Published at : 25 Jan 2021
Volume : IJtech Vol 12, No 1 (2021)
DOI : https://doi.org/10.14716/ijtech.v12i1.4281
|Muhamad Asvial||Graduate Program in Telecommunications Management, Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
|Jihar Mayangsari||Graduate Program in Telecommunications Management, Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
|Alvin Yudistriansyah||Sigma Cipta Caraka, Jakarta Selatan 12710, Indonesia|
Behavior intention; Covid-19; E-learning; UTAUT; SEM
According to Zahedi and Dehghan (2019), e-learning is considered as using electronic technology to access educational programs via internet. With the use of technology and automation will play a significant role in increasing productivity (Berawi, 2020). These days, distance is no longer a barrier to communication. Sending a mail, making a call, and text messaging can be done via the Internet, and even video calls can be done in real time. With technology, many people have meetings with other people in different places through video conferences. One of the developments in the use of video conferencing is e-learning or distance learning.
There are many benefits of e-learning for students and teachers. e-Learning can help reduce education costs and can effectively be carried out in an effective time and without geographical boundaries (Cheng, 2011; Chen, 2011). Among many others, an advantage of e-learning is the ease of accessing learning material anywhere by connecting to the internet (Ong et al., 2004; Al-Rahmi et al., 2018). Some studies found that despite its flexibility, ease, and cost-effectiveness, there are still many problems encountered in e-learning learning. Researchers have found that the problem with e-learning is the limited ability of students’ perceptions of e-learning systems (Almaiah and Mulhem, 2018). Some problems can only be solved if students and students can use this e-learning system properly (Pituch and Lee, 2006; Chaka and Govender, 2017). Students' perceptions regarding the motivation to use e-learning due to the success of the learning process with e-learning is assisted by students’ acceptance of its actual use (Salloum et al., 2019). Further, the success of e-learning is seen by increasing students’ competencies, abilities, knowledge, user satisfaction, and behavioral intentions (Al-Qahtani et al., 2013; Mohammadi, 2015; Al-Rahmi et al., 2018). Based on studies (Dachyar et al., 2015) on the development of an organizational model strategy through information systems in higher education, it shows a lack of adequate human resources in technology implementation.
The quality of education in Indonesia is still low and is below that of neighboring countries. Based on research conducted by the Organization for Economic Co-operation and Development, Program for International Student Assessment, Indonesia ranked 72 out of 77 countries and scored 371 in the reading, 379 in mathematics and 396 in mathematics science. Therefore, we assumed that e-learning could be difficult to accomplish by most middle-school students in Indonesia from the student’s competencies and knowledge point of view.
The spread of COVID-19 caused a change in activities in the world. Schools that previously used the offline method with a face-to-face system were forced to offer instruction from home through the Internet. Thus, all students in Indonesia must have supporting facilities to connect to the Internet for e-learning. The e-learning system during COVID-19 in Indonesia is called Pembelajaran Jarak Jauh (PJJ). Based on the results of research by the Indonesian Child Protection Commission (KPAI), PJJ in Indonesia currently creates an education gap between the able and disadvantaged groups. This is because the ability to buy Internet credit, computers, or smartphones that are suitable for distance learning is inadequate, added to the high cost of Internet access, electricity, and other supporting facilities that are not in accordance with the income of parents during the COVID-19 pandemic. In the KPAI study involving 246 main respondents, 1,700 comparative students, and 602 teachers, 73.2% of the teachers only gave assignments without interacting with students, which was not effective, and the students reported not liking the distance learning. Based on the results of research by KPAI, the application of e-learning in middle schools in Indonesia is not simple.
research on the behavioral intentions of students in e-learning systems in
secondary schools is needed. This study was performed in the Greater Jakarta
area. Given the high Internet costs in Indonesia and the cost of living in
Jakarta and Tangerang, a cost variable was added to the study. In particular,
Greater Jakarta was faced with many people affected by layoffs due to the
COVID-19 pandemic. Therefore, this study aimed to assess behavior intention of
e-learning at junior high schools in Jakarta and Tangerang, Indonesia due to
the impact of COVID-19.
From the results of the hypothesis test with t-value, students at the junior high school level in Jakarta and Tangerang participated in distance learning or e-learning due to COVID-19 because of parental encouragement and government regulations. They were not actually interested in learning through e-learning. Thus, we suggest that the government of Indonesia improves the digital literacy of middle schoolers, includes the ability to learn new technology easily, motivated to learn with information and communication technology, and willingness to use information and communication technology at work (Santoso et al., 2019) by minimizing the digital gap, improving teachers’ quality, and providing supportive facilities prior to establishing policies that mandate e-learning as a standard for instruction delivery.
One of the limitations of our research is the geographical distribution of the subjects. All the respondents were from Jakarta and Tangerang. Therefore, future investigations should examine a larger number of respondents from across Indonesia, especially in rural areas. Another limitation of this work is that the respondents were surveyed between June and August 2020, a period during which there was no government subsidy on internet fees. Further research on the condition of the behavioral intention of e-learning in junior high schools that includes the entire period of the impact of COVID-19 is neededs.
Al-Qahtani, M., Al-Qahtani, M., Al-Misehal, H., 2013. Learner Satisfaction of E-Learning in Workplace: Case of Oil Company in Middle East. In: The 10th International Conference on Information Technology: New Generations, pp. 294–298
Al-Rahmi, W.M., Alias, N.B., Othman, M.Z., Alzahrani, A.I., Alfarraj, O., Saged, A.A.G., Rahman, N.S.A., 2018. Use of E-Learning by University Students in Malaysian Higher Educational Institutions: A Case in Universiti Teknologi Malaysia. IEEE Access, Volume 6, pp. 14268–14276
Almaiah, D.R.M.A., Mulhem, D.R.A.A.L., 2018. A Conceptual Framework for Determining the Success Factors of e-Learning System Implementation using Delphi Technique.
Alshehri, A., Rutter, M., Smith, S., 2019. An Implementation of the UTAUT Model for Understanding Students’ Perceptions of Learning Management Systems: A Study within Tertiary Institutions in Saudi Arabia. International Journal of Distance Education Technologies, Volume 17(3), pp. 1–24
Babie, S., ?i?in-Šain, M., Bubaš, G., 2016. A Study of Factors Influencing Higher Education Teachers’ Intention to use E-Learning in Hybrid Environments. In: The 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 998–1003
Berawi, M.A., 2020. Empowering Healthcare, Economic, and Social Resilience during Global Pandemic Covid-19. International Journal of Technology, Volume 11(3), pp. 436–439
Chaka, J.G., Govender, I., 2017. Students’ Perceptions and Readiness Towards Mobile Learning in Colleges of Education: A Nigerian Perspective. South African Journal of Education, Volume 37, pp. 1–12
Chang, A., 2012. UTAUT and UTAUT 2: A Review and Agenda for Future Research. The Winners, Volume 13(2), pp. 106–114
Chen, J.-L., 2011. The Effects of Education Compatibility and Technological Expectancy on e-Learning Acceptance. Computers & Education, Volume 57(2), pp. 1501–1511
Cheng, Y.-M., 2011. Antecedents and Consequences of e-learning Acceptance. Information Systems Journal, Volume 21(3), pp. 269–299
Dachyar, M., Yadrifil, Pratama, N.R., 2015. Development of Strategy Model for Organizational. International Journal of Technology, Volume 6(2), pp. 284–290
Hasani, L.M., Adnan, H.R., Sensuse, D.I., Kautsarina, Suryono, R.R., 2020. Factors Affecting Student’s Perceived Readiness on Abrupt Distance Learning Adoption: Indonesian Higher-Education Perspectives. In: The 3rd International Conference on Computer and Informatics Engineering (IC2IE), pp. 286–292
Kanwal, F., Rehman, M., 2017. Factors Affecting E-Learning Adoption in Developing Countries–Empirical Evidence From Pakistan’s Higher Education Sector. IEEE Access, Volume 5, pp. 10968–10978
Mahande, R.D., Malago, J.D., 2019. An e-Learning Acceptance Evaluation through UTAUT Model in a Postgraduate Program. Journal of Educators Online, Volume 16(2), pp. 1–10
Min, Q., Ji, S., Qu, G., 2008. Mobile Commerce User Acceptance Study in China: A Revised UTAUT Model. Tsinghua Science and Technology, Volume 13(3), pp. 257–264
Mohammadi, H., 2015. Investigating users’ Perspectives on e-Learning: An Integration of TAM and IS Success Model. Computers in Human Behavior, Volume 45, pp. 359–374
Ngampornchai, A., Adams, J., 2016. Students’ Acceptance and Readiness for E-learning in Northeastern Thailand. International Journal of Educational Technology in Higher Education, Volume 13(34), pp. 1–13
Olasina, G., 2019. Human and Social Factors Affecting the Decision of Students to Accept e-Learning. Interactive Learning Environments, Volume 27(3), pp. 363–376
Ong, C-S., Lai, J-Y., Wang, Y-S.., 2004. Factors Affecting Engineers’ Acceptance of Asynchronous e-Learning Systems in High-tech Companies. Information & Management., Volume 41(6), pp. 795–804
Pituch, K.A., Lee, Y., 2006. The Influence of System Characteristics on E-Learning use. Computers & Education, Volume 47(2), pp. 222–244
Ramllah, Nurkhin, A., 2020. Analysis of Factors Affecting Behavioral Intention to Use E-Learning Uses the Unified Theory of Acceptance and Use of Technology Approach. KnE Social Sciences, Volume 2020, pp. 1005–1025
Salloum, S.A., Al-Emran, M., Shaalan, K., Tarhini, A., 2019. Factors Affecting the E-learning Acceptance: A Case Study from UAE. Education and Information Technologies, Volume 24(1), pp. 509–530
Samaradiwakara, G.D.M., Gunawardena, C.G., 2014. Comparison of Existing Technology Acceptance Theories and Models to Suggest a Well Improved Theory/Model. International Technical Sciences Journal, Volume 1(1), pp. 21–36
Santoso, H., Abdinagoro, S.B., Arief, M., 2019. The Role of Digital Literacy in Supporting Performance through Innovative Work Behavior: The Case of Indonesia’s Telecommunications Industry. International Journal of Technology, Volume 10(8), pp. 1558–1566
Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, Volume 27(3), pp. 425–478
Venkatesh, V., Thong, J.Y.L., Xu, X., 2012. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, Volume 36(1), pp. 157–178
Zahedi, M.H., Dehghan, Z., 2019. Effective E-learning utilizing Internet of Things. In: The 7th International and 13th Iranian Conference on E-Learning and E-Teaching, ICeLeT 2019, pp. 14–19