ICT Classroom LMSs: Examining the Various Components Affecting the Acceptance of College Students in the Use of Blackboard Systems

  • Sara Jeza AlotaibiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 498)


A number of different academic departments at both college and university level implement Learning Management System such as Blackboard System in an effort to achieve economical improvements to course management. ICT initiatives in universities also are known to have implemented this method. Nonetheless, the successful adoption of the Blackboard System in IT Graduation Project courses necessitates that students in such fields accept the system. The UTAUT (Unified Theory of Acceptance and Use of Technology) model has been applied in mind of examining the various factors known to effect the acceptance and use of the Blackboard system by students in IT Graduation Projects, which are known to comprise lab and theoretical lectures [4]. Moreover, this work seeks to represent ICT college students’ views on the various aspects known to effect the rejection or acceptance of such a system. With this in mind, a sample of 51 ICT college students was involved in the study, with ten focus group discussions carried out. The subjects communicated five key elements as impacting the application of the Blackboard System in the specific arena of IT Graduation Project classes in the KSA’s Taif University, namely effort expectancy, facilitating conditions, lab practice, performance expectancy and social influence.


Blackboard system LMS UTAUT ICT Learning management system 


  1. 1.
    Carswell, A.D., Bojanova, I.: E-learning for IT professionals: the UMUC experience. IT Professional 13(6), 16–21 (2011)Google Scholar
  2. 2.
    Kihoro, J.M., Oyier, P.A., Kiula, B.M., Wafula, J.M., Ibukah, R.W.: E-learning eco-system for mobility and effective learning: a case of JKUAT IT students. In: IST-Africa Conference and Exhibition (IST-Africa), 2013, pp. 1–9, 29–31 May (2013)Google Scholar
  3. 3.
    Elliott, R.: Do students like the flipped classroom? An investigation of student reaction to a flipped undergraduate IT course. In: Frontiers in Education Conference (FIE), 2014 IEEE, pp. 1–7, 22–25 Oct (2014)Google Scholar
  4. 4.
    Sumak, B., Polancic, G., Heričko, M.: An empirical study of virtual learning environment adoption using UTAUT. In: ELML’10. Second International Conference on Mobile, Hybrid, and On-Line Learning, 2010, pp. 17–22, 10–16 Feb (2010)Google Scholar
  5. 5.
    Venkatesh, V., Morris, M., Davis, G., Davis, F.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)Google Scholar
  6. 6.
    Pardamean, B., Susanto, M.: Assessing user acceptance toward blog technology using the UTAUT model. Int. J. Math. Comput. Simul. 6(27), 203–212 (2012)Google Scholar
  7. 7.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  8. 8.
    Moore, G., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2(3), 192–222 (1991)CrossRefGoogle Scholar
  9. 9.
    Sekaran, U., Bougie, R.: Research Methods for Business, 5th edn. Wiley (2009)Google Scholar
  10. 10.
    Al-Qeisi, K.: Analyzing the use of UTAUT model in explaining an online behaviour: internet banking adoption. Brunel University (2009)Google Scholar
  11. 11.
    Taylor, S.: Marketing Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. Int. J. Res. Mark. 12(2), 137–155 (1995)CrossRefGoogle Scholar
  12. 12.
    Krejcie, R.V., Morgan, D.W.: Determining sample size for research activities. Educ. Psychol. Measur. 30, 607–610 (1970)Google Scholar
  13. 13.
    Stat, T.: Sample Size: Simple Random Samples. Accessed May 1 2016 (2012)
  14. 14.
    Crabtree, B., Miller, W.: A template approach to text analysis: developing and using codebooks. In: Crabtree, B., Miller, W. (eds.) Doing Qualitative Research, pp. 163–177. Sage, Newbury Park, CA (1999)Google Scholar
  15. 15.
    Boyatzis, R.: Transforming Qualitative Information: Thematic Analysis and Code Development. Sage, Thousand Oaks, CA (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.e-Learning and Distance LearningCollege of Computer Sciences and Information Technlogy, Taif UniversityTaifSaudi Arabia

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