Analysis of the Effect of Information Quality, System Quality, and Support Service Quality on User Satisfaction Levels and Its Implications for Blended E-Learning Continuance Intention to Use in the New Normal Era
DOI:
https://doi.org/10.55927/fjsr.v1i7.2226Keywords:
Information Quality, System Quality, Support Service Quality, User Satisfaction, Continuance Intention to Use Blended E-LearningAbstract
Technology encourages e-learning to improve student learning and performance. The Covid-19 pandemic accelerated e-learning in Indonesian higher education. The learning method has transitioned from complete online learning to blended e-learning in new normal era. This research was conducted to determine the factors that influence students' intention to continue using blended e-learning using the IS Success Model. This research was conducted using a quantitative approach. Sampling was carried out by convenience sampling of 232 active students using blended e-learning in Indonesia. Data analysis was carried out with PLS-SEM. The hypothesis test shows that information, system, and support service quality affect blended e-learning student satisfaction. This research demonstrates that user happiness affects the intention to continue using blended e-learning
Downloads
References
Adeoye, I., Adanikin, A., & Adanikin, A. (2020). COVID-19 and E-Learning: Nigeria Tertiary Education System Experience. International Journal of Research and Innovation in Applied Science (IJRIAS) |, V(May), 2454–6194. www.rsisinternational.org
Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102(March 2019), 67–86. https://doi.org/10.1016/j.chb.2019.08.004
AlMulhem, A. (2020). Investigating the effects of quality factors and organizational factors on university students’ satisfaction of e-learning system quality. Cogent Education, 7(1). https://doi.org/10.1080/2331186X.2020.1787004
Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214. https://doi.org/10.1016/S0167-9236(01)00111-7
Bhattacherjee, A. (2001b). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370.
Chen, Y.-M. (2014). Extending the expectation-confirmation model with quality and flow to explore nurses’ continued blended e-learning intention. Information Technology & People, 27(3), 366–386. https://doi.org/10.1108/ITP-01-2013-0024
Chin, W. W. (1998). The partial least squares approach for structural equation modeling. The partial least squares approach for structural equation modeling. In Modern methods for business research. Lawrence Erlbaum Associates Publishers.
Cho, V., Cheng, T. C. E., & Lai, W. M. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers and Education, 53(2), 216–227. https://doi.org/10.1016/j.compedu.2009.01.014
Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers and Education, 122, 273–290. https://doi.org/10.1016/j.compedu.2017.12.001
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. tRoutledge. https://doi.org/https://doi.org/10.4324/9780203771587
Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management and E-Learning, 11(2), 201–214. https://doi.org/10.34105/j.kmel.2019.11.010
Dangaiso, P., Makudza, F., & Hogo, H. (2022). Modelling perceived e-learning service quality, student satisfaction and loyalty. A higher education perspective. Cogent Education, 9(1). https://doi.org/10.1080/2331186x.2022.2145805
Delone, W. H., & Mclean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1016/j.giq.2003.08.002
Delone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Management, 3(1), 60–95. https://doi.org/10.5267/j.uscm.2014.12.002
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
Ghazal, S., Aldowah, H., & Umar, I. (2018). Critical factors to learning management system acceptance and satisfaction in a blended learning environment. Lecture Notes on Data Engineering and Communications Technologies, 5, 688–698. https://doi.org/10.1007/978-3-319-59427-9_71
Hair, Joe F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM : Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139–151. https://doi.org/10.2753/MTP1069-6679190202
Hair, Joseph F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis 7th Edition. Pearson.
Hair, Joseph F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling. In SAGE (Vol. 46, Issues 1–2). https://doi.org/10.1016/j.lrp.2013.01.002
Hair, Joseph F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) Second Edition. In Sage.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20(2009), 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014
Hermita, M., Farida, Margianti, E. S., & Fanreza, R. (2019). The determinants and impact of system usage and satisfaction on e-learning success and faculty-student interaction in indonesian private universities. Malaysian Journal of Consumer and Family Economics, 23, 85–99.
Hikmah, A. N., & Chudzaifah, I. (2020). Blanded Learning: Solusi Model Pembelajaran Pasca Pandemi Covid-19. Al-Fikr: Jurnal Pendidikan Islam, 6(2), 83–94. https://doi.org/10.32489/alfikr.v6i2.84
Kumar Basak, S., Wotto, M., & Bélanger, P. (2018). E-learning, M-learning and D-learning: Conceptual definition and comparative analysis. E-Learning and Digital Media, 15(4), 191–216. https://doi.org/10.1177/2042753018785180
Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers and Education, 53(4), 1320–1329. https://doi.org/10.1016/j.compedu.2009.06.014
Lee, J. W. (2010). Online support service quality, online learning acceptance, and student satisfaction. Internet and Higher Education, 13(4), 277–283. https://doi.org/10.1016/j.iheduc.2010.08.002
Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers and Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers and Education, 51(2), 864–873. https://doi.org/10.1016/j.compedu.2007.09.005
Lin, W. S., & Wang, C. H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers and Education, 58(1), 88–99. https://doi.org/10.1016/j.compedu.2011.07.008
Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers and Education, 53(4), 1285–1296. https://doi.org/10.1016/j.compedu.2009.06.011
Pituch, K. A., & Lee, Y. kuei. (2006). The influence of system characteristics on e-learning use. Computers and Education, 47(2), 222–244. https://doi.org/10.1016/j.compedu.2004.10.007
Poelmans, S., & Wessa, P. (2015). A constructivist approach in a blended e-learning environment for statistics. Interactive Learning Environments, 23(3), 385–401. https://doi.org/10.1080/10494820.2013.766890
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2018). Partial least squares structural equation modeling in HRM research. International Journal of Human Resource Management, 31(12), 1617–1643. https://doi.org/10.1080/09585192.2017.1416655
Salam, M., & Farooq, M. S. (2020). Does sociability quality of web-based collaborative learning information system influence students’ satisfaction and system usage? International Journal of Educational Technology in Higher Education, 17(1). https://doi.org/10.1186/s41239-020-00189-z
Sullivan, G. M., & Feinn, R. (2012). Using Effect Size—or Why the P Value Is Not Enough. Journal of Graduate Medical Education, 4(3), 279–282. https://doi.org/10.4300/jgme-d-12-00156.1
Suzianti, A., & Paramadini, S. A. (2021). Continuance intention of e-learning: The condition and its connection with open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1). https://doi.org/10.3390/JOITMC7010097
Urbach, N., & Ahlemann, F. (2010). Structural Equation Modeling in Information Systems Research Using Partial Least Squares. Journal of Information Technology Theory and Application (JITTA), 11(2), 5–40. http://aisel.aisnet.org/jitta/vol11/iss2/2
Wan, L., Xie, S., & Shu, A. (2020). Toward an Understanding of University Students’ Continued Intention to Use MOOCs: When UTAUT Model Meets TTF Model. SAGE Open, 10(3). https://doi.org/10.1177/2158244020941858
Wijaya, R., Lukman, M., & Yadewani, D. (2020). Dampak Pandemi Covid19 Terhadap Pemanfaatan E Learning. Jurnal Dimensi, 9(2), 307–322. https://doi.org/10.33373/dms.v9i2.2543
Ya-Ching Lee. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517–541. https://doi.org/DOI 10.1108/14684520610706406
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Riatun, Elissa Dwi Lestari
This work is licensed under a Creative Commons Attribution 4.0 International License.