Development and psychometric evaluation of a Turkish adaptation of the social media flow scale

Po-Ching Huang; Julia Brailovskaia; Kamolthip Ruckwongpatr; Ali Gökalp; Mark D. Griffiths; Marc N.  Potenza; Servet  Üztemur; Chung-Ying  Lin

doi:10.29329/jsomer.96

Authors

Po-Ching Huang Hong Kong Metropolitan University, Hong Kong https://orcid.org/0000-0002-2047-2246
Julia Brailovskaia Ruhr University, Germany https://orcid.org/0000-0001-7607-1305
Kamolthip Ruckwongpatr Christian University of Thailand
Ali Gökalp Gaziantep University, Türkiye https://orcid.org/0000-0002-3301-8392
Mark D. Griffiths Nottingham Trent University, United Kingdom https://orcid.org/0000-0001-8880-6524
Marc N. Potenza Yale School of Medicine, USA https://orcid.org/0000-0002-6323-1354
Servet Üztemur Anadolu University, Türkiye https://orcid.org/0000-0002-1580-9123
Chung-Ying Lin National Cheng Kung University, Taiwan https://orcid.org/0000-0002-2129-4242

DOI:

https://doi.org/10.29329/jsomer.96

Keywords:

Social Media Flow Scale, Psychometric Validation , Confirmatory Factor Analysis, Scale Adaptation, Turkish Social Media Users

Abstract

The present study adapted the Social Media Flow Scale (SMFS), developed by Brailovskaia et al. (2020), into Turkish and evaluated its psychometric properties. Data from 732 social media users (N = 732; 65.4% female; M_age = 31.19 years, SD_age = 11.13) were collected by an online survey. A standard procedure, including forward and back translation, was used to ensure the linguistic validity of the Turkish SMFS. Confirmatory factor analysis supported the original five-factor structure, comprising focused attention, enjoyment, curiosity, telepresence, and time distortion. Fit indices revealed a good fit of the model (comparative fit index = .975, Tucker-Lewis index = .960, root mean square error of approximation = .066, and standardized root mean square residual = .033). All subscales demonstrated acceptable to excellent internal consistency (α = 0.789–0.888; ω = 0.791–0.942). Convergent and discriminant validity of the SMFS were supported by average variance extraction, composite reliability, and heterotrait-monotrait ratio of correlations. Analyses of concurrent validity showed that total scores on the SMFS were significantly positively related to social media continuance, social media-related fear of missing out, social media addiction, and problematic smartphone use (r = .515 to .689). The findings suggest that flow in social media use acts as a double-edged sword by both maintaining engagement and being associated with problematic use. In sum, the results indicate that the Turkish SMFS is a reliable and valid instrument for assessing multidimensional flow experiences in social media contexts and can be utilized in research on digital well-being and addictive behaviors.

References

,,Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252–262. https://doi.org/10.1037/adb0000160

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

Argiropoulou, M. I., & Vlachopanou, P. (2021). Studying vs Internet use 0-1: The mediating role of academic procrastination between flow and problematic internet use among Greek university students. Journal of Technology in Behavioral Science, 6(2), 159–165. https://doi.org/10.1007/s41347-020-00173-4

Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186–3191. https://doi.org/10.1097/00007632-200012150-00014

Borsa, J. C., Damásio, B. F., & Bandeira, D. R. (2012). Cross-cultural adaptation and validation of psychological instruments: Some considerations. Paidéia (Ribeirão Preto), 22(53), 401-410. http://dx.doi.org/10.1590/1982-43272253201314

Brailovskaia, J. (2024). The “Vicious Circle of addictive Social Media Use and Mental Health” Model. Acta Psychologica, 247, 104306. https://doi.org/10.1016/j.actpsy.2024.104306

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), e0201484. https://doi.org/10.1371/journal.pone.0201484

Brailovskaia, J., Schillack, H., & Margraf, J. (2020). Tell me why are you using social media (SM)! Relationship between reasons for use of SM, SM flow, daily stress, depression, anxiety, and addictive SM use–An exploratory investigation of young adults in Germany. Computers in Human Behavior, 113, 106511. https://doi.org/10.1016/j.chb.2020.106511

Byrne, B. M. (2016). Structural equation modeling with Amos: Basic concepts, applications, and programming (3rd ed.). Routledge.

Carlson, J., De Vries, N. J., Rahman, M. 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

Çelik, F., & Özkara, B. Y. (2022). Fear of Missing Out (FoMO) Scale: Adaptation to social media context and testing its psychometric properties. Studies in Psychology, 42(1), 71–103. https://doi.org/10.26650/SP2021-838539

Chang, C., Huang, W., Li, S., Luo, F., & Yang, C. (2022). From lurkers to habitual engaged participants: The mediating role of flow experience in online communities. Frontiers in Psychology, 13, 938461. https://doi.org/10.3389/fpsyg.2022.836303

Chang, Y. P., & Zhu, D. H. (2012). The role of perceived social capital and flow experience in building users’ continuance intention to social networking sites in China. Computers in Human Behavior, 28(3), 995–1001. https://doi.org/10.1016/j.chb.2012.01.001

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14(3), 464–504. https://doi.org/10.1080/10705510701301834

Csibi, S., Griffiths, M. D., Cook, B., Demetrovics, Z., & Szabo, A. (2018). The psychometric properties of the Smartphone Application-Based Addiction Scale (SABAS). International Journal of Mental Health and Addiction, 16(2), 393–403. https://doi.org/10.1007/s11469-017-9787-2

Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. Jossey-Bass.

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.

Csikszentmihalyi, M., & Larson, R. (2014). Validity and reliability of the experience sampling method. In M. Csikszentmihalyi (Ed.), Flow and the foundations of positive psychology (pp. 35–54). Springer.

Cuevas, R., Sánchez, M., & Meilán, J. J. G. (2021). The role of flow in social search on Instagram and its effect on purchase intention. Journal of Theoretical and Applied Electronic Commerce Research, 16(7), 3149-3163. https://doi.org/10.1108/JRIM-03-2019-0041

Demirci, İ. (2019). The adaptation of the Bergen Social Media Addiction Scale to Turkish and the evaluation its relationships with depression and anxiety symptoms. Alpha Psychiatry, 20(1), 15–22. https://doi.org/10.5455/apd.41585

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/10.2307/3151312

Gökalp, A., Fan, C.-W., İnel, Y., & Chen, I.-H. (2024). Social Media Fatigue Scale: Adaptation to Turkish culture, validity and reliability study. Journal of Social Media Research, 1(1), 38–46. https://doi.org/10.29329/jsomer.6

Gökler, M. E., & Bulut, Y. E. (2019). Validity and reliability of the Turkish version of the Smartphone Application-Based Addiction Scale. The Journal of Cognitive Behavioral Psychotherapies and Research, 8(2), 100-106. https://doi.org/10.5455/JCBPR.38288

Han, B. (2018). Social media burnout: Definition, measurement instrument, and why we care. Journal of Computer Information Systems, 58(2), 2. https://doi.org/10.1080/08874417.2016.1208064

Hemphill, J. F. (2003). Interpreting the magnitudes of correlation coefficients. American Psychologist, 58(1), 78–79. https://doi.org/10.1037/0003-066X.58.1.78

Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioral sciences. Houghton Mifflin Company.

Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50–68. https://doi.org/10.2307/1251841

Hyun, H., Thavisay, T., & Lee, S. H. (2022). Enhancing the role of flow experience in social media usage and its impact on shopping. Journal of Retailing and Consumer Services, 65, 102492. https://doi.org/10.1016/j.jretconser.2021.102492

Kaur, P., Dhir, A., Chen, S., & Rajala, R. (2016). Flow in context: Development and validation of the flow experience instrument for social networking. Computers in Human Behavior, 59, 358–367. https://doi.org/10.1016/j.chb.2016.02.039

Kemp, S. (2024). Digital 2024: Turkey. https://datareportal.com/reports/digital-2024-turkey

Kemp, S. (2026). Digital 2026: Six billion internet users. Datareportal. https://datareportal.com/reports/digital-2026-six-billion-internet-users

Kim, E., & Seo, E. (2013). The relationship of flow and self-regulated learning to active procrastination. Social Behavior and Personality, 41, 1099–1113. https://doi.org/10.2224/sbp.2013.41.7.1099 .

Kim, H. K., & Davis, K. E. (2009). Toward a comprehensive theory of problematic Internet use: Evaluating the role of self-esteem, anxiety, flow, and the self-rated importance of Internet activities. Computers in Human Behavior, 25(2), 490-500. https://doi.org/10.1016/j.chb.2008.11.001

Kim, M., Yoo, J., & Yang, H. (2020). The role of flow in social media engagement: A study of Instagram. Journal of Research in Interactive Marketing, 14(3), 277-295. https://doi.org/10.1177/1096348019887202

Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). The Guilford Press.

Kwak, K. T., Choi, S. K., & Lee, B. G. (2014). SNS flow, SNS self-disclosure and post hoc interpersonal relations change: Focused on Korean Facebook user. Computers in Human Behavior, 31, 294–304. https://doi.org/10.1016/j.chb.2013.10.046

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

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(12), 723-731. https://doi.org/10.1089/cyber.2010.0377

Miranda, F. J., Chamorro-Mera, A., & Rubio, S. (2023). The role of sense of belonging and flow experience in the development of social media addiction. Journal of Behavioral Addictions, 12(1), 199-211. https://doi.org/10.1016/j.techfore.2022.122280

Novak, T. P., Hoffman, D. L., & Yung, Y. F. (2000). Measuring the flow construct in online environments: A structural modeling approach. Marketing Science, 19, 22-42. http://dx.doi.org/10.1287/mksc.19.1.22.15184

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.

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

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

Psailla, G., & Wagner, R. (2007). E-commerce and web technologies.8th International Conference, EC-Web 2007, Regensburg, Germany, September 3-7, Proceedings, 4655. Springer. https://doi.org/10.1007/978-3-540-74563-1

Roberts, J. A., & David, M. E. (2023). Instagram and TikTok flow states and their association with psychological well-being. Cyberpsychology, Behavior, and Social Networking, 26(2), 80-89. https://doi.org/10.1089/cyber.2022.0117

Santamaría, A. C., Riaño Gil, C., & Ruiz Vega, A. V. (2024). The power of social commerce: Understanding the role of social word-of-mouth behaviors and flow experience on social media users’ purchase intention. Sage Open, 14(3), 1-16. https://doi.org/10.1177/21582440241278452

Saura, J. R., Palacios-Marqués, D., & Iturricha-Fernández, A. (2021). Ethical design in social media: Assessing the main performance measurements of user online behavior modification. Journal of Business Research, 129, 271-281. https://doi.org/10.1016/j.jbusres.2021.03.001

Singh, S. (2026). How many people use social media in 2026? (Statistics). Retrieved January 27, 2026, from: https://www.demandsage.com/social-media-users/

Tuncer, T. (2021). The effect of flow experience on purchase intention in social commerce. Journal of Business Research, 134, 536-546. https://doi.org/10.1016/j.techsoc.2021.101567

Turkish Ministry of Transport and Infrastructure (2024). Türkiye’de 303 milyon aktif sosyal medya hesabı var. Retrieved January 27, 2025, from: https://www.uab.gov.tr/haberler/turkiye-de-303-milyon-aktif-sosyal-medya-hesabi-var

Turkish Statistical Institute (2024). Hanehalkı Bilişim Teknolojileri (BT) Kullanım Araştırması, 2024. Retrieved January 27, 2025, from: https://data.tuik.gov.tr/Bulten/Index?p=Hanehalki-Bilisim-Teknolojileri-(BT)-Kullanim-Arastirmasi-2024-53492)

Üztemur, S., Lin, C. Y., Gökalp, A., Kartol, A., Avcı, G., & Pakpour, A. H. (2025). Social media burnout and social anxiety as antecedents of discontinuous usage in the stressor-strain-outcome framework. Scientific Reports, 15(1), 23220. https://doi.org/10.1038/s41598-025-04119-9

Wu, T., & Tien, K.-Y. (2024). An empirical study on the effectiveness of e-commerce entrepreneurial learning: the mediating effect of social media flow experience. Sage Open, 14(2), 21582440241261131. https://doi.org/10.1177/21582440241261131

Yang, H., Zhang, S., Diao, Z., & Sun, D. (2023). What motivates users to continue using current short video applications? A dual-path examination of flow experience and cognitive lock-in. Telematics and Informatics, 85, 102050. https://doi.org/10.1016/j.tele.2023.102050

Yao, S., Xie, L., & Chen, Y. (2023). Effect of active social media use on flow experience: Mediating role of academic self-efficacy. Education and Information Technologies, 28(5), 5833–5848. https://doi.org/10.1007/s10639-022-11428-3

Zhang, Z., Jiménez, F. R. & Cicala, J. E. (2020). Fear of Missing Out Scale: A self‐concept perspective. Psychology & Marketing, 37(11), 1619–1634. https://doi.org/10.1002/mar.2140

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

Zheng, C. (2023). Research on the flow experience and social influences of users of short online videos. A case study of DouYin. Scientific Reports, 13(1), 3312. https://doi.org/10.1038/s41598-023-30525-y

Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management, 13(1), 27-37. https://doi.org/10.1007/s10799-011-0111-8

Zhou, T., Li, H., & Liu, Y. (2010). The effect of flow experience on mobile SNS users’ loyalty. Industrial Management & Data Systems, 110(6), 930-946. https://doi.org/10.1108/02635571011055126