[1] Abbad, M.M. (2021). Using the utaut model to understand studentn usage of e-learning systems in developing countries. Education and Information Technologies, 1–20.

[2] Abdel-Hameed, F.S.M., Tomczyk Ł., & Hu, C. (2021). The editorial of special issue on education, it, and the covid-19 pandemic. Education and information technologies, 26(6), 6563–6566.

[3] Abdelguiom, G.A., & Iahad, N.A. (2020). Potential benefits of social media to healthcare: A systematic literature review. In International Conference of Reliable Information and Communication Technology, Springer, pp 230–241.

[4] Ahmed, V., & Opoku, A. (2022). Technology supported learning and pedagogy in times of crisis: the case of covid-19 pandemic. Education and Information Technologies, 27(1), 365–405.

[5] Ahmed, Y.A., Ahmad, M.N., Ahmad, N., & et al. (2019). Social media for knowledge-sharing: A systematic literature review. Telematics and informatics, 37, 72–112.

[6] Ajzen, I (1985). From intentions to actions: A theory of planned behavior. In Action control. Springer, p 11–39.

[7] Alshurafat, H., Al Shbail, M.O., Masadeh, W.M., & et al. (2021). Factors affecting online accounting education during the covid-19 pandemic: an integrated perspective of social capital theory, the theory of reasoned action and the technology acceptance model. Education and Information Technologies, 1–19.

[8] Anderson, J.C., & Gerbing, D.W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49(2), 155–173.

[9] Barnett, T., Pearson, A.W., Pearson, R., & et al. (2015). Five-factor model personality traits as predictors of perceived and actual usage of technology. European Journal of Information Systems, 24(4), 374–390.

[10] Barrick, M.R., & Mount, M.K. (1991). The big five personality dimensions and job performance: a meta-analysis. Personnel psychology, 44(1), 1–26.

[11] Beaunoyer, E., Arsenault, M., Lomanowska, A.M., & et al. (2017). Understanding online health information: Evaluation, tools, and strategies. Patient education and counseling, 100(2), 183–189.

[12] Beaunoyer, E., Dupéré, S, & Guitton, M.J. (2020). Covid-19 and digital inequalities: Reciprocal impacts and mitigation strategies. Computers in Human Behavior, 111, 106,424.

[13] Black, W. (1983). Discontinuance and diffusion: Examination of the post adoption decision process. ACR North American Advances.

[14] Booker, C.L., Kelly, Y.J., & Sacker, A. (2018). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the uk. BMC Public Health, 18(1), 1–12.

[15] Calaguas, N.P., & Consunji, P.M.P. (2022). A structural equation model predicting adults’ online learning self-efficacy. Education and Information Technologies, 1–17.

[16] Cao, X., & Sun, J. (2018). Exploring the effect of overload on the discontinuous intention of social media users: An sor perspective. Computers in Human Behavior, 81, 10–18.

[17] Chuang, H.M., & Liao, Y.D. (2021). Sustainability of the benefits of social media on socializing and learning: An empirical case of facebook. Sustainability, 13(12), 6731.

[18] Chugh, R., Grose, R., & Macht, S.A. (2021). Social media usage by higher education academics: A scoping review of the literature. Education and Information Technologies, 26(1), 983–999.

[19] Costa P.T. Jr, McCrae, R.R., & Dye, D.A. (1991). Facet scales for agreeableness and conscientiousness: A revision of the neo personality inventory. Personality and Individual Differences, 12(9), 887–898.

[20] Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.

[21] Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111–1132.

[22] 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.

[23] Dontre, A.J. (2021). The influence of technology on academic distraction: A review. Human Behavior and Emerging Technologies, 3(3), 379–390.

[24] Eksail, F.A.A., & Afari, E. (2020). Factors affecting trainee teachers’ intention to use technology: A structural equation modeling approach. Education and Information Technologies, 25(4), 2681–2697.

[25] El-Khoury, J., Haidar, R., Kanj, R.R., & et al. (2021). Characteristics of social media ‘detoxification’in university students. Libyan Journal of Medicine, 16(1), 1846,861.

[26] Ellis, K., & Goggin, G. (2013). Disability and social media. In The social media handbook. Routledge, p 134–151.

[27] Fishbein, M. (1979). A theory of reasoned action: some applications and implications.

[28] Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2).

[29] Fishbein, M., & Ajzen, I. (2011). Predicting and changing behavior: The reasoned action approach. Psychology press.

[30] González Bravo, L., Fernández Sagredo, M., Torres Martínez, P., & et al. (2020). Psychometric analysis of a measure of acceptance of new technologies (utaut), applied to the use of haptic virtual simulators in dental students. European Journal of Dental Education, 24(4), 706–714.

[31] Guitton, M.J. (2021). Something good out of something bad: ehealth and telemedicine in the post-covid era. Computers in Human Behavior, 123, 106,882.

[32] Hammond, M. (2010). What is an affordance and can it help us understand the use of ict in education?. Education and Information Technologies, 15 (3), 205–217.

[33] Hanney, R., & Skirkeviciutey, G. (2020). Reflection, identity, community: Affordances of blogging for social interaction and reflective dialogue. Education and Information Technologies, 25(3), 1553–1569.

[34] Henseler, J. (2012). Why generalized structured component analysis is not universally preferable to structural equation modeling. Journal of the Academy of Marketing Science, 40(3), 402–413.

[35] Humble, N. (2021). Teacher observations of programming affordances for k-12 mathematics and technology. Education and Information Technologies, 1–18.

[36] Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A component-based approach to structural equation modeling. CRC Press.

[37] Jarman, H.K., Marques, M.D., McLean, S.A., & et al. (2021). Motivations for social media use: Associations with social media engagement and body satisfaction and well-being among adolescents. Journal of Youth and Adolescence, 1–15.

[38] John, O.P., Srivastava, S., & et al. (1999). The Big-Five trait taxonomy: History, measurement, and theoretical perspectives, vol 2. University of California Berkeley.

[39] Jung, K., Nguyen, V.T., Piscarac, D., & et al. (2020). Meet the virtual jeju dol harubang—the mixed vr/ar application for cultural immersion in korea’s main heritage. ISPRS International Journal of Geo-Information, 9(6), 367.

[40] Jung, K., Nguyen, V.T., & Lee, J. (2021). Blocklyxr: An interactive extended reality toolkit for digital storytelling. Applied Sciences, 11(3), 1073.

[41] Karahanna, E., & Straub, D.W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management, 35(4), 237–250.

[42] Keles, B., McCrae, N., & Grealish, A. (2020). A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. International Journal of Adolescence and Youth, 25(1), 79–93.

[43] 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.

[44] Kietzmann, J.H., Hermkens, K., McCarthy, I.P., & et al. (2011). Social media? get serious! understanding the functional building blocks of social media. Business Horizons, 54(3), 241–251.

[45] Klar, S., Krupnikov, Y., Ryan, J.B., & et al. (2020). Using social media to promote academic research: Identifying the benefits of twitter for sharing academic work. PloS One, 15(4), e0229,446.

[46] Kline, R.B. (2015). Principles and practice of structural equation modeling. Guilford publications.

[47] Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in pls-sem: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261.

[48] Krouska, A., Troussas, C., & Sgouropoulou, C. (2021). Mobile game-based learning as a solution in covid-19 era: Modeling the pedagogical affordance and student interactions. Education and Information Technologies, 1–13.

[49] Kuo, B.C., Roldan-Bau, A., & Lowinger, R. (2015). Psychological help-seeking among latin american immigrants in canada: Testing a culturally-expanded model of the theory of reasoned action using path analysis. International Journal for the Advancement of Counselling, 37(2), 179–197.

[50] Lakhal, S., & Khechine, H. (2017). Relating personality (big five) to the core constructs of the unified theory of acceptance and use of technology. Journal of Computers in Education, 4(3), 251–282.

[51] Lavidas, K., Achriani, A., Athanassopoulos, S., & et al. (2020). University students’ intention to use search engines for research purposes: A structural equation modeling approach. Education and Information Technologies, 25(4), 2463–2479.

[52] Lederman, R., Wadley, G., Gleeson, J., & et al. (2014). Moderated online social therapy: Designing and evaluating technology for mental health. ACM Transactions on Computer-Human Interaction (TOCHI), 21(1), 1–26.

[53] Lemay, D, Doleck, T, & Bazelais, P (2018). The influence of the social, cognitive, and instructional dimensions on technology acceptance decisions among college-level students. EURASIA Journal of Mathematics, Science and Technology Education, 14(12), em1635.

[54] Lesch, L. (2014). Creative learning for the information age: how classrooms can better prepare students. Rowman & Littlefield.

[55] Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: An empirical study in vietnam. Education and Information Technologies, 1–21.

[56] Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the covid-19 pandemic: An extended tam model. Education and Information Technologies, 1–21.

[57] Manosuthi, N., Lee, J.S., & Han, H. (2020). Impact of distance on the arrivals, behaviours and attitudes of international tourists in hong kong: A longitudinal approach. Tourism Management, 78, 103,963.

[58] Mao, J. (2014). Social media for learning: A mixed methods study on high school students’ technology affordances and perspectives. Computers in Human Behavior, 33, 213–223.

[59] Meier, A., & Reinecke, L. (2020). Computer-mediated communication, social media, and mental health: A conceptual and empirical meta-review. Communication Research, 0093650220958224.

[60] Naslund, J.A., Bondre, A., Torous, J., & et al. (2020). Social media and mental health: benefits, risks, and opportunities for research and practice. Journal of Technology in Behavioral Science, 5(3), 245–257.

[61] Nazir, S., Ahram, T., & Karwowski, W. (2020). Advances in human factors in training, education, and learning sciences. Springer.

[62] Nguyen, V.T., Jung, K., & Dang, T. (2020). Blocklyar: A visual programming interface for creating augmented reality experiences. Electronics, 9(8), 1205.

[63] Obar, J.A., & Wildman, S.S. (2015). Social media definition and the governance challenge: An introduction to the special issue. Telecommunications Policy, 39(9), 745–750.

[64] Osterberg, L. (2021). 7 unexpected benefits of a digital detox. https://www.huffpost.com/entry/digital-detox_b_8361700/, Last accessed date on November 9, 2021.

[65] Przybylski, A.K., Thuy-vy, T.N., Law, W., & et al. (2021). Does taking a short break from social media have a positive effect on well-being? evidence from three preregistered field experiments. Journal of Technology in Behavioral Science, 1–8.

[66] Radtke, T., Apel, T., Schenkel, K., & et al. (2021). Digital detox: An effective solution in the smartphone era? a systematic literature review. Mobile Media & Communication, 20501579211028647.

[67] Rogers, E.M. (2010). Diffusion of innovations. Simon and Schuster.

[68] Rosen, P.A., & Kluemper, D.H. (2008). The impact of the big five personality traits on the acceptance of social networking website. AMCIS 2008 proceedings, 274.

[69] Schmitt, J.B., Breuer, J., & Wulf, T. (2021). From cognitive overload to digital detox: Psychological implications of telework during the covid-19 pandemic. Computers in Human Behavior, 106899.

[70] Schmuck, D. (2020). Does digital detox work? exploring the role of digital detox applications for problematic smartphone use and well-being of young adults using multigroup analysis. Cyberpsychology, Behavior, and Social Networking, 23(8), 526–532.

[71] Schønning, V., Hjetland, G.J., Aarø, L.E., & et al. (2020). Social media use and mental health and well-being among adolescents–a scoping review. Frontiers in psychology, 11, 1949.

[72] Shachak, A., Kuziemsky, C., & Petersen, C. (2019). Beyond tam and utaut: Future directions for hit implementation research. Journal of Biomedical Informatics, 100, 103,315.

[73] Skogen, J.C., Hjetland, G.J., et al., & Bøe, T (2021). Through the looking glass of social media. focus on self-presentation and association with mental health and quality of life. a cross-sectional survey-based study. International Journal of Environmental Research and Public Health, 18(6), 3319.

[74] Smailhodzic, E., Hooijsma, W., Boonstra, A., & et al. (2016). Social media use in healthcare: A systematic review of effects on patients and on their relationship with healthcare professionals. BMC health services research, 16(1), 1–14.

[75] Soper, D. (2016). Calculator: a-priori sample size for structural equation models. Retrieved October.

[76] Statista Research Department. (2021). Number of social network users worldwide from 2017 to 2025. https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/. Last accessed date on November 7, 2021.

[77] Svendsen, G.B., Johnsen, J.A.K., Almås-Sørensen, L., & et al. (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.

[78] Syvertsen, T., & Enli, G. (2020). Digital detox: Media resistance and the promise of authenticity. Convergence, 26(5-6), 1269–1283.

[79] Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia manufacturing, 22, 960–967.

[80] Teasdale, J.D. (1978). Self-efficacy: Toward a unifying theory of behavioural change?. Advances in Behaviour Research and Therapy, 1(4), 211–215.

[81] 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.

[82] Thompson, R.L., Higgins, C.A., & Howell, J.M. (1994). Influence of experience on personal computer utilization: Testing a conceptual model. Journal of Management Information Systems, 11(1), 167–187.

[83] Turel, O., & Vaghefi, I. (2019). Social media detox: Relapse predictors. Psychiatry Research, 112488.

[84] Ugur, N.G., & Koc, T. (2015). Time for digital detox: Misuse of mobile technology and phubbing. Procedia-Social and Behavioral Sciences, 195, 1022–1031.

[85] Valentine, L., McEnery, C., D’Alfonso, S., & et al. (2019). Harnessing the potential of social media to develop the next generation of digital health treatments in youth mental health. Current Treatment Options in Psychiatry, 6(4), 325–336.

[86] Valkenburg, P.M., Meier, A., & Beyens, I. (2021). Social media use and its impact on adolescent mental health: An umbrella review of the evidence. Current Opinion in Psychology.

[87] Venkatesh, V., & Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

[88] Venkatesh, V., Morris, M.G., Davis, G.B., & et al. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.

[89] Venkatesh, V., Thong, J.Y., & 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.

[90] White, T.R. (2013). Digital social media detox (dsmd): Responding to a culture of interconnectivity. In Social media and the new academic environment: Pedagogical challenges. IGI Global, 414–430.

[91] Wilcockson, T.D., Osborne, A.M., & Ellis, D.A. (2019). Digital detox: The effect of smartphone abstinence on mood, anxiety, and craving. Addictive Behaviors, 99, 106,013.

[92] Williams, M.L., Saunderson, I.P., & Dhoest, A. (2021). Students’ perceptions of the adoption and use of social media in academic libraries: A utaut study. Communicatio, 47(1), 76–94.

[93] Yee, M.L.S., & Abdullah, M.S. (2021). A review of utaut and extended model as a conceptual framework in education research. Jurnal Pendidikan Sains Dan Matematik Malaysia, 11, 1–20.

[94] Yu, L., Cao, X., Liu, Z., & et al. (2018). Excessive social media use at work: Exploring the effects of social media overload on job performance. Information Technology & People.