Abstract
To explore the effect of active social media use (ASMU) on the mechanisms of flow experience (FE), 433 questionnaires incorporating an active social media use scale, academic self-efficacy (ASE) scale, and flow experience scale were collected from college students. This study is expected to enrich flow experience theory and provide a foundation for follow-up studies. The theoretical model for this study analyses positive social media use as an independent variable, flow experience as a dependent variable, and academic self-efficacy as an intermediary variable. Relevant data were collected via questionnaires. Subsequently, linear regression analysis was used, and the PROCESS v3.3 statistical tool of SPSS 23.0 software was used to process and analyze data. This study found that active social media use had a significant and positive impact on the flow experience. Additionally, active social media use had a significant and positive impact on academic self-efficacy.
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Data availability
The data that support the findings of this study are available from [Jiangxi Provincial Education Department of China] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [Jiangxi Provincial Education Department of China].
References
Asakawa, K. (2010). Flow experience, culture, and well-being: How do autotelic Japanese college students feel, behave, and think in their daily lives? Journal of Happiness Studies., 11(2), 205–223. https://doi.org/10.1007/s10902-008-9132-3
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
Boahene, K. O., Fang, J., & Sampong, F. (2019). Social Media Usage and Tertiary Students’ Academic Performance: Examining the Influences of Academic Self-Efficacy and Innovation Characteristics. Sustainability., 11(8), 2431. https://doi.org/10.3390/su11082431
Brailovskaia, J., & Margraf, J. (2019). I present myself and have a lot of Facebook friends – Am I a happy narcissist!? Personality and Individual Differences., 148, 11–16. https://doi.org/10.1016/j.paid.2019.05.022
CAI, L. and JIA, X. (2020). The Effect of Academic Self-Efficacy on Online Learning Engage-ment: The Chain Mediating Role of Learning Motivation and Flow Experience. Studies of Psychology and Behavior. 18(6), 805–811. https://kns.cnki.net/kcms/detail/detail.aspx. Accessed 20 May 2021
Carlson, J., de Vries, N. J., Rahman, M., & Taylor, A. (2017). Go with the flow: Engineering flow experiences for customer engagement value creation in branded social media environments. Journal of Brand Management., 24(4), 334–348. https://doi.org/10.1057/s41262-017-0054-4
Charles, S. (2019). Social media linked to rise in mental health disorders in teens, survey finds. Accessed online at: https://www.nbcnews.com/health/mental-health/social-media-linked-rise-mental-health-disorders-teens-survey-finds-n982526. Accessed 20 May 2021.
Chen, H., Wigand, R. T., & Nilan, M. (2000). Exploring Web Users’ Optimal Flow Experiences. Information Technology & People., 13(4), 263–281. https://doi.org/10.1108/09593840010359473
Chen, H., Wigand, R. T., & Nilan, M. S. (1999). Optimal experience of web activities. Computers in Human Behavior., 15(5), 585–608. https://doi.org/10.1016/s0747-5632(99)00038-2
China Internet Network Information Center (2016). Research report on Online Behavior of Chinese Adolescents in 2015. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/qsnbg/201608/t20160812_54425.htm. Accessed 20 May 2021
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly., 19(2), 189–211. https://doi.org/10.2307/249688
Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L., and Booth, M. (2019). Does time spent using social media impact mental health? An eight year longitudinal study. Computers in Human Behavior. 104.https://doi.org/10.1016/j.chb.2019.106160.
Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. Jossey- Bass.
Csikszentmihalyi, M. (1990a). Flow: The Psychology of Optimal Experience. Design Issues, 8(1), 75–77. https://doi.org/10.5465/amr.1991.4279513
Csikszentmihalyi, M. (1990b). Flow: The Psychology of Optimal Experience. Harper & Row.
Deters, F. G., & Mehl, M. R. (2012). Does posting Facebook status updates increase or decrease loneliness? An online social networking experiment. Social Psychological and Personality Science., 4(5), 579–586. https://doi.org/10.1177/1948550612469233
Frison, E., & Eggermont, S. (2016). Exploring the Relationships between Different Types of Facebook Use, Perceived Online Social Support and Adolescents’ Depressed Mood. Social Science Computer Review, 34(2), 153–171. https://doi.org/10.1177/0894439314567449
Gastelum, Z. N., and Whattam, K. M. (2013). State-of-the-art of Social Media Analytics Research. December 2020. http://www.pnnl.gov/main/publications/external/technical_reports/PNNL-22171.pdf. Accessed 20 May 2021.
Ghani, J. A., & Deshpande, S. P. (1994). Task Characteristics and the Experience of Optimal Flow in Human-Computer Interaction. The Journal of Psychology., 128(4), 381–391. https://doi.org/10.1080/00223980.1994.9712742
Gieryn, T. F. (1999). Cultural boundaries of science: Credibility on the line. University of Chicago Press.
Hair, J. F., Tatham, R. L., & Anderson, R. E. (1998). Multivariate Data Analysis. Prentice Hall.
Harasim, L., Hiltz, S., Teles, L., & Turoff, M. (1997). Learning networks. The MIT Press.
Hatfield, E., Rapson, R. L., & Le, Y.-C. L. (2009). Emotional contagion and empathy. In J. Decety & W. Ickes (Eds.), The social neuroscience of empathy (pp. 19–30). MIT Press. https://doi.org/10.7551/mitpress/9780262012973.003.0003
Ho, L. A., & Kuo, T. H. (2010). How can one amplify the effect of e-learning? An examination of high-tech employees’ computer attitude and flow experience. Computers in Human Behavior., 26(1), 23–31. https://doi.org/10.1016/j.chb.2009.07.007
Hoffman, D. L., & Novak, T. P. (1996). Marketing in Hypermedia Computer-mediated Environ-ments: Conceptual Foundations. Journal of Marketing., 60(3), 50–68. https://doi.org/10.2307/1251841
Hoffman, D. L., Novak, T. P., & Ratchford, B. T. (2009). Flow Online: Lessons Learned and Future Prospects. Journal of Interactive Marketing., 23(1), 23–34. https://doi.org/10.1016/j.intmar.2008.10.003
Holzinger, A. (2001). A Multi-Media test-bed for examining incidental learning, motivation and the Tamagotchi-Effect within a Game-Show like Computer Based Learning Module. Association for the Advancement of Computing in Education (AACE).
Huang, Z., & Benyoucef, M. (2014). User preferences of social features on social commerce websites: An empirical study. Technological Forecasting and Social Change., 95, 57–72. https://doi.org/10.1016/j.techfore.2014.03.005
Hyuna, H., Thavisay, T., & Lee, S. H. (2021). Enhancing the role of flow experience in social media usage and its impact on shopping. Journal of Retailing and Consumer Services. https://doi.org/10.1016/j.jretconser.2021.102492
Jackson, S. A., & Marsh, H. (1996). Development and Validation of a Scale to Measure Optimal Experience: The Flow State Scale. Journal of Sport & Exercise Psychology., 18(1), 17–35. https://doi.org/10.1123/jsep.18.1.17
Joo, Y. J., Oh, E., & Kim, S. M. (2015). Motivation, instructional design, flow, and academic achievement at Korean online University: A structural equation modeling study. Journal of Computing in Higher Education., 27, 28–46. https://doi.org/10.1007/s12528-015-9090-9
Junco, R. (2014). Engaging Students through Social Media: Evidence-Based Practices for Use in Student Affairs. Jossey-Bass.
Kabilan, M. K., Ahmad, N., & Abidin, M. J. Z. (2010). Facebook: An online environment for learning of English in institutions of higher education? The Internet and Higher Education., 13(4), 179–187. https://doi.org/10.1016/j.iheduc.2010.07.003
Kerka, S. (2000). Incidental learning. Trends and Issues, 18. Ohio State University: Center on Education and Training for Employment.
Kim, B., & Kim, Y. (2017). College students’ social media use and communication network heterogeneity: Implications for social capital and subjective well-being. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2017.03.033
Kim, M. J., Lee, C. K., & Bonn, M. (2017). Obtaining a better understanding about travel-related purchase intentions among senior users of mobile social network sites. International Journal of Information Management., 37(5), 484–496. https://doi.org/10.1016/j.ijinfomgt.2017.04.006
Kozinets, R. V., Belz, F. M., and McDonagh, P. (2012). Social media for social change. In D. Glen Mick, S. Pettigrew, C. Pechmann, & J. L. Ozanne (Eds.), Transformative consumer research for personal and collective well-being (pp. 205–223). Taylor and Francis.
Lee, S. (2017). The impact of qualities of social network service on the continuance usage intention. Management Decision., 55(4), 701–729. https://doi.org/10.1108/MD-102016-0731
Levinson, P. (2010). New new media. Allyn and Bacon.
Lin, J., Lin, S., Turel, O., & Xu, F. (2020). The buffering effect of flow experience on the relationship between overload and social media users’ discontinuance intentions. Telematics and Informatics., 49, 101374. https://doi.org/10.1016/j.tele.2020.101374
Liu, D., Baumeister, R. F., Yang, C., & Hu, B. (2019). Digital Communication Media Use and Psychological Well-Being: A Meta-Analysis. Journal of Computer-Mediated Communication., 24(5), 259–273. https://doi.org/10.1093/jcmc/zmz013
Mao, Yanhui; Yang, Rui; Bonaiuto, Marino; Ma, Jianhong; Harmat, Lászlà (2020). Can Flow Alleviate Anxiety? The Roles of Academic Self-Efficacy and Self-Esteem in Building Psy-chological Sustainability and Resilience. Sustainability. 12(7). https://doi.org/10.3390/su12072987.
Marty-Dugas, J., and Smilek, D. (2020). The relations between smartphone use, mood, and flow experience. Personality and Individual Differences. 164.https://doi.org/10.1016/j.paid.2020.109966.
Mauri, M., Cipresso, P., Balgera, A., Villamira, M., & Riva, G. (2011). Why is Facebook so successful? Psychophysiological measures describe a core flow state while using Facebook. Cyberpsychology, Behavior, and Social Networking., 14, 723–731. https://doi.org/10.1089/cyber.2010.0377
McFerrin, K. (1999). Incidental learning in a higher education asynchronous online distance education course. SITE 99: Society for Information Technology & Teacher Education International Conference Proceedings. Association for the Adputing in Education.
Mesurado, B., Richaud, M. C., & Mateo, N. J. (2016a). Engagement, flow, self-efficacy, and eustress of university students: A cross-national comparison between the Philippines and Argentina. The Journal of Psychology, 150(3), 281–299. https://doi.org/10.1080/00223980.2015.1024595
Mesurado, B., Richaud, M. C., & Mateo, N. J. (2016b). Engagement, Flow, Self-Efficacy, and Eustress of University Students: A Cross-National Comparison Between the Philippines and Argentina. The Journal of Psychology., 150, 281–299. https://doi.org/10.1080/00223980.2015.1024595
Michael, R. B., Garry, M., & Kirsch, I. (2012a). Suggestion, cognition, and behavior. Current Directions in Psychological Science., 21(3), 151–156. https://doi.org/10.1177/0963721412446369
Michael, R. B., Garry, M., & Kirsch, I. (2012b). Suggestion, cognition, and behavior. Current Directions in Psychological Science, 21(3), 151–156. https://doi.org/10.1177/0963721412446369
Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., Anderman, L., Freeman, K. E., Gheen, M., Kaplan, A., Kumar, R., Middleton, M. J., Nelson, J., Roeser, R., and Urdan, T. (2000). Manual for the patterns of adaptive learning scales. University of Michigan. http://websites.umich.edu/~pals/PALS%202000_V12Word97.pdf. Accessed 20 May 2021.
Northcote, M., and Kendle, A. (2001). Informal online networks for learning: Making use of incidental learning through recreation. International Education Research. http://www.aare.edu.au/data/publications/2001/nor01596.pdf. Accessed 20 May 2021.
Özhan, ŞÇ., & Kocadere, S. A. (2020). The effects of flow, emotional engagement, and motivation on success in a gamified online learning environment. Journal of Educational Computing Research., 57(8), 2006–2031. https://doi.org/10.1177/0735633118823159
Pelet, J. É., Ettis, S., & Cowart, K. (2017). Optimal experience of flow enhanced by telepresence: Evidence from social media use. Information & Management., 54(1), 115–128. https://doi.org/10.1016/j.im.2016.05.001
Pennington, M. (1989). Teaching languages with computers: The state of the art. Athelstan.
Pew Research Center. (2019). Share of U.S. adults using social media, including Facebook, is mostly unchanged since 2018. https://www.pewresearch.org/fact-tank/2019/04/10/share-of-u-s-adults-using-socialmedia-including-facebook-is-mostly-unchanged-since-2018/. Accessed 20 May 2021.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management., 12(4), 69–82. https://doi.org/10.1177/014920638601200408
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology., 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Privette, G. (1983). Peak Experience, Peak Performance, and Flow: A Comparative Analysis of Positive Human Experiences. Journal of Personality & Social Psychology., 45(6), 1361–1368. https://doi.org/10.1037/0022-3514.45.6.1361
Rafaeli, S. (1988). From new media to communication. Sage Annual Review of Communication Research: Advancing Communication Science. Sage.
Rogers, A. (1997). Learning: Can we change the discourse? Adults Learning (England). 8(5), 116−117. https://eric.ed.gov/?id=EJ540449. Accessed 20 May 2021.
Ross-Gordon, J., & Dowling, W. (1995). Adult learning in the context of African American women’s voluntary organizations. International Journal of Lifelong Education., 14(4), 306–319. https://doi.org/10.1080/0260137950140404
Selkie, E., Adkins, V., Masters, E., Bajpai, A., & Shumer, D. (2020). Transgender Adolescents’ Uses of social media for Social Support. Journal of Adolescent Health, 66(3), 275–280. https://doi.org/10.1016/j.jadohealth.2019.08.011
Siciliano, M. D. (2016). It’s the quality not the quantity of ties that matters: Social networks and self-efficacy beliefs. American Educational Research Journal., 53(2), 227–262. https://doi.org/10.3102/0002831216629207
Srivastava, K., Shukla, A., and Sharma, N. (2010). Online flow experiences: The role of need for cognition, self-efficacy, and sensation seeking tendency. International Journal of Business Insights and Transformation. 3(2), 93–101. https://www.researchgate.net/publication/280489681. Accessed 20 May 2021.
Statista. (2020). User-generated internet content per minute 2020. https://www.statista.com/statistics/195140/new-user-generated-content-uploaded-by-users-per-minute/. Accessed 20 May 2021.
Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of Communication., 42(4), 73–93. https://doi.org/10.1111/j.1460-2466.1992.tb00812.x
Taylor, A. B., MacKinnon, D. P., & Tein, J. Y. (2008). Tests of the three-path mediated effect. Organizational Research Methods., 11(2), 241–269. https://doi.org/10.1177/1094428107300344
Taylor, K., and Silver, L. (2019). Smartphone ownership is growing rapidly around the world, but not always equally. Pew Research Center. https://www.pewresearch.org/global/. Accessed 20 May 2021.
Thomas, N. J. T. (1999). Are theories of imagery theories of imagination? An active perception approach to conscious mental content. Cognitive Science., 23(2), 207–245. https://doi.org/10.1207/s15516709cog2302_3
Trevino, L. K., & Webster, J. (1992). Flow in computer-mediated communication: Electronic mail and voice mail evaluation and impacts. Communication Research., 19(5), 539–573. https://doi.org/10.1177/009365092019005001
Turban, E., Strauss, J., and Lai, L. (2012). Social Commerce: Marketing, Technology and Management. Springer. https://link.springer.com/book/10.1007%2F978-3-319-17028-2. Accessed 20 May 2021.
Twenge, J. M., Martin, G. N., & Spitzberg, B. H. (2019). Trends in US Adolescents’ media use, 1976–2016: The rise of digital media, the decline of TV, and the (near) demise of print. Psychology of Popular Media Culture., 8(4), 329–345. https://doi.org/10.1037/ppm0000203
Verduyn, P., Gugushvili, N., & Kross, E. (2021). The impact of social network sites on mental health: Distinguishing active from passive use. World Psychiatry, 20(1), 133–134. https://doi.org/10.1002/wps.20820
Verduyn, P., Lee, D. S., Park, J., Shablack, H., Orvell, A., Bayer, J., Ybarra, O., Jonides, J., & Kross, E. (2015). Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. Journal of Experimental Psychology: General., 144(2), 480–488. https://doi.org/10.1037/xge0000057
Verduyn, P., Ybarra, O., Résibois, M., Jonides, J., & Kross, E. (2017). Do social network sites enhance or undermine subjective well-being? A critical review. Social Issues and Policy Review., 11, 274–302. https://doi.org/10.1111/sipr.12033
Wang, L., Yan, J., Lin, J., & Cui, W. (2017). Let the users tell the truth: Self-disclosure intention and self-disclosure honesty in mobile social networking. International Journal of Information Management., 37(1), 1428–1440. https://doi.org/10.1016/j.ijinfomgt.2016.10.006
Yao, S., & Chung, S. (2019). The Effect of Social Media Interactions Perception among Chinese Employees on Organizational Citizenship Behavior: With Job Satisfaction as the Mediator Variable, Trust as the Moderator Variable. Korean Business Education Review, 34(4), 153–185.
Zhao, N., & Zhou, G. (2021). COVID-19 Stress and Addictive Social Media Use (SMU): Mediating Role of Active Use and Social Media Flow. Frontiers in Psychiatry, 12, 635546. https://doi.org/10.3389/fpsyt.2021.635546
Zhu, H., and Ke, J. (2019). The Impact of Communication Synchronization on Customer's Flow Experience. Taxation and Economy, 222(1), 25–33. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2019&filename=SWYJ201901005&uniplatform=NZKPT&v=g0aF7TnchOMh8cxbigTNXuuKcM7KChn0meMiaQ4leVhrQSEzvFXJ4cZ_TO9TvY2X. Accessed 20 May 2021.
Funding
This study was supported by Research on the influencing mechanism of College students' self-efficacy in social Network context, the Education Department of Jiangxi Provincial, Humanities and Social Science Project of Universities in Jiangxi Province in 2020(JC20127).
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Yao, S., Xie, L. & Chen, Y. Effect of active social media use on flow experience: Mediating role of academic self-efficacy. Educ Inf Technol 28, 5833–5848 (2023). https://doi.org/10.1007/s10639-022-11428-3
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DOI: https://doi.org/10.1007/s10639-022-11428-3