.png)
Structural Equation Modeling Factors That Influence Online Course Acceptance in University Students
Abstract
Online courses are currently growing rapidly, influenced by various factors, especially among college students. This study uses the Technology Acceptance Model, by adding the variable of facilitating conditions. This study aims to determine the effects of perceived ease of use, perceived usefulness, and facilitating conditions on the actual use of online courses, mediated by intention to use. This study is the first conducted on students in university in South Sumatra, Indonesia, who have used or are currently using online courses. The data collection was carried out from June 24, 2024, to August 9, 2024. The research instrument used was a questionnaire based on a 5-point Likert scale. The sampling technique used in this study was purposive sampling, targeting students at universities under the Higher Education Service Institution Region II in Indonesia. A total of 360 students participated as respondents. The questionnaire results were analyzed using Structural Equation Modeling – Partial Least Squares (SEM-PLS). Data analysis was conducted using the SmartPLS software, version 4.1.1.2. The results showed that perceived ease of use had a significant influence on intention to use (path coefficient = 0.281, T-statistic = 6.642, P-value = 0.000), and perceived usefulness also had a positive influence on intention to use (path coefficient = 0.155, T-statistic = 4.545, P-value = 0.000). Facilitating conditions also had a positive influence on intention to use (path coefficient = 0.476, T-statistic = 8.880, P-value = 0.000), and intention to use significantly influenced actual use (path coefficient = 0.452, T-statistic = 10.490, P-value = 0.000). These findings highlight the important role of perceived ease of use, perceived usefulness, and facilitating conditions—mediated by intention to use—in significantly influencing the actual use of online courses among students, particularly at universities under the jurisdiction of the Higher Education Service Institution Region II in Indonesia.
Keywords
Full Text:
PDFReferences
Z. Adeel, S. J. Smith, K. Brown, and S. Williams-habibi, “Student Engagement Tracks with Success In- Person and Online in a Hybrid-Flexible Course Student Engagement Tracks with Success In-Person and,” vol. 14, no. 2, 2023.
I. Bingöl, “Factors for Success and Course Completion in Massive Open Online Courses through the Lens of Participant Types,” vol. 12, no. 2, pp. 223–239, 2020.
Z. Robinson and T. Uys, “Determinants of graduate economics student preparation in an online environment Determinants of graduate economics student preparation in an online environment,” Cogent Educ., vol. 10, no. 1, 2023, doi: 10.1080/2331186X.2023.2179835.
J. Chung, S. Mckenzie, and A. Schweinsberg, “Correlates of Academic Performance in Online Higher Education : A Systematic Review,” vol. 7, no. February, 2022, doi: 10.3389/feduc.2022.820567.
S. J. Acebes, J. Melitante, N. Tuble, C. Mae, and D. Toquero, “Hopes , goals , hindrances , and solutions of students on forced digitalization of course learning amid pandemic,” vol. 4, no. 2, 2022.
I. Nedeljković, “Investigating critical factors influencing the acceptance of e-learning during,” vol. 27, no. 4, pp. 30–40, 2022, doi: 10.5937/StraMan2200019N.
I. Simm, U. Winklhofer, T. Naab, and A. N. Langmeyer, “How Children and Adolescents Perceive Their Coping With Home Learning in Times of COVID-19 : A Mixed Method Approach,” vol. 12, no. November, 2021, doi: 10.3389/fpsyg.2021.733428.
B. Duffy, R. Tully, and A. V Stanton, “An online case ‑ based teaching and assessment program on clinical history ‑ taking skills and reasoning using simulated patients in response to the COVID ‑ 19 pandemic,” BMC Med. Educ., pp. 1–11, 2023, doi: 10.1186/s12909-022-03950-2.
M. A. Ayaz, “A Lost Year ? Perspectives of foreign language students before and after the COVID-19 ＊,” no. April, 2023, doi: 10.14689/enad.34.1662.
D. John, P. Bazelais, T. Doleck, and J. A. College, “Computers in Human Behavior Reports Transition to online learning during the COVID-19 pandemic,” vol. 4, 2021.
D. E. Batmanlusy, M. A. Pendidikan-fkip-uksw, M. A. Pendidikan-fkip-uksw, and M. A. Pendidikan-fkip-uksw, “Pengembangan Modul Pelatihan Bagi Orang Tua Dalam Pendampingan Belajar Siswa Dari Rumah Pascapandemi Covid-19,” pp. 32–45, 2024.
M. Arteaga et al., “An online training and feedback module enhances the musculoskeletal examination performance of medical interns,” pp. 1–6, 2024.
M. Carvalho, G. H. Pedroso, A. B. Veber, Ú. Gomes, and R. Maruyama, “Learner Engagement and Demographic Influences in Brazilian Massive Open Online Courses : Aprenda Mais Platform Case Study,” pp. 178–193, 2024.
C. Cobanoglu, “A beginner ’ s guide and best practices for using crowdsourcing platforms for survey research : The case of Amazon Mechanical Turk ( MTurk ),” vol. 6, no. 1, pp. 92–97, 2021.
C. M. D. Hart et al., ““ I Don ’ t Think the System Will Ever be the Same ”: Distance Education Leaders ’ Predictions and Recommendations for the Use of Online Learning in Community Colleges Post-COVID “ I Don ’ t Think the System Will Ever be the Same ”: Distance Education Leaders ’ Predictions and Recommendations for,” 2025, doi: 10.1080/00221546.2024.2347810.
E. Kelly, J. Colella, and A. Sottosanti-kusnir, “Student Perceptions of Effective Educators in Online Learning,” vol. 28, no. 2024, pp. 372–397, doi: 10.24059/olj.v28i3.3534.
H. Madanat et al., “Heliyon Jordanian English language educators ’ perceived readiness for virtual learning environment,” Heliyon, vol. 10, no. 4, p. e25766, 2024, doi: 10.1016/j.heliyon.2024.e25766.
S. Tzimiris, S. Nikiforos, and K. Lida, Post ‑ pandemic pedagogy : Emergency remote teaching impact on students with functional diversity, vol. 28, no. 8. Springer US, 2023. doi: 10.1007/s10639-023-11582-2.
C. Wu, X. Gong, L. Luo, Q. Zhao, S. Hu, and Y. Mou, “Applying Control-Value Theory and Unified Theory of Acceptance and Use of Technology to Explore Pre-service Teachers ’ Academic Emotions and Learning Satisfaction,” vol. 12, no. March 2020, 2021, doi: 10.3389/fpsyg.2021.738959.
H. Lee, H. Chang, and L. Bryan, “Doctoral Students ’ Learning Success in Online-Based Leadership Programs : Intersection With Technological and Relational Factors International Review of Research in Open and Distributed Learning Doctoral Students ’ Learning Success in Online-Based Leadership Programs : Intersection With Technological and Relational Factors,” no. January 2020, 2024, doi: 10.19173/irrodl.v20i5.4462.
F. D. Davis and F. Davis, “Perceived Usefulness , Perceived Ease of Use , and User Acceptance of Information Technology,” no. September 1989, 2015, doi: 10.2307/249008.
W. Suh, “Development and Validation of a Scale Measuring Student Attitudes Toward Artificial Intelligence,” 2022, doi: 10.1177/21582440221100463.
Y. Tri et al., “Acta Psychologica Factors influencing the perceived usability of line pay : An extended technology acceptance model approach,” Acta Psychol. (Amst)., vol. 255, no. November 2024, p. 104924, 2025, doi: 10.1016/j.actpsy.2025.104924.
J. S. Kim, “An empirical comparison between China and USA market on smartphone adoption An empirical comparison between China and USA market on smartphone adoption,” Cogent Bus. Manag., vol. 9, no. 1, 2022, doi: 10.1080/23311975.2022.2036309.
M. H. Widianto, “Analysis of Pharmaceutical Company Websites using Innovation Diffusion Theory and Technology Acceptance Model,” vol. 6, no. 1, pp. 464–471, 2021.
A. Mahmood, M. Imran, and K. Adil, “Modeling Individual Beliefs to Transfigure Technology Readiness into Technology Acceptance in Financial Institutions,” no. March, pp. 1–19, 2023, doi: 10.1177/21582440221149718.
H. Outada, H. Belaouidel, A. Jaddar, A. Chetouani, and A. Dafali, “E-Learning Acceptance in the Post COVID-19 Period: A Case Study,” J. High. Educ. Theory Pract., vol. 23, no. 4, pp. 211–221, 2023, doi: 10.33423/jhetp.v23i4.5901.
R. Liao, C. Wang, F. Peng, W. E. I. Liang, and Y. Zhang, “DTM-Bearing : A Novel Framework for Speed-Invariant Bearing Fault Diagnosis Based on Diffusion Transformation Model ( DTM ),” IEEE Access, vol. PP, p. 1, 2024, doi: 10.1109/ACCESS.2024.3351935.
Y. Dai, X. Lin, and L. Li, “Technology acceptance of lms—do previous online learning experiences matter?,” J. Educ. Technol. Dev. Exch., vol. 14, no. 2, pp. 75–90, 2021, doi: 10.18785/jetde.1402.04.
L. D. Le and T. Giang, “Factors Affecting the Undergraduate Student ’ s Satisfaction in Short-Term Online Courses : A Case Study of Vietnamese Pedagogical Students,” no. January, 2024, doi: 10.13187/ejced.2023.1.105.
V. Arkorful, K. A. Barfi, N. O. Baffour, V. Arkorful, K. A. Barfi, and N. O. Baffour, “Factors affecting use of massive open online courses by Ghanaian students Factors affecting use of massive open online courses by Ghanaian students,” Cogent Educ., vol. 9, no. 1, 2022, doi: 10.1080/2331186X.2021.2023281.
M. A. Almulla, “Using Digital Technologies for Testing Online Teaching Skills and Competencies during the COVID-19 Pandemic,” Sustain., vol. 14, no. 9, 2022, doi: 10.3390/su14095455.
Y. H. Woon, “Students’ Perception about Learning using MOOC,” Int. J. Emerg. Technol. Learn., vol. 14, no. 18, pp. 203–208, 2019, doi: 10.3991/ijet.v14i18.10802.
Y. Li and M. Zhao, “A Study on the Influencing Factors of Continued Intention to Use MOOCs: UTAUT Model and CCC Moderating Effect,” Front. Psychol., vol. 12, 2021, doi: 10.3389/fpsyg.2021.528259.
A. E. Study, “Examining Higher Education Students ’ Intention of Adopting MOOCs : An Empirical Study,” vol. 18, no. 1, pp. 1–18, doi: 10.4018/IJTHI.299358.
H. Warsono, T. Yuwono, and I. Riswanti, “International Journal of Data and Network Science Analyzing technology acceptance model for collaborative governance in public administration : Empirical evidence of digital governance and perceived ease of use,” vol. 7, pp. 41–48, 2023, doi: 10.5267/j.ijdns.2022.12.008.
P. Kim, T. Minh, C. Minh, and C. Open, “Social Capital , Knowledge Sharing and Financial Performance,” vol. 14, no. 1, pp. 984–997, 2020.
J. Ali et al., “IoT Adoption Model for E-Learning in Higher Education Institutes : A Case Study in Saudi Arabia,” pp. 1–19, 2023.
A. Aminu, A. Fauzi, M. Ayub, W. Marzuki, and W. Jaafar, “Influence of Studentsr Perceived Ease of Use , Perceived Usefulness and Time Spent Towards Studentsr Continuance Intention Using MOOC Among Public University Students Influence of Students ’ Perceived Ease of Use , Perceived U sefulness and Time Spent Towards Student s ’ Continuance I ntention Using MOOC Among Public University Students,” no. January, 2018, doi: 10.2991/icems-17.2018.50.
C. Huang, “Exploring the Continuous Usage Intention of Online Learning Platforms from the Perspective of Social Capital,” 2021.
V. Venkatesh, “C ONSUMER A CCEPTANCE AND U SE OF I NFORMATION T ECHNOLOGY : E XTENDING THE U NIFIED T HEORY,” vol. 36, no. 1, pp. 157–178, 2012.
S. Chaveesuk and W. Chaiyasoonthorn, “Moocs adoption in higher education : A management perspective MOOCs ADOPTION IN HIGHER EDUCATION : A MANAGEMENT,” no. June, 2021, doi: 10.17512/pjms.2021.23.1.15.
S. G. Universities, “education sciences Factors Affecting MOOC Usage by Students in Selected Ghanaian Universities,” 2018, doi: 10.3390/educsci8020070.
A. Aldraiweesh, “Exploring Factors Influencing the Acceptance of E-Learning and Students ’ Cooperation Skills in Higher Education,” 2023.
Z. Osman and M. Yatam, “Fostering Continuous Intention to Use E-learning Platforms among Students of Open Online Flexible Distance Learning Higher Education Institutions,” no. July, 2024, doi: 10.6007/IJARBSS/v14-i6/21646.
H. Waqas and M. Awais-e-yazdan, “The effect of technostress on the teachers ’ willingness to use online teaching modes and the moderating role of job insecurity during COVID-19 pandemic in Pakistan,” no. December, 2021, doi: 10.1108/IJEM-07-2021-0291.
H. Ames, C. Glenton, and S. Lewin, “Purposive sampling in a qualitative evidence synthesis : a worked example from a synthesis on parental perceptions of vaccination communication,” vol. 4, pp. 1–9, 2019.
A. Wutich, M. Beresford, and H. R. Bernard, “Sample Sizes for 10 Types of Qualitative Data Analysis : An Integrative Review , Empirical Guidance , and Next Steps,” vol. 23, pp. 1–14, 2024, doi: 10.1177/16094069241296206.
C. Pedrosa-jesu, C. Leo, and A. Maz-machado, “Measuring attitude towards mathematics using Likert scale surveys : The weighted average,” pp. 1–15, 2020, doi: 10.1371/journal.pone.0239626.
J. F. Hair, C. M. Ringle, and M. Sarstedt, “A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM),” no. September 2021, 2022.
I. Muda, “In fl uence of Manufacture of Textiles , Clothing , and Leather and Manufacture of Paper , Printing , and Publishing on Economic Growth,” vol. 1, pp. 107–113, doi: 10.1108/978-1-78756-793-1-00048.
E. Hartono, C. W. Holsapple, K.-Y. Kim, K.-S. Na, and J. T. Simpson, “Measuring perceived security in B2C electronic commerce website usage: A respecification and validation,” Decis. Support Syst., vol. 62, pp. 11–21, 2014, doi: https://doi.org/10.1016/j.dss.2014.02.006.
DOI: https://doi.org/10.47738/jads.v6i4.863
Refbacks
- There are currently no refbacks.
Journal of Applied Data Sciences
| ISSN | : | 2723-6471 (Online) |
| Publisher | : | Bright Publisher |
| Website | : | http://bright-journal.org/JADS |
| : | taqwa@amikompurwokerto.ac.id (principal contact) | |
| support@bright-journal.org (technical issues) |
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0