“Antecedents promoting e-learner’s engagement behavior: Mediating effect of e-learner’s intention to use behavior”

References

• Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–28. https://doi.org/10.1007/s10639-021-10573-5
   PubMed Web of Science ®Google Scholar
• Abdekhoda, M., Dehnad, A., & Zarei, J. (2022). Factors influencing adoption of e-learning in healthcare: Integration of UTAUT and TTF model. BMC Medical Informatics & Decision Making, 22(1). https://doi.org/10.1186/s12911-022-02060-9
   Web of Science ®Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Ajzen, M., Fishbein, I., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Contemporary Sociology, 6(2), 244. https://doi.org/10.2307/2065853
   Web of Science ®Google Scholar
• Al-Adwan, A. S., Albelbisi, N. A., Hujran, O., Al-Rahmi, W. M., & Alkhalifah, A. (2022). Developing a holistic success model for sustainable E-learning: A structural equation modeling approach. Sustainability, 13(16), 9453. https://doi.org/10.3390/su13169453
   Web of Science ®Google Scholar
• Al-Bogami, B., & Elyas, T. (2020). Promoting middle school students’ engagement through incorporating ipad apps in EFL/ESL classes. SAGE Open, 10(2), 215824402092657. https://doi.org/10.1177/2158244020926570
   Web of Science ®Google Scholar
• Aldowah, H., Al-Samarraie, H., Alzahrani, A. I., & Alalwan, N. (2020). Factors affecting student dropout in MOOCs: A cause and effect decision‐making model. Journal of Computing in Higher Education, 32(2), 429–454. https://doi.org/10.1007/s12528-019-09241-y
   Web of Science ®Google Scholar
• Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004
   Web of Science ®Google Scholar
• Alghazi, S. S., Kamsin, A., Almaiah, M. A., Wong, S. Y., & Shuib, L. (2021). For sustainable application of mobile learning: An Extended UTAUT model to examine the effect of technical factors on the usage of mobile devices as a learning tool. Sustainability, 13(4), 1856. https://doi.org/10.3390/su13041856
   Web of Science ®Google Scholar
• Al-Nuaimi, M. N., Al Sawafi, O. S., Malik, S. I., & Al-Maroof, R. S. (2022). Extending the unified theory of acceptance and use of technology to investigate determinants of acceptance and adoption of learning management systems in the post-pandemic era: A structural equation modeling approach. Interactive Learning Environments, 1–27. https://doi.org/10.1080/10494820.2022.2127777
   Web of Science ®Google Scholar
• Alyoussef, I. Y. (2023). Acceptance of a flipped classroom to improve university students’ learning: An empirical study on the TAM model and the Unified Theory of Acceptance and Use of Technology (UTAUT). Heliyon, 8(12), e12529. https://doi.org/10.1016/j.heliyon.2022.e12529
   Web of Science ®Google Scholar
• Ameen, N., Willis, R., Abdullah, M. N., & Shah, M. (2019). Towards the successful integration of e‐learning systems in higher education in Iraq: A student perspective. British Journal of Educational Technology, 50(3), 1434–1446. https://doi.org/10.1111/bjet.12651
   Web of Science ®Google Scholar
• Andrews, J. E., Ward, H., & Yoon, J. (2021). UTAUT as a model for understanding intention to adopt AI and related technologies among librarians. The Journal of Academic Librarianship, 47(6), 102437. https://doi.org/10.1016/j.acalib.2021.102437
   Web of Science ®Google Scholar
• Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society, 18(3), 659–673. https://doi.org/10.1007/s10209-019-00685-8
   Web of Science ®Google Scholar
• Arkorful, V., Barfi, K. A., & Baffour, N. O. (2022). Factors affecting use of massive open online courses by Ghanaian students. Cogent Education, 9(1). https://doi.org/10.1080/2331186x.2021.2023281
   Web of Science ®Google Scholar
• Bajaj, P., Khan, A., Tabash, M. I., Anagreh, S., & Koo, A. C. (2021). Teachers’ intention to continue the use of online teaching tools post Covid-19. Cogent Education, 8(1). https://doi.org/10.1080/2331186x.2021.2002130
   Web of Science ®Google Scholar
• Bond, M., Buntins, K., Bedenlier, S., Richter, O. Z., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17(1). https://doi.org/10.1186/s41239-019-0176-8
   Web of Science ®Google Scholar
• Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies – Students’ behavior. Computers in Human Behavior, 72, 612–620. https://doi.org/10.1016/j.chb.2016.05.027
   Web of Science ®Google Scholar
• Buettner, R. (2017). Getting a job via career-oriented social networking markets. Electronic Markets, 27(4), 371–385. https://doi.org/10.1007/s12525-017-0248-3
   Web of Science ®Google Scholar
• Camilleri, M. A. (2019). Measuring the hoteliers’ interactive engagement through social media. Proceedings of the 14th European Conference on Innovation and Entrepreneurship, University of Peloponnese, Kalamata, Greece (pp. 1–21).
   Google Scholar
• Carroll, M., Lindsey, S., Chaparro, M., & Winslow, B. (2019). An applied model of learner engagement and strategies for increasing learner engagement in the modern educational environment. Interactive Learning Environments, 29(5), 757–771. https://doi.org/10.1080/10494820.2019.1636083
   Web of Science ®Google Scholar
• Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 10. https://doi.org/10.3389/fpsyg.2019.01652
   PubMed Web of Science ®Google Scholar
• Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443–3463. https://doi.org/10.1007/s10639-020-10159-7
   Web of Science ®Google Scholar
• Chiu, C.-M., & Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194–201. https://doi.org/10.1016/j.im.2008.02.003
   Web of Science ®Google Scholar
• Chopdar, P. K., & Sivakumar, V. J. (2018). Understanding continuance usage of mobile shopping applications in India: The role of espoused cultural values and perceived risk. Behaviour & Information Technology, 38(1), 42–64. https://doi.org/10.1080/0144929x.2018.1513563
   Web of Science ®Google Scholar
• Coates, H. (2006). The value of student engagement for higher education quality assurance. Quality in Higher Education, 11(1), 25–36. https://doi.org/10.1080/13538320500074915
   Google Scholar
• Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189. https://doi.org/10.2307/249688
   Web of Science ®Google Scholar
• Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145. https://doi.org/10.2307/249749
   Web of Science ®Google Scholar
• Dajani, D., & Abu Hegleh, A. S. (2019). Behavior intention of animation usage among university students. Heliyon, 5(10), e02536. https://doi.org/10.1016/j.heliyon.2019.e02536
   PubMed Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & 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
   Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
   Web of Science ®Google Scholar
• Deka, P. K. (2021). Factors influencing student engagement in online learning during the COVID – 19 pandemic period in India. Journal of Management in Practice, 6(1), 1–16.
   Google Scholar
• Deng, R., Benckendorff, P., & Gannaway, D. (2019). Learner engagement in MOOCs: Scale development and validation. British Journal of Educational Technology, 51(1), 245–262. https://doi.org/10.1111/bjet.12810
   Web of Science ®Google Scholar
• Digital Education Initiatives | IBEF. (2022). https://www.ibef.org/blogs/digital-education-initiatives
   Google Scholar
• Duggal, S. (2022). Factors impacting acceptance of e-learning in India: Learners’ perspective. Asian Association of Open Universities Journal, 17(2), 101–119. https://doi.org/10.1108/aaouj-01-2022-0010
   Google Scholar
• Dumford, A. D., & Miller, A. L. (2018). Online learning in higher education: Exploring advantages and disadvantages for engagement. Journal of Computing in Higher Education, 30(3), 452–465. https://doi.org/10.1007/s12528-018-9179-z
   Web of Science ®Google Scholar
• Edem Adzovie, D., & Jibril, A. B. (2022). Assessment of the effects of Covid-19 pandemic on the prospects of e-learning in higher learning institutions: The mediating role of academic innovativeness and technological growth. Cogent Education, 9(1). https://doi.org/10.1080/2331186x.2022.2041222
   Web of Science ®Google Scholar
• Education & Training Sector in India: Education System, Growth & Market Size | IBEF. (2022). https://www.ibef.org/industry/education-sector-india
   Google Scholar
• Erdoğdu, F., & Çakıroğlu, Ü. (2021). The educational power of humor on student engagement in online learning environments. Research and Practice in Technology Enhanced Learning, 16(1), 16(1. https://doi.org/10.1186/s41039-021-00158-8
   Google Scholar
• Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/brm.41.4.1149
   PubMed Web of Science ®Google Scholar
• Fianu, E., Blewett, C., Ampong, G., & Ofori, K. (2018). Factors affecting MOOC usage by students in selected ghanaian universities. Education Sciences, 8(2), 70. https://doi.org/10.3390/educsci8020070
   Web of Science ®Google Scholar
• Fu, S., Gu, H., & Yang, B. (2020). The affordances of AI‐enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China. British Journal of Educational Technology, 51(5), 1674–1692. https://doi.org/10.1111/bjet.12995
   Web of Science ®Google Scholar
• Gerlach, J., Soeren, S., & Buxmann, P. (2014). (I Can’t Get No) satisfaction: Investigating the role of goal value and mood in habitual technology use. ICIS 2014 Proceedings., 48. https://aisel.aisnet.org/icis2014/proceedings/HumanBehavior/48
   Google Scholar
• Goh, T.-T., & Yang, B. (2021). The role of e-engagement and flow on the continuance with a learning management system in a blended learning environment. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00285-8
   Web of Science ®Google Scholar
• Guo, Z., Xiao, L., Toorn, C. V., Lai, Y., & Seo, C. (2016). Promoting online learners’ continuance intention: An integrated flow framework. Information & Management, 53(2), 279–295. https://doi.org/10.1016/j.im.2015.10.010
   Web of Science ®Google Scholar
• Hair, J. F. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.
   Google Scholar
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/ebr-11-2018-0203
   Web of Science ®Google Scholar
• Han, S., & Yi, Y. J. (2018). How does the smartphone usage of college students affect academic performance? Journal of Computer Assisted Learning, 35(1), 13–22. https://doi.org/10.1111/jcal.12306
   Web of Science ®Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Heo, H., Bonk, C. J., & Doo, M. Y. (2021). Enhancing learning engagement during COVID ‐19 pandemic: Self‐efficacy in time management, technology use, and online learning environments. Journal of Computer Assisted Learning, 37(6), 1640–1652. https://doi.org/10.1111/jcal.12603
   Web of Science ®Google Scholar
• Hermita, N., Wijaya, T. T., Yusron, E., Abidin, Y., Alim, J. A., & Putra, Z. H. (2023). Extending unified theory of acceptance and use of technology to understand the acceptance of digital textbook for elementary School in Indonesia. Frontiers in Education, 8, 8. https://doi.org/10.3389/feduc.2023.958800
   Google Scholar
• Hertzog, M. A. (2008). Considerations in determining sample size for pilot studies. Research in Nursing & Health, 31(2), 180–191. https://doi.org/10.1002/nur.20247
   PubMed Web of Science ®Google Scholar
• Hixon, E., Buckenmeyer, J., Barczyk, C., Feldman, L., & Zamojski, H. (2012). Beyond the early adopters of online instruction: Motivating the reluctant majority. The Internet and Higher Education, 15(2), 102–107. https://doi.org/10.1016/j.iheduc.2011.11.005
   Web of Science ®Google Scholar
• Hoi, V. N. (2020). Understanding higher education learners’ acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 103761. https://doi.org/10.1016/j.compedu.2019.103761
   Web of Science ®Google Scholar
• Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157–168. https://doi.org/10.1016/j.compedu.2016.03.016
   Web of Science ®Google Scholar
• Huang, H., & Wang, Y. (2022). How flow experience and self-efficacy define students’ online learning intentions: View from task technology fit (framework). Frontiers in Psychology, 13, 13. https://doi.org/10.3389/fpsyg.2022.835328
   Web of Science ®Google Scholar
• Ifedayo, A. E., Ziden, A. A., & Ismail, A. B. (2021). Podcast acceptance for pedagogy: The levels and significant influences. Heliyon, 7(3), e06442. https://doi.org/10.1016/j.heliyon.2021.e06442
   PubMed Web of Science ®Google Scholar
• Isaac, O., Aldholay, A., Abdullah, Z., & Ramayah, T. (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the is success model. Computers & Education, 136, 113–129. https://doi.org/10.1016/j.compedu.2019.02.012
   Web of Science ®Google Scholar
• Jain, R., Garg, N., & Khera, S. N. (2022). Adoption of AI-Enabled tools in social development organizations in india: An extension of UTAUT model. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.893691
   Web of Science ®Google Scholar
• Jung, Y., & Lee, J. (2018). Learning engagement and persistence in Massive Open Online Courses (MOOCS). Computers & Education, 122, 9–22. https://doi.org/10.1016/j.compedu.2018.02.013
   Web of Science ®Google Scholar
• Kennedy, G. (2020). What is student engagement in online learning … and how do I know when it is there? Melbourne Cshe Discussion Paper, 1–5.
   Google Scholar
• Khechine, H., Raymond, B., & Augier, M. (2020). The adoption of a social learning system: Intrinsic value in the UTAUT model. British Journal of Educational Technology, 51(6), 2306–2325. https://doi.org/10.1111/bjet.12905
   Web of Science ®Google Scholar
• Kim, B., Yoo, M., & Yang, W. (2019). Online engagement among restaurant customers: The importance of enhancing flow for social media users. Journal of Hospitality & Tourism Research, 44(2), 109634801988720. https://doi.org/10.1177/1096348019887202
   Web of Science ®Google Scholar
• Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220–221. https://doi.org/10.1080/1743727x.2015.1005806
   Web of Science ®Google Scholar
• Lidhoo, P. (2021, November 9). India becomes Coursera’s second-largest market with 13.6 million users. Business Today.
   Google Scholar
• Li, B., Sun, J., & Oubibi, M. (2022). The acceptance behavior of blended learning in secondary vocational school students: Based on the modified UTAUT Model. Sustainability, 14(23), 15897. https://doi.org/10.3390/su142315897
   Web of Science ®Google Scholar
• Liu, N., & Pu, Q. (2020). Factors influencing learners’ continuance intention toward one-to-one online learning. Interactive Learning Environments, 31(3), 1742–1763. https://doi.org/10.1080/10494820.2020.1857785
   Web of Science ®Google Scholar
• Ma, K., Chutiyami, M., Zhang, Y., & Nicoll, S. (2021). Online teaching self-efficacy during COVID-19: Changes, its associated factors and moderators. Education and Information Technologies, 26(6), 6675–6697. https://doi.org/10.1007/s10639-021-10486-3
   PubMed Web of Science ®Google Scholar
• Mafabi, S., Nasiima, S., Muhimbise, E. M., Kasekende, F., & Nakiyonga, C. (2017). The mediation role of intention in knowledge sharing behavior. VINE Journal of Information and Knowledge Management Systems, 47(2), 172–193. https://doi.org/10.1108/vjikms-02-2016-0008
   Web of Science ®Google Scholar
• Markets, R. A. (2017, February 6). Global E-Learning market to reach $325 billion by 2025 - rapid growth in online content & digitization/innovations in wearable technologies are flourishing the E-learning industry. GlobeNewswire Newsroom. https://www.globenewswire.com/newsrelease/2017/02/06/914187/0/en/Global-E-Learning-Market-to-Reach-325-billion-by-2025-Rapid-Growth-in-Online-Content-Digitization-Innovations-in-Wearable-Technologies-are-Flourishing-the-E-learning-Industry.html
   Google Scholar
• Masito, F., Risdianto, E., Oka, I. G. A. A. M., Daryanti, Y., & Fathurrochman, I. (2021). Analysis of online LEarning system needs basedon MOOCs. AL-ISHLAH: Journal Pendidikan, 13(2), 953–959. https://doi.org/10.35445/alishlah.v13i2.670
   Google Scholar
• Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. https://doi.org/10.1287/isre.2.3.173
   Web of Science ®Google Scholar
• McCue, T. J. (2018, July 31). E Learning Climbing to $325 Billion by 2025 UF Canvas Absorb Schoology Moodle. Forbes. https://www.forbes.com/sites/tjmccue/2018/07/31/e-learning-climbing-to-325-billion-by-2025-uf-canvas-absorb-schoology-moodle/?sh=3e9300af3b39
   Google Scholar
• McKenna, G., Baxter, G., & Hainey, T. (2017). Adopting a virtual learning environment towards enhancing students’ self-efficacy. Journal of Applied Research in Higher Education, 9(1), 54–69. https://doi.org/10.1108/jarhe-05-2016-0038
   Google Scholar
• McLean, G., & Wilson, A. (2019). Shopping in the digital world: Examining customer engagement through augmented reality mobile applications. Computers in Human Behavior, 101, 210–224. https://doi.org/10.1016/j.chb.2019.07.002
   Web of Science ®Google Scholar
• Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192
   Web of Science ®Google Scholar
• Muhammad, S. S., Dey, B. L., Kamal, M. M., & Syed Alwi, S. F. (2021). Consumer engagement with social media platforms: A study of the influence of attitudinal components on cutting edge technology adaptation behaviour. Computers in Human Behavior, 121, 106802. https://doi.org/10.1016/j.chb.2021.106802
   Web of Science ®Google Scholar
• Mulik, S., Srivastava, M., Yajnik, N., & Taras, V. (2019). Antecedents and outcomes of flow experience of MOOC users. Journal of International Education in Business, 13(1), 1–19. https://doi.org/10.1108/jieb-10-2018-0049
   Web of Science ®Google Scholar
• Muthmainnah, M., Ibna Seraj, P. M., Oteir, I., & Balakrishnan, B. (2022). Playing with AI to investigate human-computer interaction technology and improving critical thinking skills to pursue 21st century age. Education Research International, 2022, 1–17. https://doi.org/10.1155/2022/6468995
   Web of Science ®Google Scholar
• Naveed, Q. N., Alam, M. M., & Tairan, N. (2020). Structural equation modeling for mobile learning acceptance by university students: An empirical study. Sustainability, 12(20), 8618. https://doi.org/10.3390/su12208618
   Web of Science ®Google Scholar
• Osei, H. V., Kwateng, K. O., & Boateng, K. A. (2022). Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic. Education and Information Technologies, 27(8), 10705–10730. https://doi.org/10.1007/s10639-022-11047-y
   PubMed Web of Science ®Google Scholar
• Pilli, O., & Admiraal, W. (2017). Students’ learning outcomes in Massive Open Online Courses (MOOCs): Some suggestions for course design. Yuksekogretim Dergisi, 7(1), 46–71. https://doi.org/10.2399/yod.17.001
   Google Scholar
• Qiao, P., Zhu, X., Guo, Y., Sun, Y., & Qin, C. (2021). The development and adoption of online learning in pre- and post-COVID-19: Combination of technological system evolution theory and unified theory of acceptance and use of technology. Journal of Risk and Financial Management, 14(4), 162. https://doi.org/10.3390/jrfm14040162
   Web of Science ®Google Scholar
• Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A.-E., & Bañeres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in Higher Education. Computers & Education, 182, 104468. https://doi.org/10.1016/j.compedu.2022.104468
   Web of Science ®Google Scholar
• Rahi, S., & Ghani, M. A. (2019). Investigating the role of UTAUT and e-service quality in internet banking adoption setting. The TQM Journal, 31(3), 491–506. https://doi.org/10.1108/tqm-02-2018-0018
   Google Scholar
• Rajabalee, Y. B., & Santally, M. I. (2020). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26(3), 2623–2656. https://doi.org/10.1007/s10639-020-10375-1
   PubMed Web of Science ®Google Scholar
• Ray, A., Bala, P. K., & Dwivedi, Y. K. (2020). Exploring values affecting e-Learning adoption from the user-generated-content: A consumption-value-theory perspective. Journal of Strategic Marketing, 29(5), 430–452. https://doi.org/10.1080/0965254x.2020.1749875
   Web of Science ®Google Scholar
• Raza, M., Abd Rani, S. H., & Md Isa, N. (2022). Do brand experience and customer engagement matter for brand love? A case of apparel industry of Pakistan. Psychology and Education, 58(5), 673–683.
   Google Scholar
• Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2020). 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
   Web of Science ®Google Scholar
• Razmerita, L., Kirchner, K., Hockerts, K., & Tan, C.-W. (2019). Modeling collaborative intentions and behavior in digital environments: The case of a Massive Open Online Course (MOOC). Academy of Management Learning & Education, 19(4), 469–502. https://doi.org/10.5465/amle.2018.0056
   Web of Science ®Google Scholar
• Risdianto, E., Wachidi, W., Riyanto, R., Alexon, A., Fathurrochman, I., & Kusen, K. (2021). Blended learning model based on Massive Open Online Courses (MOOCs) assisted by augmented reality (BMA) model as the electronic learning media in the pandemic Covid-19. AL-ISHLAH: Jurnal Pendidikan, 13(1), 228–241. https://doi.org/10.35445/alishlah.v13i1.470
   Google Scholar
• Rodríguez-Ardura, I., & Meseguer-Artola, A. (2016). What leads people to keep on e-learning? An empirical analysis of users’ experiences and their effects on continuance intention. Interactive Learning Environments, 24(6), 1030–1053. https://doi.org/10.1080/10494820.2014.926275
   Web of Science ®Google Scholar
• Rokhman, F., Mukhibad, H., Hapsoro, B. B., & Nurkhin, A. (2022). E-learning evaluation during the COVID-19 pandemic era based on the updated of Delone and McLean information systems success model. Cogent Education, 9(1), 1–25. https://doi.org/10.1080/2331186X.2022.2093490
   Web of Science ®Google Scholar
• Sanne, P. N. C., & Wiese, M. (2018). The theory of planned behaviour and user engagement applied to Facebook advertising. South African Journal of Information Management, 20(1), 1–10. https://doi.org/10.4102/sajim.v20i1.915
   Web of Science ®Google Scholar
• Schaupp, L. C., Carter, L., & McBride, M. E. (2010). E-file adoption: A study of U.S. taxpayers’ intentions. Computers in Human Behavior, 26(4), 636–644. https://doi.org/10.1016/j.chb.2009.12.017
   Web of Science ®Google Scholar
• Seetah, S. R., & Hosanoo, Z. A. (2021). An evaluation of the impact of confinement on the quality of e-learning in higher education institutions. Quality Assurance in Education, ahead-of-print(ahead-of-print). https://doi.org/10.1108/qae-03-2021-0043
   Google Scholar
• Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). John Wiley & Sons.
   Google Scholar
• Sharma, S. K., Al-Badi, A., Rana, N. P., & Al-Azizi, L. (2018). Mobile applications in government services (Mg-app) from user’s perspectives: A predictive modelling approach. Government Information Quarterly, 35(4), 557–568. https://doi.org/10.1016/j.giq.2018.07.002
   Web of Science ®Google Scholar
• Sharma, S. K., Al-Badi, A., Rana, N. P., & Al-Azizi, L. (2021). Mobile applications in government services (Mg-app) from user’s perspectives: A predictive modelling approach. Government Information Quarterly, 35(4), 557–568. https://doi.org/10.1016/j.giq.2018.07.002/
   Google Scholar
• Sun, J. C.-Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204. https://doi.org/10.1111/j.1467-8535.2010.01157.x
   Web of Science ®Google Scholar
• Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, 102269. https://doi.org/10.1016/j.ijinfomgt.2020.102269
   Web of Science ®Google Scholar
• Tani, M., Gheith, M. H., & Papaluca, O. (2021). Drivers of student engagement in higher education: A behavioral reasoning theory perspective. Higher Education, 82(3), 499–518. https://doi.org/10.1007/s10734-020-00647-7
   Web of Science ®Google Scholar
• Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561. https://doi.org/10.2307/249633
   Web of Science ®Google Scholar
• Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125. https://doi.org/10.2307/249443
   Web of Science ®Google Scholar
• Tsai, Y.-Y., Chao, C.-M., Lin, H.-M., & Cheng, B.-W. (2017). Nursing staff intentions to continuously use a blended e-learning system from an integrative perspective. Quality & Quantity, 52(6), 2495–2513. https://doi.org/10.1007/s11135-017-0540-5
   Google Scholar
• Tseng, T. H., Lin, S., Wang, Y.-S., & Liu, H.-X. (2019). Investigating teachers’ adoption of MOOCs: The perspective of UTAUT2. Interactive Learning Environments, 30(4), 635–650. https://doi.org/10.1080/10494820.2019.1674888
   Web of Science ®Google Scholar
• Uden, J. M. V., Ritzen, H., & Pieters, J. M. (2014). Engaging students: The role of teacher beliefs and interpersonal teacher behavior in fostering student engagement in vocational education. Teaching & Teacher Education, 37, 21–32. https://doi.org/10.1016/j.tate.2013.08.005
   Web of Science ®Google Scholar
• Venkatesh, V. (2021). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1–2), 641–652. https://doi.org/10.1007/s10479-020-03918-9
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Venkatesh, V., Thong, J., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428
   Web of Science ®Google Scholar
• 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, 36(1), 157–178. https://doi.org/10.2307/41410412
   Web of Science ®Google Scholar
• Wilson, C., Broughan, C., & Marselle, M. (2019). A new framework for the design and evaluation of a learning institution’s student engagement activities. Studies in Higher Education, 1–14. https://doi.org/10.1080/03075079.2018.1469123
   Web of Science ®Google Scholar
• Wu, Y.-T., & Tsai, C.-C. (2006). University students’ internet attitudes and internet self-efficacy: AStudy at three universities in Taiwan. CyberPsychology & Behavior, 9(4), 441–450. https://doi.org/10.1089/cpb.2006.9.441
   PubMedGoogle Scholar
• Xerri, M. J., Radford, K., & Shacklock, K. (2018). Student engagement in academic activities: A social support perspective. Higher Education, 75(4), 589–605. https://doi.org/10.1007/s10734-017-0162-9
   Web of Science ®Google Scholar
• Xu, W., Shen, Z.-Y., Lin, S.-J., & Chen, J.-C. (2022). Improving the behavioral intention of continuous online learning among learners in higher education during COVID-19. Frontiers in Psychology, 13, 13. https://doi.org/10.3389/fpsyg.2022.857709
   Web of Science ®Google Scholar
• Yang, Y., Asaad, Y., & Dwivedi, Y. (2017). Examining the impact of gamification on intention of engagement and brand attitude in the marketing context. Computers in Human Behavior, 73, 459–469. https://doi.org/10.1016/j.chb.2017.03.066
   Web of Science ®Google Scholar
• Yeboah, D., & Nyagorme, P. (2022). Students’ acceptance of WhatsApp as teaching and learning tool in distance higher education in sub-Saharan Africa. Cogent Education, 9(1). https://doi.org/10.1080/2331186x.2022.2077045
   Web of Science ®Google Scholar
• Yi, Y. J., You, S., & Bae, B. J. (2016). The influence of smartphones on academic performance. Library Hi Tech, 34(3), 480–499. https://doi.org/10.1108/lht-04-2016-0038
   Web of Science ®Google Scholar
• Yuan, Y.-P., Tan, G. W.-H., Ooi, K.-B., & Lim, W.-L. (2021). Can COVID-19 pandemic influence experience response in mobile learning? Telematics and Informatics, 64, 101676. https://doi.org/10.1016/j.tele.2021.101676
   PubMed Web of Science ®Google Scholar
• Yunus, M. M., Ang, W. S., & Hashim, H. (2021). Factors Affecting Teaching English as a Second Language (TESL) postgraduate students’ behavioural intention for online learning during the COVID-19 Pandemic. Sustainability, 13(6), 3524. https://doi.org/10.3390/su13063524
   Web of Science ®Google Scholar
• Zhang, Z., Cao, T., Shu, J., & Liu, H. (2021). Identifying key factors affecting college students’ adoption of the e-learning system in mandatory blended learning environments. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2020.1723113
   Web of Science ®Google Scholar
• Zhang, K., & Yu, Z. (2022). Extending the UTAUT model of gamified english vocabulary applications by adding new personality constructs. Sustainability, 14(10), 6259. https://doi.org/10.3390/su14106259
   Web of Science ®Google Scholar