Understanding students’ satisfaction and continuance intention of e-learning: Application of expectation–confirmation model
Article Sidebar
Published
Dec 30, 2015
Main Article Content
Abstract
The evolution of technologies leads to the great significance of e-learning in the domain of education. Recognition of the crucial factors which influence learners’ aims towards continued use of e-learning would guide teachers, learners and e-learning developers to increase e-learning use. To this end, the present study investigates the Expectation-Confirmation Model (ECM) factors of Post-Adoption Expectation (PAE) which is explored via using language learners’ post-adoption experiences in the use of e-learning systems. Learning process, tutor interaction, peer interaction, and course design are the four factors identified used for extending the perception of language learners’ experiences in e-learning. The survey method was used to empirically validate the suggested model (ECM) of the present study. A total sample of 120 Iranian university students participated in the study.In order to investigate the proposed model, structural equation modelling employing Smart PLS 2.0 was run. The findings indicate that learners’ confirmation of using e-learning has a significant effect on the four aforementioned factors. Learning process and course design are the only two factors that have a significant effect on users’ satisfaction and continuance intention. On the other hand, the results showed that tutor interaction and peer interaction do not have a significant effect on predicting learners’ satisfaction and continuance intention of e-learning systems. Keywords: e-learning, students’ satisfaction, students’ continuance intention, expectation-confirmation model, post-adoption expectation
Downloads
Download data is not yet available.
Article Details
How to Cite
TIYAR, Forouzan Rezaeian; KHOSHSIMA, Hooshang.
Understanding students’ satisfaction and continuance intention of e-learning: Application of expectation–confirmation model.
World Journal on Educational Technology: Current Issues, [S.l.], v. 7, n. 3, p. 157-166, dec. 2015.
ISSN 1309-0348.
Available at: <http://sproc.org/ojs/index.php/wjet/article/view/203>. Date accessed: 02 dec. 2017.
doi: https://doi.org/10.18844/wjet.v7i3.203.
Section
Articles
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
Bearden , W. O., & Teel, J. E. (1983). Selected determinants of consumer satisfaction and complaint reports. Journal of Marketing Research, 20(1), 21–28.
Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214.
Bhattacherjee, A. (2001b). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 351-370.
Brophy, J. (2000). Teaching. Educational Practices Series (Vol. 1). International Academy of Education & International Bureau of Education.
Chin , W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Chiu, C. M., Sun, S. Y., Sun, P. C., & Ju, T. L. (2007). An empirical analysis of the antecedents of web-based learning continuance. Computers & Education, 49(4), 1224-1245.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Davis, F. D., Bagozzi, R. P., & al, e. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
Fishbein , M., & Ajzen , I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. MA: Addison-Wesley.
Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of email: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389–400.
Johnson, R. D., Hornik, S., & Salas, E. (2008). An empirical examination of factors contributing to the creation of successful e-learning environments. International Journal of Human-Computer Studies, 66(5), 356-369.
Jucks, R., Paechter, M. R., & Tatar, D. G. (2003). Learning and collaboration in online discourses. International Journal Of Educational Policy Research And Practice, 4(1), 117-146.
Kim, B., & Han, L. (2009). The role of trust belief in community-driven knowledge and its antecedents. Journal of the American Society for Information Science and Technology, 60(5), 1012–1026.
Lam, S. Y., Shankar, V., & Erramilli, M. K. (2004). Customer value, satisfaction loyalty, and switching costs: An illustration from a business-to-business service context. Journal of the Academy of Marketing Science, 32(3), 293–311.
Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516.
Locke, E. A. (1976). The Nature and Causes of Job Satisfaction. New York: Holt, Reinhart & Winston.
Lu, Y., & Zhou , T. e. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29–39.
Mohammadi, N., Ghorbani, V., & Hamidi, F. (2011). Effects of e-learning on language learning. Procedia Computer Science, 3, 464–468.
Narciss, S., Proske, A., & Koerndle, H. (2007). Promoting self-regulated learning in web-based learning environments. Computers in Human Behavior, 23(3), 1126-1144.
Njenga , J. K., & Fourie, L. C. (2010). The myths about e‐learning in higher education. British Journal of Educational Technology, 41(2), 199-212.
Oliver, R. L. (1981). A Cognitive Model for the Antecedents and Consequences of Satisfaction. Journalo f MarketingR esearch, 17, 460-469.
Oliver, R. L. (1988). Cognitive, affective, and attribute bases of the satisfaction response. The Journal of Consumer Research, 20(3), 418–430.
Oliver, R. L., & DeSarbo, W. S. (1988). Response determinants in satisfaction judgments. The Journal of Consumer Research, 16(4), 495–507.
Paechter, M., Maier, B., & Macher, D. (2010). Students’ expectations of, and experiences in e-learning: Theirrelation to learning achievements and course satisfaction. Computers & Education, 54(1), 222-229.
Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
Rogers, E. M. (1995). Diffusion of Innovations (4th ed.). New York: Free Press.
Szymanski, D. M., & Henard , D. H. (2001). Customer satisfaction: A meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29(1), 16–35.
Taylor, S., & Todd, P. A. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561–570.
Thong, J. Y., Hong, S. J., & Tam, K. V. (2006). The effects of post-adoption beliefs on the expectation–confirmation model for information technology continuance. International Journal of Human–Computer Studies, 64(9), 799–810.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
Bearden , W. O., & Teel, J. E. (1983). Selected determinants of consumer satisfaction and complaint reports. Journal of Marketing Research, 20(1), 21–28.
Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214.
Bhattacherjee, A. (2001b). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 351-370.
Brophy, J. (2000). Teaching. Educational Practices Series (Vol. 1). International Academy of Education & International Bureau of Education.
Chin , W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Chiu, C. M., Sun, S. Y., Sun, P. C., & Ju, T. L. (2007). An empirical analysis of the antecedents of web-based learning continuance. Computers & Education, 49(4), 1224-1245.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Davis, F. D., Bagozzi, R. P., & al, e. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
Fishbein , M., & Ajzen , I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. MA: Addison-Wesley.
Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of email: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389–400.
Johnson, R. D., Hornik, S., & Salas, E. (2008). An empirical examination of factors contributing to the creation of successful e-learning environments. International Journal of Human-Computer Studies, 66(5), 356-369.
Jucks, R., Paechter, M. R., & Tatar, D. G. (2003). Learning and collaboration in online discourses. International Journal Of Educational Policy Research And Practice, 4(1), 117-146.
Kim, B., & Han, L. (2009). The role of trust belief in community-driven knowledge and its antecedents. Journal of the American Society for Information Science and Technology, 60(5), 1012–1026.
Lam, S. Y., Shankar, V., & Erramilli, M. K. (2004). Customer value, satisfaction loyalty, and switching costs: An illustration from a business-to-business service context. Journal of the Academy of Marketing Science, 32(3), 293–311.
Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516.
Locke, E. A. (1976). The Nature and Causes of Job Satisfaction. New York: Holt, Reinhart & Winston.
Lu, Y., & Zhou , T. e. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29–39.
Mohammadi, N., Ghorbani, V., & Hamidi, F. (2011). Effects of e-learning on language learning. Procedia Computer Science, 3, 464–468.
Narciss, S., Proske, A., & Koerndle, H. (2007). Promoting self-regulated learning in web-based learning environments. Computers in Human Behavior, 23(3), 1126-1144.
Njenga , J. K., & Fourie, L. C. (2010). The myths about e‐learning in higher education. British Journal of Educational Technology, 41(2), 199-212.
Oliver, R. L. (1981). A Cognitive Model for the Antecedents and Consequences of Satisfaction. Journalo f MarketingR esearch, 17, 460-469.
Oliver, R. L. (1988). Cognitive, affective, and attribute bases of the satisfaction response. The Journal of Consumer Research, 20(3), 418–430.
Oliver, R. L., & DeSarbo, W. S. (1988). Response determinants in satisfaction judgments. The Journal of Consumer Research, 16(4), 495–507.
Paechter, M., Maier, B., & Macher, D. (2010). Students’ expectations of, and experiences in e-learning: Theirrelation to learning achievements and course satisfaction. Computers & Education, 54(1), 222-229.
Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
Rogers, E. M. (1995). Diffusion of Innovations (4th ed.). New York: Free Press.
Szymanski, D. M., & Henard , D. H. (2001). Customer satisfaction: A meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29(1), 16–35.
Taylor, S., & Todd, P. A. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561–570.
Thong, J. Y., Hong, S. J., & Tam, K. V. (2006). The effects of post-adoption beliefs on the expectation–confirmation model for information technology continuance. International Journal of Human–Computer Studies, 64(9), 799–810.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.