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

Authors

  • Riatun Universitas Multimedia Nusantara
  • Elissa Dwi Lestari Universitas Multimedia Nusantara

DOI:

https://doi.org/10.55927/fjsr.v1i7.2226

Keywords:

Information Quality, System Quality, Support Service Quality, User Satisfaction, Continuance Intention to Use Blended E-Learning

Abstract

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

Download data is not yet available.

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

2022-12-27

How to Cite

Riatun, & Elissa Dwi Lestari. (2022). 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. Formosa Journal of Sustainable Research, 1(7), 1067–1082. https://doi.org/10.55927/fjsr.v1i7.2226