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Investigating the Performance of Antecedents of Behavioral Intentions to Use Online Learning Technologies Proceeding

, , McGill University, Canada

EdMedia + Innovate Learning, in Vancouver, BC, Canada ISBN 978-1-939797-24-7 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC


Technology acceptance is a stream of research geared toward understanding why users adopt and use technology. Most technology acceptance research is concerned with unearthing the antecedent factors of either behavioral intentions and/or use of technology. While standard structural equation modeling analysis highlights the relative importance of constructs in the structural model, it can also be useful to be cognizant of the performance of the constructs. As such, we highlight the utility of a technique called Importance-Performance Map Analysis (IPMA) for examining the performance of antecedents of behavioral intentions to use online learning technologies.


Bazelais, P. & Doleck, T. (2016). Investigating the Performance of Antecedents of Behavioral Intentions to Use Online Learning Technologies. In Proceedings of EdMedia 2016--World Conference on Educational Media and Technology (pp. 360-364). Vancouver, BC, Canada: Association for the Advancement of Computing in Education (AACE). Retrieved October 19, 2018 from .

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  1. Adams, D.A., Nelson, R.R., & Todd, P.A. (1992). Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication. MIS Quarterly, 16(2), pp. 227-247
  2. Chau, P.Y.K. (1996). An empirical investigation on factors affecting the acceptance of CASE by systems developers. Information & Management, 30, 269–280.
  3. Chin, W.W. (1998). The partial least squares approach for structural equation modeling. InMarcoulides, G.A. (Ed.), Modern Methods for business research (pp. 295-336). Mahwah, NJ:Erlbaum.
  4. Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319.
  5. Davis, F.D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International Journal of Human-Computer Studies, 45(1), 19-45.
  6. Hair, J., Ringle, C., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. The Journal of Marketing Theory and Practice, 19(2), 139-152.
  7. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20(2), 195-204.
  8. Lederer, A.L., Maupin, D.J., Sena, M.P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support System, 29(3), 269-282.
  9. Ringle, C.M., Wende, S., & Becker, J. (2015). SmartPLS 3. Retrieved from
  10. Venkatesh, V., & Davis, F. (1996). A Model of the Antecedents of Perceived Ease of Use: Development and Test. Decision Sciences, 27(3), 451-481.
  11. Venkatesh, V., & Davis F. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186-204.
  12. Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478.

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Cited By

  1. Perceptions of Online Learning and Technology Use: A Study of CEGEP Students

    Paul Bazelais & Tenzin Doleck, McGill University, Canada

    E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2016 (Nov 14, 2016) pp. 987–991

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