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Simon Petrus Wenehenubun
Johny Natu Prihanto


This study aims to analyze the influence of lecturers’ perceived behavioral control on the intention to use the online system in teaching and learning. The impact of Covid 19 requires every university to provide learning support facilities, including the Learning Management System (LMS). Lectures need to make use of LMS effectively in achieving learning objectives. Data were collected using a questionnaire with a google form. There were 162 lecturers from various private universities in Jabodetabek. For the data analysis, we used structural equation modeling (SEM) PLS 3.0. The results revealed that the lecturers felt confident in using the LMS provided by the university. Lecturers have behavioral beliefs, normative beliefs, and control beliefs in facilitating learning for students. They do believe that the LMS provided by the university is effective in the learning process and makes it easier for them to teach. However, there are still obstacles to operating the available LMS features. The practical implications derived from this research are that online learning can be applied in the post-COVID-19 era and can become an effective learning model in private universities with adequate LMS facilities. Further research should be conducted with a broader scope, a larger number of respondents, and specific details regarding lecturers' PBC based on their study programs.

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Akay, E. and Gumusoglu, E.K. (2020), The impact of learning management systems on students’ achievement in language exams, Turkish Online Journal of Distance Education-TOJDE, 21(4).

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.

Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior, Journal of Applied Social Psychology, 32, 4, pp. 665-683.

Bag, S., Aich, P., & Islam, M. A. (2022). Behavioral intention of “digital natives” toward adapting the online education system in higher education. Journal of Applied Research in Higher Education, 14(1), 16-40.

Barua, P. (2013). The moderating role of perceived behavioral control: The literature criticism and methodological considerations. International Journal of Business and Social Science, 4(10).

Calisir, F., Altin Gumussoy, C., & Bayram, A. (2009). Predicting the behavioral intention to use enterprise resource planning systems: An exploratory extension of the technology acceptance model. Management research news, 32(7), 597-613.

Cardullo, V., Wang, C. H., Burton, M., & Dong, J. (2021). K-12 teachers’ remote teaching self-efficacy during the pandemic. Journal of research in innovative teaching & learning, 14(1), 32-45.

Deslonde, V., & Becerra, M. (2018). The Technology Acceptance Model (TAM): Exploring School Counselors' Acceptance and Use of Naviance. Professional Counselor, 8(4), 369-382.

Dulkaman, N. S., & Ali, A. M. (2016). Factors influencing the success of learning management system (LMS) on students’ academic performance. International Young Scholars Journal of Languages, 1(1), 36-49.

Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2).

Gong, M., Xu, Y. & Yu, Y. (2004), And enhanced technology acceptance model for web based learning, Journal of Information Systems Education, 15(4).

Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M. (2017a). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.

Humida, T., Al Mamun, M. H., & Keikhosrokiani, P. (2022). Predicting behavioral intention to use e-learning system: A case-study in Begum Rokeya University, Rangpur, Bangladesh. Education and information technologies, 27(2), 2241-2265.

Khan, S. A., Zainuddin, M., Mahi, M. A., & Arif, I. (2020, December). Behavioral intention to use online learning during COVID-19: An analysis of the technology acceptance model. In International Conference on Innovative Methods of Teaching and Technological Advancements in Higher Education (IMTTAHE). Tbilisi, Georgia.

Kock, N. (2016). Hypothesis testing with confidence intervals and P values in PLS-SEM. International Journal of e-Collaboration (IJeC), 12(3), 1-6.

Kulviwat, S., C. Bruner II, G., & P. Neelankavil, J. (2014). Self-efficacy as an antecedent of cognition and affect in technology acceptance. Journal of Consumer Marketing, 31(3), 190-199.

Kurniasari, F., Prihanto, J. N., Andre, N. (2023). Identifying determinant factors influencing user’s behavioral intention to use Traveloka Paylater. Eastern-European Journal of Enterprise Technologies, 122(13), 52–61.

Liao, S., Hong, J. C., Wen, M. H., & Pan, Y. C. (2018). Applying technology acceptance model (TAM) to explore users’ behavioral intention to adopt a performance assessment system for E-book production. EURASIA Journal of Mathematics, Science and Technology Education, 14(10).

Ryadi, W., T., Kurniasari, F., & Sudiyono, K. A. (2021). Factors influencing consumer's intention towards e-grocery shopping: An extended technology acceptance model approach. Economics, Management and Sustainability, 6(2), 146-159.

Rohmawati, R. (2022), Efektivitas penggunaan media pembelajaran learning management system (LMS) sebagai media pembelajaran daring mahasiswa saat pandemi Covid 19, Sinau: Jurnal Ilmu Pendidikan dan Humaniora, 8(1), 29-35.

Sudaryati, E., Agustia, D. and Syahputra, M.I. (2017). The influence of perceived usefulness, perceived ease of use, attitude, subjectif norm, and perceived behavioral control to actual usage PSAK 45 Revision on 2011 with Intention as Intervening Variable in UNAIR Financial Departement, International Conference of Organizational Innovation (ICOI 2017), Advances in Intelligent Systems Research, 131. 86-92. Atlantis Press.

Turnbull. D., Chugh, R., and Luck, Jo. (2019). Learning management systems: An overview, Encyclopedia of Education and Information Technologies,

Vasquez, A. K., Foditsch, C., Dulièpre, S. A. C., Siler, J. D., Just, D. R., Warnick, L. D., ... & Sok, J. (2019). Understanding the effect of producers’ attitudes, perceived norms, and perceived behavioral control on intentions to use antimicrobials prudently on New York dairy farms. PloS one, 14(9),

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.

Yuzulia, I. (2021). The challenges of online learning during pandemic: Students’ voice. Wanastra: Jurnal Bahasa dan Sastra, 13(1), 08-12.