End-User Computing Satisfaction (EUCS) Model: Implementation of Learning Management System (LMS) on Students Satisfaction at Universities
Abstract
The purpose of this research is to find out the extent to which user satisfaction with the Learning Management System (LMS) service is so that it can provide better service and can satisfy/meet the needs of its users by measuring user satisfaction with information systems using the End-User Computing Satisfaction (EUCS) model approach. The researcher adopted the EUCS model developed by Doll and Torkzadeh regarding the satisfaction of end users of information systems. Related to the level of contentment experienced by end users of information systems, the researcher chose to implement the EUCS model that Doll and Torkzadeh had developed. The collected research samples came from a total of two hundred (200) students enrolled in the Arts, Drama, Dance, and Music Education Study Program in batches of 2021 and 2022. Confirmatory Factor Analysis (CFA), a subset of the Structural Equation Modeling (SEM) method, was used to analyze this research project's data. User satisfaction will also affect if the variable of ease of use in learning applications used by students is easy to access and can be accessed anywhere and anytime. This study found that students were highly satisfied with the Application of LMS in the learning process. It was also found that the five EUCS factors significantly increased satisfaction with learning technology.
References
N. Shrestha et al., “The impact of COVID-19 on globalization,” One Heal., vol. 11, p. 100180, 2020.
S. A. Tabish, “COVID-19 pandemic: Emerging perspectives and future trends,” J. Public health Res., vol. 9, no. 1, p. jphr-2020, 2020.
A. Haleem, M. Javaid, and R. Vaishya, “Effects of COVID-19 pandemic in daily life,” Curr. Med. Res. Pract., vol. 10, no. 2, p. 78, 2020.
World Health Organization, “WHO Director-General’s opening remarks at the media briefing on COVID-19-11 March 2020.” Geneva, Switzerland, 2020.
S. J. Ball, The education debate. Policy Press, 2021.
N. Mustafa, “Impact of the 2019–20 coronavirus pandemic on education,” Int. J. Heal. Prefer. Res., vol. 4, no. 1, pp. 25–30, 2020.
A. W. T. Bates and A. Sangra, Managing technology in higher education: Strategies for transforming teaching and learning. John Wiley & Sons, 2011.
M. Ouadoud, A. Nejjari, M. Y. Chkouri, and K. E. El Kadiri, “Educational modeling of a learning management system,” in 2017 international conference on electrical and information technologies (ICEIT), 2017, pp. 1–6.
B. Bhattacharya and A. Chaubey, “Learning Management System in Higher Education,” IJSTE Int. J. Sci. Technol. Enginering, vol. 2, no. 3, pp. 29–51, 2019.
C. C. Aydin and G. Tirkes, “Open source learning management systems in e-learning and Moodle,” in IEEE EDUCON 2010 Conference, 2010, pp. 593–600.
S. Rapuano and F. Zoino, “A learning management system including laboratory experiments on measurement instrumentation,” IEEE Trans. Instrum. Meas., vol. 55, no. 5, pp. 1757–1766, 2006.
A. Sharma and S. Vatta, “Role of learning management systems in education,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 3, no. 6, pp. 997–1002, 2013.
C. Müller, M. Stahl, M. Alder, and M. Müller, “Learning effectiveness and students’ perceptions in a flexible learning course,” Eur. J. Open, Distance E-Learning, vol. 21, no. 2, pp. 44–52, 2018.
W. H. DeLone and E. R. McLean, “Information systems success: The quest for the dependent variable,” Inf. Syst. Res., vol. 3, no. 1, pp. 60–95, 1992.
N. Urbach and B. Müller, “The updated DeLone and McLean model of information systems success,” in Information systems theory, Springer, 2012, pp. 1–18.
V. P. Aggelidis and P. D. Chatzoglou, “Hospital information systems: Measuring end user computing satisfaction (EUCS),” J. Biomed. Inform., vol. 45, no. 3, pp. 566–579, 2012.
E. S. Kassim, S. F. A. K. Jailani, H. Hairuddin, and N. H. Zamzuri, “Information system acceptance and user satisfaction: The mediating role of trust,” Procedia-Social Behav. Sci., vol. 57, pp. 412–418, 2012.
W. Goddard and S. Melville, Research methodology: An introduction. Juta and Company Ltd, 2004.
F. N. Kerlinger and H. B. Lee, Foundations of Behavioral Research, 4th ed. New York: Holt, Rinehart and Winston, 2000.
J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 7th ed. Harlow, England: Pearson New International Edition, 2014.
T. A. Whittaker and R. E. Schumacker, A Beginner’s Guide to Structural Equation Modeling. Routledge, 2022.
W. J. Doll and G. Torkzadeh, “The measurement of end-user computing satisfaction,” MIS Q., pp. 259–274, 1988.
R. D’Agostino, Goodness-of-fit-techniques. Routledge, 2017.
K. G. Jöreskog and D. Sörbom, LISREL 8: Structural equation modeling with the SIMPLIS command language. Scientific Software International, 1993.
G. D. Garson, Partial Least Squares: Regression & structural equation modeling. Asheboro, USA: Statistical Publishing Associates, 2016.
J. J. Hox and T. M. Bechger, “An introduction to structural equation modeling,” 2007.
B. Wheaton, B. Muthen, D. F. Alwin, and G. F. Summers, “Assessing reliability and stability in panel models,” Sociol. Methodol., vol. 8, pp. 84–136, 1977.
E. G. Carmines, “Analyzing models with unobserved variables,” Soc. Meas. Curr. issues, vol. 80, 1981.
R. E. Schumacher and R. G. Lomax, A Beginner’s Guide to Structural Equation Modeling: Third Edition, 3rd ed. Mahwah, NJ: Lawrence Erlbaum Associates, 2010.
J. S. Tanaka and G. J. Huba, “A general coefficient of determination for covariance structure models under arbitrary GLS estimation,” Br. J. Math. Stat. Psychol., vol. 42, no. 2, pp. 233–239, 1989.
J. H. Steiger and J. C. Lind, “Statistically based tests for the number of common factors,” 1980.
M. W. Browne and R. Cudeck, “Alternative ways of assessing model fit,” Sage Focus Ed., vol. 154, p. 136, 1993.
L. J. Williams and E. O’Boyle Jr, “The myth of global fit indices and alternatives for assessing latent variable relations,” Organ. Res. Methods, vol. 14, no. 2, pp. 350–369, 2011.
F. Chen, P. J. Curran, K. A. Bollen, J. Kirby, and P. Paxton, “An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models,” Sociol. Methods Res., vol. 36, no. 4, pp. 462–494, 2008.
L. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,” Struct. Equ. Model. a Multidiscip. J., vol. 6, no. 1, pp. 1–55, 1999.
L. R. Tucker and C. Lewis, “A reliability coefficient for maximum likelihood factor analysis,” Psychometrika, vol. 38, no. 1, pp. 1–10, 1973.
P. M. Bentler and L. T. Hu, “Evaluating model fit,” in Structural equation modeling: Concepts, issues, and applications, Thousand Oaks, CA: SAGE Publications, 1995, pp. 76–99.
P. M. Bentler, “SEM with simplicity and accuracy,” J. Consum. Psychol., vol. 20, no. 2, pp. 215–220, 2010, doi: 10.1016/j.jcps.2010.03.002.
T. A. Brown, Confirmatory Factor Analysis for Applied Research, 2nd ed. New York: The Guilford Press, 2015.
K. A. Bollen, “A new incremental fit index for general structural equation models,” Sociol. Methods Res., vol. 17, no. 3, pp. 303–316, 1989.
S. Sharma, Applied Multivariate Techniques. New York, USA: John Wiley & Sons, Inc., 1996.
C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” J. Mark. Res., vol. 18, no. 1, pp. 39–50, 1981.
E. Grigoroudis, C. Litos, V. A. Moustakis, Y. Politis, and L. Tsironis, “The assessment of user-perceived web quality: Application of a satisfaction benchmarking approach,” Eur. J. Oper. Res., vol. 187, no. 3, pp. 1346–1357, 2008.
X. Zhu and S. Gauch, “Incorporating quality metrics in centralized/distributed information retrieval on the World Wide Web,” in Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, 2000, pp. 288–295.
S. Beck, “The good, the bad and the ugly: or, why it’sa good idea to evaluate web sources,” Retrieved May, vol. 14, p. 2003, 1997.
R. J. Holden and B.-T. Karsh, “The technology acceptance model: its past and its future in health care,” J. Biomed. Inform., vol. 43, no. 1, pp. 159–172, 2010.
D. R. Garrison, T. Anderson, and W. Archer, “Critical inquiry in a text-based environment: Computer conferencing in higher education,” internet High. Educ., vol. 2, no. 2–3, pp. 87–105, 1999.
G. Singh, J. O’Donoghue, and H. Worton, “A study into the effects of elearning on higher education,” J. Univ. Teach. Learn. Pract., vol. 2, no. 1, pp. 16–27, 2005.
W. H. Delone and E. R. McLean, “The DeLone and McLean model of information systems success: a ten-year update,” J. Manag. Inf. Syst., vol. 19, no. 4, pp. 9–30, 2003.
M. M. Yusof, J. Kuljis, A. Papazafeiropoulou, and L. K. Stergioulas, “An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit),” Int. J. Med. Inform., vol. 77, no. 6, pp. 386–398, 2008.
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