Modelling the acceptance of e-learning during the pandemic of COVID-19-A study of South Korea

Abdullah, 2016, Investigating the influence of the most commonly used external variables of TAM on students' Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios, Computers in Human Behavior, 63, 75, 10.1016/j.chb.2016.05.014 Aixia, 2011, Factors influencing learner attitudes toward e-learning and development of e-learning environment based on the integrated e-learning platform, International Journal of e-Education, e-Business, e-Management and e-Learning, 1, 264 Akarasriworn, 2013, Graduate students'knowledge construction and attitudes toward online synchronous videoconferencing collaborative learning environments, Quarterly Review of Distance Education, 14 Al-Azawei, 2017, Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM), Australasian Journal of Educational Technology, 33 Al-Fraihat, 2020, Evaluating E-learning systems success: An empirical study, Computers in Human Behavior, 102, 67, 10.1016/j.chb.2019.08.004 Al-hawari, 2010 Al-Okaily, 2020, Dataset on the acceptance of e-learning system among universities students' under the COVID-19 pandemic conditions, Data in Brief, 32, 106176, 10.1016/j.dib.2020.106176 Alamri, 2017, Factors affecting learners with disabilities–instructor interaction in online learning, Journal of Special Education Technology, 32, 59, 10.1177/0162643416681497 Alpay, 2006, Self-theories of intelligence of engineering students, European Journal of Engineering Education, 31, 169, 10.1080/03043790600567027 Alqahtani, 2020, E-Learning critical success factors during the covid-19 pandemic: A comprehensive analysis of e-learning managerial perspectives, Education Sciences, 10, 216, 10.3390/educsci10090216 Arbaugh, 2000, How classroom environment and student engagement affect learning in Internet-based MBA courses, Business Communication Quarterly, 63, 9, 10.1177/108056990006300402 Ascough, 2002, Designing for online distance education: Putting pedagogy before technology, Teaching Theology & Religion, 5, 17, 10.1111/1467-9647.00114 Baber, 2020, Determinants of students' perceived learning outcome and satisfaction in online learning during the pandemic of COVID-19, Journal of Education and e-Learning Research, 7, 285, 10.20448/journal.509.2020.73.285.292 Baber, 2021, Social interaction and effectiveness of the online learning–A moderating role of maintaining social distance during the pandemic COVID-19, Asian Education and Development Studies, 10.1108/AEDS-09-2020-0209 Baby, 2020, Network path analysis for developing an enhanced TAM model: A user-centric e-learning perspective, Computers in Human Behavior, 107, 106081, 10.1016/j.chb.2019.07.024 Bahhouth, 2011, Significance of e-learning in traditional classes, International Journal of Educational Research, 6, 1 Bao, 2020, COVID‐19 and online teaching in higher education: A case study of Peking University, Human Behavior and Emerging Technologies, 2, 113, 10.1002/hbe2.191 Bawane, 2009, Prioritization of online instructor roles: Implications for competency‐based teacher education programs, Distance Education, 30, 383, 10.1080/01587910903236536 Baylari, 2009, Design a personalized e-learning system based on item response theory and artificial neural network approach, Expert Systems with Applications, 36, 8013, 10.1016/j.eswa.2008.10.080 Bellamy, 1996, Designing educational technology: Computer-mediated change, Context and Consciousness: Activity Theory and Human-Computer Interaction, 123 Bilgiç, 2011, Current situation of online learning in Turkish higher education institutions: Needs, problems, and possible solutions, Yükseköğretim Dergisi, 1, 80 Blut, 2019, Technology readiness: A meta-analysis of conceptualizations of the construct and its impact on technology usage, Journal of the Academy of Marketing Science, 1 Brooks, 2020, The psychological impact of quarantine and how to reduce it: rapid review of the evidence, The lancet, 395, 912, 10.1016/S0140-6736(20)30460-8 Bunz, 2020, From TAM to AVRTS: Development and validation of the attitudes toward virtual reality technology scale, Virtual Reality, 25, 31, 10.1007/s10055-020-00437-7 Chen, 2011, The effects of education compatibility and technological expectancy on e-learning acceptance, Computers & Education, 57, 1501, 10.1016/j.compedu.2011.02.009 Cheng, 2012, The effects of organizational learning environment factors on e-learning acceptance, Computers & Education, 58, 885, 10.1016/j.compedu.2011.10.014 Chen, 2011, Recent related research in technology acceptance model: A literature review, Australian Journal of Business and Management Research, 1, 124, 10.52283/NSWRCA.AJBMR.20110109A14 Chen, 2012, Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan, Evaluation and Program Planning, 35, 398, 10.1016/j.evalprogplan.2011.11.007 Cho, 2015, The influence of self‐efficacy, subjective norms, and risk perception on behavioral intentions related to the H1N1 flu pandemic: A comparison between K orea and the US, Asian Journal of Social Psychology, 18, 311, 10.1111/ajsp.12104 Chyung, 2009, Improving students' learning in precalculus with E-learning activities and through analyses of student learning styles and motivational characteristics, Vol. 1873, 2009 Davis, 1989, Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 319, 10.2307/249008 Davis, 1989, User acceptance of computer technology: A comparison of two theoretical models, Management Science, 35, 982, 10.1287/mnsc.35.8.982 Davis, 2007, Embedding blended learning in a university's teaching culture: Experiences and reflections, British Journal of Educational Technology, 38, 817, 10.1111/j.1467-8535.2007.00756.x Dweck, 2006 Elkaseh, 2016, Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis, International Journal of Information and Education Technology, 6, 192, 10.7763/IJIET.2016.V6.683 Eom, 2016, The determinants of students' perceived learning outcomes and satisfaction in university online education: An update, Decision Sciences Journal of Innovative Education, 14, 185, 10.1111/dsji.12097 Eom, 2006, The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation, Decision Sciences Journal of Innovative Education, 4, 215, 10.1111/j.1540-4609.2006.00114.x Eraslan Yalcin, 2019, Examination of students' acceptance of and intention to use learning management systems using extended TAM, British Journal of Educational Technology, 50, 2414, 10.1111/bjet.12798 Estis-Sumerel, 2020 Farahat, 2012, Applying the technology acceptance model to online learning in the Egyptian universities, Procedia-Social and Behavioral Sciences, 64, 95, 10.1016/j.sbspro.2012.11.012 Fornell, 1981 Garrison, 2005, Facilitating cognitive presence in online learning: Interaction is not enough, The American journal of distance education, 19, 133, 10.1207/s15389286ajde1903_2 Glahn, 2020, Designing for context-aware and contextualized learning, 21 Goh, 2020, Why do university teachers use E-learning systems?, International Review of Research in Open and Distance Learning, 21, 136, 10.19173/irrodl.v21i2.3720 Grolnick, 1987, Autonomy in children's learning: An experimental and individual difference investigation, Journal of Personality and Social Psychology, 52, 890, 10.1037/0022-3514.52.5.890 Guglielmino, 2003, Identifying learners who are ready for e-learning and supporting their success, Preparing Learners for e-learning, 18 Hair, 2019, When to use and how to report the results of PLS-SEM, European Business Review, 31, 2, 10.1108/EBR-11-2018-0203 Hamzah, 2015, Influence of gamification on students' motivation in using e-learning applications based on the motivational design model, International Journal of Emerging Technologies in Learning (iJET), 10, 30, 10.3991/ijet.v10i2.4355 Harandi, 2015, Effects of e-learning on students' motivation, Procedia-Social and Behavioral Sciences, 181, 423, 10.1016/j.sbspro.2015.04.905 Hiltz, 2000, Measuring the importance of collaborative learning for the effectiveness of ALN: A multi-measure, multi-method approach, Journal of Asynchronous Learning Networks, 4, 103 Hong, 1999, Implicit theories, attributions, and coping: A meaning system approach, Journal of Personality and Social Psychology, 77, 588, 10.1037/0022-3514.77.3.588 Huang, 2020, Chinese students' intentions to use the Internet-based technology for learning, Educational Technology Research & Development, 68, 575, 10.1007/s11423-019-09695-y Ibrahim, 2017, E-learning acceptance based on technology acceptance model (TAM), Journal of Fundamental and Applied Sciences, 9, 871, 10.4314/jfas.v9i4S.50 Jin, 2005, Analyzing student-student and student-instructor interaction through multiple communication tools in web-based learning, International Journal of Instructional Media, 32, 59 Kang, 2013, Factors of learner–instructor interaction which predict perceived learning outcomes in online learning environment, Journal of Computer Assisted Learning, 29, 292, 10.1111/jcal.12005 Kauffman, 2015, A review of predictive factors of student success in and satisfaction with online learning, Research in Learning Technology, 23, 10.3402/rlt.v23.26507 Keller, 2004, Learner motivation and e-learning design: A multinationally validated process, Journal of Educational Media, 29, 229, 10.1080/1358165042000283084 Kerr, 2006, Student characteristics for online learning success, The Internet and Higher Education, 9, 91, 10.1016/j.iheduc.2006.03.002 Kersaint, 2003, Technology beliefs and practices of mathematics education faculty, Journal of Technology and Teacher Education, 11, 549 Kimathi, 2019, Exploring the general extended technology acceptance model for e-learning approach on student's usage intention on e-learning system in University of Dar es Salaam, Creative Education, 10, 208, 10.4236/ce.2019.101017 Kim, 2011, Changes in student motivation during online learning, Journal of Educational Computing Research, 44, 1, 10.2190/EC.44.1.a Kok, 2010, Behavioural intentions in response to an influenza pandemic, BMC Public Health, 10, 174, 10.1186/1471-2458-10-174 Koç, 2016, Acceptance and usage of a mobile information system in higher education: An empirical study with structural equation modeling, International Journal of Management in Education, 14, 286 Lazar, 2020, Digital technology adoption scale in the blended learning context in higher education: Development, validation and testing of a specific tool, PloS One, 15, 10.1371/journal.pone.0235957 Lee, 2010, Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation–confirmation model, Computers & Education, 54, 506, 10.1016/j.compedu.2009.09.002 Lee, 2018, Vol. 122 Liang, 2019 Liaw, 2008, Users' attitudes toward Web-based collaborative learning systems for knowledge management, Computers & Education, 50, 950, 10.1016/j.compedu.2006.09.007 Liaw, 2002, How web technology can facilitate learning, Information Systems Management, 19, 56, 10.1201/1078/43199.19.1.20020101/31477.8 Liaw, 2007, Surveying instructor and learner attitudes toward e-learning, Computers & Education, 49, 1066, 10.1016/j.compedu.2006.01.001 Limayem, 2008, Understanding information systems continuance: The case of Internet-based learning technologies, Information & Management, 45, 227, 10.1016/j.im.2008.02.005 Liu, 2010, Extending the TAM model to explore the factors that affect intention to use an online learning community, Computers & Education, 54, 600, 10.1016/j.compedu.2009.09.009 Li, 2017, Rethinking distance tutoring in e-learning environments: A study of the priority of roles and competencies of open university tutors in China, International Review of Research in Open and Distance Learning, 18, 189, 10.19173/irrodl.v18i2.2752 Lowry, 2014, Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it, IEEE Transactions on Professional Communications, 57, 123, 10.1109/TPC.2014.2312452 Mahdizadeh, 2007 Mansouri, 2016, Leadership is skin deep: A new way of being through inside-out effect of leadership and its strategies in teaching, Journal of Advances in Humanities and Social Sciences, 2, 133 Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14(1), 81-95. Menchaca, 2008, Learner and instructor identified success factors in distance education, Distance Education, 29, 231, 10.1080/01587910802395771 Mohammadi, 2015, Investigating users' perspectives on e-learning: An integration of TAM and IS success model, Computers in Human Behavior, 45, 359, 10.1016/j.chb.2014.07.044 Ozkan, 2009, Multi-dimensional students' evaluation of e-learning systems in the higher education context: An empirical investigation, Computers & Education, 53, 1285, 10.1016/j.compedu.2009.06.011 Park, 2009, An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning, Journal of Educational Technology & Society, 12, 150 Piskurich, 2003 Quah, 2004, Crisis prevention and management during SARS outbreak, Singapore, Emerging Infectious Diseases, 10, 364, 10.3201/eid1002.030418 Rahiem, 2021, Remaining motivated despite the limitations: University students' learning propensity during the COVID-19 pandemic, Children and Youth Services Review, 120, 105802, 10.1016/j.childyouth.2020.105802 Ratten, 2020, Covid-19 and entrepreneurship education: Implications for advancing research and practice, International Journal of Management in Education, 100432 Revythi, 2019, Extension of technology acceptance model by using system usability scale to assess behavioral intention to use e-learning, Education and Information Technologies, 24, 2341, 10.1007/s10639-019-09869-4 Rhema, 2014, Analysis of student attitudes towards e-learning: The case of engineering students in Libya, Issues in Informing Science and Information Technology, 11, 169, 10.28945/1987 Rogers, 1995 Rovai, 2007, A comparative analysis of student motivation in traditional classroom and e-learning courses, International Journal on E-Learning, 6, 413 Saadé, 2005, The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model, Information & Management, 42, 317, 10.1016/j.im.2003.12.013 Saadé, 2007, Exploring dimensions to online learning, Computers in Human Behavior, 23, 1721, 10.1016/j.chb.2005.10.002 Salloum, 2019, Exploring students' acceptance of E-learning through the development of a comprehensive technology acceptance model, IEEE Access, 7, 128445, 10.1109/ACCESS.2019.2939467 Salmon, 2012 Saxena, 2021, Examining the moderating effect of perceived benefits of maintaining social distance on e-learning quality during COVID-19 pandemic, Journal of Educational Technology Systems, 49, 532, 10.1177/0047239520977798 Schepers, 2007, A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects, Information & management, 44, 90, 10.1016/j.im.2006.10.007 Scherer, 2019, The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education, Computers & Education, 128, 13, 10.1016/j.compedu.2018.09.009 Seifert, 2004, Understanding student motivation, Educational Research, 46, 137, 10.1080/0013188042000222421 Selim, 2007, Critical success factors for e-learning acceptance: Confirmatory factor models, Computers & Education, 49, 396, 10.1016/j.compedu.2005.09.004 Soong, 2001, Critical success factors for on-line course resources, Computers & Education, 36, 101, 10.1016/S0360-1315(00)00044-0 Spector, 2001 ŠUmak, 2011, A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types, Computers in Human Behavior, 27, 2067, 10.1016/j.chb.2011.08.005 Tarhini, 2013, Extending the TAM model to empirically investigate the students' behavioural intention to use e-learning in developing countries, 732 Tarhini, 2013 Taylor, 1995, Effects of mindset on positive illusions, Journal of Personality and Social Psychology, 69, 213, 10.1037/0022-3514.69.2.213 Tesar, 2020 Vallerand, 1992, Intrinsic, extrinsic, and a motivational styles as predictors of behavior: A prospective study, Journal of Personality, 60, 599, 10.1111/j.1467-6494.1992.tb00922.x Venkatesh, 2012, Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology, MIS Quarterly, 36, 157, 10.2307/41410412 Watson, 1998 Weiser, 2018, How do medium naturalness, teaching-learning interactions and Students' personality traits affect participation in synchronous E-learning?, The Internet and Higher Education, 37, 40, 10.1016/j.iheduc.2018.01.001 Woodrow, 1992, The influence of programming training on the computer literacy and attitudes of preservice teachers, Journal of Research on Computing in Education, 25, 200, 10.1080/08886504.1992.10782044 Xhaferi, 2018, Teacher’attitudes towards e-learning in higher education in Macedonia Case study: University of Tetovo, European Journal of Electrical Engineering and Computer Science, 2, 10.24018/ejece.2018.2.5.26