Technostress and Continuance Intention of Online Learning in Higher Education: Evidence from Indonesia




College student, Continuance intention, Online learning, Technostress


As a developing country, Indonesia faces many obstacles in implementing online learning due to the lack of infrastructure and technical skills. The mandated online learning policy during the spread of the covid-19 virus became a turning point and made massive use. However, the online learning policy raised unforeseen issues such as stress, especially among students. This study focuses on the continuance intention of online learning among college students in Indonesia. The person-environment fit theory serves as a theoretical anchor, with technostress being examined as a predictor. This research uses an online questionnaire to reach 466 college students as research participants. We used partial least square structural equation modeling (PLS-SEM) to examine the research model. The result shows that three types of technostress (techno-overload, techno-invasion, and techno-uncertainty) are confirmed to have a significant negative effect on the continuance intention of online learning. Meanwhile, the other two (techno-complexity and techno-insecurity) do not affect online learning continuance intention. The current study contributes to the literature regarding the technostress and continuance intention of online learning topics, especially in developing countries such as Indonesia. Furthermore, the research provides valuable insight for policymakers and university administrators, enabling them to formulate effective policies for mandated online learning.


Acharya, B., and Lee, J. (2018) ‘Users’ perspective on the adoption of e-learning in developing countries: The case of Nepal with a conjoint-based discrete choice approach’, Telematics and Informatics, Vol. 35, No. 6, pp. 1733–1743.

Al Rawashdeh, A. Z., Mohammed, E. Y., Al Arab, A. R., Alara, M., Al-Rawashdeh, B., and Al-Rawashdeh, B. (2021) ‘Advantages and Disadvantages of Using e-Learning in University Education: Analyzing Students’ Perspectives’, Electronic Journal of e-Learning, Vol. 19, No. 3, pp. 107–117.

Al-Samarraie, H., Teng, B. K., Alzahrani, A. I., and Alalwan, N. (2018) ‘E-learning continuance satisfaction in higher education: a unified perspective from instructors and students’, Studies in Higher Education, Vol. 43, No. 11, pp. 2003–2019.

Alsabawy, A. Y., Cater-Steel, A., and Soar, J. (2016) ‘Determinants of perceived usefulness of e-learning systems’, Computers in Human Behavior, Vol. 64, No., pp. 843–858.

Amoroso, D. L., and Chen, Y. (2017) ‘Constructs Affecting Continuance intention in consumers with mobile financial apps : a dual factor approach’, Journal of Information Technology Management, Vol. 28, No. 3, pp. 1–24.

Andrews, B., and Wilding, J. M. (2004) ‘The relation of depression and anxiety to life-stress and achievement in students’, British Journal of Psychology, Vol. 95, No. 4, pp. 509–521.

Anggraeni, D. M., and Sole, F. B. (2018) ‘E-Learning Moodle, Media Pembelajaran Fisika Abad 21’, Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika, Vol. 1, No. 2, pp. 57.

Ashrafi, A., Zareravasan, A., Rabiee Savoji, S., and Amani, M. (2022) ‘Exploring factors influencing students’ continuance intention to use the learning management system (LMS): a multi-perspective framework’, Interactive Learning Environments, Vol. 30, No. 8, pp. 1475–1497.

Bhattacherjee, A. (2001) ‘Understanding Information Systems Continuance: An Expectation-Confirmation Model’, MIS Quarterly, Vol. 25, No. 3, pp. 351–370.

Boateng, R., Mbrokoh Alfred, S., Boateng, L., Senyo Prince, K., and Ansong, E. (2016) ‘Determinants of e-learning adoption among students of developing countries’, The International Journal of Information and Learning Technology, Vol. 33, No. 4, pp. 248–262.

Brown, P. (2016) The invisible problem? Improving students’ mental health, Banbury Road: Higher Education Policy Institute.

Califf, C. B., and Brooks, S. (2020) ‘An empirical study of techno-stressors, literacy facilitation, burnout, and turnover intention as experienced by K-12 teachers’, Computers & Education, Vol. 157, 103971.

Chaeruman, U. A. (2018) ‘Encouraging E-Learning Implementation’, Jurnal Teknodik, Vol. 12, No. 1, pp. 25–31.

Cheng, Y.-M. (2014) ‘Extending the expectation-confirmation model with quality and flow to explore nurses’ continued blended e-learning intention’, Information Technology & People, Vol. 27, No. 3, pp. 230–258.

Chou, H.-L., and Chou, C. (2021) ‘A multigroup analysis of factors underlying teachers’ technostress and their continuance intention toward online teaching’, Computers & Education, Vol. 175, 104335.

Chow, W. S., and Shi, S. (2014) ‘Investigating Students’ Satisfaction and Continuance Intention toward E-learning: An Extension of the Expectation – Confirmation Model’, Procedia - Social and Behavioral Sciences, Vol. 141, No., pp. 1145–1149.

Cohen, J. (1988) Statistical Power Analysis for the Behavioral Sciences, 2nd edition, New York: Routledge.

Cojocariu, V.-M., Lazar, I., Nedeff, V., and Lazar, G. (2014) ‘SWOT Anlysis of E-learning Educational Services from the Perspective of their Beneficiaries’, Procedia - Social and Behavioral Sciences, Vol. 116, pp. 1999–2003.

Dhawan, S. (2020) ‘Online Learning: A Panacea in the Time of COVID-19 Crisis’, Journal of Educational Technology Systems, Vol. 49, No. 1, pp. 5–22.

Fawaz, M., and Samaha, A. (2021) ‘E‐learning: Depression, anxiety, and stress symptomatology among Lebanese university students during COVID‐19 quarantine’, Nursing Forum, Vol. 56, No. 1, pp. 52–57.

Fornell, C., and Larcker, D. F. (1981) ‘Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics’, Journal of Marketing Research, Vol. 18, No. 3, pp. 382–388.

Franque, F. B., Oliveira, T., and Tam, C. (2021) ‘Understanding the factors of mobile payment continuance intention: empirical test in an African context’, Heliyon, Vol. 7, No. 8, E07807.

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM), 2nd edition, Thousand Oaks: Sage.

Hair, J. F., Sarstedt, M., Hopkins, L., and G. Kuppelwieser, V. (2014) ‘Partial least squares structural equation modeling (PLS-SEM)’, European Business Review, Vol. 26, No. 2, pp. 106–121.

Han, M., Wu, J., Wang, Y., and Hong, M. (2018) ‘A Model and Empirical Study on the User’s Continuance Intention in Online China Brand Communities Based on Customer-Perceived Benefits’, Journal of Open Innovation: Technology, Market, and Complexity, Vol. 4, No. 4, 46.

Handarini, O. I., and Wulandari, S. S. (2020) ‘Pembelajaran Daring Sebagai Upaya Study From Home (SFH) Selama Pandemi Covid 19’, JPAP (Jurnal Pendidikan Administrasi Perkantoran), Vol. 8, No. 3, pp. 496–503.

Jena, R. K. (2015) ‘Technostress in ICT enabled collaborative learning environment: An empirical study among Indian academician’, Computers in Human Behavior, Vol. 51, pp. 1116–1123.

Jia, Q., Guo, Y., and Barnes, S. J. (2017) ‘Enterprise 2.0 post-adoption: Extending the information system continuance model based on the technology-Organization-environment framework’, Computers in Human Behavior, Vol. 67, pp. 95–105.

Johari, J., Yean Tan, F., and Tjik Zulkarnain, Z. I. (2018) ‘Autonomy, workload, work-life balance and job performance among teachers’, International Journal of Educational Management, Vol. 32, No. 1, pp. 107–120.

Joo, Y. J., Lim, K. Y., and Kim, N. H. (2016) ‘The effects of secondary teachers’ technostress on the intention to use technology in South Korea’, Computers & Education, Vol. 95, pp. 114–122.

Joshanloo, M., and Jovanović, V. (2020) ‘The relationship between gender and life satisfaction: analysis across demographic groups and global regions’, Archives of Women’s Mental Health, Vol. 23, No. 3, pp. 331–338.

Jovanović, V. (2017) ‘Measurement Invariance of the Serbian Version of the Satisfaction With Life Scale Across Age, Gender, and Time’, European Journal of Psychological Assessment, Vol. 35, No. 4, pp. 555–563.

Karana, K. P. (2020) The survey during learning from home policy among the students in Indonesia, [Online], Available: [27 Dec 2021].

Kaunang, S. T. G., and Usagawa, T. (2017) ‘A New Approach for Delivering e-Learning Complex Courses in Indonesia’, International Journal of e-Education, e-Business, e-Management e-Learning, Vol. 7, No. 2, pp. 132–145.

Kemenpppa. (2020) Hearing Indonesian children’s voices about covid-19 through the AADC-19 survey, [Online], Available: [27 Dec 2021].

Kim, H.-W., Chan, H. C., and Chan, Y. P. (2007) ‘A balanced thinking–feelings model of information systems continuance’, International Journal of Human-Computer Studies, Vol. 65, No. 6, pp. 511–525.

Kuntoro, R. D., and Al-Hawamdeh, S. (2003) ‘E-Learning in Higher Educational Institutions in Indonesia’, Journal of Information & Knowledge Management, Vol. 2, No. 4, pp. 361–374.

Lee, M.-C. (2010) ‘Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model’, Computers & Education, Vol. 54, No. 2, pp. 506–516.

Lestariyanti, E. (2020) ‘Mini-Review Pembelajaran Daring Selama Pandemi Covid-19: Keuntungan dan Tantangan’, Jurnal Prakarsa Paedagogia, Vol. 3, No. 1, pp. 89–96.

Li, L., and Wang, X. (2021) ‘Technostress inhibitors and creators and their impacts on university teachers’ work performance in higher education’, Cognition, Technology & Work, Vol. 23, No. 2, pp. 315–330.

Limayem, M., and Cheung, C. M. K. (2011) ‘Predicting the continued use of Internet-based learning technologies: the role of habit’, Behaviour & Information Technology, Vol. 30, No. 1, pp. 91–99.

Mheidly, N., Fares, M. Y., and Fares, J. (2020) ‘Coping With Stress and Burnout Associated With Telecommunication and Online Learning [Review]‘, Frontiers in Public Health, Vol. 8, 574969.

Okpara, J. O., Squillace, M., and Erondu, E. A. (2005) ‘Gender differences and job satisfaction: a study of university teachers in the United States’, Women in Management Review, Vol. 20, No. 3, pp. 177–190.

Ong, C.-S., and Lin, M. Y.-C. (2016) ‘Is being satisfied enough? Well-being and IT post-adoption behavior:An empirical study of Facebook’, Information Development, Vol. 32, No. 4, pp. 1042–1054.

Özgür, H. (2020) ‘Relationships between teachers’ technostress, technological pedagogical content knowledge (TPACK), school support and demographic variables: A structural equation modeling’, Computers in Human Behavior, Vol. 112, 106468.

Panisoara, I. O., Lazar, I., Panisoara, G., Chirca, R., and Ursu, A. S. (2020) ‘Motivation and Continuance Intention towards Online Instruction among Teachers during the COVID-19 Pandemic: The Mediating Effect of Burnout and Technostress’, International Journal of Environmental Research and Public Health, Vol. 17, No. 21, 8002.

Pasca, R. (2014) ‘Person-Environment Fit Theory’, in Michalos A. C. (ed.), Encyclopedia of Quality of Life and Well-Being Research, pp. 4776–4778, Dordrecht: Springer.

Penado Abilleira, M., Rodicio-García, M.-L., Ríos-de Deus, M. P., and Mosquera-González, M. J. (2021) ‘Technostress in Spanish University Teachers During the COVID-19 Pandemic [Original Research]’, Frontiers in Psychology, Vol. 12, 617650.

Pratama, H. F. A., and Arief, S. (2019) ‘Pengaruh pemanfaatan e-learning, lingkungan teman sebaya, dan motivasi belajar terhadap prestasi belajar’, J-PIPS (Jurnal Pendidikan Ilmu Pengetahuan Sosial), Vol. 6, No. 1, pp. 1–12.

Qi, C. (2019) ‘A double-edged sword? Exploring the impact of students’ academic usage of mobile devices on technostress and academic performance’, Behaviour & Information Technology, Vol. 38, No. 12, pp. 1337–1354.

Rafsanjani, M. A., Pamungkas, H. P., Laily, N., and Prabowo, A. E. (2022) ‘Online Learning During the Covid-19 Pandemic: Readiness and Satisfaction among Indonesian Students’, Center for Educational Policy Studies Journal, Vol. 12, No. 3, pp. 149–165.

Rafsanjani, M. A., Wijaya, P. A., Baskara, A., and Wahyudi, H. D. (2023) ‘Mental health and learning achievement during the COVID-19 outbreak: A lesson from online learning among Indonesian college students’, Obrazovanie i Nauka, Vol. 25, No. 3, pp. 155–173.

Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., and Tu, Q. (2008) ‘The Consequences of Technostress for End Users in Organizations: Conceptual Development and Empirical Validation’, Information Systems Research, Vol. 19, No. 4, pp. 417–433.

Rahman, M. N. A. R., Zamri, S. N. A. S., and Leong , K. E. (2017) ‘A Meta-Analysis Study of Satisfaction and Continuance Intention to Use Educational Technology’, International Journal of Academic Research in Business and Social Sciences, Vol. 7, No. 4, pp. 1059–1072.

Salo, M., Pirkkalainen, H., and Koskelainen, T. (2019) ‘Technostress and social networking services: Explaining users’ concentration, sleep, identity, and social relation problems’, Information Systems Journal, Vol. 29, No. 2, pp. 408–435.

Singh, V., and Thurman, A. (2019) ‘How Many Ways Can We Define Online Learning? A Systematic Literature Review of Definitions of Online Learning (1988–2018)’, American Journal of Distance Education, Vol. 33, No. 4, pp. 289–306.

Sokal, L., Trudel, L. E., and Babb, J. (2020) ‘Canadian teachers’ attitudes toward change, efficacy, and burnout during the COVID-19 pandemic’, International Journal of Educational Research Open, Vol. 1, 100016.

Tagoe, M. (2012) ‘Students’ perceptions on incorporating elearning into teaching and learning at the University of Ghana’, International Journal of Education and Development using Information and Communication Technology (IJEDICT), Vol. 8, No. 1, pp. 91–103.

Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., and Ragu-Nathan, T. S. (2007) ‘The Impact of Technostress on Role Stress and Productivity’, Journal of Management Information Systems, Vol. 24, No. 1, pp. 301–328.

Tarafdar, M., Tu, Q., and Ragu-Nathan, T. S. (2010) ‘Impact of Technostress on End-User Satisfaction and Performance’, Journal of Management Information Systems, Vol. 27, No. 3, pp. 303–334.

Thepwongsa, I., Sripa, P., Muthukumar, R., Jenwitheesuk, K., Virasiri, S., and Nonjui, P. (2021) ‘The effects of a newly established online learning management system: the perspectives of Thai medical students in a public medical school’, Heliyon, Vol. 7, No. 10, E08182.

Truzoli, R., Pirola, V., and Conte, S. (2021) ‘The impact of risk and protective factors on online teaching experience in high school Italian teachers during the COVID-19 pandemic’, Journal of Computer Assisted Learning, Vol. 37, No. 4, pp. 940–952.

Unicef. (2020) Plans for back to school during Covid-19, [Online], Available: [14 Jan 2022].

Upadhyaya, P., and Vrinda. (2021) ‘Impact of technostress on academic productivity of university students’, Education and Information Technologies, Vol. 26, No. 2, pp. 1647–1664.

Wang, L.-Y.-K., Lew, S.-L., Lau, S.-H., and Leow, M.-C. (2019) ‘Usability factors predicting continuance of intention to use cloud e-learning application’, Heliyon, Vol. 5, No. 6, E01788.

Wang, X., and Li, B. (2019) ‘Technostress Among University Teachers in Higher Education: A Study Using Multidimensional Person-Environment Misfit Theory [Original Research]‘, Frontiers in Psychology, Vol. 10, 1791.

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How to Cite

Rafsanjani, M. A., Prakoso, A. F., Nurlaili, E. I., Kurniawan, R. Y. and Wulandari, W. (2023) ’Technostress and Continuance Intention of Online Learning in Higher Education: Evidence from Indonesia’, Journal on Efficiency and Responsibility in Education and Science, vol. 16, no. 3, pp. 220–230.



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