References
- 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
- Al-Adwan, A. S., Al-Madadha, A., & Zvirzdinaite, Z. (2018). Modeling students’ readiness to adopt mobile learning in higher education: An empirical study. The International Review of Research in Open and Distributed Learning, 19(1), 221–241. (1 SE-Research Articles). https://doi.org/10.19173/irrodl.v19i1.3256
- Aljukhadar, M., Senecal, S., & Nantel, J. (2014). Is more always better? Investigating the task-technology fit theory in an online user context. Information & Management, 51(4), 391–397. https://doi.org/10.1016/j.im.2013.10.003
- 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(3), 1–20. https://doi.org/10.1007/s10639-020-10219-y
- Alonso, F., Manrique, D., Martinez, L., & Vines, J. M. (2011). How blended learning reduces underachievement in higher education: An experience in teaching computer sciences. IEEE Transactions on Education, 54(3), 471–478. https://doi.org/10.1109/TE.2010.2083665
- Anthony, B., Kamaludin, A., Romli, A., Raffei, A. F. M., Nincarean AL Eh Phon, D., Abdullah, A., Ming, G. L., Shukor, N. A., Nordin, M. S., & Baba, S. (2019). Exploring the role of blended learning for teaching and learning effectiveness in institutions of higher learning: An empirical investigation. Education and Information Technologies, 24(6), 3433–3466. https://doi.org/10.1007/s10639-019-09941-z
- Arroyo, I., Woolf, B. P., Royer, J. M., & Tai, M. (2009). Affective gendered learning companions. Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems That Care: From Knowledge Representation to Affective Modelling, Brighton, UK, 41–48.
- Bartlett, R. M., & Strough, J. (2003). Multimedia versus traditional course instruction in introductory social psychology. Teaching of Psychology, 30(4), 335–338. https://doi.org/10.1207/S15328023TOP3004_07
- Baylor, A. L., & Kim, Y. (2004). Pedagogical agent design: The impact of agent realism, gender, ethnicity, and instructional role. In J. C. Lester, R. M. Vicari, & F. Paraguaçu (Eds.), Intelligent Tutoring Systems (pp. 592–603). Springer Berlin Heidelberg.
- Beaunoyer, E., Dupéré, S., & Guitton, M. J. (2020). COVID-19 and digital inequalities: Reciprocal impacts and mitigation strategies. Computers in Human Behavior, 111, 106424. https://doi.org/10.1016/j.chb.2020.106424
- Cabero-Almenara, J., Fernández-Batanero, J. M., & Barroso-Osuna, J. (2019). Adoption of augmented reality technology by university students. Heliyon, 5(5), e01597. https://doi.org/10.1016/j.heliyon.2019.e01597
- Chang, H. H. (2010). Task-technology fit and user acceptance of online auction. In International Journal of Human-Computer Studies (Vol. 68, Issues 1–2, pp. 69–89). Elsevier Science. https://doi.org/10.1016/j.ijhcs.2009.09.010
- Condie, R., & Livingston, K. (2007). Blending online learning with traditional approaches: Changing practices. British Journal of Educational Technology, 38(2), 337–348. https://doi.org/10.1111/j.1467-8535.2006.00630.x
- Dalal, M. (2014). Impact of Multi-Media Tutorials in a Computer Science Laboratory Course–An Empirical Study. Electronic Journal of E-Learning, 12(4), 366–374. https://eric.ed.gov/?id=EJ1035651
- Dang, Y. M., Zhang, Y. G., Ravindran, S., & Osmonbekov, T. (2016). Examining student satisfaction and gender differences in technology-supported, blended learning. Journal of Information Systems Education, 27(2), 119. https://aisel.aisnet.org/jise/vol27/iss2/5
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008
- 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
- de Jong, P. G. M., Pickering, J. D., Hendriks, R. A., Swinnerton, B. J., Goshtasbpour, F., & Reinders, M. E. J. (2020). Twelve tips for integrating massive open online course content into classroom teaching. Medical Teacher, 42(4), 393–397. https://doi.org/10.1080/0142159X.2019.1571569
- Demaidi, M. N., Qamhieh, M., & Afeefi, A. (2019). Applying blended learning in programming courses. IEEE Access, 7, 156824–156833. https://doi.org/10.1109/ACCESS.2019.2949927
- Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9–21. https://doi.org/10.1016/S0378-7206(98)00101-3
- Eagly, A. H., Mladinic, A., & Otto, S. (1991). Are women evaluated more favorably than men?: An analysis of attitudes, beliefs, and emotions. Psychology of Women Quarterly, 15(2), 203–216. https://doi.org/10.1111/j.1471-6402.1991.tb00792.x
- Estriegana, R., Medina-Merodio, J.-A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers & Education, 135, 1–14. https://doi.org/10.1016/j.compedu.2019.02.010
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
- Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105. https://doi.org/10.1016/j.iheduc.2004.02.001
- González-Gómez, F., Guardiola, J., Martín Rodríguez, Ó., & Montero Alonso, M. Á. (2012). Gender differences in e-learning satisfaction. Computers & Education, 58(1), 283–290. https://doi.org/10.1016/j.compedu.2011.08.017
- Goodhue, D. L., Klein, B. D., & March, S. T. (2000). User evaluations of IS as surrogates for objective performance. Information & Management, 38(2), 87–101. https://doi.org/10.1016/S0378-7206(00)00057-4
- Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689
- Guitton, M. J. (2020). Cyberpsychology research and COVID-19. Computers in Human Behavior, 111, 106357. https://doi.org/10.1016/j.chb.2020.106357
- Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis, (Vol. 5). Prentice hall Upper Saddle River, NJ. Issue 3.
- Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications. https://books.google.co.th/books?id=JDWmCwAAQBAJ
- Hargittai, E. (2003). The digital divide and what to do about it. Springer Nature.
- 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
- Hoic-Bozic, N., Mornar, V., & Boticki, I. (2009). A blended learning approach to course design and implementation. IEEE Transactions on Education, 52(1), 19–30. https://doi.org/10.1109/TE.2007.914945
- Hoogerheide, V., Loyens, S. M. M., & van Gog, T. (2016). Learning from video modeling examples: Does gender matter? Instructional Science, 44(1), 69–86. https://doi.org/10.1007/s11251-015-9360-y
- Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<95::aid-smj13>3.0.CO;2-7
- Iivari, N., Sharma, S., & Ventä-Olkkonen, L. (2020). Digital transformation of everyday life – How COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? International Journal of Information Management, 55, 102183. https://doi.org/10.1016/j.ijinfomgt.2020.102183
- Joe, H., L., H. C., B., R. A., & Loong, C. A. Y. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
- Johnson, D. A., & Christensen, J. (2011). A comparison of simplified-visually rich and traditional presentation styles. Teaching of Psychology, 38(4), 293–297. https://doi.org/10.1177/0098628311421333
- Johnson, R. D. (2011). Gender differences in E-learning: Communication, social presence, and learning outcomes. Journal of Organizational and End User Computing, 23(1), 79–94. https://doi.org/10.4018/joeuc.2011010105
- Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., Barman, B., Das, P., & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Children and Youth Services Review, 116, 105194. https://doi.org/10.1016/j.childyouth.2020.105194
- Kay, R. H. (2009). Examining gender differences in attitudes toward interactive classroom communications systems (ICCS). Computers & Education, 52(4), 730–740. https://doi.org/10.1016/j.compedu.2008.11.015
- Kay, R. H. (2012). Exploring the use of video podcasts in education: A comprehensive review of the literature. Computers in Human Behavior, 28(3), 820–831. https://doi.org/10.1016/j.chb.2012.01.011
- Keil, M., Tan, B. C. Y., Wei, -K.-K., Saarinen, T., Tuunainen, V., & Wassenaar, A. (2000). A cross-cultural study on escalation of commitment behavior in software projects. MIS Quarterly, 24(2), 299–325. https://doi.org/10.2307/3250940
- Kissi, P. S., Nat, M., & Armah, R. B. (2018). The effects of learning–family conflict, perceived control over time and task-fit technology factors on urban–rural high school students’ acceptance of video-based instruction in flipped learning approach. Educational Technology Research and Development, 66(6), 1547–1569. https://doi.org/10.1007/s11423-018-9623-9
- Kortum, P., & Sorber, M. (2015). Measuring the usability of mobile applications for phones and tablets. International Journal of Human–Computer Interaction, 31(8), 518–529. https://doi.org/10.1080/10447318.2015.1064658
- Lah, U., Lewis, J. R., & Šumak, B. (2020). Perceived usability and the modified technology acceptance model. International Journal of Human–Computer Interaction, 36(13), 1216–1230. https://doi.org/10.1080/10447318.2020.1727262
- Lai, W.-T. (2015). Exploring Use Intention of a Smart Bike-Sharing System-Extending Technology Acceptance Model with Trust BT - LISS 2014: Proceedings of 4th International Conference on Logistics, Informatics and Service Science (Z. Zhang, Z. M. Shen, J. Zhang, & R. Zhang (eds.); pp. 1597–1603). USA: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-43871-8_230
- Lee, D. Y., & Lehto, M. R. (2013). User acceptance of youtube for procedural learning: An extension of the technology acceptance model. Computers & Education, 61(Suppl. C), 193–208. https://doi.org/10.1016/j.compedu.2012.10.001
- Lee, D. Y., & Ryu, H. (2013). Learner acceptance of a multimedia-based learning system. International Journal of Human–Computer Interaction, 29(6), 419–437. https://doi.org/10.1080/10447318.2012.715278
- Lee, D. Y., & Shin, D.-H. (2011). Effects of spatial ability and richness of motion cue on learning in mechanically complex domain. Computers in Human Behavior, 27(5), 1665–1674. https://doi.org/10.1016/j.chb.2011.02.005
- Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4
- Li, L.-Y. (2019). Effect of prior knowledge on attitudes, behavior, and learning performance in video lecture viewing. International Journal of Human–Computer Interaction, 35(4–5), 415–426. https://doi.org/10.1080/10447318.2018.1543086
- Lin, T.-C., & Huang, -C.-C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410–417. https://doi.org/10.1016/j.im.2008.06.004
- Maniar, N., Bennett, E., Hand, S., & Allan, G. (2008). The effect of mobile phone screen size on video based learning (Vol. 3(4), pp. 51–61). International Academy Publishing (IAP).
- 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. https://doi.org/10.1007/s10209-014-0348-1
- Marko, S., Jörg, H., & M., R. C. (2011). Multigroup analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. In M. Sarstedt, M. Schwaiger, & C. R. Taylor (Eds.), Measurement and research methods in international marketing (Vol. 22, pp. 195–218). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2011)0000022012
- Mikalef, P., Pappas, I. O., & Giannakos, M. N. (2016). Investigating determinants of video-based learning acceptance BT - state-of-the-art and future directions of smart learning (Y. Li, M. Chang, M. Kravcik, E. Popescu, R. Huang, Kinshuk, & N.-S. Chen, eds). Springer Singapore. 483–491.
- Molnar, A. (2017). Content type and perceived multimedia quality in mobile learning. Multimedia Tools and Applications, 76(20), 21613–21627. https://doi.org/10.1007/s11042-016-4062-2
- 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
- Nagy, J. T. (2018). Evaluation of online video usage and learning satisfaction: An extension of the technology acceptance model. The International Review of Research in Open and Distributed Learning, 19(1), 160–185. https://doi.org/10.19173/irrodl.v19i1.2886
- Nai-Hua, C. (2019). Extending a TAM–TTF model with perceptions toward telematics adoption. Asia Pacific Journal of Marketing and Logistics, 31(1), 37–54. https://doi.org/10.1108/APJML-02-2018-0074
- Nguyen, Q. N., Ta, A., & Prybutok, V. (2019). An integrated model of voice-user interface continuance intention: The gender effect. International Journal of Human–Computer Interaction, 35(15), 1362–1377. https://doi.org/10.1080/10447318.2018.1525023
- O’Callaghan, F. V., Neumann, D. L., Jones, L., & Creed, P. A. (2017). The use of lecture recordings in higher education: A review of institutional, student, and lecturer issues. Education and Information Technologies, 22(1), 399–415. https://doi.org/10.1007/s10639-015-9451-z
- Ozogul, G., Johnson, A. M., Atkinson, R. K., & Reisslein, M. (2013). Investigating the impact of pedagogical agent gender matching and learner choice on learning outcomes and perceptions. Computers & Education, 67, 36–50. https://doi.org/10.1016/j.compedu.2013.02.006
- P., N. P., Rajani, M., Georg, G., Lynnea, E., & Raghu, R. (2018). Towards an inclusive digital literacy framework for digital India. Education + Training, 60(6), 516–528. https://doi.org/10.1108/ET-03-2018-0061
- Pagani, M. (2006). Determinants of adoption of high speed data services in the business market: Evidence for a combined technology acceptance model with task technology fit model. Information & Management, 43(7), 847–860. https://doi.org/10.1016/j.im.2006.08.003
- Pal, D., Arpnikanondt, C., Funilkul, S., & Chutimaskul, W. (2020). The adoption analysis of voice based smart IoT products. IEEE Internet of Things Journal, 7(11), 1. https://doi.org/10.1109/JIOT.2020.2991791
- Pal, D., & Triyason, T. (2018). User intention towards a music streaming service: A Thailand case study. KnE Social Sciences, 3(1), 1–16. https://doi.org/10.18502/kss.v3i1.1393
- Pal, D., & Vanijja, V. (2017a). A no-reference modular video quality prediction model for H.265/HEVC and VP9 codecs on a mobile device. Advances in Multimedia, 2017, 1–19. https://doi.org/10.1155/2017/8317590
- Pal, D., & Vanijja, V. (2017b). Model for mobile online video viewed on samsung galaxy note 5. KSII Transactions on Internet and Information Systems, 11(11), 5392–5418. https://doi.org/10.3837/tiis.2017.11.012
- Pal, D., & Vanijja, V. (2020). Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India. Children and Youth Services Review, 119, 105535. https://doi.org/10.1016/j.childyouth.2020.105535
- Pappas, I. O., Giannakos, M. N., & Mikalef, P. (2017). Investigating students’ use and adoption of with-video assignments: Lessons learnt for video-based open educational resources. Journal of Computing in Higher Education, 29(1), 160–177. https://doi.org/10.1007/s12528-017-9132-6
- Pappas, I. O., Mikalef, P., & Giannakos, M. (2016). Video-based learning adoption: A typology of learners. SE@VBL@LAK, 1579, 34–41. http://ceur-ws.org/Vol-1579/
- Park, C., Kim, D., Cho, S., & Han, H.-J. (2019). Adoption of multimedia technology for learning and gender difference. Computers in Human Behavior, 92, 288–296. https://doi.org/10.1016/j.chb.2018.11.029
- Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150–162. http://www.jstor.org/stable/jeductechsoci.12.3.150
- Patrick, M., O., P. I., & Michail, G. (2016). An integrative adoption model of video-based learning. The International Journal of Information and Learning Technology, 33(4), 219–235. https://doi.org/10.1108/IJILT-01-2016-0007
- Ramírez-Correa, P., Mariano-Melo, A., & Alfaro-Pérez, J. (2019). Predicting and explaining the acceptance of social video platforms for learning: The case of Brazilian youtube users. Sustainability, 11(24), 7115. https://doi.org/10.3390/su11247115
- Rismark, M., & Sølvberg, A. M. (2019). Video as a learner scaffolding tool. International Journal of Learning, Teaching and Educational Research, 18(1), 62–75. https://doi.org/10.26803/ijlter.18.1.5
- Sablić, M., Mirosavljević, A., & Škugor, A. (2020). Video-based learning (VBL)—past, present and future: An overview of the research published from 2008 to 2019. Technology, Knowledge and Learning, 1–17. https://doi.org/10.1007/s10758-020-09455-5
- Sarstedt, M., & Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: A software review. Journal of Marketing Analytics, 7(3), 196–202. https://doi.org/10.1057/s41270-019-00058-3
- Scagnoli, N. I., Choo, J., & Tian, J. (2019). Students’ insights on the use of video lectures in online classes. British Journal of Educational Technology, 50(1), 399–414. https://doi.org/10.1111/bjet.12572
- Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
- Scherer, R., Siddiq, F., & Tondeur, J. (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, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
- Shao, Z., Zhang, L., Li, X., & Guo, Y. (2019). Antecedents of trust and continuance intention in mobile payment platforms: The moderating effect of gender. Electronic Commerce Research and Applications, 33, 100823. https://doi.org/10.1016/j.elerap.2018.100823
- Shen, R., Wang, M., Gao, W., Novak, D., & Tang, L. (2009). Mobile learning in a large blended computer science classroom: System function, pedagogies, and their impact on learning. IEEE Transactions on Education, 52(4), 538–546. https://doi.org/10.1109/TE.2008.930794
- Shih, -Y.-Y., & Chen, C.-Y. (2013). The study of behavioral intention for mobile commerce: Via integrated model of TAM and TTF. Quality & Quantity, 47(2), 1009–1020. https://doi.org/10.1007/s11135-011-9579-x
- Surgenor, D., Hollywood, L., Furey, S., Lavelle, F., McGowan, L., Spence, M., Raats, M., McCloat, A., Mooney, E., Caraher, M., & Dean, M. (2017). The impact of video technology on learning: A cooking skills experiment. Appetite, 114, 306–312. https://doi.org/10.1016/j.appet.2017.03.037
- Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on E-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163–184. https://doi.org/10.2190/EC.51.2.b
- Terzis, V., & Economides, A. A. (2011). Computer based assessment: Gender differences in perceptions and acceptance. Computers in Human Behavior, 27(6), 2108–2122. https://doi.org/10.1016/j.chb.2011.06.005
- 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
- Turan, Z., & Cetintas, H. B. (2020). Investigating university students’ adoption of video lessons. Open Learning: The Journal of Open, Distance and e-Learning, 35(2), 122–139. https://doi.org/10.1080/02680513.2019.1691518
- Ulrich, F., Helms, N. H., Frandsen, U. P., & Rafn, A. V. (2019). Learning effectiveness of 360° video: Experiences from a controlled experiment in healthcare education. Interactive Learning Environments, 26(1), 1–14. https://doi.org/10.1080/10494820.2019.1579234
- UNESCO. (2020). Quality education. In COVID-19 educational disruption and response. https://en.unesco.org/news/covid-19-educational-disruption-and-response
- UNESCO. (n.d.). No title. COVID-19 Educational Disruption and Response. Retrieved June 30, 2020, from https://en.unesco.org/news/covid-19-educational-disruption-and-response
- van Dijk, J., & Hacker, K. (2003). The digital divide as a complex and dynamic phenomenon. The Information Society, 19(4), 315–326. https://doi.org/10.1080/01972240309487
- van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852. https://doi.org/10.1016/j.compedu.2006.09.001
- 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
- Watson, S. L. (2017). Facilitating attitudinal learning in an animal behaviour and welfare MOOC. Open Learning: The Journal of Open, Distance and e-Learning, 32(3), 262–278. https://doi.org/10.1080/02680513.2017.1357465
- Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028
- Wu, J., & Du, H. (2012). Toward a better understanding of behavioral intention and system usage constructs. European Journal of Information Systems, 21(6), 680–698. https://doi.org/10.1057/ejis.2012.15
- Yen, D. C., Wu, C.-S., Cheng, -F.-F., & Huang, Y.-W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906–915. https://doi.org/10.1016/j.chb.2010.02.005
- Yu, T.-K., & Yu, T.-Y. (2010). Modelling the factors that affect individuals’ utilisation of online learning systems: An empirical study combining the task technology fit model with the theory of planned behaviour. British Journal of Educational Technology, 41(6), 1003–1017. https://doi.org/10.1111/j.1467-8535.2010.01054.x
- Zhang, D., Zhao, J. L., Zhou, L., & Nunamaker, J. F. (2004). Can E-Learning Replace Classroom Learning? Communications of the ACM, 47(5), 75–79. https://doi.org/10.1145/986213.986216
- Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker, J. F. (2006). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & Management, 43(1), 15–27. https://doi.org/10.1016/j.im.2005.01.004
- Zhou, J., Rau, P.-L. P., & Salvendy, G. (2014). Older adults’ text entry on smartphones and tablets: Investigating effects of display size and input method on acceptance and performance. International Journal of Human–Computer Interaction, 30(9), 727–739. https://doi.org/10.1080/10447318.2014.924348
- Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767. https://doi.org/10.1016/j.chb.2010.01.013