FACTORS INFLUENCING STUDENTS' INTENTION TO ADOPT E-LEARNING WITH EXTENDED UTAUT
DOI:
https://doi.org/10.31539/costing.v7i6.13747Keywords:
UTAUT, E-learning, Student Intention, Learning Convenience.Abstract
This study aims to analyze the factors that influence students' intention to adopt e-learning in Surabaya, using a modified theoretical model of the Unified Theory of Acceptance and Use of Technology (UTAUT). Through a quantitative approach, data were collected from 489 respondents who had used e-learning, using a questionnaire distributed online. The results showed that Performance Expectancy, Effort Expectancy, and Learning Convenience had a significant influence on students' Behavioral Intention to use e-learning. Social Influence and Facilitating Conditions were proven to have no influence on Behavioral Intention. In addition, Educational Level acts as a moderating variable that strengthens the relationship between Learning Convenience and Behavioral Intention, with a stronger effect on postgraduate students. This study provides theoretical contributions by enriching the UTAUT study through the addition of new factors, as well as practical contributions for e-learning developers and educators in designing more effective and user-friendly platforms. These findings are expected to provide broader insights into the acceptance of e-learning across educational levels and geographic contexts.
References
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Abdou, D., & Jasimuddin, S. M. (2020). The use of the UTAUT model in the adoption of e-learning technologies: An empirical study in France based banks. Journal of Global Information Management (JGIM), 28(4), 38-51.
Acharjya, B., & Das, S. (2022). Adoption of e-Learning during the COVID-19 pandemic: The moderating role of age and gender. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 17(2), 1-14.
Alrawi, M. A. S., Samy, G. N., Yusoff, R. C. M., Shanmugam, B., Lakshmiganthan, R., Maarop, N., & Kamaruddin, N. (2020). Examining factors that effect on the acceptance of mobile commerce in Malaysia based on revised UTAUT. Indonesian Journal of Electrical Engineering and Computer Science, 20(3), 1173-1184.
Ardi, N., & Isnayanti. (2020). Structural Equation Modelling-Partial Least Square to Determine the Correlation of Factors Affecting Poverty in Indonesian Provinces. IOP Conference Series: Materials Science and Engineering, 846(1), 0–13. https://doi.org/10.1088/1757-899X/846/1/012054
Aurora, A., & Effendi, H. (2019). Pengaruh penggunaan media pembelajaran e-learning terhadap motivasi belajar mahasiswa di Universitas Negeri Padang. JTEV (Jurnal Teknik Elektro Dan Vokasional), 5(2), 11-16.
Aziz, F. A. I. Q., Safiai, A. Z. I. Z. I., Wahat, N. A., Hamzah, S. R. A., Ahrari, S. E. Y. E. D. A. L. I., & Mahadi, N. O. M. A. H. A. Z. A. (2023). Academics’ behavioral intention and usage of IoT in e-learning: moderation of gender and experience. Journal of Theoretical and Applied Information Technology, 101(4), 1376-186.
Baso, F., Safanah, N. A. A., Al Faruq, A. F., Ansya, A., & AM, A. M. A. (2023). Evaluasi Sistem Pembelajaran Blended Learning Berbasis Model UTAUT di Jurusan Teknik Informatika dan Komputer, Universitas Negeri Makassar. Jurnal Pendidikan Terapan, 36-45.
Chen, Y. H., & Keng, C. J. (2019). Utilizing the Push-Pull-Mooring-Habit framework to explore users’ intention to switch from offline to online real-person English learning platform. Internet Research, 29(1), 167-193.
Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Fort Worth: Harcourt Brace Jovanovich College Publishers.
Cross, Jay. (2004). An informal history of eLearning. on The Horizon. 12. 103-110. 10.1108/10748120410555340.
Cureton, E.E., & D'Agostino, R.B. (1983). Factor analysis: An applied approach. Hillsdale, NJ: Erlbaum.
Dianaris, A. A., Pramana, E., & Budianto, H. (2022). Faktor-Faktor yang Mempengaruhi Adopsi E-learning untuk Siswa SMA di Indonesia dengan Menggunakan Extended Technology Acceptance Model. Journal of Information System, Graphics, Hospitality and Technology, 4(01), 13-26.
Dorthy, H., & Sinaga, E. (2020). The Effect of Using E-Learning to Students’ Interest In Learning Statistic Subject. International Conference on Social, Sciences and Information Technolog, 4509
Ebner, C., & Gegenfurtner, A. (2019). Learning and satisfaction in webinar, online, and face-to-face instruction: a meta-analysis. In Frontiersin Education, 4(92). https://doi.org/10.3389/feduc.2019.00092.
Ermilinda, L., Handarkho, Y. D., & Emanuel, A. W. R. (2024). Factors Influencing Student Intention to continue using E-learning Platform Post Covid-19 Pandemic: Case Study of University of Nusa Nipa Indonesia. Procedia Computer Science, 234, 1043-1052.
Ernawati, Ernawati. (2023). Analisis Pembelajaran Daring Mata Kuliah Praktek dengan Uji T Berpasangan dan Matriks Ipa. Syntax Literate; Jurnal Ilmiah Indonesia. 8. 5420-5431. 10.36418/syntax-Literate.v8i10.13702.
Farliana, N., Setiaji, K., Rusdarti, R., & Hardianto, H. (2023). Behavioral Switching Model To Hybrid Learning Based on Push Pull Mooring Framework. Journal of Education Research and Evaluation, 7(1).
Ghozali, I. (2016). Aplikasi Analisis Multivariate dengan Program SPSS Semarang: Badan Penerbit Universitas Diponegoro.
Hartley, Darin E. (2001). Selling E-Learning, American Society for Training and Development
Hunde, M. K., Demsash, A. W., & Walle, A. D. (2023). Behavioral intention to use e-learning and its associated factors among health science students in Mettu university, southwest Ethiopia: Using modified UTAUT model. Informatics in Medicine Unlocked, 36, 101154.
Hurley, Peter, and Nina Van Dyke. (2020). Australian Investment in Education: Higher Education. Melbourne: Mitchell Institute.
Israel, G. D. (1992). Determining sample size.
Jameel, A. S., Karem, M. A., & Ahmad, A. R. (2022). Behavioral intention to use e-learning among academic staff during COVID-19 pandemic based on UTAUT model. In Proceedings of International Conference on Emerging Technologies and Intelligent Systems: ICETIS 2021 (Volume 1) (pp. 187-196). Springer International Publishing.
Khan, B. H. (2005). Managing E-Learning: Design, Delivery, Implementation, and Evaluation. Information Science Publishing.
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Lai, C. Y., Cheung, K. Y., Chan, C. S., & Law, K. K. (2024). Integrating the adapted UTAUT model with moral obligation, trust and perceived risk to predict ChatGPT adoption for assessment support: A survey with students. Computers and Education: Artificial Intelligence, 6, 100246.
Li, C., He, L., & Wong, I. A. (2021). Determinants predicting undergraduates’ intention to adopt e-learning for studying english in chinese higher education context: A structural equation modelling approach. Education and Information Technologies, 26, 4221-4239.
Liao, Y. W., Huang, Y. M., Huang, S. H., Chen, H. C., & Wei, C. W. (2019). Exploring the switching intention of learners on social network-based learning platforms: a perspective of the push–pull–mooring model. Eurasia Journal of Mathematics, Science and Technology Education, 15(9), em1747
Lin, C. L., Jin, Y. Q., Zhao, Q., Yu, S. W., & Su, Y. S. (2021). Factors influence students’ switching behavior to online learning under COVID-19 pandemic: A push–pull–mooring model perspective. The Asia-Pacific Education Researcher, 30, 229-245.
Lisana, L. (2023). Factors affecting university students switching intention to mobile learning: A push-pull-mooring theory perspective. Education and Information Technologies, 28(5), 5341-5361.
Mallery, P., & George, D. (2000). SPSS for windows step by step. Allyn & Bacon, Inc..
Martínez-Cerdá, J. F., Torrent-Sellens, J., & González-González, I. (2020). Socio-technical e-learning innovation and ways of learning in the ICT-space-time continuum to improve the employability skills of adults. Computers in Human Behavior, 107, 105753.
Nabity-Grover, T., Cheung, C. M., & Thatcher, J. B. (2020). Inside out and outside in: How the COVID-19 pandemic affects self-disclosure on social media. International Journal of Information Management, 55, 102188.
Qiao, P., Zhu, X., Guo, Y., Sun, Y., & Qin, C. (2021). The development and adoption of online learning in pre-and post-COVID-19: Combination of technological system evolution theory and unified theory of acceptance and use of technology. Journal of Risk and Financial Management, 14(4), 162.
Ramadhan, A., Hidayanto, A. N., Salsabila, G. A., Wulandari, I., Jaury, J. A., & Anjani, N. N. (2022). The effect of usability on the intention to use the e-learning system in a sustainable way: A case study at Universitas Indonesia. Education and Information Technologies, 1-34.
Rawat, B., & Dwivedi, S. K. (2019). Discovering Learners’ characteristics through cluster analysis for recommendation of courses in E-learning environment. International Journal of Information and Communication Technology Education (IJICTE), 15(1), 42–66.
Santoso, S. (2012). Analisis SPSS pada statistik parametrik. Jakarta: PT. Elex Media Komputindo
Straub, D., Boudreau, M. C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information systems, 13(1), 24.
Sudiksa, I., Divayana, D., & Warpala, I. (2020). Pengaruh E-Learning Dan Lingkungan Kampus Terhadap Minat Belajar Mahasiswa Dengan Motivasi Belajar Sebagai Variabel Mediasi. Jurnal Teknologi Pembelajaran Indonesia, 10(2), 86–97.
Sujiwo, D. A. C., & A'yun, Q. (2020). Pengaruh Pemanfaatan E-learning Terhadap Motivasi Belajar Mahasiswa. JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia), 5(2), 53-59
Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2013). Using multivariate statistics (Vol. 6, pp. 497-516). Boston, MA: pearson.
Venkatesh, Morris, Davis, Davis, (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27, 425. https://doi.org/10.2307/30036540
Xu, H., Wang, J., Tai, Z., & Lin, H. C. (2021). Empirical study on the factors affecting user switching behavior of online learning platform based on push-pull-mooring theory. Sustainability, 13(13), 7087.
Yakubu, M. N., & Dasuki, S. I. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492-502.
Abdou, D., & Jasimuddin, S. M. (2020). The use of the UTAUT model in the adoption of e-learning technologies: An empirical study in France based banks. Journal of Global Information Management (JGIM), 28(4), 38-51.
Acharjya, B., & Das, S. (2022). Adoption of e-Learning during the COVID-19 pandemic: The moderating role of age and gender. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 17(2), 1-14.
Alrawi, M. A. S., Samy, G. N., Yusoff, R. C. M., Shanmugam, B., Lakshmiganthan, R., Maarop, N., & Kamaruddin, N. (2020). Examining factors that effect on the acceptance of mobile commerce in Malaysia based on revised UTAUT. Indonesian Journal of Electrical Engineering and Computer Science, 20(3), 1173-1184.
Ardi, N., & Isnayanti. (2020). Structural Equation Modelling-Partial Least Square to Determine the Correlation of Factors Affecting Poverty in Indonesian Provinces. IOP Conference Series: Materials Science and Engineering, 846(1), 0–13. https://doi.org/10.1088/1757-899X/846/1/012054
Aurora, A., & Effendi, H. (2019). Pengaruh penggunaan media pembelajaran e-learning terhadap motivasi belajar mahasiswa di Universitas Negeri Padang. JTEV (Jurnal Teknik Elektro Dan Vokasional), 5(2), 11-16.
Aziz, F. A. I. Q., Safiai, A. Z. I. Z. I., Wahat, N. A., Hamzah, S. R. A., Ahrari, S. E. Y. E. D. A. L. I., & Mahadi, N. O. M. A. H. A. Z. A. (2023). Academics’ behavioral intention and usage of IoT in e-learning: moderation of gender and experience. Journal of Theoretical and Applied Information Technology, 101(4), 1376-186.
Baso, F., Safanah, N. A. A., Al Faruq, A. F., Ansya, A., & AM, A. M. A. (2023). Evaluasi Sistem Pembelajaran Blended Learning Berbasis Model UTAUT di Jurusan Teknik Informatika dan Komputer, Universitas Negeri Makassar. Jurnal Pendidikan Terapan, 36-45.
Chen, Y. H., & Keng, C. J. (2019). Utilizing the Push-Pull-Mooring-Habit framework to explore users’ intention to switch from offline to online real-person English learning platform. Internet Research, 29(1), 167-193.
Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Fort Worth: Harcourt Brace Jovanovich College Publishers.
Cross, Jay. (2004). An informal history of eLearning. on The Horizon. 12. 103-110. 10.1108/10748120410555340.
Cureton, E.E., & D'Agostino, R.B. (1983). Factor analysis: An applied approach. Hillsdale, NJ: Erlbaum.
Dianaris, A. A., Pramana, E., & Budianto, H. (2022). Faktor-Faktor yang Mempengaruhi Adopsi E-learning untuk Siswa SMA di Indonesia dengan Menggunakan Extended Technology Acceptance Model. Journal of Information System, Graphics, Hospitality and Technology, 4(01), 13-26.
Dorthy, H., & Sinaga, E. (2020). The Effect of Using E-Learning to Students’ Interest In Learning Statistic Subject. International Conference on Social, Sciences and Information Technolog, 4509
Ebner, C., & Gegenfurtner, A. (2019). Learning and satisfaction in webinar, online, and face-to-face instruction: a meta-analysis. In Frontiersin Education, 4(92). https://doi.org/10.3389/feduc.2019.00092.
Ermilinda, L., Handarkho, Y. D., & Emanuel, A. W. R. (2024). Factors Influencing Student Intention to continue using E-learning Platform Post Covid-19 Pandemic: Case Study of University of Nusa Nipa Indonesia. Procedia Computer Science, 234, 1043-1052.
Ernawati, Ernawati. (2023). Analisis Pembelajaran Daring Mata Kuliah Praktek dengan Uji T Berpasangan dan Matriks Ipa. Syntax Literate; Jurnal Ilmiah Indonesia. 8. 5420-5431. 10.36418/syntax-Literate.v8i10.13702.
Farliana, N., Setiaji, K., Rusdarti, R., & Hardianto, H. (2023). Behavioral Switching Model To Hybrid Learning Based on Push Pull Mooring Framework. Journal of Education Research and Evaluation, 7(1).
Ghozali, I. (2016). Aplikasi Analisis Multivariate dengan Program SPSS Semarang: Badan Penerbit Universitas Diponegoro.
Hartley, Darin E. (2001). Selling E-Learning, American Society for Training and Development
Hunde, M. K., Demsash, A. W., & Walle, A. D. (2023). Behavioral intention to use e-learning and its associated factors among health science students in Mettu university, southwest Ethiopia: Using modified UTAUT model. Informatics in Medicine Unlocked, 36, 101154.
Hurley, Peter, and Nina Van Dyke. (2020). Australian Investment in Education: Higher Education. Melbourne: Mitchell Institute.
Israel, G. D. (1992). Determining sample size.
Jameel, A. S., Karem, M. A., & Ahmad, A. R. (2022). Behavioral intention to use e-learning among academic staff during COVID-19 pandemic based on UTAUT model. In Proceedings of International Conference on Emerging Technologies and Intelligent Systems: ICETIS 2021 (Volume 1) (pp. 187-196). Springer International Publishing.
Khan, B. H. (2005). Managing E-Learning: Design, Delivery, Implementation, and Evaluation. Information Science Publishing.
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Lai, C. Y., Cheung, K. Y., Chan, C. S., & Law, K. K. (2024). Integrating the adapted UTAUT model with moral obligation, trust and perceived risk to predict ChatGPT adoption for assessment support: A survey with students. Computers and Education: Artificial Intelligence, 6, 100246.
Li, C., He, L., & Wong, I. A. (2021). Determinants predicting undergraduates’ intention to adopt e-learning for studying english in chinese higher education context: A structural equation modelling approach. Education and Information Technologies, 26, 4221-4239.
Liao, Y. W., Huang, Y. M., Huang, S. H., Chen, H. C., & Wei, C. W. (2019). Exploring the switching intention of learners on social network-based learning platforms: a perspective of the push–pull–mooring model. Eurasia Journal of Mathematics, Science and Technology Education, 15(9), em1747
Lin, C. L., Jin, Y. Q., Zhao, Q., Yu, S. W., & Su, Y. S. (2021). Factors influence students’ switching behavior to online learning under COVID-19 pandemic: A push–pull–mooring model perspective. The Asia-Pacific Education Researcher, 30, 229-245.
Lisana, L. (2023). Factors affecting university students switching intention to mobile learning: A push-pull-mooring theory perspective. Education and Information Technologies, 28(5), 5341-5361.
Mallery, P., & George, D. (2000). SPSS for windows step by step. Allyn & Bacon, Inc..
Martínez-Cerdá, J. F., Torrent-Sellens, J., & González-González, I. (2020). Socio-technical e-learning innovation and ways of learning in the ICT-space-time continuum to improve the employability skills of adults. Computers in Human Behavior, 107, 105753.
Nabity-Grover, T., Cheung, C. M., & Thatcher, J. B. (2020). Inside out and outside in: How the COVID-19 pandemic affects self-disclosure on social media. International Journal of Information Management, 55, 102188.
Qiao, P., Zhu, X., Guo, Y., Sun, Y., & Qin, C. (2021). The development and adoption of online learning in pre-and post-COVID-19: Combination of technological system evolution theory and unified theory of acceptance and use of technology. Journal of Risk and Financial Management, 14(4), 162.
Ramadhan, A., Hidayanto, A. N., Salsabila, G. A., Wulandari, I., Jaury, J. A., & Anjani, N. N. (2022). The effect of usability on the intention to use the e-learning system in a sustainable way: A case study at Universitas Indonesia. Education and Information Technologies, 1-34.
Rawat, B., & Dwivedi, S. K. (2019). Discovering Learners’ characteristics through cluster analysis for recommendation of courses in E-learning environment. International Journal of Information and Communication Technology Education (IJICTE), 15(1), 42–66.
Santoso, S. (2012). Analisis SPSS pada statistik parametrik. Jakarta: PT. Elex Media Komputindo
Straub, D., Boudreau, M. C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information systems, 13(1), 24.
Sudiksa, I., Divayana, D., & Warpala, I. (2020). Pengaruh E-Learning Dan Lingkungan Kampus Terhadap Minat Belajar Mahasiswa Dengan Motivasi Belajar Sebagai Variabel Mediasi. Jurnal Teknologi Pembelajaran Indonesia, 10(2), 86–97.
Sujiwo, D. A. C., & A'yun, Q. (2020). Pengaruh Pemanfaatan E-learning Terhadap Motivasi Belajar Mahasiswa. JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia), 5(2), 53-59
Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2013). Using multivariate statistics (Vol. 6, pp. 497-516). Boston, MA: pearson.
Venkatesh, Morris, Davis, Davis, (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27, 425. https://doi.org/10.2307/30036540
Xu, H., Wang, J., Tai, Z., & Lin, H. C. (2021). Empirical study on the factors affecting user switching behavior of online learning platform based on push-pull-mooring theory. Sustainability, 13(13), 7087.
Yakubu, M. N., & Dasuki, S. I. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492-502.
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2024-12-23
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