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Extension of technology acceptance model by using system usability scale to assess behavioral intention to use e-learning

  • Anastasia Revythi
  • Nikolaos TseliosEmail author
Article

Abstract

This study examines the acceptance of technology and behavioral intention to use learning management systems (LMS). In specific, the aim of the research reported in this paper is to examine whether students ultimately accept LMSs such as eClass and the impact of behavioral intention on their decision to use them. An extended version of technology acceptance model has been proposed and used by employing one of the most reliable measures of perceived eased of use, the System Usability Scale. 345 university students participated in the study. The data analysis was based on partial least squares method. The majority of the research hypotheses were confirmed. In particular, social norm, system access and self-efficacy were found to significantly affect behavioral intention to use. As a result, it is suggested that e-learning developers and stakeholders should focus on these factors to increase acceptance and effectiveness of learning management systems.

Keywords

Learning management system Behavioral intention to use Technology acceptance model System usability scale Partial least squares 

Notes

References

  1. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  2. Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27–50.MathSciNetGoogle Scholar
  3. Altanopoulou, P., & Tselios, N. (2017). Assessing acceptance toward wiki technology in the context of higher education. The International Review of Research in Open and Distributed Learning (IRRODL), 18(6), 127–149.Google Scholar
  4. Altanopoulou, P., Tselios, N., Katsanos, C., Georgoutsou, M., & Panagiotaki, A. (2015). Wiki-mediated activities in higher education: Evidence-based analysis of learning effectiveness across three studies. Educational Technology & Society, 18(4), 511–522.Google Scholar
  5. Ayad, K., & Rigas, D. (2010). Comparing virtual classroom, game-based learning and storytelling teachings in e-learning. International Journal of Education and Information Technologies, 4(1), 15–23.Google Scholar
  6. Bandura, A. (1994). Self-efficacy. John Wiley & Sons, Inc..Google Scholar
  7. Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the system usability scale. International Journal of Human-Computer Interaction, 24(6), 574–594.Google Scholar
  8. Brooke, J. (1996). SUS: A “quick and dirty” usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, & I. L. McClelland (Eds.), Usability evaluation in industry (pp. 189–194). London: Taylor & Francis.Google Scholar
  9. Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–358). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  10. Dasgupta, S., Granger, M., & McGarry, N. (2002). User acceptance of e-collaboration technology: An extension of the technology acceptance model. Group Decision and Negotiation, 11(2), 87–100.Google Scholar
  11. Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man Machine Studies, 38(3), 475–487.Google Scholar
  12. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.Google Scholar
  13. Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2), 210–232.Google Scholar
  14. Grandon, E., Alshare, O., & Kwan, O. (2005). Factors influencing student intention to adopt online classes: A cross-cultural study. Journal of Computing Sciences in Colleges, 20(4), 46–56.Google Scholar
  15. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equationmodeling. Journal of the Academy of Marketing Science, 43(1), 115–135.Google Scholar
  16. Hsu, H. H., & Chang, Y. Y. (2013). Extended TAM model: Impacts of convenience on acceptance and use of Moodle. Online Submission, 3(4), 211–218.Google Scholar
  17. Katsanos, C., Tselios, N., & Avouris, N. (2008). AutoCardSorter: Designing the information architecture of a web site using latent semantic analysis. In Proceedings of the SIGCHI conference on human factors in computing systems, CHI 2008 {Vol1, ISBN:978–1–60558-011-1}, pp. 875–878. Florence, Italy: ACM press April 5-10, 2008.Google Scholar
  18. Katsanos, C., Tselios, N., & Xenos, M. (2012). Perceived Usability Evaluation of Learning Management Systems: A First Step towards Standardization of the System Usability Scale in Greek. 16th Panhellenic Conference on Informatics (pp. 302–307). IΕΕΕ.  https://doi.org/10.1109/PCi.2012.38.
  19. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.Google Scholar
  20. Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the blackboard system. Computers & Education, 51(2), 864–873.Google Scholar
  21. Nikou, S. A., & Economides, A. A. (2018). Factors that influence behavioral intention to use mobile-based assessment: A STEM teachers’ perspective. British Journal of Educational Technology.  https://doi.org/10.1111/bjet.12609.
  22. Orfanou, K., Tselios, N., & Katsanos, C. (2015). Perceived usability evaluation of learning management systems: Empirical evaluation of the system usability scale. The International Review of Research in Open and Distributed Learning, 16(2).Google Scholar
  23. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150–162.Google Scholar
  24. Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (Beta). Hamburg. Available in http://www.smartpls.de. Accessed 1 Oct 2018.
  25. Rothmann, S., & Coetzer, E. P. (2003). The big five personality dimensions and job performance. SA Journal of Industrial Psychology, 29(1), 68–74.Google Scholar
  26. Scherer, R., Siddiq, F., & Teo, T. (2015). Becoming more specific: Measuring and modeling teachers' perceived usefulness of ICT in the context of teaching and learning. Computers & Education, 88, 202–214.  https://doi.org/10.1016/j.compedu.2015.05.005.Google Scholar
  27. Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202.Google Scholar
  28. Teo, T. (2011). Technology acceptance in education. Springer Science & Bussiness Media.Google Scholar
  29. Tselios, N., & Avouris, N. M. (2003). Cognitive Task Modeling for system design and evaluation of non-routine task domains. In E. Hollnagel's (Ed.), Handbook of Cognitive Task Design, Lawrence Erlbaum Associates (pp. 307–332).Google Scholar
  30. Tselios, N., Avouris, N., & Kordaki, M. (2002). Student task modeling in design and evaluation of open problem-solving environments. Journal of Education and Information Technologies, 7(1), 19–42.Google Scholar
  31. Tullis, T. S. & Stetson, J. N. (2004). A comparison of questionnaires for assessing.Google Scholar
  32. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.Google Scholar
  33. Yueh, H. P., Huang, J. Y., & Chang, C. (2015). Exploring factors affecting students continued wiki use for individual and collaborative learning: An extended UTAUT perspective. Australasian Journal of Educational Technology, 31(1), 16–31.Google Scholar
  34. Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution, 1(1), 3–14.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.ICT in Education Group, Department of Educational Sciences and Early Childhood EducationUniversity of PatrasPatrasGreece

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