Religiosity, Social Control, and Peer Control in Relation to Swearing Normalization: A PLS-SEM Study
DOI:
https://doi.org/10.55578/c1tg2033Keywords:
religiosity, social control, peer control, swearing normalization, social norms, PLS-SEMAbstract
Swearing is increasingly understood not merely as a form of offensive language but as a socially situated practice whose acceptability is shaped by interpersonal relationships, normative expectations, and group contexts. Previous studies have demonstrated that swearing may function as an expression of emotion, solidarity, group identity, and social affiliation, while religious and social norms may simultaneously influence the boundaries of acceptable language use. However, empirical research integrating religiosity, social control, and peer control into a single explanatory model of swearing normalization remains limited. This study examines the relationships between religiosity, social control, peer control, and swearing normalization using partial least squares structural equation modeling (PLS-SEM). The analysis was conducted using 300 valid observations and four reflective constructs. The measurement model demonstrated satisfactory indicator reliability, internal consistency, and convergent validity, with outer loadings ranging from 0.746 to 0.839, Cronbach's alpha from 0.945 to 0.974, composite reliability from 0.952 to 0.976, and AVE from 0.609 to 0.673. Discriminant validity was also supported, with HTMT values ranging from 0.095 to 0.648. The structural model explained 53.5% of the variance in swearing normalization (R² = 0.535). Religiosity showed a significant positive relationship with swearing normalization (β = 0.461, t = 10.778, p < 0.001), followed by social control (β = 0.328, t = 7.659, p < 0.001), whereas peer control showed a significant negative relationship (β = −0.269, t = 6.576, p < 0.001). Effect sizes were moderate for all three predictors, while Q²predict values ranged from 0.298 to 0.393. The positive association between religiosity and swearing normalization provides a theoretically important insight, suggesting that religious commitment may not translate uniformly into behavioral restraint across different forms of everyday language use. This finding highlights the context-dependent expression of religious norms when language practices are embedded in informal social interaction. Overall, the findings suggest that swearing normalization is embedded in a complex normative environment in which religious identity, broader social regulation, and peer-based control may operate in different directions. Given the cross-sectional design, these findings are interpreted as statistical associations rather than causal effects.
References
[1] K. Stapleton, K. Beers Fägersten, R. Stephens, and C. Loveday, “The power of swearing: What we know and what we don’t,” Lingua, vol. 277, p. 103406, Oct. 2022, doi: 10.1016/j.lingua.2022.103406.
[2] S. J. Cho and Y. Tian, “Why Do They Keep Swearing? The Role of Outcome Expectations between Descriptive Norms and Swearing among Korean Youths: A Test of the Theory of Normative Social Behavior,” West. J. Commun., vol. 84, no. 2, pp. 227–244, Mar. 2020, doi: 10.1080/10570314.2019.1659993.
[3] D. P. Saefudin, A. Puadah, A. R. A. Prestika, E. V. Mutiarawati, S. Nurani, and R. Permana, “Swearing as Social Bonding: A Case Study of Peer Friendships in Early Adulthood,” IJLECR - Int. J. Lang. Educ. Cult. Rev., vol. 11, no. 2, pp. 204–213, Dec. 2025, doi: 10.21009/ijlecr.v11i2.60899.
[4] M. Brauer and N. Chaurand, “Descriptive norms, prescriptive norms, and social control: An intercultural comparison of people’s reactions to uncivil behaviors,” Eur. J. Soc. Psychol., vol. 40, no. 3, pp. 490–499, Apr. 2010, doi: 10.1002/ejsp.640.
[5] J. K. Cochran, P. B. Wood, and B. J. Arnejlev, “Is the Religiosity-Delinquency Relationship Spurious? A Test of Arousal and Social Control Theories,” J. Res. Crime Delinq., vol. 31, no. 1, pp. 92–123, Feb. 1994, doi: 10.1177/0022427894031001004.
[6] M. Sarstedt, J. F. Hair, M. Pick, B. D. Liengaard, L. Radomir, and C. M. Ringle, “Progress in partial least squares structural equation modeling use in marketing research in the last decade,” Psychol. Mark., vol. 39, no. 5, pp. 1035–1064, May 2022, doi: 10.1002/mar.21640.
[7] J.-M. Ringle, C. M., Wende, S., & Becker, “SmartPLS 4. SmartPLS.,” 2024.
[8] P. Guenther, M. Guenther, C. M. Ringle, G. Zaefarian, and S. Cartwright, “PLS-SEM and reflective constructs: A response to recent criticism and a constructive path forward,” Ind. Mark. Manag., vol. 128, pp. 1–9, Jul. 2025, doi: 10.1016/j.indmarman.2025.05.003.
[9] J. Cohen, Statistical Power Analysis for the Behavioral Sciences. Routledge, 2013. doi: 10.4324/9780203771587.
[10] T. K. Dijkstra and J. Henseler, “Consistent Partial Least Squares Path Modeling1,” MIS Q., vol. 39, no. 2, pp. 297–316, Jun. 2015, doi: 10.25300/MISQ/2015/39.2.02.
[11] C. M. Ringle, M. Sarstedt, N. Sinkovics, and R. R. Sinkovics, “A perspective on using partial least squares structural equation modelling in data articles,” Data Br., vol. 48, p. 109074, Jun. 2023, doi: 10.1016/j.dib.2023.109074.
[12] J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, Jan. 2015, doi: 10.1007/s11747-014-0403-8.
[13] C. Fornell and D. F. Larcker, “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,” J. Mark. Res., vol. 18, no. 1, pp. 39–50, Feb. 1981, doi: 10.1177/002224378101800104.
[14] G. Shmueli et al., “Predictive model assessment in PLS-SEM: guidelines for using PLSpredict,” Eur. J. Mark., vol. 53, no. 11, pp. 2322–2347, Nov. 2019, doi: 10.1108/EJM-02-2019-0189.
[15] M. Sarstedt, C. M. Ringle, and J. F. Hair, “Partial Least Squares Structural Equation Modeling,” in Handbook of Market Research, Cham: Springer Nature Switzerland, 2025, pp. 1–56. doi: 10.1007/978-3-319-05542-8_15-3.
[16] J. F. Hair, P. N. Sharma, W. W. Chin, M. Sarstedt, and C. M. Ringle, “A Multimethod SEM Framework for Analyzing Models with Latent Variables,” J. Glob. Mark., vol. 39, no. 2, pp. 167–182, Mar. 2026, doi: 10.1080/08911762.2026.2638909.
[17] D. J. Ketchen, “A Primer on Partial Least Squares Structural Equation Modeling,” Long Range Plann., vol. 46, no. 1–2, pp. 184–185, Feb. 2013, doi: 10.1016/j.lrp.2013.01.002.
[18] P. N. Sharma, M. Sarstedt, C. M. Ringle, J.-H. Cheah, A. Herfurth, and J. F. Hair, “A framework for enhancing the replicability of behavioral MIS research using prediction oriented techniques,” Int. J. Inf. Manage., vol. 78, p. 102805, Oct. 2024, doi: 10.1016/j.ijinfomgt.2024.102805.
[19] Y. Song, Q. Lin, K. H. Kwon, C. H. Y. Choy, and R. Xu, “Contagion of offensive speech online: An interactional analysis of political swearing,” Comput. Human Behav., vol. 127, p. 107046, Feb. 2022, doi: 10.1016/j.chb.2021.107046.
Downloads
Published
Data Availability Statement
The dataset generated and analyzed during the current study is not publicly available because it contains data collected from human participants and is subject to confidentiality and ethical restrictions. Aggregated data supporting the findings are available from the corresponding author upon reasonable request, subject to applicable ethical and institutional restrictions.
Issue
Section
License
Copyright (c) 2026 Fadhlurrahman, Hasruddin, Riswanto, Firman, Sutiyono, Agus Martins, Yuliana (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.