A Meta-Analytical Review of the Determinants of Social Media Discontinuance Intentions

References

• Adhikari, K., & Panda, R. K. (2020). Examining the role of social networking fatigue toward discontinuance intention: The multigroup effects of gender and age. Journal of Internet Commerce, 19(2), 125–152. https://doi.org/10.1080/15332861.2019.1698265
   Web of Science ®Google Scholar
• Al-Menayes, J. (2015). Motivations for using social media: An exploratory factor analysis. International Journal of Psychological Studies, 7(1). https://doi.org/10.5539/ijps.v7n1p43
   Google Scholar
• Auxier, B., & Anderson, M. (2021). Social media use in 2021. Pew Research Center, Issue. https://www.pewresearch.org/internet/wp-content/uploads/sites/9/2021/04/PI_2021.04.07_Social-Media-Use_FINAL.pdf
   Google Scholar
• Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831–858. https://doi.org/10.2307/41409963
   Web of Science ®Google Scholar
• Baumeister, R. F., Stillwell, A. M., & Heatherton, T. F. (1994). Guilt: An interpersonal approach. Psychological Bulletin, 115(2), 243–267. https://doi.org/10.1037/0033-2909.115.2.243
   PubMed Web of Science ®Google Scholar
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
   Web of Science ®Google Scholar
• Bhattacherjee, A., Limayem, M., & Cheung, C. M. K. (2012). User switching of information technology: A theoretical synthesis and empirical test. Information & Management, 49(7), 327–333. https://doi.org/10.1016/j.im.2012.06.002
   Web of Science ®Google Scholar
• Blumler, J. G., & Katz, E. (1974). The uses of mass communications: Current perspectives on gratifications research. Sage Publications.
   Google Scholar
• Boulianne, S. (2015). Social media use and participation: A meta-analysis of current research. Information, Communication & Society, 18(5), 524–538. https://doi.org/10.1080/1369118X.2015.1008542
   Web of Science ®Google Scholar
• Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.x
   Web of Science ®Google Scholar
• Bright, L. F., & Logan, K. (2018). Is my fear of missing out (FOMO) causing fatigue? Advertising, social media fatigue, and the implications for consumers and brands. Internet Research, 28(5), 1213–1227. https://doi.org/10.1108/IntR-03-2017-0112
   Web of Science ®Google Scholar
• Cao, X., Khan, A. N., Ali, A., & Khan, N. A. (2019). Consequences of cyberbullying and social overload while using SNSs: A study of users’ discontinuous usage behavior in SNSs. Information Systems Frontiers, 22(6), 1343–1356. https://doi.org/10.1007/s10796-019-09936-8
   Web of Science ®Google Scholar
• Cao, X., & Sun, J. (2018). Exploring the effect of overload on the discontinuous intention of social media users: An S-O-R perspective. Computers in Human Behavior, 81, 10–18. https://doi.org/10.1016/j.chb.2017.11.035
   Web of Science ®Google Scholar
• Cao, Y., Long, Q., Hu, B., Li, J., & Qin, X. (2021). Exploring elderly users’ MSNS intermittent discontinuance: A dual-mechanism model. Telematics and Informatics, 62, 101629. https://doi.org/10.1016/j.tele.2021.101629
   Web of Science ®Google Scholar
• Chaouali, W. (2016). Once a user, always a user: Enablers and inhibitors of continuance intention of mobile social networking sites. Telematics and Informatics, 33(4), 1022–1033. https://doi.org/10.1016/j.tele.2016.03.006
   Web of Science ®Google Scholar
• Chetty, K., Qigui, L., Gcora, N., Josie, J., Wenwei, L., & Fang, C. (2018). Bridging the digital divide: Measuring digital literacy. Economics, 12(1). https://doi.org/10.5018/economics-ejournal.ja.2018-23
   Google Scholar
• Cho, J., Ramgolam, D. I., Schaefer, K. M., & Sandlin, A. N. (2011). The rate and delay in overload: An investigation of communication overload and channel synchronicity on identification and job satisfaction. Journal of Applied Communication Research, 39(1), 38–54. https://doi.org/10.1080/00909882.2010.536847
   Web of Science ®Google Scholar
• Chu, S.-C., & Kim, Y. (2011). Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1), 47–75. https://doi.org/10.2501/IJA-30-1-047-075
   Web of Science ®Google Scholar
• 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), Article 6731. https://doi.org/10.3390/su13126731
   Web of Science ®Google Scholar
• Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Elsevier Science. https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=923159
   Google Scholar
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2015). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge. https://doi.org/10.4324/9781410606266
   Google Scholar
• Cramer, E. M., Song, H., & Drent, A. M. (2016). Social comparison on Facebook: Motivation, affective consequences, self-esteem, and Facebook fatigue. Computers in Human Behavior, 64, 739–746. https://doi.org/10.1016/j.chb.2016.07.049
   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
• Dhir, A., Kaur, P., Chen, S., & Pallesen, S. (2019). Antecedents and consequences of social media fatigue. International Journal of Information Management, 48, 193–202. https://doi.org/10.1016/j.ijinfomgt.2019.05.021
   Web of Science ®Google Scholar
• Edison Research. (2021). The infinite dial 2021. Edison Research. http://www.edisonresearch.com/wp-content/uploads/2021/03/The-Infinite-Dial-2021.pdf
   Google Scholar
• Ellison, J. (2021, January 15). Should we quit social media, too? Financial Times. https://www.ft.com/content/2d7478ca-05bf-4908-969b-890af31c3c4c
   Google Scholar
• Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168. https://doi.org/10.1111/j.1083-6101.2007.00367.x
   Web of Science ®Google Scholar
• Faelens, L., Hoorelbeke, K., Soenens, B., Van Gaeveren, K., De Marez, L., De Raedt, R., & Koster, E. H. W. (2021). Social media use and well-being: A prospective experience-sampling study. Computers in Human Behavior, 114, 106510. https://doi.org/10.1016/j.chb.2020.106510
   Web of Science ®Google Scholar
• Fardouly, J., Diedrichs, P. C., Vartanian, L. R., & Halliwell, E. (2015). Social comparisons on social media: The impact of Facebook on young women’s body image concerns and mood. Body Image, 13, 38–45. https://doi.org/10.1016/j.bodyim.2014.12.002
   PubMed Web of Science ®Google Scholar
• Farooq, A., Dahabiyeh, L., & Maier, C. (2023). Social media discontinuation: A systematic literature review on drivers and inhibitors. Telematics and Informatics, 77, 101924. https://doi.org/10.1016/j.tele.2022.101924
   Google Scholar
• Feng, G. C. (2014). Intercoder reliability indices: Disuse, misuse, and abuse. Quality & Quantity, 48(3), 1803–1815. https://doi.org/10.1007/s11135-013-9956-8
   Google Scholar
• Feng, G. C. (2015). Mistakes and how to avoid mistakes in using intercoder reliability indices. Methodology, 11(1), 13–22. https://doi.org/10.1027/1614-2241/a000086
   Web of Science ®Google Scholar
• Feng, G. C., Su, X., Lin, Z., He, Y., Luo, N., & Zhang, Y. (2020). Determinants of technology acceptance: Two model-based meta-analytic reviews. Journalism & Mass Communication Quarterly, 98(1), 83–104. https://doi.org/10.1177/1077699020952400
   Web of Science ®Google Scholar
• Feng, G. C., Zhang, Y., & Lin, Z. (2019). A meta-analysis of the effects of sociodemographic factors on social media adoption. International Journal of Communication, 13(April). https://ijoc.org/index.php/ijoc/article/view/10088
   Google Scholar
• Feng, G. C., & Zhao, X. (2016). Do not force agreement. Methodology, 12(4), 145–148. https://doi.org/10.1027/1614-2241/a000120
   Google Scholar
• Fu, S., Li, H., Liu, Y., Pirkkalainen, H., & Salo, M. (2020). Social media overload, exhaustion, and use discontinuance: Examining the effects of information overload, system feature overload, and social overload. Information Processing & Management, 57(6), 102307. https://doi.org/10.1016/j.ipm.2020.102307
   Web of Science ®Google Scholar
• Gil de Zúñiga, H., Jung, N., & Valenzuela, S. (2012). Social media use for news and individuals’ social capital, civic engagement and political participation. Journal of Computer-Mediated Communication, 17(3), 319–336. https://doi.org/10.1111/j.1083-6101.2012.01574.x
   Web of Science ®Google Scholar
• Guan, L., Liang, H., & Zhu, J. J. H. (2022). Predicting reposting latency of news content in social media: A focus on issue attention, temporal usage pattern, and information redundancy. Computers in Human Behavior, 127, 107080. https://doi.org/10.1016/j.chb.2021.107080
   Web of Science ®Google Scholar
• Guo, Y., Lu, Z., Kuang, H., & Wang, C. (2020). Information avoidance behavior on social network sites: Information irrelevance, overload, and the moderating role of time pressure. International Journal of Information Management, 52, 102067. https://doi.org/10.1016/j.ijinfomgt.2020.102067
   Web of Science ®Google Scholar
• Henderson, M., Selwyn, N., & Aston, R. (2017). What works and why? Student perceptions of ‘useful’ digital technology in university teaching and learning. Studies in Higher Education, 42(8), 1567–1579. https://doi.org/10.1080/03075079.2015.1007946
   Web of Science ®Google Scholar
• Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539–1558. https://doi.org/10.1002/sim.1186
   PubMed Web of Science ®Google Scholar
• Hunter, J. E., & Schmidt, F. L. (2015). Methods of meta-analysis: Correcting error and bias in research findings. SAGE. https://doi.org/10.4135/9781483398105
   Google Scholar
• Jackson, D., & Turner, R. (2017). Power analysis for random-effects meta-analysis. Research Synthesis Methods, 8(3), 290–302. https://doi.org/10.1002/jrsm.1240
   PubMed Web of Science ®Google Scholar
• Ji, Q., Ha, L., & Sypher, U. (2014). The role of news media use and demographic characteristics in the possibility of information overload prediction. International Journal of Communication, 8, 16. https://ijoc.org/index.php/ijoc/article/view/2419
   Web of Science ®Google Scholar
• Joshanloo, M., & Jarden, A. (2016). Individualism as the moderator of the relationship between hedonism and happiness: A study in 19 nations. Personality and Individual Differences, 94, 149–152. https://doi.org/10.1016/j.paid.2016.01.025
   Web of Science ®Google Scholar
• Kang, I., Zhang, Y., & Yoo, S. (2020). Elaboration of social media performance measures: From the perspective of social media discontinuance behavior. Sustainability, 12(19), 7962. https://doi.org/10.3390/su12197962
   Web of Science ®Google Scholar
• Karr-Wisniewski, P., & Lu, Y. (2010). When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity. Computers in Human Behavior, 26(5), 1061–1072. https://doi.org/10.1016/j.chb.2010.03.008
   Web of Science ®Google Scholar
• Kim, B. (2012). Understanding key factors of users’ intentions to repurchase and Recommend digital items in social virtual worlds. Cyberpsychology, Behavior, and Social Networking, 15(10), 543–550. https://doi.org/10.1089/cyber.2012.0128
   PubMed Web of Science ®Google Scholar
• Koeske, G. F., & Koeske, R. D. (1993). A preliminary test of a stress-strain-outcome model for reconceptualizing the burnout phenomenon. Journal of Social Service Research, 17(3–4), 107–135. https://doi.org/10.1300/J079v17n03_06
   Google Scholar
• Ku, Y.-C., Chen, R., & Zhang, H. (2013). Why do users continue using social networking sites? An exploratory study of members in the United States and Taiwan. Information & Management, 50(7), 571–581. https://doi.org/10.1016/j.im.2013.07.011
   Web of Science ®Google Scholar
• Kugler, K., & Jones, W. H. (1992). On conceptualizing and assessing guilt. Journal of Personality and Social Psychology, 62(2), 318–327. https://doi.org/10.1037/0022-3514.62.2.318
   Web of Science ®Google Scholar
• Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. Springer.
   Google Scholar
• Lee, A. R., Son, S.-M., & Kim, K. K. (2016). Information and communication technology overload and social networking service fatigue: A stress perspective. Computers in Human Behavior, 55, 51–61. https://doi.org/10.1016/j.chb.2015.08.011
   Web of Science ®Google Scholar
• Liao, C., Liu, C.-C., Liu, Y.-P., To, P.-L., & Lin, H.-N. (2011). Applying the expectancy disconfirmation and regret theories to online consumer behavior. Cyberpsychology, Behavior, and Social Networking, 14(4), 241–246. https://doi.org/10.1089/cyber.2009.0236
   PubMed Web of Science ®Google Scholar
• Lin, S., Lin, J., Luo, X., & Liu, S. (2021). Juxtaposed effect of social media overload on discontinuous usage intention: The perspective of stress coping strategies. Information Processing & Management, 58(1), 102419. https://doi.org/10.1016/j.ipm.2020.102419
   Web of Science ®Google Scholar
• Liu, F., & Xiao, B. (2014, January 6). Do I switch? Understanding users’ intention to switch between social network sites. 2014 47th Hawaii International Conference on System Sciences. https://ieeexplore.ieee.org/document/6758672/
   Google Scholar
• Liu, L., Suh, A., & Wagner, C. (2016). Watching online videos interactively: The impact of media capabilities in Chinese danmaku video sites. Chinese Journal of Communication, 9(3), 283–303. https://doi.org/10.1080/17544750.2016.1202853
   Web of Science ®Google Scholar
• Liu, Q., Shao, Z., & Fan, W. (2018). The impact of users’ sense of belonging on social media habit formation: Empirical evidence from social networking and microblogging websites in China. International Journal of Information Management, 43, 209–223. https://doi.org/10.1016/j.ijinfomgt.2018.08.005
   Web of Science ®Google Scholar
• Lo, J. (2019). Exploring the buffer effect of receiving social support on lonely and emotionally unstable social networking users. Computers in Human Behavior, 90, 103–116. https://doi.org/10.1016/j.chb.2018.08.052
   Web of Science ®Google Scholar
• Luqman, A., Masood, A., & Ali, A. (2018). An SDT and TPB-based integrated approach to explore the role of autonomous and controlled motivations in “SNS discontinuance intention”. Computers in Human Behavior, 85, 298–307. https://doi.org/10.1016/j.chb.2018.04.016
   Web of Science ®Google Scholar
• Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2015). Giving too much social support: Social overload on social networking sites. European Journal of Information Systems, 24(5), 447–464. https://doi.org/10.1057/ejis.2014.3
   Web of Science ®Google Scholar
• Maier, C., Laumer, S., Weinert, C., & Weitzel, T. (2015). The effects of technostress and switching stress on discontinued use of social networking services: A study of Facebook use. Information Systems Journal, 25(3), 275–308. https://doi.org/10.1111/isj.12068
   Web of Science ®Google Scholar
• Mamun, M. R. A., Prybutok, V. R., Peak, D. A., Torres, R., & Pavur, R. J. (2022). The role of emotional attachment in IPA continuance intention: An emotional attachment model. Information Technology & People, 36(2), 867–894. https://doi.org/10.1108/ITP-09-2020-0643
   Web of Science ®Google Scholar
• Marino, C., Gini, G., Angelini, F., Vieno, A., & Spada, M. M. (2020). Social norms and e-motions in problematic social media use among adolescents. Addictive Behaviors Reports, 11, 100250. https://doi.org/10.1016/j.abrep.2020.100250
   PubMedGoogle Scholar
• Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. http://books.google.com/books?id=EthOAAAAMAAJ
   Google Scholar
• Michie, S., & Prestwich, A. (2010). Are interventions theory-based? Development of a theory coding scheme. Health Psychology, 29(1), 1–8. https://doi.org/10.1037/a0016939
   PubMed Web of Science ®Google Scholar
• Milgram, S. (1970). The experience of living in cities. Science, 167(3924), 1461–1468. https://doi.org/10.1126/science.167.3924.1461
   PubMed Web of Science ®Google Scholar
• Miniwatts Marketing Group. (2021, October 10). World Internet Users Statistics and 2021 World Population Stats. https://www.internetworldstats.com/stats.htm
   Google Scholar
• Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151(4), 264–269. https://doi.org/10.7326/0003-4819-151-4-200908180-00135
   PubMed Web of Science ®Google Scholar
• Niu, G., Yao, L., Tian, Y., Sun, X., & Zhou, Z. (2020). Information overload and the intention to reduce SNS usage: The mediating roles of negative social comparison and fatigue. Current Psychology, 41(8), 5212–5219. https://doi.org/10.1007/s12144-020-01043-1
   Web of Science ®Google Scholar
• Pai, P., & Arnott, D. C. (2013). User adoption of social networking sites: Eliciting uses and gratifications through a means–end approach. Computers in Human Behavior, 29(3), 1039–1053. https://doi.org/10.1016/j.chb.2012.06.025
   Web of Science ®Google Scholar
• Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 12(6), 729–733. https://doi.org/10.1089/cpb.2009.0003
   PubMedGoogle Scholar
• Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. The Journal of Applied Psychology, 90(1), 175–181. https://doi.org/10.1037/0021-9010.90.1.175
   PubMed Web of Science ®Google Scholar
• Ravindran, T., Kuan, A. C. Y., & Lian, D. G. H. (2014). Antecedents and effects of social network fatigue. Journal of the Association for Information Science and Technology, 65(11), 2306–2320. https://doi.org/10.1002/asi.23122
   Web of Science ®Google Scholar
• Reinecke, L., Aufenanger, S., Beutel, M. E., Dreier, M., Quiring, O., Stark, B., Wölfling, K., & Müller, K. W. (2016). Digital stress over the life span: The effects of communication load and internet multitasking on perceived stress and psychological health impairments in a German probability sample. Media Psychology, 20(1), 90–115. https://doi.org/10.1080/15213269.2015.1121832
   Web of Science ®Google Scholar
• Rogers, E. M. (2003). Diffusion of innovations. Free Press.
   Google Scholar
• Rosenthal, R. (1991). Effect sizes: Pearson’s correlation, its display via the BESD, and alternative indices. American Psychologist, 46(10), 1086–1087. https://doi.org/10.1037/0003-066X.46.10.1086
   Web of Science ®Google Scholar
• Schmid, E. J., Koch, G. G., & LaVange, L. M. (1991). An overview of statistical issues and methods of meta-analysis. Journal of Biopharmaceutical Statistics, 1(1), 103–120. https://doi.org/10.1080/10543409108835008
   PubMedGoogle Scholar
• 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, 124, 106899. https://doi.org/10.1016/j.chb.2021.106899
   PubMed Web of Science ®Google Scholar
• Soliman, W., & Rinta-Kahila, T. (2020). Toward a refined conceptualization of is discontinuance: Reflection on the past and a way forward. Information & Management, 57(2), 103167. https://doi.org/10.1016/j.im.2019.05.002
   Web of Science ®Google Scholar
• Tugtekin, U., Barut Tugtekin, E., Kurt, A. A., & Demir, K. (2020). Associations between fear of missing out, problematic smartphone use, and social networking services fatigue among young adults. Social Media + Society, 6(4), 205630512096376. https://doi.org/10.1177/2056305120963760
   Web of Science ®Google Scholar
• Turel, O. (2014). Quitting the use of a habituated hedonic information system: A theoretical model and empirical examination of Facebook users. European Journal of Information Systems, 24(4), 431–446. https://doi.org/10.1057/ejis.2014.19
   Web of Science ®Google Scholar
• Turel, O. (2016). Untangling the complex role of guilt in rational decisions to discontinue the use of a hedonic information system. European Journal of Information Systems, 25(5), 432–447. https://doi.org/10.1057/s41303-016-0002-5
   Web of Science ®Google Scholar
• Umberson, D., & Montez, J. K. (2010). Social relationships and health: A flashpoint for health policy. Journal of Health & Social Behavior, 51(Suppl), S54–S66. https://doi.org/10.1177/0022146510383501
   PubMed Web of Science ®Google Scholar
• Vaghefi, I., & Qahri-Saremi, H. (2017, January 4). From IT addiction to discontinued use: A cognitive dissonance perspective. Proceedings of the 50th Hawaii International Conference on System Sciences (2017). https://scholarspace.manoa.hawaii.edu/bitstream/10125/41844/1/paper0695.pdf
   Google Scholar
• Vaghefi, I., Qahri-Saremi, H., & Turel, O. (2020). Dealing with social networking site addiction: A cognitive-affective model of discontinuance decisions. Internet Research, 30(5), 1427–1453. https://doi.org/10.1108/Intr-10-2019-0418
   Web of Science ®Google Scholar
• Valenzuela, S., Park, N., & Kee, K. F. (2009). Is there social capital in a social network site?: Facebook use and college students’ life satisfaction, trust, and participation1. Journal of Computer-Mediated Communication, 14(4), 875–901. https://doi.org/10.1111/j.1083-6101.2009.01474.x
   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
• Zha, X., Yang, H., Yan, Y., Liu, K., & Huang, C. (2018). Exploring the effect of social media information quality, source credibility and reputation on informational fit-to-task: Moderating role of focused immersion. Computers in Human Behavior, 79, 227–237. https://doi.org/10.1016/j.chb.2017.10.038
   Web of Science ®Google Scholar
• Zhang, S., Zhao, L., Lu, Y., & Yang, J. (2016). Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services. Information & Management, 53(7), 904–914. https://doi.org/10.1016/j.im.2016.03.006
   Web of Science ®Google Scholar
• Zhang, X., Ding, X.-Y., & Ma, L. (2020). The influences of information overload and social overload on intention to switch in social media. Behaviour & Information Technology, 41(2), 1–14. https://doi.org/10.1080/0144929x.2020.1800820
   Web of Science ®Google Scholar
• Zhang, Y. B., Lin, M.-C., Nonaka, A., & Beom, K. (2005). Harmony, Hierarchy and conservatism: A cross-cultural comparison of confucian values in China, Korea, Japan, and Taiwan. Communication Research Reports, 22(2), 107–115. https://doi.org/10.1080/00036810500130539
   Google Scholar
• Zheng, H., & Ling, R. (2021). Drivers of social media fatigue: A systematic review. Telematics and Informatics, 64, 101696. https://doi.org/10.1016/j.tele.2021.101696
   Web of Science ®Google Scholar