The Influence of Social Isolation, Technostress, and Personality on the Acceptance of Online Meeting Platforms during the COVID-19 Pandemic

References

• Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036
   Web of Science ®Google Scholar
• Agyei, J., Sun, S., Abrokwah, E., Penney, E. K., & Ofori-Boafo, R. (2020). Mobile banking adoption: Examining the role of personality traits. SAGE Open, 10(2), 215824402093215–215824402093291. https://doi.org/10.1177/2158244020932918
   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
• Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2020). Fear from COVID-19 and technology adoption: The impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 1–16. https://doi.org/10.1080/10494820.2020.1830121
   Web of Science ®Google Scholar
• Alohali, M., Clarke, N., Li, F., & Furnell, S. (2018). Identifying and predicting the factors affecting end-users’ risk-taking behavior. Information & Computer Security, 26(3), 306–326. https://doi.org/10.1108/ICS-03-2018-0037
   Web of Science ®Google Scholar
• Asanov, I., Flores, F., McKenzie, D., Mensmann, M., & Schulte, M. (2021). Remote-learning, time-use, and mental health of Ecuadorian high-school students during the COVID-19 quarantine. World Development, 138, 105225. https://doi.org/10.1016/j.worlddev.2020.105225
   PubMed Web of Science ®Google Scholar
• Bailey, D. (2022). Interactivity during Covid-19: Mediation of learner interactions on social presence and expected learning outcome within videoconference EFL courses. Journal of Computers in Education, 9(2), 291–313. https://doi.org/10.1007/s40692-021-00204-w
   Web of Science ®Google Scholar
• Banerjee, D., & Rai, M. (2020). Social isolation in Covid-19: The impact of loneliness. The International Journal of Social Psychiatry, 66(6), 525–527. https://doi.org/10.1177/0020764020922269
   PubMed Web of Science ®Google Scholar
• Bennell, K. L., Lawford, B. J., Metcalf, B., Mackenzie, D., Russell, T., van den Berg, M., Finnin, K., Crowther, S., Aiken, J., Fleming, J., & Hinman, R. S. (2021). Physiotherapists and patients report positive experiences overall with telehealth during the COVID-19 pandemic: A mixed-methods study. Journal of Physiotherapy, 67(3), 201–209. https://doi.org/10.1016/j.jphys.2021.06.009
   PubMed Web of Science ®Google Scholar
• Bennett, A. A., Campion, E. D., Keeler, K. R., & Keener, S. K. (2021). Videoconference fatigue? Exploring changes in fatigue after videoconference meetings during COVID-19. The Journal of Applied Psychology, 106(3), 330–344. https://doi.org/10.1037/apl0000906
   PubMed Web of Science ®Google Scholar
• Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588
   Web of Science ®Google Scholar
• Bergner, R. M. (2020). What is personality? Two myths and a definition. New Ideas in Psychology, 57, 100759. https://doi.org/10.1016/j.newideapsych.2019.100759
   Web of Science ®Google Scholar
• Bernotat, J., & Eyssel, F. (2017). A robot at home – How affect, technology commitment, and personality traits influence user experience in an intelligent robotics apartment. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (pp. 641–646). IEEE. https://doi.org/10.1109/ROMAN.2017.8172370
   Google Scholar
• Bielik, L., Samoilenko, O., Mudretska, H., Voloshanivska, T., & Titunina, K. (2020). Features of criminal proceedings (pre-trial and trial investigation) in the time of pandemic COVID-19. Ius Humani. Law Journal, 9(2), 203–224. https://doi.org/10.31207/ih.v9i2.251
   Google Scholar
• Borle, P., Reichel, K., Niebuhr, F., & Voelter-Mahlknecht, S. (2021). How are techno-stressors associated with mental health and work outcomes? A systematic review of occupational exposure to information and communication technologies within the technostress model. International Journal of Environmental Research and Public Health, 18(16), 8673. https://doi.org/10.3390/ijerph18168673
   PubMed Web of Science ®Google Scholar
• Brearly, T. W., Shura, R. D., Martindale, S. L., Lazowski, R. A., Luxton, D. D., Shenal, B. V., & Rowland, J. A. (2017). Neuropsychological test administration by videoconference: A systematic review and meta-analysis. Neuropsychology Review, 27(2), 174–186. https://doi.org/10.1007/s11065-017-9349-1
   PubMed Web of Science ®Google Scholar
• Brod, C. (1984). Technostress: The human cost of the computer revolution. Addison-Wesley.
   Google Scholar
• Califf, C. B., Sarker, S., & Sarker, S. (2020). The bright and dark sides of technostress: A mixed-methods study involving healthcare IT. MIS Quarterly, 44(2), 809–856. https://doi.org/10.25300/MISQ/2020/14818
   Web of Science ®Google Scholar
• Camadan, F., Reisoglu, I., Ursavas, Ö. F., & Mcilroy, D. (2018). How teachers’ personality affect on their behavioral intention to use tablet PC. The International Journal of Information and Learning Technology, 35(1), 12–28. https://doi.org/10.1108/IJILT-06-2017-0055
   Web of Science ®Google Scholar
• Camarena, L., & Fusi, F. (2022). Always connected: Technology use increases technostress among public managers. The American Review of Public Administration, 52(2), 154–168. https://doi.org/10.1177/02750740211050387
   Web of Science ®Google Scholar
• Chen, K., & Chan, A. (2013). Use or non-use of gerontechnology – A qualitative study. International Journal of Environmental Research and Public Health, 10(10), 4645–4666. https://doi.org/10.3390/ijerph10104645
   PubMed Web of Science ®Google Scholar
• Chen, Z., Chen, L., & Chen, H. (2021). The impact of COVID-19 on the clinical trial. PLoS One, 16(5), e0251410. https://doi.org/10.1371/journal.pone.0251410
   PubMed Web of Science ®Google Scholar
• Chuang, S., Lin, S., Chang, T., & Kaewmeesri, R. (2017). Behavioral intention of using social networking site: A comparative study of Taiwanese and Thai Facebook users. International Journal of Technology and Human Interaction, 13(1), 61–81. https://doi.org/10.4018/IJTHI.2017010104
   Google Scholar
• Cicha, K., Rizun, M., Rutecka, P., & Strzelecki, A. (2021). COVID-19 and higher education: First-year students’ expectations toward distance learning. Sustainability, 13(4), 1889. https://doi.org/10.3390/su13041889
   Web of Science ®Google Scholar
• Cooper, A. J., Smillie, L. D., & Corr, P. J. (2010). A confirmatory factor analysis of the Mini-IPIP five-factor model personality scale. Personality and Individual Differences, 48(5), 688–691. https://doi.org/10.1016/j.paid.2010.01.004
   Web of Science ®Google Scholar
• Correa, T., Hinsley, A. W., & de Zúñiga, H. G. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247–253. https://doi.org/10.1016/j.chb.2009.09.003
   Web of Science ®Google Scholar
• Costa, P., & McCrae, R. (1992). Normal personality assessment in clinical practice: The NEO personality inventory. Psychological Assessment, 4(1), 5–13. https://doi.org/10.1037/1040-3590.4.1.5
   Google Scholar
• Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. SAGE Publications.
   Google Scholar
• Cullum, C. M., Hynan, L. S., Grosch, M., Parikh, M., & Weiner, M. F. (2014). Teleneuropsychology: Evidence for video teleconference-based neuropsychological assessment. Journal of the International Neuropsychological Society, 20(10), 1028–1033. https://doi.org/10.1017/S1355617714000873
   PubMed Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research note – How does personality matter? Relating the five-factor model to technology acceptance and use. Information Systems Research, 19(1), 93–105. https://doi.org/10.1287/isre.1070.0153
   Web of Science ®Google Scholar
• Ebadi, S., & Bashiri, S. (2021). Psychological benefits and challenges of Ph.D. entrance exam virtual interviews during COVID-19 pandemic: Does gender play a role? Frontiers in Psychology, 12, 800715. https://doi.org/10.3389/fpsyg.2021.800715
   PubMed Web of Science ®Google Scholar
• Elliot, D. L., & Makara, K. A. (2021). An online community of international scholars: Enabling spaces for reciprocal academic and psychological support. Innovations in Education and Teaching International, 58(6), 693–703. https://doi.org/10.1080/14703297.2021.1991424
   Web of Science ®Google Scholar
• Etzioni, S., Hamadneh, J., Elvarsson, A. B., Esztergár-Kiss, D., Djukanovic, M., Neophytou, S. N., Sodnik, J., Polydoropoulou, A., Tsouros, I., Pronello, C., Thomopoulos, N., & Shiftan, Y. (2020). Modeling cross-national differences in automated vehicle acceptance. Sustainability, 12(22), 9765. https://doi.org/10.3390/su12229765
   Web of Science ®Google Scholar
• Feher, A., & Vernon, P. A. (2021). Looking beyond the Big Five: A selective review of alternatives to the Big Five model of personality. Personality and Individual Differences, 169, 110002. https://doi.org/10.1016/j.paid.2020.110002
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Gabr, H. M., Soliman, S. S., Allam, H. K., & Raouf, S. Y. A. (2021). Effects of remote virtual work environment during COVID-19 pandemic on technostress among Menoufia University Staff, Egypt: A cross-sectional study. Environmental Science and Pollution Research International, 28(38), 53746–53753. https://doi.org/10.1007/s11356-021-14588-w
   PubMed Web of Science ®Google Scholar
• Genis-Mendoza, A. D., Martínez-Magaña, J. J., López-Narváez, M. L., González-Castro, T. B., Juárez-Rojop, I. E., Nicolini, H., Tovilla-Zárate, C. A., & Castillo-Avila, R. G. (2021). Mental health problems due to social isolation during the COVID-19 pandemic in a Mexican population. Frontiers in Public Health, 9, 703450. https://doi.org/10.3389/fpubh.2021.703450
   PubMed Web of Science ®Google Scholar
• Grady, Z. J., Gallo, L. K., Lin, H., Magod, B. L., Coulthard, S. L., Flink, B. J., Knauer, E. M., Winer, J. H., Papandria, D., & Pettitt, B. J. (2022). From the operating room to online: Medical student surgery education in the time of COVID-19. The Journal of Surgical Research, 270, 145–150. https://doi.org/10.1016/j.jss.2021.08.020
   PubMed Web of Science ®Google Scholar
• Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough?: An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903
   Web of Science ®Google Scholar
• Hair, J. F. (2009). Multivariate data analysis. Pearson Education.
   Google Scholar
• Hanif, A., Naeem, S. B., & Bhatti, R. (2021). Library professional’s resistance to innovation: An empirical examination of technostress in Punjab’s university libraries. International Transaction Journal of Engineering Management & Applied Sciences & Technologies, 12(1), 1–8. https://doi.org/10.14456/ITJEMAST.2021.9
   Web of Science ®Google Scholar
• Hensel, J. M., Yang, R., Vigod, S. N., & Desveaux, L. (2021). Videoconferencing at home for psychotherapy in the postpartum period: Identifying drivers of successful engagement and important therapeutic conditions for meaningful use. Counselling and Psychotherapy Research, 21(3), 535–544. https://doi.org/10.1002/capr.12372
   Web of Science ®Google Scholar
• Heponiemi, T., Kaihlanen, A. M., Gluschkoff, K., Saranto, K., Nissinen, S., Laukka, E., & Vehko, T. (2021). The association between using a mobile version of an electronic health record and the well-being of nurses: Cross-sectional survey study. JMIR Medical Informatics, 9(7), e28729. https://doi.org/10.2196/28729
   PubMed Web of Science ®Google Scholar
• Hilsdorf, M., & Bullerjahn, C. (2021). Modulation of negative affect predicts acceptance of music streaming services, while personality does not. Frontiers in Psychology, 12, 659062. https://doi.org/10.3389/fpsyg.2021.659062
   PubMed Web of Science ®Google Scholar
• Huang, C. (2019). Social network site use and big five personality traits: A meta-analysis. Computers in Human Behavior, 97, 280–290. https://doi.org/10.1016/j.chb.2019.03.009
   Web of Science ®Google Scholar
• Huang, D., & Chueh, H. (2022). Usage intention model of mobile apps in membership application. Journal of Business Research, 139, 1255–1260. https://doi.org/10.1016/j.jbusres.2021.10.062
   Web of Science ®Google Scholar
• Hussein, N. H. (2022). COVID-19 in a collectivist culture: Social isolation and maintenance of social relations. International Journal of Sociology and Social Policy, 42(3/4), 276–288. https://doi.org/10.1108/IJSSP-07-2020-0297
   Web of Science ®Google Scholar
• Joo, Y. J., Park, S., & Lim, E. (2018). Factors influencing preservice teachers’ intention to use technology: TPACK, teacher self-efficacy, and technology acceptance model. Educational Technology & Society, 21(3), 48–59. https://www.jstor.org/stable/26458506.
   Web of Science ®Google Scholar
• Katayama, O., Lee, S., Bae, S., Makino, K., Chiba, I., Harada, K., Morikawa, M., Tomida, K., & Shimada, H. (2022). Are non-face-to-face interactions an effective strategy for maintaining mental and physical health? Archives of Gerontology and Geriatrics, 98, 104560. https://doi.org/10.1016/j.archger.2021.104560
   PubMed Web of Science ®Google Scholar
• Khan, A. N., Cao, X., & Pitafi, A. H. (2019). Personality traits as predictor of m-payment systems: A SEM-neural networks approach. Journal of Organizational and End User Computing, 31(4), 89–110. https://doi.org/10.4018/JOEUC.2019100105
   Web of Science ®Google Scholar
• Khan, A. U., Zhang, Z., Ahvanooey, M. T., & Rafique, W. (2022). Opinion mining towards blockchain technology adoption for accessing digital library resources. Aslib Journal of Information Management, 74(1), 135–157. https://doi.org/10.1108/AJIM-01-2021-0016
   Web of Science ®Google Scholar
• Kim, K. J., & Shin, D. H. (2015). An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Research, 25(4), 527–541. https://doi.org/10.1108/IntR-05-2014-0126
   Web of Science ®Google Scholar
• Kircaburun, K., Alhabash, S., Tosuntaş, Ş. B., & Griffiths, M. D. (2020). Uses and gratifications of problematic social media use among university students: A simultaneous examination of the Big Five of personality traits, social media platforms, and social media use motives. International Journal of Mental Health and Addiction, 18(3), 525–547. https://doi.org/10.1007/s11469-018-9940-6
   Web of Science ®Google Scholar
• Kisanga, D. H. (2016). Determinants of teachers’ attitudes towards e-learning in Tanzanian higher learning institutions. The International Review of Research in Open and Distributed Learning, 17(5), 109–125. https://doi.org/10.19173/irrodl.v17i5.2720
   Web of Science ®Google Scholar
• Landers, R. N., & Lounsbury, J. W. (2006). An investigation of Big Five and narrow personality traits in relation to Internet usage. Computers in Human Behavior, 22(2), 283–293. https://doi.org/10.1016/j.chb.2004.06.001
   Web of Science ®Google Scholar
• Latikka, R., Savela, N., Koivula, A., & Oksanen, A. (2021). Attitudes toward robots as equipment and coworkers and the impact of robot autonomy level. International Journal of Social Robotics, 13(7), 1747–1759. https://doi.org/10.1007/s12369-020-00743-9
   Web of Science ®Google Scholar
• Lee, J. (2016). Does stress from cell phone use increase negative emotions at work? Social Behavior and Personality, 44(5), 705–715. https://doi.org/10.2224/sbp.2016.44.5.705
   Web of Science ®Google Scholar
• Lee, M. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141. https://doi.org/10.1016/j.elerap.2008.11.006
   Web of Science ®Google Scholar
• Lee, M. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
   Web of Science ®Google Scholar
• Lee, V., Hew, J., Leong, L., Tan, G. W., & Ooi, K. (2021). The dark side of compulsory e-education: Are students really happy and learning during the COVID-19 pandemic? International Journal of Human–Computer Interaction, 38(12), 1168–1181. https://doi.org/10.1080/10447318.2021.1987681
   Web of Science ®Google Scholar
• Lee, E., Lee, J., Moon, J., & Sung, Y. (2015). Pictures speak louder than words: Motivations for using Instagram. Cyberpsychology, Behavior and Social Networking, 18(9), 552–556. https://doi.org/10.1089/cyber.2015.0157
   PubMed Web of Science ®Google Scholar
• Liang, Y., Lee, S., & Workman, J. (2022). How do consumers perceive mobile self-checkout in fashion retail stores? International Journal of Retail & Distribution Management, 50(6), 677–691. https://doi.org/10.1108/IJRDM-08-2020-0299
   Web of Science ®Google Scholar
• Liébana-Cabanillas, F., Japutra, A., Molinillo, S., Singh, N., & Sinha, N. (2020). Assessment of mobile technology use in the emerging market: Analyzing intention to use m-payment services in India. Telecommunications Policy, 44(9), 102009. https://doi.org/10.1016/j.telpol.2020.102009
   Web of Science ®Google Scholar
• Lin, C., Chih, Y., & Tsay, Y. (2022). Determinants of the adoption of green building simulation technologies in architectural design practices in Taiwan. Journal of Construction Engineering and Management, 148(1), 04021190. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002223
   Web of Science ®Google Scholar
• Linden, K., & Gonzalez, P. (2021). Zoom invigilated exams: A protocol for rapid adoption to remote examinations. British Journal of Educational Technology, 52(4), 1323–1337. https://doi.org/10.1111/bjet.13109
   Web of Science ®Google Scholar
• Lissitsa, S., & Kol, O. (2021). Four generational cohorts and hedonic m-shopping: Association between personality traits and purchase intention. Electronic Commerce Research, 21(2), 545–570. https://doi.org/10.1007/s10660-019-09381-4
   Web of Science ®Google Scholar
• Liu, C., Cheng, T., & Chen, C. (2019). Exploring the factors that influence physician technostress from using mobile electronic medical records. Informatics for Health & Social Care, 44(1), 92–104. https://doi.org/10.1080/17538157.2017.1364250
   PubMed Web of Science ®Google Scholar
• Loades, M. E., Chatburn, E., Higson-Sweeney, N., Reynolds, S., Shafran, R., Brigden, A., Linney, C., McManus, M. N., Borwick, C., & Crawley, E. (2020). Rapid systematic review: The impact of social isolation and loneliness on the mental health of children and adolescents in the context of COVID-19. Journal of the American Academy of Child and Adolescent Psychiatry, 59(11), 1218–1239. https://doi.org/10.1016/j.jaac.2020.05.009
   PubMed Web of Science ®Google Scholar
• Luo, Y., Yang, S., & Lu, C. (2021). Development of a multidimensional polychronicity scale for information technology learning. Journal of Computer Assisted Learning, 37(2), 387–395. https://doi.org/10.1111/jcal.12496
   Web of Science ®Google Scholar
• MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130
   Web of Science ®Google Scholar
• Manolika, M., Kotsakis, R., Matsiola, M., & Kalliris, G. (2022). Direct and indirect associations of personality with audiovisual technology acceptance through general self-efficacy. Psychological Reports, 125(2), 1165–1185. https://doi.org/10.1177/0033294121997784
   PubMed Web of Science ®Google Scholar
• Marsh, H. W., Hau, K., Balla, J. R., & Grayson, D. (1998). Is more ever too much? The number of indicators per factor in confirmatory factor analysis. Multivariate Behavioral Research, 33(2), 181–220. https://doi.org/10.1207/s15327906mbr3302_1
   PubMed Web of Science ®Google Scholar
• Matheson, B. E., Bohon, C., & Lock, J. (2020). Family-based treatment via videoconference: Clinical recommendations for treatment providers during COVID-19 and beyond. The International Journal of Eating Disorders, 53(7), 1142–1154. https://doi.org/10.1002/eat.23326
   PubMed Web of Science ®Google Scholar
• Maurino, M. A., Eymann, A., Santarsieri, N., Vainman, S., Krauss, M., Catsicaris, C., & Mulli, V. (2022). Adolescent behaviors during the preventive and mandatory social isolation in Argentina in 2020. Archivos Argentinos de Pediatria, 120(1), 39–45. https://doi.org/10.5546/aap.2022.eng.39
   PubMed Web of Science ®Google Scholar
• McElroy, J. C., Hendrickson, A. R., Townsend, A. M., & DeMarie, S. M. (2007). Dispositional factors in internet use: Personality versus cognitive style. MIS Quarterly, 31(4), 809–820. https://doi.org/10.2307/25148821
   Web of Science ®Google Scholar
• McLeay, F., Olya, H., Liu, H., Jayawardhena, C., & Dennis, C. (2022). A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles. Technological Forecasting and Social Change, 174, 121252. https://doi.org/10.1016/j.techfore.2021.121252
   Web of Science ®Google Scholar
• Mitzner, T. L., Rogers, W. A., Fisk, A. D., Boot, W. R., Charness, N., Czaja, S. J., & Sharit, J. (2016). Predicting older adults’ perceptions about a computer system designed for seniors. Universal Access in the Information Society, 15(2), 271–280. https://doi.org/10.1007/s10209-014-0383-y
   PubMed Web of Science ®Google Scholar
• Moore, K., & Craciun, G. (2021). Fear of missing out and personality as predictors of social networking sites usage: The Instagram case. Psychological Reports, 124(4), 1761–1787. https://doi.org/10.1177/0033294120936184
   PubMed Web of Science ®Google Scholar
• Moretti, F., van Vliet, L., Bensing, J., Deledda, G., Mazzi, M., Rimondini, M., Zimmermann, C., & Fletcher, I. (2011). A standardized approach to qualitative content analysis of focus group discussions from different countries. Patient Education and Counseling, 82(3), 420–428. https://doi.org/10.1016/j.pec.2011.01.005
   PubMed Web of Science ®Google Scholar
• Moslehpour, M., Pham, V., Wong, W. K., & Bilgiçli, İ. (2018). E-purchase intention of Taiwanese consumers: Sustainable mediation of perceived usefulness and perceived ease of use. Sustainability, 10(1), 234. https://doi.org/10.3390/su10010234
   Web of Science ®Google Scholar
• Mukhopadhyay, S., Booth, A. L., Calkins, S. M., Doxtader, E. E., Fine, S. W., Gardner, J. M., Gonzalez, R. S., Mirza, K. M., & Jiang, X. (2020). Leveraging technology for remote learning in the era of COVID-19 and social distancing. Archives of Pathology & Laboratory Medicine, 144(9), 1027–1036. https://doi.org/10.5858/arpa.2020-0201-ED
   PubMed Web of Science ®Google Scholar
• Muñoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing - ESIC, 21(1), 25–38. https://doi.org/10.1016/j.sjme.2016.12.001
   Google Scholar
• Nemteanu, M. S., & Dabija, D. C. (2021). The influence of internal marketing and job satisfaction on task performance and counterproductive work behavior in an emerging market during the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18(7), 3670. https://doi.org/10.3390/ijerph18073670
   PubMed Web of Science ®Google Scholar
• Nikou, S. A. (2021). Web-based videoconferencing for teaching online: Continuance intention to use in the post-COVID-19 period. Interaction Design and Architecture(s), 47(47), 123–143. https://doi.org/10.55612/s-5002-047-006
   Google Scholar
• Oentoro, W. (2021). Mobile payment adoption process: A serial of multiple mediation and moderation analysis. The Bottom Line, 34(3/4), 225–244. https://doi.org/10.1108/BL-09-2020-0059
   Web of Science ®Google Scholar
• Özbek, V., Alnıaçık, Ü., Koc, F., Akkılıç, M. E., & Kaş, E. (2014). The impact of personality on technology acceptance: A study on smart phone users. Procedia - Social and Behavioral Sciences, 150, 541–551. https://doi.org/10.1016/j.sbspro.2014.09.073
   Google Scholar
• Pal, D., & Patra, S. (2021). University students’ perception of video-based learning in times of COVID-19: A TAM/TTF perspective. International Journal of Human–Computer Interaction, 37(10), 903–921. https://doi.org/10.1080/10447318.2020.1848164
   Web of Science ®Google Scholar
• Pedaste, M., & Kasemets, M. (2021). Challenges in organizing online conferences: Lessons of the COVID 19 era. Educational Technology & Society, 24(1), 92–104. Retrieved from https://www.jstor.org/stable/26977859
   Web of Science ®Google Scholar
• Pereira, A. I., Muris, P., Roberto, M. S., Stallard, P., Garcia-Lopez, L. J., Tulbure, B. T., Podina, I., Simon, E., Sousa, M., & Barros, L. (2021). Cumulative risk exposure and social isolation as correlates of career and child mental health during the COVID-19 pandemic: An online study with families from various Europeans countries. Child Psychiatry & Human Development, Advanced online publication. https://doi.org/10.1007/s10578-021-01233-3
   PubMed Web of Science ®Google Scholar
• Pinho, C., Franco, M., & Mendes, L. (2020). Acceptance and use of information technology: Context of Portuguese universities. Information and Learning Sciences, 121(11/12), 869–887. https://doi.org/10.1108/ILS-02-2020-0030
   Web of Science ®Google Scholar
• Prasetyo, Y. T., Ong, A. K. S., Concepcion, G. K. F., Navata, F. M. B., Robles, R. A. V., Tomagos, I. J. T., Young, M. N., Diaz, J. F. T., Nadlifatin, R., & Redi, A. A. N. P. (2021). Determining factors affecting acceptance of e-learning platforms during the COVID-19 pandemic: Integrating extended technology acceptance model and DeLone & McLean IS success model. Sustainability, 13(15), 8365. https://doi.org/10.3390/su13158365
   Web of Science ®Google Scholar
• Prijambodo, K. C., & Lie, A. (2021). Senior high school students’ readiness and motivation to learn English using synchronous video conferences. Journal of Information Technology Education: Research, 20, 429–457. https://doi.org/10.28945/4880
   Web of Science ®Google Scholar
• Purnomo, Y. W. (2017). A scale for measuring teachers’ mathematics-related beliefs: A validity and reliability study. International Journal of Instruction, 10(1), 23–38. https://doi.org/10.12973/iji.2017.1022a
   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
• Quadir, B., & Zhou, M. (2021). Students perceptions, system characteristics and online learning during the COVID-19 epidemic school disruption. International Journal of Distance Education Technologies, 19(2), 15–33. https://doi.org/10.4018/IJDET.20210401.oa1
   Web of Science ®Google Scholar
• Quintana, S. M., & Maxwell, S. E. (1999). Implications of recent developments in structural equation modeling for counseling psychology. The Counseling Psychologist, 27(4), 485–527. https://doi.org/10.1177/0011000099274002
   Web of Science ®Google Scholar
• Rahi, S., & Abd Ghani, M. (2019). Does gamified elements influence on user’s intention to adopt and intention to recommend internet banking? The International Journal of Information and Learning Technology, 36(1), 2–20. https://doi.org/10.1108/IJILT-05-2018-0045
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Riedl, R. (2022). On the stress potential of videoconferencing: Definition and root causes of Zoom fatigue. Electronic Markets, 32(1), 153–177. https://doi.org/10.1007/s12525-021-00501-3
   PubMed Web of Science ®Google Scholar
• Roos, G., Oláh, J., Ingle, R., Kobayashi, R., & Feldt, M. (2020). Online conferences - Towards a new (virtual) reality. Computational and Theoretical Chemistry, 1189, 112975. https://doi.org/10.1016/j.comptc.2020.112975
   Web of Science ®Google Scholar
• Ryan, T., & Xenos, S. (2011). Who uses Facebook? An investigation into the relationship between the Big Five, shyness, narcissism, loneliness, and Facebook usage. Computers in Human Behavior, 27(5), 1658–1664. https://doi.org/10.1016/j.chb.2011.02.004
   Web of Science ®Google Scholar
• Saleem, F., Malik, M. I., Qureshi, S. S., Farid, M. F., & Qamar, S. (2021). Technostress and employee performance nexus during COVID-19: Training and creative self-efficacy as moderators. Frontiers in Psychology, 12, 595119. https://doi.org/10.3389/fpsyg.2021.595119
   PubMed Web of Science ®Google Scholar
• Saliba, M. (2020). Getting to grips with online conferences. Nature Energy, 5(7), 488–490. https://doi.org/10.1038/s41560-020-0656-z
   Web of Science ®Google Scholar
• Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632–1640. https://doi.org/10.1016/j.chb.2010.06.011
   Web of Science ®Google Scholar
• Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644–654. https://doi.org/10.1016/j.chb.2016.09.061
   Web of Science ®Google Scholar
• Shadiev, R., & Sintawati, W. (2020). A review of research on intercultural learning supported by technology. Educational Research Review, 31, 100338. https://doi.org/10.1016/j.edurev.2020.100338
   Web of Science ®Google Scholar
• Singh, N., Sinha, N., & Liébana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191–205. https://doi.org/10.1016/j.ijinfomgt.2019.05.022
   Web of Science ®Google Scholar
• Smith, D., Leonis, T., & Anandavalli, S. (2021). Belonging and loneliness in cyberspace: Impacts of social media on adolescents’ well-being. Australian Journal of Psychology, 73(1), 12–23. https://doi.org/10.1080/00049530.2021.1898914
   Web of Science ®Google Scholar
• Stevens, J. P. (2012). Applied multivariate statistics for the social sciences. Routledge.
   Google Scholar
• Svendsen, G. B., Johnsen, J. K., Almås-Sørensen, L., & Vittersø, J. (2013). Personality and technology acceptance: The influence of personality factors on the core constructs of the technology acceptance model. Behaviour & Information Technology, 32(4), 323–334. https://doi.org/10.1080/0144929X.2011.553740
   Web of Science ®Google Scholar
• Swilley, E. (2010). Technology rejection: The case of the wallet phone. Journal of Consumer Marketing, 27(4), 304–312. https://doi.org/10.1108/07363761011052341
   Google Scholar
• Taser, D., Aydin, E., Torgaloz, A. O., & Rofcanin, Y. (2022). An examination of remote e-working and flow experience: The role of technostress and loneliness. Computers in Human Behavior, 127, 107020. https://doi.org/10.1016/j.chb.2021.107020
   Web of Science ®Google Scholar
• Terzis, V., Moridis, C. N., & Economides, A. A. (2012). How student’s personality traits affect computer based assessment acceptance: Integrating BFI with CBAAM. Computers in Human Behavior, 28(5), 1985–1996. https://doi.org/10.1016/j.chb.2012.05.019
   Web of Science ®Google Scholar
• Uffen, J., Kaemmerer, N., & Breitner, M. H. (2013). Personality traits and cognitive determinants – An empirical investigation of the use of smartphone security measures. Journal of Information Security, 04(04), 203–212. https://doi.org/10.4236/jis.2013.44023
   Google Scholar
• Upadhyaya, P. (2021). Impact of technostress on academic productivity of university students. Education and Information Technologies, 26(2), 1647–1664. https://doi.org/10.1007/s10639-020-10319-9
   Web of Science ®Google Scholar
• Vaziri, H., Casper, W. J., Wayne, J. H., & Matthews, R. A. (2020). Changes to the work-family interface during the COVID-19 pandemic: Examining predictors and implications using latent transition analysis. The Journal of Applied Psychology, 105(10), 1073–1087. https://doi.org/10.1037/apl0000819
   PubMed Web of Science ®Google Scholar
• Venkatesh, V., Sykes, T. A., & Venkatraman, S. (2014). Understanding e-Government portal use in rural India: Role of demographic and personality characteristics. Information Systems Journal, 24(3), 249–269. https://doi.org/10.1111/isj.12008
   Web of Science ®Google Scholar
• Verkijika, S. F. (2019). Digital textbooks are useful but not everyone wants them: The role of technostress. Computers & Education, 140, 103591. https://doi.org/10.1016/j.compedu.2019.05.017
   Web of Science ®Google Scholar
• Vlachogianni, P., & Tselios, N. (2022). Investigating the impact of personality traits on perceived usability evaluation of e-learning platforms. Interactive Technology and Smart Education, 19(2), 202–221. https://doi.org/10.1108/ITSE-02-2021-0024
   Web of Science ®Google Scholar
• Vladova, G., Ullrich, A., Bender, B., & Gronau, N. (2021). Students’ acceptance of technology-mediated teaching – How it was influenced during the COVID-19 pandemic in 2020: A study from Germany. Frontiers in Psychology, 12, 636086. https://doi.org/10.3389/fpsyg.2021.636086
   PubMedGoogle Scholar
• Wang, W., Ngai, E. W., & Wei, H. (2012). Explaining instant messaging continuance intention: The role of personality. International Journal of Human-Computer Interaction, 28(8), 500–510. https://doi.org/10.1080/10447318.2011.622971
   Web of Science ®Google Scholar
• Wang, X., Tan, S., & Li, L. (2020). Technostress in university students’ technology-enhanced learning: An investigation from multidimensional person-environment misfit. Computers in Human Behavior, 105, 106208. https://doi.org/10.1016/j.chb.2019.106208
   Web of Science ®Google Scholar
• Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. https://doi.org/10.1016/j.elerap.2010.07.003
   Web of Science ®Google Scholar
• Witt, E. A., Massman, A. J., & Jackson, L. A. (2011). Trends in youth’s videogame playing, overall computer use, and communication technology use: The impact of self-esteem and the Big Five personality factors. Computers in Human Behavior, 27(2), 763–769. https://doi.org/10.1016/j.chb.2010.10.025
   Web of Science ®Google Scholar
• Xu, R., Frey, R. M., Fleisch, E., & Ilic, A. (2016). Understanding the impact of personality traits on mobile app adoption – Insights from a large-scale field study. Computers in Human Behavior, 62, 244–256. https://doi.org/10.1016/j.chb.2016.04.011
   Web of Science ®Google Scholar
• Yao, J., & Cao, X. (2017). The balancing mechanism of social networking overuse and rational usage. Computers in Human Behavior, 75, 415–422. https://doi.org/10.1016/j.chb.2017.04.055
   Web of Science ®Google Scholar
• Yu, Z. (2020). Extending the learning technology acceptance model of WeChat by adding new psychological constructs. Journal of Educational Computing Research, 58(6), 1121–1143. https://doi.org/10.1177/0735633120923772
   Web of Science ®Google Scholar
• Yu, Z. (2021). The effects of gender, educational level, and personality on online learning outcomes during the COVID-19 pandemic. International Journal of Educational Technology in Higher Education, 18(1), 14. https://doi.org/10.1186/s41239-021-00252-3
   Google Scholar
• Yu, Z., & Yu, X. (2019). An extended technology acceptance model of a mobile learning technology. Computer Applications in Engineering Education, 27(3), 721–732. https://doi.org/10.1002/cae.22111
   Web of Science ®Google Scholar
• Zhang, T., Tao, D., Qu, X., Zhang, X., Zeng, J., Zhu, H., & Zhu, H. (2020). Automated vehicle acceptance in China: Social influence and initial trust are key determinants. Transportation Research Part C: Emerging Technologies, 112, 220–233. https://doi.org/10.1016/j.trc.2020.01.027
   Web of Science ®Google Scholar
• Zhang, Z., & Zheng, L. (2021). Consumer community cognition, brand loyalty, and behaviour intentions within online publishing communities: An empirical study of Epubit in China. Learned Publishing, 34(2), 116–127. https://doi.org/10.1002/leap.1327
   Web of Science ®Google Scholar