The Impact of System Quality and User Satisfaction: The Mediating Role of Ease of Use and Usefulness in E-Learning Systems

Fitria Fitria; Muhammad Yahya; M. Ichsan Ali; Purnamawati Purnamawati; Abdul Muis Mappalotteng

doi:10.55151/ijeedu.v6i2.134

Fitria Fitria Department of Vocational Engineering Education, Postgraduate Program, Universitas Negeri Makassar, Makassar 90222, South Sulawesi, Indonesia
Muhammad Yahya Department of Automotive Engineering Education, Universitas Negeri Makassar, Makassar 90222, South Sulawesi, Indonesia
M. Ichsan Ali Department of Department of Civil Engineering and Planning Education, Universitas Negeri Makassar, Makassar 90224, South Sulawesi, Indonesia
Purnamawati Purnamawati Department of Electronics Engineering Education, Universitas Negeri Makassar, Makassar 90224, South Sulawesi, Indonesia
Abdul Muis Mappalotteng Department of Electrical Engineering Education, Universitas Negeri Makassar, Makassar 90224, South Sulawesi, Indonesia

DOI: https://doi.org/10.55151/ijeedu.v6i2.134

Keywords: Educational Technology, Information System Success Model, Structural Equation Model, Technology Acceptance Model

Abstract

The research aims to understand how the system's quality influences users' perceptions of its usability and ease of use, affecting their overall satisfaction with the e-learning system. This analysis provides insights into the factors contributing to a positive user experience and the sustainable use of e-learning platforms. The study employs a quantitative approach with a survey method. The sample comprises 470 students from five universities using e-learning information systems, selected through purposive sampling. Data was collected via a questionnaire survey distributed to respondents and analyzed using Structural Equation Modeling (SEM) with the IBM AMOS Program. The results indicate that System Quality (SYQ) significantly affects Perceived Ease of Use (PEOU) with a probability value of 0.019 (p < 0.05), System Quality (SYQ) significantly affects Perceived Usefulness (PU) with a probability value of 0.036 (p < 0.05), Perceived Usefulness (PU) significantly affects User Satisfaction (USA) with a probability value of 0.028 (p < 0.05), and Perceived Ease of Use (PEOU) significantly affects User Satisfaction (USA) with a probability value of 0.000 (p < 0.05). The study concludes that integrating TAM and ISSM provides a comprehensive framework for understanding the factors influencing the sustainable use of e-learning systems. The practical implications of this research underscore the importance of giving e-learning systems that are not only easy to use and useful but also possess high system, information, and service quality to enhance user satisfaction and sustain usage.

References

H. Rodrigues, F. Almeida, V. Figueiredo, and S. L. Lopes, “Tracking e-learning through published papers: A systematic review,” Comput. Educ., vol. 136, pp. 87–98, 2019, doi: 10.1016/j.compedu.2019.03.007.

S. Ozkan and R. Koseler, “Multi-dimensional evaluation of E-learning systems in the higher education context: An empirical investigation of a computer literacy course,” Proc. - Front. Educ. Conf. FIE, vol. 53, no. 4, pp. 1285–1296, 2009, doi: 10.1109/FIE.2009.5350590.

A. Keramati, M. Afshari-Mofrad, and A. Kamrani, “The role of readiness factors in E-learning outcomes: An empirical study,” Comput. Educ., vol. 57, no. 3, pp. 1919–1929, 2011, doi: 10.1016/j.compedu.2011.04.005.

U. M. Azeiteiro, P. Bacelar-Nicolau, F. J. P. Caetano, and S. Caeiro, “Education for sustainable development through e-learning in higher education: Experiences from Portugal,” J. Clean. Prod., vol. 106, pp. 308–319, 2015, doi: 10.1016/j.jclepro.2014.11.056.

D. Persico, S. Manca, and F. Pozzi, “Adapting the technology acceptance model to evaluate the innovative potential of e-learning systems,” Comput. Human Behav., vol. 30, pp. 614–622, 2014, doi: 10.1016/j.chb.2013.07.045.

T. Govindasamy, “Successful implementation of e-Learning Pedagogical considerations,” Internet High. Educ., vol. 4, no. 3–4, pp. 287–299, 2001, doi: 10.1016/S1096-7516(01)00071-9.

B. Al Kurdi, M. Alshurideh, and S. A. Salloum, “Investigating a theoretical framework for e-learning technology acceptance,” Int. J. Electr. Comput. Eng., vol. 10, no. 6, pp. 6484–6496, 2020, doi: 10.11591/IJECE.V10I6.PP6484-6496.

F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Q. Manag. Inf. Syst., vol. 13, no. 3, pp. 319–339, 1989, doi: 10.2307/249008.

M. E. Eltahir, “E-Learning in Developing Countries: Is it a Panacea? A Case Study of Sudan,” IEEE Access, vol. 7, pp. 97784–97792, 2019, doi: 10.1109/ACCESS.2019.2930411.

J. Valverde-Berrocoso, M. del Carmen Garrido-Arroyo, C. Burgos-Videla, and M. B. Morales-Cevallos, “Trends in educational research about e-Learning: A systematic literature review (2009-2018),” Sustain., vol. 12, no. 12, p. 5153, 2020, doi: 10.3390/su12125153.

T. Zhang, Z. A. Shaikh, A. V. Yumashev, and M. Chład, “Applied model of E-learning in the framework of education for sustainable development,” Sustain., vol. 12, no. 16, p. 6420, 2020, doi: 10.3390/SU12166420.

S. Grassini, “Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational Settings,” Educ. Sci., vol. 13, no. 7, p. 692, 2023, doi: 10.3390/educsci13070692.

A. H. Muhammad et al., “A hierarchical model to evaluate the quality of web-based E-learning systems,” Sustain., vol. 12, no. 10, p. 4071, 2020, doi: 10.3390/SU12104071.

A. M. Al-Rahmi et al., “The influence of information system success and technology acceptance model on social media factors in education,” Sustain., vol. 13, no. 14, p. 7770, 2021, doi: 10.3390/su13147770.

K. J. Patel and H. J. Patel, “Adoption of internet banking services in Gujarat: An extension of TAM with perceived security and social influence,” Int. J. Bank Mark., vol. 36, no. 1, pp. 147–169, 2018, doi: 10.1108/IJBM-08-2016-0104.

N. Singh, N. Sinha, and F. J. Liébana-Cabanillas, “Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence,” Int. J. Inf. Manage., vol. 50, pp. 191–205, 2020, doi: 10.1016/j.ijinfomgt.2019.05.022.

T. Zhang et al., “Automated vehicle acceptance in China: Social influence and initial trust are key determinants,” Transp. Res. Part C Emerg. Technol., vol. 112, pp. 220–233, 2020, doi: 10.1016/j.trc.2020.01.027.

M. Kayali and S. Alaaraj, “Adoption of Cloud Based E-learning in Developing Countries: A Combination of DOI, TAM and UTAUT IJCMIT IJCMIT International Journal of Contemporary Management and information Technology Adoption of Cloud Based E-learning in Developing Countries: A Combina,” Int. J. Contemp. Manag. Inf. Technol., vol. 1, no. 1, pp. 1–7, 2020, [Online]. Available: www.ijcmit.com.

L. L. Zhou, J. Owusu-Marfo, H. Asante Antwi, M. O. Antwi, A. D. T. Kachie, and S. Ampon-Wireko, “‘assessment of the social influence and facilitating conditions that support nurses’’ adoption of hospital electronic information management systems (HEIMS) in Ghana using the unified theory of acceptance and use of technology (UTAUT) model",’” BMC Med. Inform. Decis. Mak., vol. 19, no. 1, pp. 1–9, 2019, doi: 10.1186/s12911-019-0956-z.

N. Abasi, N. Azad, and K. F. Hafashjani, “Information systems success: The quest for the dependent variable,” Uncertain Supply Chain Manag., vol. 3, no. 2, pp. 181–188, 2015, doi: 10.5267/j.uscm.2014.12.002.

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, doi: 10.1080/07421222.2003.11045748.

W. H. DeLone and E. R. McLean, “Information Systems Success Measurement,” Inf. Syst. Success Meas., vol. 2, no. 1, pp. 1–116, 2016, doi: 10.1561/9781680831436.

S. Petter, W. DeLone, and E. McLean, “Measuring information systems success: Models, dimensions, measures, and interrelationships,” Eur. J. Inf. Syst., vol. 17, no. 3, pp. 236–263, 2008, doi: 10.1057/ejis.2008.15.

V. Venkatesh and H. Bala, “Technology acceptance model 3 and a research agenda on interventions,” Decis. Sci., vol. 39, no. 2, pp. 273–315, 2008, doi: 10.1111/j.1540-5915.2008.00192.x.

Y. M. Cheng, “Students’ satisfaction and continuance intention of the cloud-based e-learning system: roles of interactivity and course quality factors,” Educ. Train., vol. 62, no. 9, pp. 1037–1059, 2020, doi: 10.1108/ET-10-2019-0245.

A. AL-Hawamleh, “Exploring the Satisfaction and Continuance Intention to Use E-Learning Systems,” Int. J. Electr. Comput. Eng. Syst., vol. 15, no. 2, pp. 201–214, 2024, doi: 10.32985/ijeces.15.2.8.

E. Junnonyang, “Integrating TAM, Perceived Risk, Trust, Relative Advantage, Government Support, Social Influence and User Satisfaction as Predictors of Mobile Government Adoption Behavior in Thailand,” Int. J. Ebus. eGovernment Stud., vol. 13, no. 1, pp. 159–178, 2021.

S. A. Nikou and A. A. Economides, “Continuance Intention to Use Mobile Learning in Terms of Motivation and Technology Acceptance,” Res. E-Learning ICT Educ., pp. 1–14, 2021, doi: 10.1007/978-3-030-64363-8_1.

J. Molano and N. Arevalo, “De la salud ocupacional a la gesti??n de la seguridad y salud en el trabajo: M??s que sem??ntica, una transformaci??n del sistema general de riesgos laborales,” Innovar, vol. 23, no. 48, pp. 21–32, 2013, doi: http://dx.doi.org/10.1108/17506200710779521.

T. Kamal and A. Illiyan, “School teachers’ perception and challenges towards online teaching during COVID-19 pandemic in India: an econometric analysis,” Asian Assoc. Open Univ. J., vol. 16, no. 3, pp. 311–325, 2021, doi: 10.1108/AAOUJ-10-2021-0122.

T. Chen, G. Cong, L. Peng, X. Yin, J. Rong, and J. Yang, “Analysis of user satisfaction with online education platforms in china during the covid-19 pandemic,” Healthc., vol. 8, no. 3, p. 7329, 2020, doi: 10.3390/healthcare8030200.

M. Ramkumar, T. Schoenherr, S. M. Wagner, and M. Jenamani, “Q-TAM: A quality technology acceptance model for predicting organizational buyers’ continuance intentions for e-procurement services,” Int. J. Prod. Econ., vol. 216, pp. 333–348, 2019, doi: 10.1016/j.ijpe.2019.06.003.

B. Wu and X. Chen, “Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model,” Comput. Human Behav., vol. 67, pp. 221–232, 2017, doi: 10.1016/j.chb.2016.10.028.

J. A. Smith and S. A. Sivo, “Predicting continued use of online teacher professional development and the influence of social presence and sociability,” Br. J. Educ. Technol., vol. 43, no. 6, pp. 871–882, 2012, doi: 10.1111/j.1467-8535.2011.01223.x.

P. K, “Continuance Intention to Use Facebook: A Study of Perceived Enjoyment and TAM,” Bonfring Int. J. Ind. Eng. Manag. Sci., vol. 4, no. 1, pp. 24–29, 2014, doi: 10.9756/bijiems.4794.

E. M. Abu-Taieh et al., “Continued Intention to Use of M-Banking in Jordan by Integrating UTAUT, TPB, TAM and Service Quality with ML,” J. Open Innov. Technol. Mark. Complex., vol. 8, no. 3, p. 120, 2022, doi: 10.3390/joitmc8030120.

D. Gefen, “TAM or just plain habit: A look at experienced online shoppers,” J. End User Comput., vol. 15, no. 3, pp. 1–13, 2003.

A. S. Almogren, “Art education lecturers’ intention to continue using the blackboard during and after the COVID-19 pandemic: An empirical investigation into the UTAUT and TAM model,” Front. Psychol., vol. 13, p. 944335, 2022, doi: 10.3389/fpsyg.2022.944335.

S. J. Hong, J. Y. L. Thong, and K. Y. Tam, “Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet,” Decis. Support Syst., vol. 42, no. 3, pp. 1819–1834, 2006, doi: 10.1016/j.dss.2006.03.009.

M. J. Kang and Y. C. Hwang, “Exploring the Factors Affecting the Continued Usage Intention of IoT-Based Healthcare Wearable Devices Using the TAM Model,” Sustain., vol. 14, no. 19, p. 12492, 2022, doi: 10.3390/su141912492.

B. Hawash, U. A. Mokhtar, and Z. M. Yusof, “Users’ acceptance of an electronic record management system in the context of the oil and gas sector in Yemen: An application of ISSM-TAM,” Int. J. Manag. Enterp. Dev., vol. 20, no. 1, pp. 75–98, 2021, doi: 10.1504/IJMED.2021.113661.

C. C. Chen and J. L. Tsai, “Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM,” Futur. Gener. Comput. Syst., vol. 96, pp. 628–638, 2019, doi: 10.1016/j.future.2017.02.028.

J. F. Chen, J. F. Chang, C. W. Kao, and Y. M. Huang, “Integrating ISSM into TAM to enhance digital library services: A case study of the Taiwan digital meta-library,” Electron. Libr., vol. 34, no. 1, pp. 58–73, 2016, doi: 10.1108/EL-01-2014-0016.

Z. Aldammagh, R. Abdaljawad, and T. Obaid, “Factors Driving E-Learning Adoption in Palestine: An Integration of Technology Acceptance Model and is Success Model,” Financ. Internet Q., vol. 17, no. 1, pp. 41–49, 2021, doi: 10.2478/fiqf-2021-0005.

C. A. . Compeau, D. R., & Higgins, “Computer self-efficacy: Development of a measure and initial test.,” MIS Q. Manag. Inf. Syst., vol. 19, no. 2, pp. 189–210, 1995.

S. I. Al Zoubi and A. I. Alzoubi, “E-learning benchmarking adoption: A case study of sur university college,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 11, pp. 463–470, 2019, doi: 10.14569/IJACSA.2019.0101164.

C. Ho, “Continuance Intention of E-Learning Platform : Toward an Integrated Model,” Int. J. Electron. Bus. Manag., vol. 8, no. 3, pp. 206–215, 2010.

A. Ramadhan, A. N. Hidayanto, G. A. Salsabila, I. Wulandari, J. A. Jaury, and N. N. Anjani, “The effect of usability on the intention to use the e-learning system in a sustainable way: A case study at Universitas Indonesia,” Educ. Inf. Technol., vol. 27, no. 2, pp. 1489–1522, 2022, doi: 10.1007/s10639-021-10613-0.

H. Al-Samarraie, B. K. Teng, A. I. Alzahrani, and N. Alalwan, “E-learning continuance satisfaction in higher education: a unified perspective from instructors and students,” Stud. High. Educ., vol. 43, no. 11, pp. 2003–2019, 2018, doi: 10.1080/03075079.2017.1298088.

M. N. Tran, “A holistic success model for sustainable e-learning based on the stakeholder approach: Case of Vietnamese students during the COVID-19 pandemic,” Cogent Bus. Manag., vol. 10, no. 2, p. 2236298, 2023, doi: 10.1080/23311975.2023.2236298.

R. S. Weerarathna, N. M. Rathnayake, U. P. G. Y. Pathirana, D. S. H. Weerasinghe, D. S. P. Biyanwila, and S. D. Bogahage, “Effect of E-learning on Management Undergraduates’ Academic Success during COVID-19: A Study at Non-state Universities in Sri Lanka,” Heliyon, vol. 9, no. 9, 2023, doi: 10.1016/j.heliyon.2023.e19293.

S. Farid, R. Ahmad, M. Alam, A. Akbar, and V. Chang, “A sustainable quality assessment model for the information delivery in E-learning systems,” Inf. Discov. Deliv., vol. 46, no. 1, pp. 1–25, 2018, doi: 10.1108/IDD-11-2016-0047.

A. AlMulhem, “Investigating the effects of quality factors and organizational factors on university students’ satisfaction of e-learning system quality,” Cogent Educ., vol. 7, no. 1, p. 1787004, 2020, doi: 10.1080/2331186X.2020.1787004.

M. Chuttur, “Working Papers on Information Systems Overview of the Technology Acceptance Model : Origins, Developments and Future Directions,” Work. Pap. Inf. Syst., vol. 9, no. 2009, pp. 9-37, 2009, [Online]. Available: http://sprouts.aisnet.org/9-37.

H. R. Chen and H. F. Tseng, “Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan,” Eval. Program Plann., vol. 35, no. 3, pp. 398–406, 2012, doi: 10.1016/j.evalprogplan.2011.11.007.

V. Venkatesh and F. D. Davis, “Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies,” Manage. Sci., vol. 46, no. 2, pp. 186–204, 2000, doi: 10.1287/mnsc.46.2.186.11926.

D. Gefen and D. Straub, “The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption,” J. Assoc. Inf. Syst., vol. 1, no. 1, pp. 1–30, 2000, doi: 10.17705/1jais.00008.

I. M. Klopping and E. Mckinney, “Extending the technology acceptance model and the task-technology fit model to consumer e-consumer,” Inf. Technol. Learn. Perform. J., vol. 22, no. 1, pp. 35–48, 2004.

A. L. Lederer, D. J. Maupin, M. P. Sena, and Y. Zhuang, “Technology acceptance model and the World Wide Web,” Decis. Support Syst., vol. 29, no. 3, pp. 269–282, 2000, doi: 10.1016/S0167-9236(00)00076-2.

T. S. H. Teo, V. K. G. Lim, and R. Y. C. Lai, “Intrinsic and extrinsic motivation in Internet usage,” Omega, vol. 27, no. 1, pp. 25–37, 1999, doi: 10.1016/S0305-0483(98)00028-0.

F. A. D. M. Pereira, A. S. M. Ramos, M. A. Gouvêa, and M. F. Da Costa, “Satisfaction and continuous use intention of e-learning service in Brazilian public organizations,” Comput. Human Behav., vol. 46, pp. 139–148, 2015, doi: 10.1016/j.chb.2015.01.016.

I. Adamson and J. Shine, “Extending TAM to measure end user computer satisfaction in a mandatory environment: A bank’s treasury,” Technol. Anal. Strateg. Manag. J., vol. 15, no. 4, pp. 441–454, 2003.

C. C. Hsiao and J. S. Chiou, “The impact of online community position on online game continuance intention: Do game knowledge and community size matter?,” Inf. Manag., vol. 49, no. 6, pp. 292–300, 2012, doi: 10.1016/j.im.2012.09.002.

Aparício, “E-Learning Systems Success :,” Distance Learning, vol. 15, no. 3. University of Southern Queensland, pp. 57–74, 2016, [Online]. Available: https://eprints.usq.edu.au/27422.

N. Urbach and B. Müller, “The Updated DeLone and McLean Model of Information Systems Success,” in Information systems theory, Springer, 2012, pp. 1–18.

M. Shah and S. Clarke, E-banking management: Issues, solutions, and strategies: Issues, Solutions, and Strategies. IGI Global, 2009.

Jogiyanti, “Sistem informasi keperilakuan / Jogiyanto,” ijse.bsi.ac.id IJSE-Indonesian J. Softw. Eng., no. 2, pp. 600–604, 2007.

R. Salsabilla, “Daftar Jurusan Kuliah Paling Diminati Mahasiswa Indonesia,” CNBC Indonesia, 2023. https://www.cnbcindonesia.com/lifestyle/20230321102650-33-423466/daftar-jurusan-kuliah-paling-diminati-mahasiswa-indonesia (accessed Nov. 20, 2023).

C. L. Ho and R. J. Dzeng, “Construction safety training via e-Learning: Learning effectiveness and user satisfaction,” Comput. Educ., vol. 55, no. 2, pp. 858–867, 2010, doi: 10.1016/j.compedu.2010.03.017.

F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models,” Manage. Sci., vol. 35, no. 8, pp. 982–1003, 1989, doi: 10.1287/mnsc.35.8.982.

A. Hassanzadeh, F. Kanaani, and S. Elahi, “A model for measuring e-learning systems success in universities,” Expert Syst. Appl., vol. 39, no. 12, pp. 10959–10966, 2012, doi: 10.1016/j.eswa.2012.03.028.

P. C. Sun, R. J. Tsai, G. Finger, Y. Y. Chen, and D. Yeh, “What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction,” Comput. Educ., vol. 50, no. 4, pp. 1183–1202, May 2008, doi: 10.1016/j.compedu.2006.11.007.

J. Fox, C. Murray, and A. Warm, “Conducting research using web-based questionnaires: Practical, methodological, and ethical considerations,” Int. J. Soc. Res. Methodol. Theory Pract., vol. 6, no. 2, pp. 167–180, 2003, doi: 10.1080/13645570210142883.

B. Wright and P. H. Schwager, “Online survey research: Can response factors be improved?,” J. Internet Commer., vol. 7, no. 2, pp. 253–269, 2008, doi: 10.1080/15332860802067730.

M. Sarstedt, C. M. Ringle, and J. F. Hair, Partial Least Squares Structural Equation Modeling. Asheboro, USA: Statistical Publishing Associates, 2021.

P. M. Bentler and D. G. Bonett, “Significance tests and goodness of fit in the analysis of covariance structures,” Psychol. Bull., vol. 88, no. 3, pp. 588–606, 1980, doi: 10.1037/0033-2909.88.3.588.

N. Tabri and C. M. Elliott, Principles and Practice of Structural Equation Modeling, vol. 1, no. 1. Guilford publications, 2012.

E. Carmines and J. McIver, “Analyzing models with unobserved variables: Analysis of covariance structures,” Soc. Meas. Curr. issues, vol. 80, pp. 65–115, 1981.

R. E. Schumacker and R. G. Lomax, A Beginner’s Guide to Structural Equation Modeling, 3rd ed. Mahwah, NJ: Lawrence Erlbaum Associates, 2015.

L. J. Williams and E. O’Boyle, “The myth of global fit indices and alternatives for assessing latent variable relations,” Organ. Res. Methods, vol. 14, no. 2, pp. 350–369, 2011, doi: 10.1177/1094428110391472.

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, doi: 10.1177/0049124108314720.

M. W. Browne and R. Cudeck, “Alternative Ways of Assessing Model Fit,” Sociol. Methods Res., vol. 21, no. 2, pp. 230–258, 1992, doi: 10.1177/0049124192021002005.

L. T. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,” Struct. Equ. Model., vol. 6, no. 1, pp. 1–55, 1999, doi: 10.1080/10705519909540118.

K. P. Burnham and D. R. Anderson, “Model Selection and Inference,” Model Sel. Inference, vol. 63, no. 2020, p. 10, 1998, doi: 10.1007/978-1-4757-2917-7.

E. J. Wagenmakers and S. Farrell, “AIC model selection using Akaike weights,” Psychon. Bull. Rev., vol. 11, no. 1, pp. 192–196, 2004, doi: 10.3758/BF03206482.

M. C. Hill and C. R. Tiedeman, “Evaluating Model Fit,” in Effective Groundwater Model Calibration, Thousand Oaks, CA: SAGE Publications, 2007, pp. 93–123.

P. Wood, Confirmatory Factor Analysis for Applied Research, 2nd ed., vol. 62, no. 1. New York: The Guilford Press, 2008.

H. Latan and I. Ghozali, Structural Equation Modeling: Konsep dan Aplikasi Menggunakan Program SmartPLS 4.0 (Edisi 2). Bandung: Alfabeta, 2022.

H. W. Marsh, K. T. Hau, and Z. Wen, “In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings,” Struct. Equ. Model., vol. 11, no. 3, pp. 320–341, 2004, doi: 10.1207/s15328007sem1103_2.

I. Y. Alyoussef, “Acceptance of e-learning in higher education: The role of task-technology fit with the information systems success model,” Heliyon, vol. 9, no. 3, 2023, doi: 10.1016/j.heliyon.2023.e13751.

R. F. Chen and J. L. Hsiao, “An investigation on physicians’ acceptance of hospital information systems: A case study,” Int. J. Med. Inform., vol. 81, no. 12, pp. 810–820, 2012, doi: 10.1016/j.ijmedinf.2012.05.003.

T. Ahn, S. Ryu, and I. Han, “The impact of Web quality and playfulness on user acceptance of online retailing,” Inf. Manag., vol. 44, no. 3, pp. 263–275, 2007, doi: 10.1016/j.im.2006.12.008.

S. H. Liu, H. L. Liao, and J. A. Pratt, “Impact of media richness and flow on e-learning technology acceptance,” Comput. Educ., vol. 52, no. 3, pp. 599–607, 2009, doi: 10.1016/j.compedu.2008.11.002.

A. M. Sayaf, “Adoption of E-learning systems: An integration of ISSM and constructivism theories in higher education,” Heliyon, vol. 9, no. 2, 2023, doi: 10.1016/j.heliyon.2023.e13014.

D. Legramante, A. Azevedo, and J. M. Azevedo, “Integration of the technology acceptance model and the information systems success model in the analysis of Moodle’s satisfaction and continuity of use,” Int. J. Inf. Learn. Technol., vol. 40, no. 5, pp. 467–484, 2023, doi: 10.1108/IJILT-12-2022-0231.

H. Li and D. Li, “An empirical study of influencing factors of absorbing FDI in Henan,” 2011 Int. Conf. E-bus. E-Government, ICEE2011 - Proc., pp. 6730–6732, 2011, doi: 10.1109/ICEBEG.2011.5886785.