A Model of Factors Influencing Social Media Addiction in University Students

Phayam  Kandee; Darawan  Thapinta; Sombat  Skulphan; Petsunee  Thungjaroenkul

Authors

Phayam Kandee PhD (Candidate), RN, Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand.
Darawan Thapinta PhD, RN, Professor, Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand.
Sombat Skulphan PhD, RN, Assistant Professor, Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand.
Petsunee Thungjaroenkul PhD, RN, Associate Professor, Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand

Keywords:

Addictive behavior, Depression, Extraversion, Neuroticism, Self-regulation Social media addiction, Subjective norm, University students

Abstract

Social media addiction is an important mental health concern with steadily increasing prevalence in young people along with physical, psychological, and academic issues. To reduce social media addiction, an understanding about its influencing factors is required. This cross-sectional study aimed to develop and test a model of factors influencing social media addiction in university students. Multi-stage sampling was used to obtain 550 undergraduate students from five faculties of a public university in Thailand. Data were collected using a demographic data form, the Bergen Social Media Addiction Scale, the Internet Self-efficacy Scale, the Internet Use Expectancies Scale, the Self-regulation Scale, the Center for Epidemiologic Studies Depression Scale, a neuroticism subscale and an extraversion subscale of the Neuroticism Extraversion Openness to experience Five-Factor Inventory, the Multi-Dimensional Scale of Perceived Social Support, and the Subjective Norm Scale. SPSS version 26.0 and the AMOS program were applied for model testing.
Findings revealed that the modified model fitted with empirical data and explained 86% of variance in social media addiction. Depression was the strongest predictor influencing social media addiction both directly and indirectly via Internet use expectancies and self-regulation. Subjective norm directly and indirectly influenced social media addiction via self-regulation. Extraversion and neuroticism directly influenced social media addiction whereas Internet self-efficacy and perceived social support had no direct or indirect effects on social media addiction. In conclusion, personal and environment factors together exert powerful effect on social media addiction. Therefore, nurses and other health professionals can design and test the effectiveness of interventions to reduce social media addiction by decreasing depression, changing subjective norm to improve self-regulation and reduce Internet use expectancies, and screening and modifying extraversion and neuroticism personality.

References

Sulaiman AH. Social networking (SNS) addiction among university students: a literature review and research directions. J Educ Soc Behav Sci. 2020;33(1):11-23.doi: 10.9734/JESBS/2020/v33i130191.

Gómez-Galán J, Martínez-López JÁ, Lázaro-Pérez C, Sarasola Sánchez-Serrano JL. Social networks consumption and addiction in college students during the COVID-19 pandemic:educational approach to responsible use. Sustainability.2020;7737:1-17. doi:10.3390/su12187737.

Nguyen TH, Lin KH, Rahman FF, Ou, JP, Wong WK. Study of depression, anxiety, and social media addiction among undergraduate students. J Manag Inf Decis Sci. 2020;23(4):257-76.

National Statistical Office. The 2022 household survey on the use of information and communication technology (quarter 1). Bangkok: National Statistical Office; 2022(in Thai).

Keles B, McCrae N, Grealish A. A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int J Adolesc Youth.2020;25(1):79-93.doi: 10.1080/02673843. 2019.1590851.

Griffiths, MD, Kuss D. Adolescent social media addiction (revisited). Educ Health. 2017;35(3):49-52.

Kajai C, Thapinta D, Skulphan S. The relationship between Facebook addiction and depression among adolescents attending state university in Chiang Mai Province. SCNJ.2018;5(2):57-69 (in Thai).

Kulsawat T, Narkwatchara P. Factors affecting social media addiction of undergraduate students in the eastern region university. JMND. 2021;8(10):1-15 (in Thai).

Wong HY, Mo HY, Potenza MN, Chan MNM, Lau WM, Chui TK, et al. Relationships between severity of internet gaming disorder, severity of problematic social media use, sleep quality and psychological distress. Int J Environ Res Public Health. 2020;17(6):1-13. doi:10.3390/ijerph17061879.

Zimmer JC. Problematic social network use: its antecedents and impact upon classroom performance. Comput Educ. 2022;104368. doi:10.1016/j.compedu.2021.104368.

Simsek A, Elciyar K, Kizilhan T. A comparative study on social media addiction of high school and university students. Contemp Educ Psychol. 2019;10(2):106-19. doi:10.30935/cet.554452.

Bandura A. Social foundations of thought and action: a social cognitive theory. New Jersey: Prentice-Hall; 1986.

Rezaei H, Mazaheri MA, Dastgerdi ZF, Rahimi M, Eslami AA. Assessment of the questionnaire of cognitive factors and adolescent smoking behavior: a psychometric study.J Subst Use. 2022;27(2):181-7. doi:10.1080/14659891.2021.1916847.

Yu S, Wu AMS, Pesigan IJA. Cognitive and psychosocial health risk factors of social networking addiction. Int J Ment Health Addict. 2016;14:550-64. doi: 10.1007/s11469-015-9612-8.

Kim Y, Glassman M. Beyond search and communication: development and validation of the Internet Self-efficacy Scale (ISS). Comput Hum Behav. 2013;29(4):1421-9.doi:10.1016/j.chb.2013.01.018.

Brand M, Laier C, Young KS. Internet addiction: coping styles, expectancies, and treatment implications. Front Psychol.2014;5:1-14. doi: 10.3389/fpsyg.2014. 01256.

Osatuyi B, Turel O. Tug of war between social self-regulation and habit: explaining the experience of momentary social media addiction symptoms. Comput Hum Behav. 2018;85:95-105. doi:10.1016/j.chb.2018.03.037.

Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385-401.

Haand R, Shuwang Z. The relationship between social media addiction and depression: a quantitative study among university students in Khost, Afghanistan. Int J Adolesc Youth. 2020;25(1):780-6. doi:10.1080/02673843.2020.1741407.

Bandura A. Social cognitive theory of self-regulation. Organ Behav Hum Decis Process.1991;50:248-87.

Costa PT, McCrae RR. The five-factor model of personality and its relevance to personality disorders. J Pers Disord. 1992;6(4):343-59.

Blackwell D, Leaman C, Tramposch R, Osborne C, Liss M. Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Pers Individ Differ. 2017;116:69-72. doi:10.1016/j.paid.2017.04.039.

Kircaburun K, Alhabash S, Tosuntaş ŞB, Griffiths MD. 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. Int J Ment Health Addict.

;18:525-47. doi: 10.1007/s11469-018-9940-6.

Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. J Pers Assess. 1988;52(1):30-41.

Bilgin O, Taş İ. Effects of perceived social support and psychological resilience on social media addiction among university students. Univers J Educ Res. 2018;6(4):751-8.doi:10.13189/ujer.2018.060418.

Ho SS, Lwin MO, Lee EWJ. Till logout do us part? Comparison of factors predicting excessive social network sites use and addiction between Singaporean adolescents and adults. Comput Hum Behav. 2017;75:632-42.doi:10.1016/j.chb.2017.06.002.

Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis (8th ed.). Hampshire, UK: Cengage Learning; 2019.

Kline RB. Principles and practice of structural equation modeling. 4th ed. New York: Guilford publications; 2016. 29. Brislin RW. Back-translation for cross-cultural research.J Cross-Cult Psychol. 1970;1(3):185-216. doi:10.1177/135910457000100301.

Andreassen CS, Torsheim T, Brunborg GS, Pallesen S. Development of a Facebook Addiction Scale. Psychol Rep. 2012;110(2):501-17. doi: 10.2466/02.09.18.PR0.110.2.501-517.

Schwarzer R, Diehl M, Schmitz GS. Self-Regulation Scale. 1999 [cited 2019 Feb 14]. Available from: http://userpage.fu-berlin.de/~health/selfreg_e.htm

Trangkasombat U, Larpboomsup V, Hawanont P. CES-D as a screen for depression in adolescents. J Psychiatr Assoc Thailand. 1997;42(1):2-13 (in Thai).

Sangjeen K. The relationship of personality and physical attractiveness to social Status [thesis].[Bangkok, Thailand]:Chulalongkorn University; 2003 (in Thai).

Wongpakaran N, Wongpakaran T. A revised Thai multidimensional scale of perceived social support. Span J Psychol. 2012;15(3):1503-09. doi:10.5209/rev_SJOP.2012.v15.n3.39434.

Jasso-Medrano JL, López-Rosales F. Measuring the relationship between social media use and addictive behavior and depression and suicide ideation among university students. Comput Hum Behav. 2018;87:183-91. doi:10.1016/j.chb.2018.05.003.

Marino C, Gini G, Angelini F, Vieno A, Spada MM. Social norms and emotions in problematic social media use among adolescents. Addict Behav Rep. 2020;11:1-7.doi:10.1016/j.abrep.2020.100250.

Erik EH. Identity: Youth and crisis. New York: WW Norton; 1968.

Cao X, Khan AN, Ali A, Khan NA. Consequences of cyberbullying and social overload while using SNSs: a study of users’ discontinuous usage behavior in SNSs. Inf Syst Front. 2019;22:1343-56. doi:10.1007/s10796-019-09936-8.

Chu X, Li Y, Wang P, Zeng P, Lei L. Social support and cyberbullying for university students: the mediating role of internet addiction and the moderating role of stress. Curr Psychol. 2021 Mar 15;1-9. doi:10.1007/s12144-021-01607-9.