Volume 22, Issue 4 (7-2023)                   JRUMS 2023, 22(4): 319-332 | Back to browse issues page

Ethics code: IR.UMA.REC.1401.080


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ahmadi S, Basharpoor ُ. Predicting Cyberbullying Based on Online Disinhibition and Perceived Mother Acceptance with the Mediating Role of Emotional Stability: A Descriptive Study. JRUMS 2023; 22 (4) :319-332
URL: http://journal.rums.ac.ir/article-1-6953-en.html
mohaghegh ardabili
Abstract:   (1910 Views)
Background and Objectives: Cyberbullying has increased every year among teenagers as the most internet users. The present study was conducted with the aim of predicting cyberbullying based on online disinhibition and perceived mother acceptance with the mediating role of emotional stability.
Materials and Methods: This study is of a descriptive type, and its statistical population included all male students of the second period of high school studying in the state schools of Ardabil City in the academic year of 2023. From this population, a sample of 166 people were selected by convenience sampling method and answered the questionnaires of Cyberbullying, Emotional Stability, Online Disinhibition, and Perceived Mother Acceptance. In order to analyze the data, structural equation modeling was used.
Results: The results showed that online disinhibition with a coefficient value of 0.52 and perceived mother acceptance with a coefficient value of -0.24 are directly related to cyberbullying (p<0.001). Also, online disinhibition with a coefficient value of 0.25 and perceived mother acceptance with a coefficient value of 0.08 have an indirect significant relationship with cyberbullying through the mediation of emotional stability (p<0.001).
Conclusion: In general, the results of the present study showed that online disinhibition and perceived mother acceptance directly and indirectly through the mediation of emotional stability have a significant effect on cyberbullying. In terms of the application of interpersonal psychotherapy interventions, social network use management training and emotion and anger management are recommended for the development of parent-child communication, online disinhibition, emotional stability, and reduction of aggression, respectively.
Key words: Cyberbullying, Emotional stability, Online disinhibition, Perceived mother acceptance

Funding: This study was funded by University of Mohaghegh Ardabili.
Conflict of interest: None declared.
Ethical approval: The Ethics Committee of Mohaghegh Ardabili University of Medical Sciences approved the study (IR.UMA.REC.1401.080).

How to cite this article: Ahmadi Shirin, Basharpoor Sajjad. Predicting Cyberbullying Based on Online Disinhibition and Perceived Mother Acceptance with the Mediating Role of Emotional Stability: A Descriptive Study. J Rafsanjan Univ Med Sci 2023; 22 (4): 319-32. [Farsi]
 
Full-Text [PDF 353 kb]   (997 Downloads) |   |   Full-Text (HTML)  (1180 Views)  
Type of Study: Research | Subject: Psychiatry
Received: 2023/04/22 | Accepted: 2023/06/18 | Published: 2023/07/19

References
1. Hülür G, Macdonald B. Rethinking social relationships in old age: Digitalization and the social lives of older adults. J Am Psycho 2020; 75(4): 554-66.
2. Aizenkot D. WhatsApp cyberbullying among children and adolescents in Israel: A pilot research. Couns Educ 2017; 20: 363-89.
3. Wang W, Xie X, Wang X, Lei L, Hu Q, Jiang S. Cyberbullying and depression among Chinese college students: A moderated mediation model of social anxiety and neuroticism. J Affect Disord 2019; 256(1): 54-61.
4. Wright MF, Harper BD, Wachs S. The associations between cyberbullying and callous-unemotional traits among adolescents: The moderating effect of online disinhibition. J Pers Individ Differ 2019; 140: 41-5.
5. Yang J, Wang N, Gao L, Wang X. Deviant peer affiliation and adolescents’ cyberbullying perpetration: Online disinhibition and perceived social support as moderators. J Child Youth Serv Rev 2021; 127: 106066.
6. Zhang X, Chu X, Fan C. Peer victimization and cyberbullying: A mediating moderation model. Chin J Clin Psychol 2019; 27(1): 148-52.
7. Cheung CM, Wong RYM, Chan TK. Online disinhibition: conceptualization, measurement, and implications for online deviant behavior. J Ind Manag Data Syst 2021; 121(1): 48-64.
8. Basharpoor S, Ahmadi SH. The Designing a structural relationship of cyber bullying based on Parental monitoring and emotion regulation strategies mediated by anger rumination in adolescent students. J Crim Justice 2022; 10(1): 249-74. [Farsi]
9. Gómez-Ortiz O, Romera EM, Ortega-Ruiz R, Del Rey R. Parenting practices as risk or preventive factors for adolescent involvement in cyberbullying: Contribution of children and parent gender. Int J Environ Res Public Health 2018; 15(12): 2664-87.
10. Brock DM, Sarason IG, Sanghvi H, Gurung RA. The perceived acceptance scale: Development and validation. J Soc Pers Relatsh 1998; 15(1): 5-21.
11. Garaigordobil M. Prevention of cyberbullying: Personal and family predictive variables of cyber-aggression. J Rev Psicol Clín Niños Adolesc 2019; 6: 9-17.
12. Qu J, Lei L, Wang X, Xie X, Wang P. Mother phubbing and adolescent cyberbullying: the mediating role of perceived mother acceptance and the moderating role of emotional stability. J Interpers Violence 2022; 37(11-12): 9591-612.
13. Swearer SM, Hymel S. Understanding the psychology of bullying: Moving toward a social-ecological diathesis–stress model. J Am Psycho 2015; 70(4): 344-53.
14. Weiss M, Zacher H. Why and when does voice lead to increased job engagement? The role of perceived voice appreciation and emotional stability. J Vocat Behav 2022; 132: 103662.
15. Bai Q, Bai S, Dan Q, Lei L, Wang P. Mother phubbing and adolescent academic burnout: The mediating role of mental health and the moderating role of agreeableness and neuroticism. J Pers Individ Differ 2020; 155: 109622.
16. McCreery MP, Krach SK. How the human is the catalyst: Personality, aggressive fantasy, and proactive-reactive aggression among users of social media. J Pers Individ Differ 2018; 133: 91-5.
17. Charaschanya A, Blauw J. A study of the direct and indirect relationships between online disinhibition and depression and stress being mediated by the frequency of cyberbullying from victim and perpetrator perspectives. J Hum Sci 2017; 9(2): 275-301.
18. Kurek A, Jose PE, Stuart J. ‘I did it for the LULZ’: How the dark personality predicts online disinhibition and aggressive online behavior in adolescence. J Comput Hum Behav 2019; 98: 31-40.
19. Wang X, Gao L, Yang J, Zhao F, Wang P. Parental phubbing and adolescents’ depressive symptoms: Self-esteem and perceived social support as moderators. J Youth Adolesc 2020; 49: 427-37.
20. Hoyle RH. The structural equation modeling approach: Basic concepts and fundamental issues. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and. Sage Publications, Inc 1995; 1-15.
21. Kannan KS, Manoj K, Arumugam S. Labeling methods for identifying outliers. Int j stat appl 2015; 10(2): 231-8.
22. Lopez C. Peer victimization: Preliminary validation of a multidimensional self-report measure for children and young adolescents. Unpublished master’s thesis, University of Missouri-Columbia. 1997.
23. Campfield DC. Cyber bullying and victimization: Psychosocial characteristics of bullies, victims, and bully/victims: University of Montana; 2008; 41-163.
24. Basharpoor S, Ahmadi Sh. The Designing a structural relationship of cyber bullying based on Parental monitoring and emotion regulation strategies mediated by anger rumination in adolescent students. Ardabil: University of Mohaghegh Ardabili; 2021; 28. [Farsi]
25. Udris R. Cyberbullying among high school students in Japan: Development and validation of the Online Disinhibition Scale. Comput Hum Behav 2014; 41: 253-61.
26. Rammstedt B, John OP. Kurzversion des big five inventory (BFI-K). Diagnostica 2005; 51(4): 195-206.
27. Rammstedt B, John OP. Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. J Res Pers 2007; 41(1): 203-12.
28. Khormaei F, Farmani A. Psychometric properties of the short form of goldberg’s 50-item personality scale. J Psychol Methods 2014; 4(16): 29-39. [Farsi]
29. Pavlov G, Maydeu-Olivares A, Shi D. Using the standardized root mean squared residual (SRMR) to assess exact fit in structural equation models. J Educ Psychol Meas 2021; 81(1): 110-30.
30. Suler J. The online disinhibition effect. Cyberpsychology & behavior 2004; 7(3): 321-6.
31. Schmidt EM, Jankowski PJ. Predictors of relational aggression and the moderating role of religiousness. J Aggress Maltreatment Trauma 2014; 23(4): 333-50.
32. Putnick DL, Bornstein MH, Lansford JE, Malone PS, Pastorelli C, Skinner AT, et al. Perceived mother and father acceptance‐rejection predict four unique aspects of child adjustment across nine countries. J Child Psychol Psychiatry 2015; 56(8): 923-32. ‌
33.  

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2025 CC BY-NC 4.0 | Journal of Rafsanjan University of Medical Sciences

Designed & Developed by : Yektaweb