Perceptual Antecedents of Student Attitudes Toward a Learning Management System: The Case of Using Canvas

Authors

  • Yuan Gao Ramapo College of New Jersey

DOI:

https://doi.org/10.33423/jhetp.v24i11.7446

Keywords:

higher education, learning management system (LMS), technology acceptance model (TAM), UTAUT, technology-enabled learning, student attitudes

Abstract

This paper explores the application of the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) to a course management system in student learning. Through a TAM-aligned UTAUT model, it examines the influences of performance expectancy, effort expectancy, perceived enjoyment, social influence, and facilitating conditions on student attitude toward using a learning management system. The hypothesized relationships in the model were tested via Canvas. Results provide evidence that the TAM-aligned UTAUT model is applicable to examining factors influencing learner attitude and behavioral intentions in the use of technology-supported learning management systems.

References

Acharjya, B., & Das, S. (2022). Adoption of e-learning during the COVID-19 pandemic: The moderating role of age and gender. International Journal of Web-Based Learning and Teaching Technologies, 17(2), 1–14. https://doi.org/10.4018/IJWLTT.20220301.oa4

Alyoussef, I.Y. (2021). E-learning acceptance: The role of task–technology fit as sustainability in higher education. Sustainability, 13(11), 6450. Retrieved from https://www.mdpi.com/2071-1050/13/11/6450

Alhussain, T., Al-Rahmi, W.M., & Othman, M.S. (2020). Students’ perceptions of social networks platforms use in higher education: A qualitative research. International Journal of Advanced Trends in Computer Science and Engineering, 9, 2589–2603. Retrieved from https://www.warse.org/IJATCSE/static/pdf/file/ijatcse16932020.pdf

Alwahaishi, S., & Snásel, V. (2013). Consumers’ acceptance and use of information and communications technology: A UTAUT and flow-based theoretical model. Journal of Technology Management & Innovation, 8(2), 61–73. http://dx.doi.org/10.4067/S0718-27242013000200005

Al-Fraihat, D., Joy, M., Masa’Deh, R., & Sinclair, J. (2020). Evaluating e-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004

Al-Rahmi, W.M., Yahaya, N., Aldraiweesh, A.A., Alamri, M.M., Aljarboa, N.A., Alturki, U., & Aljeraiwi, A.A. (2019). Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use e-learning systems. IEEE Access, 7, 26797–26809. Retrieved from https://ieeexplore.ieee.org/document/8643360

Batucan, G.B., Gonzales, G.G., Balbuena, M.G., Pasaol, K.R.B., Seno, D.N., & Gonzales, R.R. (2022). An extended UTAUT model to explain factors affecting online learning system amidst COVID-19 pandemic: The case of a developing economy. Frontiers in Artificial Intelligence, 5, 768831. Retrieved from https://www.frontiersin.org/articles/10.3389/frai.2022.768831/full

Chao, C. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652. Retrieved from https://www.frontiersin.org/articles/10.3389/fpsyg.2019.01652/full

Chen, P.Y., & Hwang, G.J. (2019). An empirical examination of the effect of self-regulation and the unified theory of acceptance and use of technology (UTAUT) factors on the online learning behavioral intention of college students. Asia Pacific Journal of Education, 39(1), 79–95. https://doi.org/10.1080/02188791.2019.1575184

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.

Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.

Evans, N.D., & Roux, J.I. (2015). Modeling the acceptance and use of electronic learning at the University of Zululand. South Africa Journal of Libraries and Information Science, 81(2), 26–38. https://doi.org/10.7553/81-2-1562

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.

Hrastinski, S., & Keller, C. (2007). Computer-mediated communication in education: A review of recent research. Educational Media International, 44(1), 61–77.

Hu, L.T., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.

Jaradat, M., Ababneh, H., Faqih, K., & Nusairat, N. (2020). Exploring cloud computing adoption in higher educational environment: An extension of the UTAUT model with trust. International Journal of Advanced Science and Technology, 29(5), 8282–8306. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/18643

Khechine, H., & Augier, M. (2019). Adoption of a social learning platform in higher education: An extended UTAUT model implementation. Proceedings of the 52nd Hawaii International Conference on System Sciences, Grand Wailea, Maui. Retrieved from https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/807fbbe3-6259-40e2-ad76-916b29d9a38e/content

Koç, T., Turana, A.H., & Okursoyb, A. (2016). Acceptance and usage of a mobile information system in higher education: An empirical study with structural equation modeling. The International Journal of Management Education, 14(3), 286–300. https://doi.org/10.1016/j.ijme.2016.06.001

Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205–223.

Lee, J.H., & Song, C.H. (2013). Effect of trust and perceived risk on user acceptance of a new technology service. Social Behavior and Personality, 41(4), 587–598. http://dx.doi.org/10.2224/sbp.2013.41.4.587

Magsamen-Conrad, K., Upadhyaya, S., Joa, C.Y., & Dowd, J. (2015). Bridging the divide: Using UTAUT To predict multigenerational tablet adoption practices. Computers in Human Behavior, 50, 186–196. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0747563215002228

Malhotra, N.K. (1993). Marketing Research: An Applied Orientation. Englewood Cliffs, NJ: Prentice-Hall.

McQuail, D. (1983). Mass Communication Theory: An Introduction. London: Sage Publications.

Moon, J., & Kim, Y. (2001). Extending the TAM for a World-Wide-Web Context. Information & Management, 38(4), 217–230.

Naveed, Q.N., Alam, M.M., & Tairan, N. (2020). Structural equation modeling for mobile learning acceptance by university students: An empirical study. Sustainability, 12(80), 8618. Retrieved from https://www.mdpi.com/2071-1050/12/20/8618

Sangeeta, Tandon, U. (2021). Factors influencing adoption of online teaching by school teachers: A study during COVID-19 pandemic. Journal of Public Affairs, 21, 2503. https://doi.org/10.1002/pa.2503

Septiani, R., Handayani, P.W., & Azzahro, F. (2017). Factors that affecting behavioral intention in online transportation service: Case study of GO-JEK. Procedia Computer Science, 124, 504–512. https://doi.org/10.1016/j.procs.2017.12.183

Tewari, A., Singh, R., Mathur, S., & Pande, S. (2023). A modified UTAUT framework to predict students' intention to adopt online learning: Moderating role of openness to change. International Journal of Information and Learning Technology, 40(2), 130–147. https://doi.org/10.1108/IJILT-04-2022-0093

Thomas, T.D., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 9(3), 71–85.

Tullis, M. (2023, June 1). Instructure achieves adoption milestone: All ten top-ranked universities in US now use canvas. TechBuzz New. Retrieved from https://techbuzz.news/instructure-reaches-symbolic-milestone-all-ten-top-ranked-universities-in-us-now-use-canvas/

Vasconcelos, P., Furtado, E.S., Pinheiro, P., & Furtado, L. (2020). Multidisciplinary criteria for the quality of e-learning services design. Computers in Human Behavior, 107. https://doi.org/10.1016/j.chb.2019.04.003

Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.

Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 12(1), 157–178.

Welch, R., Alade, T., & Nichol, L. (2020). Using the unified theory of acceptance and use of technology (UTAUT) model to determine factors affecting a mobile learning adoption in the workplace: A study of the science museum group. IADIS International Journal on Computer Science and Information Systems, 15(1), 85–98. https://doi.org/10.33965/ijcsis_2020150107

Wu, B., & Chen, X. (2017). Continuance intention to Use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028

Wu, X., & Gao, Y. (2011). Applying the extended technology acceptance model to the use of clickers in student learning: Some evidence from macroeconomics classes. American Journal of Business Education, 4(7), 43–50.

Yilmaz, R. (2017). Exploring the role of e-learning readiness on student satisfaction and motivation in flipped classroom. Computers in Human Behavior, 70, 251–260. https://doi.org/10.1016/j.chb.2016.12.085

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Published

2024-12-31

How to Cite

Gao, Y. (2024). Perceptual Antecedents of Student Attitudes Toward a Learning Management System: The Case of Using Canvas. Journal of Higher Education Theory and Practice, 24(11). https://doi.org/10.33423/jhetp.v24i11.7446

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