Education and Information Technologies

, Volume 24, Issue 2, pp 1395–1431 | Cite as

A model for technological aspect of e-learning readiness in higher education

  • Asma Ali Mosa Al-araibiEmail author
  • Mohd Naz’ri bin Mahrin
  • Rasimah Che Mohd Yusoff
  • Suriayati Binti Chuprat


The rate of adoption of e-learning has increased significantly in most higher education institutions in the world. E-learning refers to the use of electronic media, educational technology, also; information and communication technology (ICT) in the educational process. The aim for adopting e-learning is to provide students with educational services via the use of ICT. Thus, students can access educational resources from anywhere and at any time. However, the successful implementation of e-learning relies on the readiness to be able to initiate this system because, without proper readiness, the project will probably fail. E-learning readiness refers to the assessment of how ready an institution is to adopt and implement an e-learning project. One of the most important aspects of e-learning readiness is the technological aspect, which plays an important role in implementing an effective and efficient e-learning system. There is currently a lack of arguments concerning the factors that shape the technological aspect of e-learning readiness. The focus of this study is concentrated on the technological aspect of e-learning readiness. A model is proposed which includes eight technological factors, specifically: Software; Hardware; Connectivity; Security; Flexibility of the system; Technical Skills and Support; cloud computing; and Data center. A quantitative study was conducted at six Malaysian public universities, with survey responses from 374 Academic staff members who use e-learning. The empirical study confirmed that seven of the technological factors have a significant effect on e-learning readiness, while one factor (cloud computing) has not yet had a significant impact on e-learning readiness.


E-learning E-learning readiness Technological aspect Higher education 



The author would like to thank her family members for their support in the completion of this paper. I would also like to thank the three experts who have been chosen to review the draft of survey for their participation in this study, and their contribution to the results. In addition, I thank all the academic staff for their participation in the survey. I also would like to express my gratitude to the reviewers for their helpful comments which helped to improve the quality of this paper.


  1. Akaslan, D., & Law, E. (2011). Measuring student E-learning readiness: A case about the subject of Electricity in Higher Education Institutions in Turkey. In H. Leung, E. Popescu, Y. Cao, R. H. Lau, & W. Nejdl (Eds.), Advances in Web-Based Learning - ICWL 2011 (Vol. 7048, pp. 209–218): Springer Berlin Heidelberg.Google Scholar
  2. Al-araibi, A. A. M., Mahrin, M. N. r. B., & Yusoff, R. C. M. (2016). A systematic literature review of technological factors for E-learning readiness in higher education. Journal of Theoretical & Applied Information Technology, 93(2), 500–521.Google Scholar
  3. Al-araibi, A. A. M., Mahrin, M. N. R. B., & Yusoff, R. C. M. (2018). Technological Aspect Factors of E-learning Readiness in Higher Education Institutions: Delphi Technique. Education and Information Technologies.Google Scholar
  4. Albarrak, A. (2010). Designing E-learning Systems in Medical Education: A case study. International Journal of Excellence in Healthcare Management, 3(1), 1–8.Google Scholar
  5. Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2013). IT infrastructure services as a requirement for E-learning system success. Computers & Education, 69, 431–451.Google Scholar
  6. Alshaher, A. (2013). The Mckinsey 7S model framework for E-learning system readiness assessment. International Journal of Advances in Engineering & Technology, 6(5).Google Scholar
  7. Atoji, Y., Koiso, T., & Nishida, S. (2002). Information filtering for emergency management. Cybernetics and Systems, 34(3), 193–206.zbMATHGoogle Scholar
  8. Aydin, C., & Tasci, D. (2005). Measuring readiness for e-learning: Reflections from an emerging country. Educational Technology & Society, 8(4), 244–257.Google Scholar
  9. Azimi, H. (2013). Readiness for implementation of E-learning in colleges of education. Journal of Novel Applied Sciences, 2(12), 769–775.Google Scholar
  10. Baars, M., Henneman, L., & ten Kate, L. (2005). Deficiency of knowledge of genetics and genetic tests among general practitioners, gynecologists, and pediatricians: A global problem. Genetics in Medicine, 7(9), 605–610.Google Scholar
  11. Bagozzi, R., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), 8–34.Google Scholar
  12. Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J., & Ciganek, A. (2012). Critical success factors for E-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843–855.Google Scholar
  13. Borotis, S. & Poulymenakou, A. (2004). E-Learning Readiness Components: Key Issues to Consider Before Adopting e-Learning Interventions. In J. Nall & R. Robson (Eds.), Proceedings of E-Learn 2004--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1622-1629). Washington, DC, USA: Association for the Advancement of Computing in Education (AACE). Retrieved February 10, 2018 from
  14. Byrne, B. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York: Routledge.Google Scholar
  15. Chalise, S., Golshani, A., Awasthi, S. R., Ma, S., Shrestha, B. R., Bajracharya, L., … Tonkoski, R. (2015). Data Center Energy Systems: Current Technology and Future Direction. Paper presented at the Power & Energy Society General Meeting. IEEE.Google Scholar
  16. Chapnick, S. (2000). Are you ready for e learning. ASTD’s Online Magazine All About Learning, 9.Google Scholar
  17. Chinda, T., & Mohamed, S. (2008). Structural equation model of construction safety culture. Engineering Construction and Architectural Management, 15(2), 114–131.Google Scholar
  18. Cochran, W. (1977). Sampling Techniques (3rd ed.). New York: Wiley.zbMATHGoogle Scholar
  19. Contreras, J., & Hilles, S. (2015). Assessment in E-learning environment readiness of teaching staff, administrators, and students of Faculty of Nursing-Benghazi University. International Journal of Computer Integrated Manufacturing, 23(1), 53–58.Google Scholar
  20. Corrado, E. M., & Moulaison, H. L. (2011). Getting Started with Cloud Computing: A LITA Guide: Neal-Schuman Publishers.Google Scholar
  21. Darab, B., & Montazer, G. (2011). An eclectic model for assessing E-learning readiness in the Iranian universities. Computers & Education, 56(3), 900–910.Google Scholar
  22. Doculan, J. (2016). E-learning readiness assessment tool for Philippine higher education institutions. International Journal on Integrating Technology in Education (IJITE), 5(2), 33–43.Google Scholar
  23. Doloi, H., Iyer, K., & Sawhney, A. (2010). Structural equation model for assessing impacts of Contractor's performance on project success. International Journal of Project Management, 29(6), 687–695.Google Scholar
  24. Driscoll, M. (2010). Web-based training: Creating E-learning experiences: Wiley.Google Scholar
  25. Engholm, P. (2002). What Determines An organisation’s Readiness For Elearning? (Bachelor). Australia: Monash University.Google Scholar
  26. Evans, T. (2011). Explanation of Cooling and Air Conditioning Terminology for IT Professionals white paper 11. Schneider Electric’s.Google Scholar
  27. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research( JMR), 18(1), 39–50.Google Scholar
  28. Ghavamifar, A., Beig, L., & Montazer, G. (2008). The Comparison of Different E-Readiness Assessment Tools. Paper presented at the 3rd international conference on information and communication technologies: From theory to applications, 2008. ICTTA 2008.Google Scholar
  29. Gunjan, C., Bhure, & Sneha, M. B. (2014). E-learning Using Cloud Computing. International Journal of Information and Computation Technology, 4(1), 41–46.Google Scholar
  30. Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate Data Analysis (7th ed.). New Jersey: Pearson prentice Hall.Google Scholar
  31. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis. New Jersey: Prentice-Hall.Google Scholar
  32. Hair Jr., J. F., Hult, G. T., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage Publications.zbMATHGoogle Scholar
  33. Ho, Y.-S. (2006). Review of second-order models for adsorption systems. Journal of Hazardous Materials, 136(3), 681–689.Google Scholar
  34. Hussain, A. (2016). Infrastructure requirements for E-learning implementation and delivery. CommLab India. Retrieved March 9, 2017 from
  35. Hussein, R., Shahriza Abdul Karim, N., & Hasan Selamat, M. (2007). The impact of technological factors on information systems success in the electronic-government context. Business Process Management Journal, 13(5), 613–627.Google Scholar
  36. Iacobucci, D. (2008). Mediation analysis. London: SAGE Publications.Google Scholar
  37. Informatica. (2017). What is a data center? Retrieved  25, January 2017 from
  38. Karami, R. (2011). Factor Infusing Achievement Motivation in Leadership Role of Extension Agents in Iran. (Unpublished Doctoral Dissertation), Universiti Putra Malaysia, Malaysia.Google Scholar
  39. Keramati, A., Afshari-Mofrad, M., & Kamrani, A. (2011). The role of readiness factors in E-learning outcomes: An empirical study. Computers & Education, 57(3), 1919–1929.Google Scholar
  40. Kituyi, G., & Tusubira, I. (2013). A framework for the integration of E-learning in higher education institutions in developing countries. International Journal of Education and Development using Information and Communication Technology, 9(2), 19–36.Google Scholar
  41. Klug, W., & Bai, X. (2015). Factors affecting cloud computing adoption among universities and colleges in the United States and Canada. Issues in Information Systems, 16(3), 1–10.Google Scholar
  42. Laohajaratsang, T. (2009). E-learning readiness in the academic sector of Thailand. International Journal on E-Learning, 8(4), 539–547.Google Scholar
  43. Lopes, C. (2007). Evaluating E-learning readiness in a health sciences higher education institution. Paper presented at the proceedings of IADIS international conference of E-learning, Porto.Google Scholar
  44. Machado, C. (2007). Developing an e-readiness model for higher education institutions: Results of a focus group study. British Journal of Educational Technology, 38(1), 72–82. Scholar
  45. Mercado, C. (2008). Readiness assessment tool for an eLearning environment implementation. Paper presented at the Fifth International Conference on E-Leraning for Knowledge based Society.Google Scholar
  46. Mosa, A. A., Naz’ri bin Mahrin, M., & Ibrrahim, R. (2016). Technological Aspects of E-learning Readiness in Higher Education: A review of the Literature. Computer and Information Science, 9(1), 113–127.Google Scholar
  47. Naresh, B., & Reddy, B. S. (2015). Challenges and opportunity of E-learning in developed and developing countries-a review. International Journal of Emerging Research in Management &Technology, 4(6), 259–262.Google Scholar
  48. Njihia, J., & Oketch, H. (2014). E-learning readiness assessment model in Kenyas’ higher education institutions: A case study of University of Nairobi. International Journal of Scientific Knowledge, 5(6).Google Scholar
  49. Nyandara, Z. (2012). Challenges and Opportunities of Technology Based Instruction in Open and Distance Learning: A comparative Study of Tanzania and China. Paper presented at the 5th UbuntuNet Alliance annual conference.Google Scholar
  50. Odunaike, S., Olugbara, O., & Ojo, S. (2013). E-learning implementation critical success factors. Innovation, 3, 4.Google Scholar
  51. Oke, A., Ogunsami, D., & Ogunlana, S. (2012). Establishing a common ground for the use of structural equation modelling for construction related research studies. Australasian Journal of Construction Economics and Building, 12(3), 89–94.Google Scholar
  52. Oketch, H. A. (2013). E-learning Readiness Assessment Model In Kenyas’ Higher Education Institutions: A Case Study Of University Of Nairobi. (Master), University Of Nairobi.Google Scholar
  53. Oketch, H., Njihia, J., & Wausi, A. (2014). E-learning readiness assessment model in Kenyas’ higher education institutions: A case study of University of Nairobi. International Journal of Scientific Knowledge, 5(6), 29–41.Google Scholar
  54. Oliver, R., & Towers, S. (2000). Up time: Information Communication Technology: Literacy and Access for Tertiary Students in Australia. Canberra: Department of Education: Training and Youth Affairs.Google Scholar
  55. Olson, J., Codde, J. R., deMaagd, K., Tarkelson, E., Sinclair, J., Yook, S., & Egidio, R. (2011). An Analysis of E-Learning Impacts & Best Practices in Developing Countries: With Reference to Secondary School Education in Tanzania . pp. 1–54. Retrieved January 15, 2017 from
  56. Omoda-Onyait, G., & Lubega, J. (2011). E-learning readiness assessment model: A case study of higher institutions of learning in Uganda. Hybrid Learning (pp. 200–211): Springer.Google Scholar
  57. Pallant, J. (2010). A step by Step Guide to Data Analysis Using SPSS. New York, NY: McGraw-Hill.Google Scholar
  58. Palo Alto Networks. (2017). What is a Data Center? Retrieved February 12, 2017 from
  59. Parasuraman, A., & Colby, C. L. (2007). Techno-ready Marketing: How and Why Your Customers Adopt Technology: The Free Press.Google Scholar
  60. Pocatilu, P., Alecu, F., & Vetrici, M. (2009). Using Cloud Computing for E-learning Systems. Paper presented at the 8th International Conference on Data Networks, Communications, Computers. World Scientific and Engineering Academy and Society (WSEAS).Google Scholar
  61. Riahi, G. (2015). E-learning systems based on cloud computing: A review. Procedia Computer Science, 62, 352–359.Google Scholar
  62. Saleh, J., Hastings, D., & Newman, D. (2003). Flexibility in system design and implications for aerospace systems. Acta Astronautica, 53(12), 927–944.Google Scholar
  63. Schreurs, J., & Al-Huneidi, A. (2012). E-learning readiness in organizations. International Journal of Advanced Corporate Learning (iJAC), 5(1), 4–7.Google Scholar
  64. Schreurs, J., Ehlers, U., & Sammour, G. (2008). ERA - E-learning readiness analysis: A eHealth case study of E-learning readiness. International Journal of Knowledge and Learning, 4(5), 496–508. Scholar
  65. SearchDataCenter. (2017). What is data center? Retrieved November 12, 2016 from
  66. Sharma, P. (2014). E-learning using cloud computing and IT. Advances in Computer Science and Information Technology (ACSIT), 1(1), 6–10.Google Scholar
  67. Singh, R. (2009). Does my structural model represent the real phenomenon?: A review of the appropriate use of structural equation modelling (SEM) model fit indices. The Marketing Review, 9(3), 199–212.Google Scholar
  68. SPSS Inc. (2012). IBM SPSS statistics version 21. Boston, Mass: International Business Machines Corp.Google Scholar
  69. Webopedia. (2017). What is a data center?. Retrieved November 12, 2016 from
  70. Zikmund, W. G. (2003). Business research methods (7th ed.). Cincinnati: Thomson.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.RAZAK Faculty of Technology and InformaticsUniversiti Teknologi Malaysia (UTM)Kuala LumpurMalaysia

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