovativeness Satisfaction Subjective norm Student’s intention to use E-learning 

References

  1. 1.
    Al-Emran, M., Mezhuyev, V., Kamaludin, A., AlSinani, M.: Development of M-learning application based on knowledge management processes. In: 2018 7th International conference on Software and Computer Applications (ICSCA 2018) (2018)Google Scholar
  2. 2.
    Al-Emran, M., Mezhuyev, V., Kamaludin, A.: Students’ perceptions towards the integration of knowledge management processes in M-learning systems: a preliminary study. Int. J. Eng. Educ. 34(2), 371–380 (2018)Google Scholar
  3. 3.
    Al-Emran, M., Salloum, S.A.: Students’ attitudes towards the use of mobile technologies in e-evaluation. Int. J. Interact. Mob. Technol. 11(5), 195–202 (2017)CrossRefGoogle Scholar
  4. 4.
    Salloum, S.A., Al-Emran, M., Monem, A.A., Shaalan, K.: Using text mining techniques for extracting information from research articles. In: Studies in Computational Intelligence, vol. 740. Springer, Berlin (2018)Google Scholar
  5. 5.
    Al-Emran, M., Shaalan, K.: Academics’ awareness towards mobile learning in Oman. Int. J. Comput. Digit. Syst. 6(1), 45–50 (2017)CrossRefGoogle Scholar
  6. 6.
    Al-Emran, M., Malik, S.I.: The impact of Google apps at work: higher educational perspective. Int. J. Interact. Mob. Technol. 10(4), 85–88 (2016)CrossRefGoogle Scholar
  7. 7.
    Al-Emran, M., Shaalan, K.: Attitudes towards the use of mobile learning: a case study from the Gulf region. Int. J. Interact. Mob. Technol. 9(3), 75–78 (2015)CrossRefGoogle Scholar
  8. 8.
    Catenazzi, N.: The evaluation of electronic book guidelines from two practical experiences. J. Educ. Multimed. Hypermedia 6(1), 91–114 (1997)Google Scholar
  9. 9.
    Shin, D.-H.: Understanding e-book users: uses and gratification expectancy model. New Media Soc. 13(2), 260–278 (2011)CrossRefGoogle Scholar
  10. 10.
    Lam, P., Lam, S.L., Lam, J., McNaught, C.: Usability and usefulness of eBooks on PPCs: how students’ opinions vary over time. Austr. J. Educ. Technol. 25(1), 30–44 (2009) Google Scholar
  11. 11.
    Poon, J.K.L.: Empirical analysis of factors affecting the e-book adoption—research agenda. Open J. Soc. Sci. 2(05), 51 (2014)Google Scholar
  12. 12.
    Letchumanan, M., Tarmizi, R.: Assessing the intention to use e-book among engineering undergraduates in Universiti Putra Malaysia, Malaysia. Libr. Hi Tech 29(3), 512–528 (2011)CrossRefGoogle Scholar
  13. 13.
    Nelson, K., Webb, H.: Exploring student perceptions of an electronic textbook: a TAM perspective. In: AMCIS 2007 Proceedings, p. 107 (2007)Google Scholar
  14. 14.
    Ngafeeson, M.N., Sun, J.: The effects of technology innovativeness and system exposure on student acceptance of e-textbooks. J. Inf. Technol. Educ. Res. 14, 55–71 (2015)Google Scholar
  15. 15.
    Tri-Agif, I., Noorhidawati, A., Ghalebandi, S.G.: Continuance intention of using ebook among higher education students. Malays. J. Libr. Inf. Sci. 21(1), 19–33 (2016)Google Scholar
  16. 16.
    Al-Emran, M.: Hierarchical reinforcement learning: a survey. Int. J. Comput. Digit. Syst. 4(2), 137–143 (2015)CrossRefGoogle Scholar
  17. 17.
    Al-Emran, M., Mezhuyev, V., Kamaludin, A.: Technology acceptance model in M-learning context: a systematic review. Comput. Educ. 125, 389–412 (2018)CrossRefGoogle Scholar
  18. 18.
    Salloum, S.A., Al-Emran, M., Shaalan, K.: A survey of lexical functional grammar in the Arabic context. Int. J. Comput. Netw. Technol. 4(3), 141–147 (2016)CrossRefGoogle Scholar
  19. 19.
    Salloum, S.A., AlHamad, A.Q., Al-Emran, M., Shaalan, K.: A survey of Arabic text mining. In: Studies in Computational Intelligence, vol. 740. Springer, Berlin (2018)Google Scholar
  20. 20.
    Salloum, S.A., Al-Emran, M., Monem, A., Shaalan, K.: A survey of text mining in social media: Facebook and Twitter perspectives. Adv. Sci. Technol. Eng. Syst. J. 2(1), 127–133 (2017)CrossRefGoogle Scholar
  21. 21.
    Al Emran, M., Shaalan, K.: A survey of intelligent language tutoring systems. In: Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014, pp. 393–399 (2014)Google Scholar
  22. 22.
    Salloum, S.A., Al-Emran, M., Abdallah, S., Shaalan, K.: Analyzing the Arab Gulf newspapers using text mining techniques. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 396–405 (2017)Google Scholar
  23. 23.
    Salloum, S.A., Mhamdi, C., Al-Emran, M., Shaalan, K.: Analysis and classification of Arabic Newspapers’ Facebook pages using text mining techniques. Int. J. Inf. Technol. Lang. Stud. 1(2), 8–17 (2017)Google Scholar
  24. 24.
    Salloum, S.A., Al-Emran, M., Shaalan, K.: Mining social media text: extracting knowledge from Facebook. Int. J. Comput. Digit. Syst. 6(2), 73–81 (2017)CrossRefGoogle Scholar
  25. 25.
    Al-Emran, M., Shaalan, K.: Learners and educators attitudes towards mobile learning in higher education: state of the art. In: 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, pp. 907–913 (2015)Google Scholar
  26. 26.
    Ngai, E.W.T., Poon, J.K.L., Chan, Y.H.C.: Empirical examination of the adoption of WebCT using TAM. Comput. Educ. 48(2), 250–267 (2007)CrossRefGoogle Scholar
  27. 27.
    Yang, K.C.C.: Exploring factors affecting the adoption of mobile commerce in Singapore. Telemat. Inform. 22(3), 257–277 (2005)CrossRefGoogle Scholar
  28. 28.
    Spreng, R.A., Chiou, J.: A cross-cultural assessment of the satisfaction formation process. Eur. J. Mark. 36(7/8), 829–839 (2002)CrossRefGoogle Scholar
  29. 29.
    Rogers, E.M.: Diffusion of Innovations, 4th edn. The Free Press, New York (1995)Google Scholar
  30. 30.
    Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46(2), 186–204 (2000)CrossRefGoogle Scholar
  31. 31.
    Alshurideh, M.: Customer Service Retention—A Behavioural Perspective of the UK Mobile Market. Durham University, Durham (2010)Google Scholar
  32. 32.
    Jin, C.-H.: Adoption of e-book among college students: the perspective of an integrated TAM. Comput. Hum. Behav. 41, 471–477 (2014)CrossRefGoogle Scholar
  33. 33.
    Ngafeeson, M.: E-book acceptance by undergraduate students: do gender differences really exist. Int. J. Web Based Learn. Teach. Technol. (2011)Google Scholar
  34. 34.
    Tsai, W.-C.: A study of consumer behavioral intention to use e-books: the technology acceptance model perspective. Innov. Mark. 8(4), 55–66 (2012)Google Scholar
  35. 35.
    Al-Maroof, R.A.S., Al-Emran, M.: Students acceptance of Google classroom: an exploratory study using PLS-SEM approach. Int. J. Emerg. Technol. Learn. 13(6), 112–123 (2018)CrossRefGoogle Scholar
  36. 36.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3), 319–340 (1989)CrossRefGoogle Scholar
  37. 37.
    Smeda, A.M., Shiratuddin, M.F., Wong, K.W.: Factors affecting the e-book adoption amongst mathematics and statistics students at universities in Libya: a structural equation modelling approach. Int. J. e-Educ. e-Bus. e-Manag. e-Learn. 5(4), 237 (2015)CrossRefGoogle Scholar
  38. 38.
    Chiu, P.-S., Chao, I.-C., Kao, C.-C., Pu, Y.-H., Huang, Y.-M.: Implementation and evaluation of mobile e-books in a cloud bookcase using the information system success model. Libr. Hi Tech 34(2), 207–223 (2016)CrossRefGoogle Scholar
  39. 39.
    Huang, Y.-M., Pu, Y.-H., Chen, T.-S., Chiu, P.-S.: Development and evaluation of the mobile library service system success model: a case study of Taiwan. Electron. Libr. 33(6), 1174–1192 (2015)CrossRefGoogle Scholar
  40. 40.
    Park, E., Sung, J., Cho, K.: Reading experiences influencing the acceptance of e-book devices. Electron. Libr. 33(1), 120–135 (2015)CrossRefGoogle Scholar
  41. 41.
    Lai, J.-Y., Rushikesh Ulhas, K.: Understanding acceptance of dedicated e-textbook applications for learning: Involving Taiwanese university students. Electron. Libr. 30(3), 321–338 (2012)CrossRefGoogle Scholar
  42. 42.
    Venkatesh, V., Morris, M., Davis, G., Davis, F.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)CrossRefGoogle Scholar
  43. 43.
    Alshurideh, M., Masa’deh, R., Alkurdi, B.: The effect of customer satisfaction upon customer retention in the Jordanian mobile market: an empirical investigation. Eur. J. Econ. Financ. Adm. Sci. 47, 69–78 (2012)Google Scholar
  44. 44.
    Alshurideh, M.T.: Do we care about what we buy or eat? A practical study of the healthy foods eaten by Jordanian youth. Int. J. Bus. Manag. 9(4), 65 (2014)CrossRefGoogle Scholar
  45. 45.
    Bandura, A.: Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84(2), 191 (1977)CrossRefGoogle Scholar
  46. 46.
    Tri Agif, I., Noorhidawati, A., Siti Hajar, M.R.: Investigating continuance intention of using e-book among higher education students (2014)Google Scholar
  47. 47.
    Smeda, A.M., Shiratuddin, M.F., Wong, K.W.: Proposed framework of the adoption of e-book amongst mathematics and statistics students at universities in Libya. In: Proceedings of the 2nd International Virtual Conference on Advanced Scientific Results (SCIECONF) (2014)Google Scholar
  48. 48.
    Bhattacherjee, A.: Understanding information systems continuance: an expectation–confirmation model. MIS Q. 25, 351–370 (2001)CrossRefGoogle Scholar
  49. 49.
    Al-dweeri, R.M., Obeidat, Z.M., Al-dwiry, M.A., Alshurideh, M.T., Alhorani, A.M.: The impact of e-service quality and e-loyalty on online shopping: moderating effect of e-satisfaction and e-trust. Int. J. Mark. Stud. 9(2), 92 (2017)CrossRefGoogle Scholar
  50. 50.
    Chen, S.-C., Yen, D.C., Peng, S.-C.: Assessing the impact of determinants in e-magazines acceptance: an empirical study. Comput. Stand. Interfaces (2017)Google Scholar
  51. 51.
    Shin, D.-H., Shin, Y.-J., Choo, H., Beom, K.: Smartphones as smart pedagogical tools: implications for smartphones as u-learning devices. Comput. Hum. Behav. 27(6), 2207–2214 (2011)CrossRefGoogle Scholar
  52. 52.
    Midgley, D.F., Dowling, G.R.: Innovativeness: The concept and its measurement. J. Consum. Res. 4(4), 229–242 (1978)CrossRefGoogle Scholar
  53. 53.
    Lee, H., Kim, D., Ryu, J., Lee, S.: Acceptance and rejection of mobile TV among young adults: a case of college students in South Korea. Telemat. Inform. 28(4), 239–250 (2011)CrossRefGoogle Scholar
  54. 54.
    Kuo, Y.-F., Yen, S.-N.: Towards an understanding of the behavioral intention to use 3G mobile value-added services. Comput. Hum. Behav. 25(1), 103–110 (2009)CrossRefGoogle Scholar
  55. 55.
    Huang, T.K.: Investigating user acceptance of a screenshot-based interaction system in the context of advanced computer software learning. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 4956–4965 (2014)Google Scholar
  56. 56.
    Lee, S.: An integrated adoption model for e-books in a mobile environment: Evidence from South Korea. Telemat. Inform. 30(2), 165–176 (2013)CrossRefGoogle Scholar
  57. 57.
    Aharony, N.: The effect of personal and situational factors on LIS students’ and professionals’ intentions to use e-books. Libr. Inf. Sci. Res. 36(2), 106–113 (2014)CrossRefGoogle Scholar
  58. 58.
    Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39(2), 273–315 (2008)CrossRefGoogle Scholar
  59. 59.
    Torres, R., Johnson, V., Imhonde, B.: The impact of content type and availability on ebook reader adoption. J. Comput. Inf. Syst. 54(4), 42–51 (2014)Google Scholar
  60. 60.
    Hyman, J.A., Moser, M.T., Segala, L.N.: Electronic reading and digital library technologies: understanding learner expectation and usage intent for mobile learning. Educ. Technol. Res. Dev. 62(1), 35–52 (2014)CrossRefGoogle Scholar
  61. 61.
    Krejcie, R.V., Morgan, D.W.: Determining sample size for research activities. Educ. Psychol. Meas. 38(1), 607–610 (1970)CrossRefGoogle Scholar
  62. 62.
    Chuan, C.L., Penyelidikan, J.: Sample size estimation using Krejcie and Morgan and Cohen statistical power analysis: A comparison. J. Penyelid. IPBL 7, 78–86 (2006)Google Scholar
  63. 63.
    Ringle, C.M., Wende, S., Will, A.: SmartPLS 2.0 (Beta). Hamburg (2005). http://www.smartpls.de
  64. 64.
    Chin, W.W.: The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 295(2), 295–336 (1998)Google Scholar
  65. 65.
    Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L.: Multivariate Data Analysis, vol. 5, no. 3. Prentice Hall, Upper Saddle River (1998)Google Scholar
  66. 66.
    Al Kurdi, B.: Healthy-Food Choice and Purchasing Behaviour Analysis: An Exploratory Study of Families in the UK. Durham University, Durham (2016)Google Scholar
  67. 67.
    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
  68. 68.
    Alshurideh, M.: The factors predicting students’ satisfaction with universities’ healthcare clinics’ services: a case-study from the Jordanian higher education sector. Dirasat Admin. Sci. 41(2), 451–464 (2014)CrossRefGoogle Scholar
  69. 69.
    Malik, S.I., Al-Emran, M.: Social factors influence on career choices for female computer science students. Int. J. Emerg. Technol. Learn. 13(5), 56–70 (2018)CrossRefGoogle Scholar
  70. 70.
    Al-Qaysi, N., Al-Emran, M.: Code-switching usage in social media: a case study from Oman. Int. J. Inf. Technol. Lang. Stud. 1(1), 25–38 (2017)Google Scholar
  71. 71.
    Falk, R.F., Miller, N.B.: A Primer for Soft Modeling. University of Akron Press, Akron (1992)Google Scholar
  72. 72.
    Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981)CrossRefGoogle Scholar
  73. 73.
    Lin, Y.-C., Chen, Y.-C., Yeh, R.C.: Understanding college students’ continuing intentions to use multimedia e-learning systems. World Trans. Eng. Technol. Educ. 8(4), 488–493 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Engineering and ITThe British University in DubaiDubaiUAE
  2. 2.Faculty of Engineering and ITUniversity of FujairahFujairahUAE

Personalised recommendations