Do social motivations predict addiction to social media in young people? The role of flow and algorithm awareness
Supporting Agencies
- the Fundamental Research Funds for the Central Universities of China (No. 3142020010)
Abstract
Addictive use of social media has become an increasingly relevant phenomenon among young people, affecting both their psychological well-being and their online behavior. The principal objective of this study is to investigate the associations between addictive use of social media, usage motivations, flow, and algorithm awareness. Our hypothesis is that the flow experience and the algorithm awareness are two mediators through which relevant social motivations influence the development of an addiction to social media. Validated questionnaires are used to measure the study variables, including the BSMAS to assess social media addiction. Structural equation modeling (SEM) with Bootstrap tests is used for analyzing data that is collected from a sample of 580 younger users aged 18 to 22 in China (M = 20.61, SD = 1.32), with 47.7% women and 52.2% men, all undergraduate students, in order to test the research hypotheses. The results reveal that different addiction mechanisms implicate different associations with socially relevant motivations. It advances the field of addiction to social media by showing that addiction is also related to algorithm awareness, through which a new alternative mechanism of addiction is identified.
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Abdelfattah, F., Halbusi, H., & Al-Brwani, R. (2022). Influence of self-perceived creativity and social media use in predicting E-entrepreneurial intention. International Journal of Innovation Studies, 6, 119-127. https://doi.org/10.1016/j.ijis.2022.04.003
Alhabash, S., & Ma, M. (2017). A tale of four platforms: Motivations and uses of Facebook, Twitter, Instagram, and Snapchat among college students? Social Media + Society, 3(1). https://doi.org/10.1177/2056305117691544
Anderson, E.L., Steen E., & Stavropoulos, V. (2016). “Internet use and problematic internet use: A systematic review of longitudinal research trends in adolescence and emergent adulthood.” International Journal of Adolescence and Youth, 1, 1-25. https://doi.org/10.1080/02673843.2016.1227716
Anderson, J.C., & Gerbing, D.W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411. https:// doi/org/10.1037/0033-2909.103.3.411
Andreassen, C.S., Pallesen, S., & Griffiths, M.D. (2017). The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. Addictive Behaviors, 64, 287–293. https://doi.org/10.1016/j. addbeh.2016.03.006
Ardèvol-Abreu, A., Rodríguez-Wangüemert, C. & Delponti, P. (2022). “Mobile instant messaging techno-stressors: Measurement, dimensionality, and relationships with type of usage”. Profesional de la información, 31(4), e310401. https://doi.org/10.3145/epi.2022.jul.01
Bajaña Tovar, F. (2021). “Filtro burbuja: ¿Cuál es el costo de la personalización digital?” Revista chilena de derecho y tecnología, 10(1), 29-52. https://doi.org/10.5354/0719-2584.2021.54042
Bérail, P.D., Guillon, M., & Bungener, C. (2019). The relations between YouTube addiction, social anxiety and parasocial relationships with YouTubers: A moderated-mediation model based on a cognitive-behavioral framework. Computers in Human Behavior, 99, 190-204. https://doi.org/10.1016/j.chb.2019.05.007
Boerman, S.C., Kruikemeier, S., & Zuiderveen Borgesius, F.J. (2017). Online behavioral advertising: A literature review and research agenda. Journal of Advertising, 46(3), 363–376. https://doi.org/10.1080/00913367.2017.1339368
Brailovskaia, J., & Margraf, J. (2022). The relationship between active and passive Facebook use, Facebook flow, depression symptoms and Facebook Addiction: A three-month investigation. Journal of Affective Disorders Reports, 10. https://doi.org/10.1016/j.jadr.2022.100374
Brailovskaia, J., Rohmann, E., Bierhoff, H.-W., & Margraf, J. (2018). The brave blue world: Facebook flow and Facebook addiction disorder (FAD). PLoS ONE, 13(7). https://doi.org/10.1371/journal.pone.0201484
Brailovskaia, J., & Teichert, T. (2020). “I like it” and “I need it”: Relationship between implicit associations, flow, and addictive social media use. Computer in Human Behavior, 113. https://doi.org/10.1016/j.chb.2020.106509
Bucher, T. (2016). The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30–44. https://doi.org/10.1080/1369118X.2016.1154086
Byrne, B.M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming. New York: Routledge. https://doi.org/10.4324/9781315757421
Carbonell, X., Chamarro, A., Griffiths, M., Oberst, U., Cladellas, R., & Talarn, A. (2012). Uso problemático de Internet y móvil en adolescentes y jóvenes españoles. Anales de Psicología / Annals of Psychology, 28(3), 789–796. https://doi.org/10.6018/analesps.28.3.156061
Cheng, C., Ebrahimi, O.V., & Luk, J.W. (2022). Heterogeneity of prevalence of social media addiction across multiple classification schemes: Latent profile analysis. Journal of Medical Internet Research, 24(1), e27000. https://doi.org/10.2196/27000
Cheng, X., Su, X., Yang, B., Zarifis, A., & Mou, J. (2023). Understanding users’ negative emotions and continuous usage intention in short video platforms. Electronic Commerce Research and Applications, 58, 101244. https://doi.org/10.1016/j.elerap.2023.101244
Cho, J., Ahmed, S., M. Hilbert, Liu B., & Luu J. (2020). Do search algorithms endanger democracy? An experimental investigation of algorithm effects on political polarization. Journal of Broadcasting & Electronic Media, 64(2). https://doi.org/10.1080/08838151.2020.1757365
Cohen, J., & Holbert, R.L. (2021). Assessing the predictive value of parasocial relationship intensity in a political context. Communication Research, 48(4), 501–526. https://doi.org/10.1177/0093650218759446
Croes, E., & Bartels, J. (2021). Young adults’ motivations for following social influencers and their relationship to identification and buying behavior. Computers in Human Behavior, 124. https://doi.org/10.1016/j.chb.2021.106910
Csikszentmihalyi, M. (1997). Finding Flow: The Psychology of Engagement with Everyday Life. Basic Books.
Csikszentmihalyi, M. (2014). Flow and the foundations of positive psychology. Dordrecht: Springer Science+Business Media. https://doi.org/10.1007/978-94-017-9088-8
Dhir, A., Chen, G.M., & Chen, S. (2015). Why do we tag photographs on Facebook? Proposing a new gratifications scale. New Media & Society, 19(4). https://doi.org/10.1177/1461444815611062
Echeburúa, E., & de Corral, P. (2010). Adicción a las nuevas tecnologías y a las redes sociales en jóvenes: Un nuevo reto. Adicciones, 22(2), 91-95. https://doi.org/10.20882/adicciones.196
Eslami, M., Rickman, A., Vaccaro K., Aleyasen A., Vuong, A., Karahalios K., Hamilton, K., & Sandvig, C. (2015). I always assumed that I wasn't really that close to [her]": Reasoning about invisible algoritsxhms in news feeds. B. Begole, J. Kim, K. Inkpen, W. Woo (Eds.), The 33rd annual ACM conference on human factors in computing systems, Seoul, Republic of Korea, 18-23 April 2015, ACM, New York, pp. 153-162. https://doi.org/10.1145/2702123.2702556
Fernandes, B., Biswas, U.N., Tan-Mansukhani, R., Vallejo, A., & Essau, C.A. (2020). “The impact of COVID-19 lockdown on internet use and escapism in adolescents”. Revista de psicología clínica con niños y adolescentes, 7(3), 59-65. https://doi.org/10.21134/rpcna.2020.mon.2056
Fiallos-Ordoñez, A., Fiallos, C., & Figueroa, S. (2021). “Tiktok and education: discovering knowledge through learning videos”. 2021 Eighth International Conference on eDemocracy & eGovernment (ICEDEG). New York: IEEE, 172-176. ISBN: 978 1 6654 2512 4
Gómez, E., Charisi, V., & Chaudron, S. (2021). Evaluating recommender systems with and for children: towards a multi-perspective framework. Perspectives on the Evaluation of Recommender Systems Workshop (PERSPECTIVES 2021), co-located with the 15th ACM Conference on Recommender Systems, Amsterdam, The Netherlands. http://bit.ly/3VtbgGz
Griffiths, M.D. (2005). A componets model of addiction within a biopsychosocial framework. Journal of Substance Use, 10, 191-197. https://doi.org/10.1080/14659890500114359
Griffiths, M.D., Kuss, D.J., & Demetrovics, Z. (2014). Social networking addiction: An overview of preliminary findings. In: Rosenberg, K., Feder, L. (Eds.), Behavioral Addictions: Criteria, Evidence and Treatment. Elsevier, New York, 119–141. https://doi.org/10.1016/B978-0-12-407724-9.00006-9
Hall, J.A. (2018). When is social media use social interaction? Defining mediated social interaction. New Media & Society, 20(1). https://doi.org/10.1177/1461444816660782
Hoffman, D.L., & Novak, T.P. (2009). “Flow online: lessons learned and future prospects.” Journal of Interactive Marketing, 23(1). https://doi.org/10.1016/j.intmar.2008.10.003
Huang, L., Hsieh, Y., & Wu, Y. (2014). Gratifications and social network service usage: the mediating role of online experience. Information & Management, 51, 774–782. http://doi.org/10.1016/j.im.2014.05.004
Huang, S.L., & Chang, C.Y. (2020). Understanding How People Select Social Networking Services: Media Trait, Social Influences and Situational Factors. Information & Management, 57(6). https://doi.org/10.1016/j.im.2020.103323
Katz, E., Haas, H., & Gurevitch, M. (1973). On the use of the mass media for important things. American Sociological Review, 38(2). https://doi.org/10.2307/2094393
Khan, M.L. (2017). Social media engagement: What motivates user participation and consumption on YouTube? Computers in Human Behavior, 66, 236–247. https://doi.org/10.1016/j.chb.2016.09.024
Khang, H., Kim, J. K., & Kim, Y. (2013). Self-traits and motivations as antecedents of digital media flow and addiction: The Internet, mobile phones, and video games. Computers in Human Behavior, 29(6), 2416-2424. https://doi.org/10.1016/j.chb.2013.05.027
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). https://doi.org/10.1007/s11469-018-9940-6
Lim, J.S., & Zhang, J. (2022). Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency. Technology in Society, 69, 101965. https://doi.org/10.1016/j.techsoc.2022.101965
Liu, B. (2020). Effects of agency locus and transparency of artificial intelligence: Uncertainty reduction and emerging mind. [Doctoral dissertation, the Pennsylvania State University]. The Pennsylvania State University Electronic Theses and Dissertations Archive. https://bit.ly/40ZQAHa
Longobardi, C., Settanni, M., Fabris, M.A., & Marengo, D. (2020). Follow or be followed: Exploring the links between Instagram popularity, social media addiction, cyber victimization, and subjective happiness in Italian adolescents. Children and Youth Services Review, 113. https://doi.org/10.1016/j.childyouth.2020.104955
Marino, C., Vieno, A., Altoè, G., & Spada, M.M. (2017). Factorial validity of the Problematic Facebook Use Scale for adolescents and young adults. Journal of Behavioral Addictions, 6(1), 5-10. https://doi.org/10.1556/2006.6.2017.004
McCrae, N., Gettings, S., & Purssell, E. (2017). Social media and depressive symptoms in childhood and adolescence: A systematic review. Adolescent Research Review, 2(4), 315–330. https://doi.org/10.1007/s40894-017-0053-4
Menon, D. (2022). Uses and gratifications of photo sharing on Instagram. International Journal of Human - Computer Studies, 168. https://doi.org/10.1016/j.ijhcs.2022.102917
Meshia, D., Turel, O., & Henleya, D. (2020). Snapchat vs. Facebook: Differences in problematic use, behavior change attempts, and trait social reward preferences. Addictive Behaviors Reports, 12. https://doi.org/10.1016/j.abrep.2020.100294
Miranda, S., Trigo, I., Rodrigues, R., & Duarte, M. (2023). Addiction to social networking sites: Motivations, flow, and sense of belonging at the root of addiction. Technological Forecasting and Social Change, 188, 122280. https://doi.org/10.1016/j.techfore.2022.122280
Muntinga, D.G., Moorman, M., & Smit, E.G. (2011). Introducing COBRAs. Exploring motivations for brand-related social media use. International Journal of Advertising, 30(1), 13–46. https://doi.org/10.2501/IJA-30-1-013-046
Omar, B., & Dequan, W. (2020). Watch, share or create: The influence of personality traits and user motivation on TikTok mobile video usage. International Journal of Interactive Mobile Technologies, 14(4), 121-137. https://doi.org/10.3991/ijim.v14i04.12429
Peleta, J.É., Ettisb, S., & Cowart, K. (2017). Optimal experience of flow enhanced by telepresence: Evidence from social media use. Information & Management, 54. https://doi.org/10.1016/j.im.2016.05.001
Phua, J., Jin, S.V., & Kim, J.J. (2017). Gratifications of using Facebook, Twitter, Instagram, or Snapchat to follow brands: The moderating effect of social comparison, trust, tie strength, and network homophily on brand identification, brand engagement, brand commitment, and membership intention. Telematics and Informatics, 34(1), 412–424. https://doi.org/10.1016/j.tele.2016.06.004
Quelhas-Brito, P. (2012). “Teen conceptualization of digital technologies”. New Media & Society, 14(3), 513-532. https://doi.org/10.1177/1461444811420822
Rassameeroj, I., & Wu, S.F. (2019). Reverse engineering of content delivery algorithms for social media systems. Guetl, C., Lloret, J., Ceravolo, P., AL-Smadi, M., Karimi, F., &Granada, S. (Eds.), The sixth international conference on social networks analysis, management and security, IEEE, Piscataway, 196-203. http://doi.org/10.1109/SNAMS.2019.8931859
Raza, A., Usman, M., & Ali, M. (2022). Examining how and when Facebook intensive use shapes users’ online pro-social behaviors. Telematics and Informatics, 67. https://doi.org/10.1016/j.tele.2021.101753
Rodríguez, C.A. (2017). Los usuarios en su laberinto: burbujas de filtros, cámaras de ecos y mediación algorítmica en la opinión pública en línea. Virtualis, 8(16). https:// bit.ly/429O1U6
Seidman, G. (2013). Self-presentation and belonging on Facebook: How personality influences social media use and motivations. Personality and Individual Differences, 54(3). https://doi.org/10.1016/j.paid.2012.10.009
Sheldon, P., & Bryant, K. (2016). Instagram: Motivations for its use and relationship to narcissism and contextual age. Computers in Human Behavior, 58, 89–97. https://doi. org/10.1016/j.chb.2015.12.059
Sokolova, K., & Perez, C. (2021). You follow fitness influencers on YouTube. But do you actually exercise? How parasocial relationships, and watching fitness influencers, relate to intentions to exercise. Journal of Retailing and Consumer Services, 58. https://doi.org/10.1016/j.jretconser.2020.102276
Stanculescu, E., & Griffiths, M.D. (2022). Social media addiction profiles and their antecedents using latent profile analysis: The contribution of social anxiety, gender, and age. Telematics and Informatics, 74, 101879. https://doi.org/10.1016/j.tele.2022.101879
Stavropoulos, V., Alexandraki, K., & Motti-Stefanidi, F. (2013). Flow and telepresence contributing to Internet abuse: Differences according to gender and age. Computers in Human Behavior, 29(5). https://doi.org/10.1016/j.chb.2013.03.011
Swart, J. (2021) Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media. Social Media + Society, 7(2). https://doi.org/10.1177/20563051211008828
Vanman, E.J., Baker, R., & Tobin, S.J. (2018). The burden of online friends: The effects of giving up Facebook on stress and well-being. The Journal of Social Psychology, 158(4), 496–508. https://doi.org/10.1080/00224545.2018.1453467
Wang, X., & Guo, Y. (2023). “Motivations on TikTok addiction: The moderating role of algorithm awareness on young people”. Profesional de la información, 32(4), e320411. https://doi.org/10.3145/epi.2023.jul.11
Wang, X., & Shang, Q. (2024). How do social and parasocial relationships on TikTok impact the well-being of university students? The roles of algorithm awareness and compulsive use. Acta Psychologica, 248, 104369. https://doi.org/10.1016/j.actpsy.2024.104369
Wang, Z.P., Yang X., & Zhang, X.L. (2020). Relationships among boredom proneness, sensation seeking and smartphone addiction among Chinese college students: Mediating roles of pastime, flow experience and self-regulation. Technology in Society, 62. https://doi.org/10.1016/j.techsoc.2020.101319
Wei, J., Dang, J., Mi, Y., & Zhou, M. (2024). Adicción al teléfono móvil y ansiedad social entre adolescentes chinos: papel mediador de los problemas interpersonales. Anales de Psicología / Annals of Psychology, 40(1), 103–109. https://doi.org/10.6018/analesps.381801
Wu, O. (2022). Are smartphones addictive? Examining the cognitive-behavior model of motivation, leisure boredom, extended self, and fear of missing out on possible smartphone addiction. Telematics and Informatics, 71. https://doi.org/10.1016/j.tele.2022.101834
Yang, J., Ti, Y., & Ye, Y. (2022). Offline and Online Social Support and Short-Form Video Addiction among Chinese Adolescents: The Mediating Role of Emotion Suppression and Relatedness Needs. Cyberpsychology, Behavior, and Social Networking, 25(5), 316-322. https://doi.org/10.1089/cyber.2021.0323
You, L., & Hon, L. (2022). Measuring consumer digital engagement and political consumerism as outcomes of corporate political advocacy. Public Relations Review, 48(5), 1–12. https://doi.org/10.1016/j.pubrev.2022.102233
Zarouali, B., Boerman, S.C., & Vreese, C.H. (2021). Is this recommended by an algorithm? The development and validation of the algorithmic media content awareness scale (AMCA-scale). Telematics and Informatics, 62. https://doi.org/10.1016/j.tele.2021.101607
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