The extended UTAUT model and learning management system during COVID-19: evidence from PLS-SEM and conditional process modeling
Abstract
The undertaken research investigates the extended unified theory of acceptance and use of technology (UTAUT) model from the perspective of online education in the deadliest period of COVID-19. This research investigates the extended dimensions, for instance, mobile self-efficacy and perceived enjoyment besides traditional elements of the UTAUT model with the relationship of behavioural intention and user behaviour of LMS. Since the COVID-19 led to social isolation (SIS), thus, this study has incorporated SIS as mediating factor and fear of COVID-19 (FOC) as the moderating factor for the considered extended model of UTAUT. The data of 1875 respondents was collected from five different Asian countries. For the data analysis, this study employed structural equation modeling through PLS-SEM and condition process modeling. This research demonstrates that the extended dimensions such as mobile self-efficacy, besides the traditional elements of the UTAUT model, exerted a cogent impact on behavioural intention except for the perceived enjoyment. Similarly, the behavioural intention demonstrated a substantial effect on the user behaviour of LMS. Additionally, social isolation as a mediating factor and FOC has a significant effect between dimensions of extended UTAUT model and behavioural intention of LMS. The outcomes of this research demonstrate significant theoretical and practical implications during the COVID-19 pandemic.
First published online 30 November 2021
Keyword : learning management system (LMS), higher education, extended unified theory of acceptance and use of technology (UTAUT) model, fear of COVID-19, social isolation, PLS-SEM
How to Cite
Ahmed, R. R., Štreimikienė, D., & Štreimikis, J. (2022). The extended UTAUT model and learning management system during COVID-19: evidence from PLS-SEM and conditional process modeling. Journal of Business Economics and Management, 23(1), 82–104. https://doi.org/10.3846/jbem.2021.15664
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Alharbi, A., & Sohaib, O. (2021). Technology readiness and cryptocurrency adoption: PLS-SEM and deep learning neural network analysis. IEEE Access, 9, 21388–21394. https://doi.org/10.1109/ACCESS.2021.3055785
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Aliaño, Á. M., Hueros, A. M. D., Franco, M. D. G., & Aguaded, I. (2019). Mobile learning in university contexts based on the unified theory of acceptance and use of technology (UTAUT). Journal of New Approaches in Educational Research, 8(1), 7–17. https://doi.org/10.7821/naer.2019.1.317
Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261–5280. https://doi.org/10.1007/s10639-020-10219-y
Almisad, B., & Alsalim, M. (2020). Kuwaiti female university students’ acceptance of the integration of smartphones in their learning: An investigation guided by a modified version of the unified theory of acceptance and use of technology (UTAUT). International Journal of Technology Enhanced Learning, 12(1), 1–19. https://doi.org/10.1504/IJTEL.2020.103812
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Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. https://psycnet.apa.org/record/1985-98423-000
Chen, P.-Y., & Hwang, G.-J. (2019). An empirical examination of the effect of self-regulation and the Unified Theory of Acceptance and Use of Technology (UTAUT) factors on the online learning behavioural intention of college students. Asia Pacific Journal of Education, 39(1), 79–95. https://doi.org/10.1080/02188791.2019.1575184
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Decman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in Human Behavior, 49, 272–281. https://doi.org/10.1016/j.chb.2015.03.022
El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, 65, 743–763. https://doi.org/10.1007/s11423-016-9508-8
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Fornell, C., & Larker, D. (1981). Structural equation modeling and regression: Guidelines for research practice. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Garrett, L. (2020). COVID-19: The medium is the message. Lancet, 395(10228), 942–943. https://doi.org/10.1016/S0140-6736(20)30600-0
Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002
Hou, H.-Y., Lo, Y.-L., & Lee, C.-F. (2020). Predicting network behavior model of e-learning partner program in PLS-SEM. Applied Sciences, 10(13), 4656. https://doi.org/10.3390/app10134656
Iqbal, S. (2011). Learning management systems (LMS): Inside matters. Information Management and Business Review, 3(4), 206–216. https://doi.org/10.22610/imbr.v3i4.935
Iyer, G. R., Blut, M., Xiao, S. H., & Grewal, D. (2020). Impulse buying: a meta-analytic review. Journal of the Academy of Marketing Science, 48, 384–404. https://doi.org/10.1007/s11747-019-00670-w
Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460–474. https://doi.org/10.1016/j.chb.2017.01.001
Khechine, H., Lakhal, N., & Ndjambou, P. (2016). A meta‐analysis of the UTAUT model: Eleven years later. Canadian Journal of Administrative Sciences, 33(2), 138–152. https://doi.org/10.1002/cjas.1381
Kufi, E. F., Negassa, T., Melaku, R., & Mergo, R. (2020). Impact of corona pandemic on educational undertakings and possible breakthrough mechanisms. BizEcons Quarterly, 11, 3–14. https://ideas.repec.org/a/ris/buecqu/0022.html
Lai, H.-J. (2020). Investigating older adults’ decisions to use mobile devices for learning, based on the unified theory of acceptance and use of technology. Interactive Learning Environments, 28(7), 890–901. https://doi.org/10.1080/10494820.2018.1546748
Law, L., & Fong, N. (2020). Applying partial least squares structural equation modeling (PLS-SEM) in an investigation of undergraduate students’ learning transfer of academic English. Journal of English for Academic Purposes, 46, 100884. https://doi.org/10.1016/j.jeap.2020.100884
Limaye, R. J., Sauer, M., Ali, J., Bernstein, J., Wahl, B., Barnhill, A., & Labrique, A. (2020). Building trust while influencing online COVID-19 content in the social media world. Lancet Digital Health, 2(6), e277–e278. https://doi.org/10.1016/S2589-7500(20)30084-4
Lin, C. Y. (2020). Social reaction toward the 2019 novel coronavirus (COVID-19). Social Health and Behavior, 3(1), 1–2. https://doi.org/10.4103/SHB.SHB_11_20
Mattila, M. (2004). Contested decisions: Empirical analysis of voting in the European Union Council of Ministers. European Journal of Political Research, 43(1), 29–50. https://doi.org/10.1111/j.1475-6765.2004.00144.x
Mertens, G., Gerritsen, L., Duijndam, S., Salemink, E., & Engelhard, I. M. (2020). Fear of the Coronavirus (COVID-19): Predictors in an online study conducted in March 2020. Journal of Anxiety Disorders, 74, 102258. https://doi.org/10.1016/j.janxdis.2020.102258
Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: investigating the factors that influence behavioral intention to use. Computer & Education, 109, 56–73. https://doi.org/10.1016/j.compedu.2017.02.005
Pakpour, A. H., & Griffiths, M. D. (2020). The fear of Covid-19 and its role in preventive behaviors. Journal of Concurrent Disorders, 2(1), 58–63. http://irep.ntu.ac.uk/id/eprint/39561/
Persada, S. F., Miraja, B. A., & Nadlifatin, R. (2019). Understanding the generation z behavior on D-learning: A Unified Theory of Acceptance and Use of Technology (UTAUT) approach. International Journal of Emerging Technologies in Learning, 14(5), 20–33. https://doi.org/10.3991/ijet.v14i05.9993
Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social isolation and acceptance of the Learning Management System (LMS) in the time of COVID-19 Pandemic: An expansion of the UTAUT Model. Journal of Educational Computing Research, 59(2), 183–208. https://doi.org/10.1177/0735633120960421
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Simon and Schuster. https://www.simonandschuster.com/books/Diffusion-of-Innovations-5th-Edition/Everett-M-Rogers/9780743222099
Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2021). Effects of COVID-19 in E-learning on higher education institution students: The group comparison between male and female. Quality and Quantity, 55, 805–826. https://doi.org/10.1007/s11135-020-01028-z
Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre- and post-adopters. Computers in Human Behavior, 64, 602–620. https://doi.org/10.1016/j.chb.2016.07.037
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143. https://doi.org/10.2307/249443
Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. In M. P. Zanna (Ed.), Advances in experimental social psychology (pp. 271–360). Academic Press. https://doi.org/10.1016/S0065-2601(08)60019-2
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Wilder-Smith, A., & Freedman, D. O. (2020). Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine, 27(2), taaa020. https://doi.org/10.1093/jtm/taaa020
Xian, X. (2019). Empirical investigation of E-learning adoption of university teachers: A PLS-SEM approach. In Communications in computer and information science: Vol. 1048. Technology in education: Pedagogical innovations (pp. 169–178). Springer, Singapore. https://doi.org/10.1007/978-981-13-9895-7_15
Ahmed, R. R., Štreimikienė, D., Rolle, J. A., & Due, P. A. (2020). The COVID-19 Pandemic and the antecedents for the Impulse buying behavior of US Citizens. Journal of Competitiveness, 12(3), 5–27. https://doi.org/10.7441/joc.2020.03.01
Ain, N., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning management system use: An extension of UTAUT2. Information Development, 32(5), 1306–1321. https://doi.org/10.1177/0266666915597546
Alharbi, A., & Sohaib, O. (2021). Technology readiness and cryptocurrency adoption: PLS-SEM and deep learning neural network analysis. IEEE Access, 9, 21388–21394. https://doi.org/10.1109/ACCESS.2021.3055785
Alhramelah, A., & Al-Shahrani, H. (2020). Saudi graduate student acceptance of blended learning courses based upon the unified theory of acceptance and use of technology. Australian Educational Computing, 35(1), 1–22. https://www.researchgate.net/publication/344711887_Saudi_graduate_student_acceptance_of_blended_learning_courses_based_upon_the_unified_theory_of_acceptance_and_use_of_technology
Aliaño, Á. M., Hueros, A. M. D., Franco, M. D. G., & Aguaded, I. (2019). Mobile learning in university contexts based on the unified theory of acceptance and use of technology (UTAUT). Journal of New Approaches in Educational Research, 8(1), 7–17. https://doi.org/10.7821/naer.2019.1.317
Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261–5280. https://doi.org/10.1007/s10639-020-10219-y
Almisad, B., & Alsalim, M. (2020). Kuwaiti female university students’ acceptance of the integration of smartphones in their learning: An investigation guided by a modified version of the unified theory of acceptance and use of technology (UTAUT). International Journal of Technology Enhanced Learning, 12(1), 1–19. https://doi.org/10.1504/IJTEL.2020.103812
Alshurideh, M., Al Kurdi, B., Salloum, S. A., Arpaci, I., & Al-Emran, M. (2020). Predicting the actual use of m-learning systems: A comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2020.1826982
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. https://psycnet.apa.org/record/1985-98423-000
Chen, P.-Y., & Hwang, G.-J. (2019). An empirical examination of the effect of self-regulation and the Unified Theory of Acceptance and Use of Technology (UTAUT) factors on the online learning behavioural intention of college students. Asia Pacific Journal of Education, 39(1), 79–95. https://doi.org/10.1080/02188791.2019.1575184
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, RP, & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
De Jong Gierveld, J., Van Tilburg, T., & Dykstra, P. (2016). Loneliness and social isolation. In A. Vangelisti & D. Perlman (Eds.), The Cambridge handbook of personal relationships (pp. 1–30). Cambridge University Press. http://hdl.handle.net/1765/93235
Decman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in Human Behavior, 49, 272–281. https://doi.org/10.1016/j.chb.2015.03.022
El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, 65, 743–763. https://doi.org/10.1007/s11423-016-9508-8
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley, Reading, MA. https://www.researchgate.net/publication/233897090_Belief_attitude_intention_and_behaviour_An_introduction_to_theory_and_research
Fornell, C., & Larker, D. (1981). Structural equation modeling and regression: Guidelines for research practice. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Garrett, L. (2020). COVID-19: The medium is the message. Lancet, 395(10228), 942–943. https://doi.org/10.1016/S0140-6736(20)30600-0
Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002
Hou, H.-Y., Lo, Y.-L., & Lee, C.-F. (2020). Predicting network behavior model of e-learning partner program in PLS-SEM. Applied Sciences, 10(13), 4656. https://doi.org/10.3390/app10134656
Iqbal, S. (2011). Learning management systems (LMS): Inside matters. Information Management and Business Review, 3(4), 206–216. https://doi.org/10.22610/imbr.v3i4.935
Iyer, G. R., Blut, M., Xiao, S. H., & Grewal, D. (2020). Impulse buying: a meta-analytic review. Journal of the Academy of Marketing Science, 48, 384–404. https://doi.org/10.1007/s11747-019-00670-w
Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460–474. https://doi.org/10.1016/j.chb.2017.01.001
Khechine, H., Lakhal, N., & Ndjambou, P. (2016). A meta‐analysis of the UTAUT model: Eleven years later. Canadian Journal of Administrative Sciences, 33(2), 138–152. https://doi.org/10.1002/cjas.1381
Kufi, E. F., Negassa, T., Melaku, R., & Mergo, R. (2020). Impact of corona pandemic on educational undertakings and possible breakthrough mechanisms. BizEcons Quarterly, 11, 3–14. https://ideas.repec.org/a/ris/buecqu/0022.html
Lai, H.-J. (2020). Investigating older adults’ decisions to use mobile devices for learning, based on the unified theory of acceptance and use of technology. Interactive Learning Environments, 28(7), 890–901. https://doi.org/10.1080/10494820.2018.1546748
Law, L., & Fong, N. (2020). Applying partial least squares structural equation modeling (PLS-SEM) in an investigation of undergraduate students’ learning transfer of academic English. Journal of English for Academic Purposes, 46, 100884. https://doi.org/10.1016/j.jeap.2020.100884
Limaye, R. J., Sauer, M., Ali, J., Bernstein, J., Wahl, B., Barnhill, A., & Labrique, A. (2020). Building trust while influencing online COVID-19 content in the social media world. Lancet Digital Health, 2(6), e277–e278. https://doi.org/10.1016/S2589-7500(20)30084-4
Lin, C. Y. (2020). Social reaction toward the 2019 novel coronavirus (COVID-19). Social Health and Behavior, 3(1), 1–2. https://doi.org/10.4103/SHB.SHB_11_20
Mattila, M. (2004). Contested decisions: Empirical analysis of voting in the European Union Council of Ministers. European Journal of Political Research, 43(1), 29–50. https://doi.org/10.1111/j.1475-6765.2004.00144.x
Mertens, G., Gerritsen, L., Duijndam, S., Salemink, E., & Engelhard, I. M. (2020). Fear of the Coronavirus (COVID-19): Predictors in an online study conducted in March 2020. Journal of Anxiety Disorders, 74, 102258. https://doi.org/10.1016/j.janxdis.2020.102258
Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: investigating the factors that influence behavioral intention to use. Computer & Education, 109, 56–73. https://doi.org/10.1016/j.compedu.2017.02.005
Pakpour, A. H., & Griffiths, M. D. (2020). The fear of Covid-19 and its role in preventive behaviors. Journal of Concurrent Disorders, 2(1), 58–63. http://irep.ntu.ac.uk/id/eprint/39561/
Persada, S. F., Miraja, B. A., & Nadlifatin, R. (2019). Understanding the generation z behavior on D-learning: A Unified Theory of Acceptance and Use of Technology (UTAUT) approach. International Journal of Emerging Technologies in Learning, 14(5), 20–33. https://doi.org/10.3991/ijet.v14i05.9993
Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social isolation and acceptance of the Learning Management System (LMS) in the time of COVID-19 Pandemic: An expansion of the UTAUT Model. Journal of Educational Computing Research, 59(2), 183–208. https://doi.org/10.1177/0735633120960421
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Simon and Schuster. https://www.simonandschuster.com/books/Diffusion-of-Innovations-5th-Edition/Everett-M-Rogers/9780743222099
Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2021). Effects of COVID-19 in E-learning on higher education institution students: The group comparison between male and female. Quality and Quantity, 55, 805–826. https://doi.org/10.1007/s11135-020-01028-z
Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre- and post-adopters. Computers in Human Behavior, 64, 602–620. https://doi.org/10.1016/j.chb.2016.07.037
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143. https://doi.org/10.2307/249443
Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. In M. P. Zanna (Ed.), Advances in experimental social psychology (pp. 271–360). Academic Press. https://doi.org/10.1016/S0065-2601(08)60019-2
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Wilder-Smith, A., & Freedman, D. O. (2020). Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine, 27(2), taaa020. https://doi.org/10.1093/jtm/taaa020
Xian, X. (2019). Empirical investigation of E-learning adoption of university teachers: A PLS-SEM approach. In Communications in computer and information science: Vol. 1048. Technology in education: Pedagogical innovations (pp. 169–178). Springer, Singapore. https://doi.org/10.1007/978-981-13-9895-7_15