The Influence of the Social, Cognitive, and Instructional Dimensions on Technology Acceptance Decisions among College-Level Students

David Lemay; Tenzin Doleck; Paul Bazelais

doi:10.29333/ejmste/97192

Full Text (PDF)

David Lemay ¹, Tenzin Doleck ¹ ^*, Paul Bazelais ¹

More Detail

¹ McGill University, Montreal, Quebec, CANADA^* Corresponding Author

Abstract

Technology acceptance models are primarily focused on the cognitive dimension of user beliefs. However, researchers have identified a range of situational and contextual factors that influence user attitudes and behavioral intention towards a given technology. We advance a situated model of e-learning acceptance among college students combining factors from the community of inquiry (COI) framework and the technology acceptance model (TAM), specifying core relationships within, and theoretically informed path relationships between the two frameworks. Using a sample of 121 respondents, we test a structural model using generalized structured component analysis. Collectively the situated model helped explain 63.7% variance in Behavioral Intention and 25% of the variance in Use suggesting that our model has strong explanatory power. Policymakers can leverage this information to boost acceptance of e-learning and platforms among their academic communities by promoting e-learning environments with strong Teacher, Social, and Cognitive Presence.

Keywords

community of inquiry
technology acceptance
e-learning

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, Volume 14, Issue 12, December 2018, Article No: em1635

https://doi.org/10.29333/ejmste/97192

Publication date: 28 Sep 2018

Article Views: 2618

Article Downloads: 1569

Open Access References How to cite this article

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall, NJ.
Barwise, J. (1981). Scenes and other situations. Journal of Philosophy, 78(7), 369–397. https://doi.org/10.2307/2026481.
Barwise, J., & Perry, J. (1981). Situations and attitudes. Journal of Philosophy, 78(11), 668–691. https://doi.org/10.2307/2026578.
Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in the technology acceptance model. Information and Management, 43(6), 706–717. https://doi.org/10.1016/j.im.2006.03.007.
Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah, NJ: Erlbaum.
Churchill, G. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.2307/3150876.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008.
Davis, F. D., Bagozzi, R., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982.
Doleck, T., Bazelais, P., & Lemay, D. J. (2017a). Examining the Antecedents of Social Networking Sites Use among CEGEP Students. Education and Information Technologies, 22(5), 2103–2123. https://doi.org/10.1007/s10639-016-9535-4.
Doleck, T., Bazelais, P., & Lemay, D. J. (2017b). Examining CEGEP Students’ Acceptance of CBLEs: A Test of Acceptance Models. Education and Information Technologies, 22(5), 2523–2543. https://doi.org/10.1007/s10639-016-9559-9.
Doleck, T., Bazelais, P., & Lemay, D. J. (2018). Is a General Extended Technology Acceptance Model for E-learning Generalizable? Knowledge Management & E-Learning, 10(2), 133–147.
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.
Fornell, C., & Larcker, D. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312.
Garrison, D. R. (2017). E-Learning in the 21st Century: A Community of Inquiry Framework for Research and Practice (Vol. 3). New York: Routledge.
Garrison, D. R., & Akyol, Z. (2013). The Community of Inquiry Theoretical Framework. In M. G. Moore (Ed.), Handbook of Distance Education (3rd ed., p. 753). Hoboken, NJ: Taylor & Francis. https://doi.org/10.4324/9780203803738.ch7.
Garrison, D. R., Anderson, T., & Archer, W. (2010). The first decade of the community of inquiry framework: A retrospective. Internet and Higher Education, 13(1–2), 5–9. https://doi.org/10.1016/j.iheduc.2009.10.003.
Garrison, D. R., Cleveland-Innes, M., & Fung, T. S. (2010). Exploring causal relationships among teaching, cognitive and social presence: Student perceptions of the community of inquiry framework. Internet and Higher Education, 13(1–2), 31–36. https://doi.org/10.1016/j.iheduc.2009.10.002.
Goffman, E. (1974). Frame analysis: An essay on the organization of experience. Cambridge, MA: Harvard University Press.
Greeno, J. G. (1994). Gibson’s affordances. Psychological Review, 101(2), 336–342. doi:10.1037/0033-295X.101.2.336.
Greeno, J. G. (1998). The situativity of knowing, learning, and research. American Psychologist, 53(1), 5–26. https://doi.org/10.1037/0003-066X.53.1.5.
Hwang, H. & Takane, Y. (2004). Generalized structured component analysis. Psychometrika, 69(1), 81-99. https://doi.org/10.1007/bf02295841.
Hwang, H. (2008). VisualGSCA 1.0 - A graphical user interface software program for generalized structured component analysis. In K. Shigemasu, A. Okada, T. Imaizumi, & T. Hoshino (Eds.). New Trends in Psychometrics (pp. 111-120). Tokyo: University Academic Press.
Hwang, H. (2011). GeSCA User’s Manual. Retrieved from http://www.sem-gesca.org/GeSCA_Manual.pdf.
Kim, S., Cardwell, R., & Hwang, H. (2016). Using R Package gesca for generalized structured component analysis. Behaviormetrika, 44(1), 3-23. https://doi.org/10.1007/s41237-016-0002-8.
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43, 740-755. https://doi.org/10.1016/j.im.2006.05.003.
Lee, Y., Kozar, K., & Larsen, K. (2003). The technology acceptance model: Past, present, and, future. Communications of the Association for Information Systems, 12(50), 752–780.
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40(3), 1-14. https://doi.org/10.1016/S0378-7206(01)00143-4.
Lemay, D. J., Doleck, T., & Bazelais, P. (2017). “Passion and Concern for Privacy” as Factors Affecting Snapchat Use: A Situated Perspective on Technology Acceptance. Computers in Human Behavior, 75, 264–271. https://doi.org/10.1016/j.chb.2017.05.022.
Lemay, D. J., Morin, M. M., Bazelais, P., & Doleck, T. (2018). Modeling Students' Perceptions of Simulation-Based Learning Using the Technology Acceptance Model. Clinical Simulation in Nursing, 20, 28–37. https://doi.org/10.1016/j.ecns.2018.04.004.
Liaw, 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. https://doi.org/10.1016/j.compedu.2007.09.005.
McFarland, D. J., & Hamilton, D. (2006). Adding contextual specificity to the technology acceptance model. Computers in Human Behavior, 22(3), 427–447. https://doi.org/10.1016/j.chb.2004.09.009.
Ryoo, J., & Hwang, H. (2017). Model Evaluation in Generalized Structured Component Analysis Using Confirmatory Tetrad Analysis. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00916.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information and Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007.
Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human Computer Studies, 64(2), 53–78. https://doi.org/j.ijhcs.2005.04.013.
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302-312. https://doi.org/10.1016/j.compedu.2008.08.006.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27, 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb01822.x.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926.
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. https://doi.org/10.2307/30036540.

How to cite this article

APA

Lemay, D., Doleck, T., & Bazelais, P. (2018). The Influence of the Social, Cognitive, and Instructional Dimensions on Technology Acceptance Decisions among College-Level Students. Eurasia Journal of Mathematics, Science and Technology Education, 14(12), em1635. https://doi.org/10.29333/ejmste/97192

Vancouver

Lemay D, Doleck T, Bazelais P. The Influence of the Social, Cognitive, and Instructional Dimensions on Technology Acceptance Decisions among College-Level Students. EURASIA J Math Sci Tech Ed. 2018;14(12):em1635. https://doi.org/10.29333/ejmste/97192

AMA

Lemay D, Doleck T, Bazelais P. The Influence of the Social, Cognitive, and Instructional Dimensions on Technology Acceptance Decisions among College-Level Students. EURASIA J Math Sci Tech Ed. 2018;14(12), em1635. https://doi.org/10.29333/ejmste/97192

Chicago

Lemay, David, Tenzin Doleck, and Paul Bazelais. "The Influence of the Social, Cognitive, and Instructional Dimensions on Technology Acceptance Decisions among College-Level Students". Eurasia Journal of Mathematics, Science and Technology Education 2018 14 no. 12 (2018): em1635. https://doi.org/10.29333/ejmste/97192

Harvard

Lemay, D., Doleck, T., and Bazelais, P. (2018). The Influence of the Social, Cognitive, and Instructional Dimensions on Technology Acceptance Decisions among College-Level Students. Eurasia Journal of Mathematics, Science and Technology Education, 14(12), em1635. https://doi.org/10.29333/ejmste/97192

MLA

Lemay, David et al. "The Influence of the Social, Cognitive, and Instructional Dimensions on Technology Acceptance Decisions among College-Level Students". Eurasia Journal of Mathematics, Science and Technology Education, vol. 14, no. 12, 2018, em1635. https://doi.org/10.29333/ejmste/97192