Determinants of Cybersecurity Behavior among Social Media Users: The Moderating Role of Self-Efficacy in the Relationship between Cybersecurity Knowledge and Social Media Use Intensity

Hadi Kurnia Saputra; Refdinal Refdinal; Rijal Abdullah; Jonni Mardizal; Ambiyar Ambiyar; Fadhilah Fadhilah

doi:10.24036/voteteknika.v13i4.137117

Determinants of Cybersecurity Behavior among Social Media Users: The Moderating Role of Self-Efficacy in the Relationship between Cybersecurity Knowledge and Social Media Use Intensity

Hadi Kurnia Saputra⁽¹⁾, Refdinal Refdinal⁽²⁾, Rijal Abdullah⁽³⁾, Jonni Mardizal⁽⁴⁾, Ambiyar Ambiyar⁽⁵⁾, Fadhilah Fadhilah⁽⁶⁾,
(1) Universitas Negeri Padang

Indonesia
(2) Universitas Negeri Padang

Indonesia
(3) Universitas Negeri Padang

Indonesia
(4) Universitas Negeri Padang

Indonesia
(5) Universitas Negeri Padang

Indonesia
(6) Universitas Negeri Padang

Indonesia

Corresponding Author

DOI : https://doi.org/10.24036/voteteknika.v13i4.137117

Full Text:

Language : id

Abstract

Social media has become an integral part of daily life, but its intensive use also increases users’ exposure to various cybersecurity threats, making it important to understand the determinants of cybersecurity behavior. This study examines the effects of cybersecurity knowledge and social media usage intensity on cybersecurity behavior, as well as the role of cybersecurity self-efficacy as both a direct predictor and a moderating variable. A quantitative approach was employed using a survey of 115 university students who actively use social media, and the data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results indicate that cybersecurity knowledge has a positive and significant effect on cybersecurity behavior (β = 0.309, t = 3.383, p < 0.01), while cybersecurity self-efficacy emerges as the strongest predictor of cybersecurity behavior (β = 0.479, t = 4.725, p < 0.001). In contrast, social media usage intensity does not show a significant effect on cybersecurity behavior (β = 0.003, t = 0.032, p > 0.05). Furthermore, moderation analysis reveals that cybersecurity self-efficacy does not moderate the relationships between cybersecurity knowledge or social media usage intensity and cybersecurity behavior. The structural model explains 58.3% of the variance in cybersecurity behavior (R² = 0.583). These findings suggest that cybersecurity behavior among social media users is more strongly driven by cognitive and psychological factors than by usage intensity alone. Practically, effective cybersecurity interventions should prioritize strengthening users’ cybersecurity knowledge and self-efficacy rather than merely restricting social media use.

Keywords— Cybersecurity behavior; Cybersecurity knowledge; Self-efficacy; Social media usage intensity; SEM-PLS

References

L. M. Ganiem, R. Setiawati, S. Suardi, N. Nurhayai, and R. Ramdhani, “Society in the Digital Era: Adaptation, Change, and Response to Communication Technology,” J. Int. Dakwah Commun., vol. 4, no. 1, pp. 123–135, June 2024, doi: 10.55849/jidc.v4i1.639.

A. M. Kaplan and M. Haenlein, “Users of the world, unite! The challenges and opportunities of Social Media,” Bus. Horiz., vol. 53, no. 1, pp. 59–68, Jan. 2010, doi: 10.1016/j.bushor.2009.09.003.

N. B. Ellison, C. Steinfield, and C. Lampe, “The Benefits of Facebook ‘Friends:’ Social Capital and College Students’ Use of Online Social Network Sites,” J. Comput.-Mediat. Commun., vol. 12, no. 4, pp. 1143–1168, July 2007, doi: 10.1111/j.1083-6101.2007.00367.x.

D. M. Boyd and N. B. Ellison, “Social Network Sites: Definition, History, and Scholarship,” J. Comput.-Mediat. Commun., vol. 13, no. 1, pp. 210–230, Oct. 2007, doi: 10.1111/j.1083-6101.2007.00393.x.

E. Mouncey and S. Ciobotaru, “Phishing scams on social media: An evaluation of cyber awareness education on impact and effectiveness,” J. Econ. Criminol., vol. 7, p. 100125, Mar. 2025, doi: 10.1016/j.jeconc.2025.100125.

Z. Alkhalil, C. Hewage, L. Nawaf, and I. Khan, “Phishing Attacks: A Recent Comprehensive Study and a New Anatomy,” Front. Comput. Sci., vol. 3, p. 563060, Mar. 2021, doi: 10.3389/fcomp.2021.563060.

M. Zwilling, G. Klien, D. Lesjak, Ł. Wiechetek, F. Cetin, and H. N. Basim, “Cyber Security Awareness, Knowledge and Behavior: A Comparative Study,” J. Comput. Inf. Syst., vol. 62, no. 1, pp. 82–97, Jan. 2022, doi: 10.1080/08874417.2020.1712269.

H.-S. Rhee, C. Kim, and Y. U. Ryu, “Self-efficacy in information security: Its influence on end users’ information security practice behavior,” Comput. Secur., vol. 28, no. 8, pp. 816–826, Nov. 2009, doi: 10.1016/j.cose.2009.05.008.

U. Kiran, N. F. Khan, H. Murtaza, A. Farooq, and H. Pirkkalainen, “Explanatory and predictive modeling of cybersecurity behaviors using protection motivation theory,” Comput. Secur., vol. 149, p. 104204, Feb. 2025, doi: 10.1016/j.cose.2024.104204.

N. Borgert, L. Jansen, I. Böse, J. Friedauer, M. A. Sasse, and M. Elson, “Self-Efficacy and Security Behavior: Results from a Systematic Review of Research Methods,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, Honolulu HI USA: ACM, May 2024, pp. 1–32. doi: 10.1145/3613904.3642432.

S. Prabhu and P. Pathak, “Impact of Social Media User Anxieties, Privacy and Security Issues, and Self-Regulation on User Behavior,” J. Scientometr. Res., vol. 14, no. 2, pp. 510–524, Sept. 2025, doi: 10.5530/jscires.20251200.

S. F. Nagari and S. Raharja, “Cyber Security Awareness, Knowledge and Behavior of Digital Banking Users in Salatiga,” Asia Pac. Fraud J., vol. 10, no. 1, pp. 15–29, July 2025, doi: 10.21532/apfjournal.v10i1.398.

William Vortia, “Modelling cybersecurity awareness, perceived threats and secure online behavioral intentions among Ghanaian university students: A PLS-SEM Approach,” Magna Sci. Adv. Res. Rev., vol. 14, no. 2, pp. 096–111, Aug. 2025, doi: 10.30574/msarr.2025.14.2.0094.

M. Gustara, E. R. Cahyadi, and B. Sartono, “ANALYSIS OF CYBERSECURITY AWARENESS AND BEHAVIOR AMONG STUDENTS OF IPB UNIVERSITY: AN INTEGRATION OF PROTECTION MOTIVATION THEORY AND THEORY OF PLANNED BEHAVIOR,” vol. 8, no. 3, 2025.

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.

J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, “When to use and how to report the results of PLS-SEM,” Eur. Bus. Rev., vol. 31, no. 1, pp. 2–24, Jan. 2019, doi: 10.1108/EBR-11-2018-0203.

L. M. Pereira, V. Sanchez Rodrigues, and F. G. M. Freires, “Use of Partial Least Squares Structural Equation Modeling (PLS-SEM) to Improve Plastic Waste Management,” Appl. Sci., vol. 14, no. 2, p. 628, Jan. 2024, doi: 10.3390/app14020628.

J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, and S. Ray, Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook. in Classroom Companion: Business. Cham: Springer International Publishing, 2021. doi: 10.1007/978-3-030-80519-7.

R. M. O’brien, “A Caution Regarding Rules of Thumb for Variance Inflation Factors,” Qual. Quant., vol. 41, no. 5, pp. 673–690, Sept. 2007, doi: 10.1007/s11135-006-9018-6.

A. Sukhov, M. Friman, and L. E. Olsson, “Unlocking potential: An integrated approach using PLS-SEM, NCA, and fsQCA for informed decision making,” J. Retail. Consum. Serv., vol. 74, p. 103424, Sept. 2023, doi: 10.1016/j.jretconser.2023.103424.

R. Shillair, S. R. Cotten, H.-Y. S. Tsai, S. Alhabash, R. LaRose, and N. J. Rifon, “Online safety begins with you and me: Convincing Internet users to protect themselves,” Comput. Hum. Behav., vol. 48, pp. 199–207, July 2015, doi: 10.1016/j.chb.2015.01.046.

S. Zhai, J. Hash, T. M. Ward, W. Yuwen, and J. Sonney, “Analysis, evaluation, and reformulation of social cognitive theory: Toward parent-child shared management in sleep health,” J. Pediatr. Nurs., vol. 73, pp. e65–e74, Nov. 2023, doi: 10.1016/j.pedn.2023.07.011.

R. Wood and A. Bandura, “Social Cognitive Theory of Organizational Management,” Acad. Manage. Rev., vol. 14, no. 3, p. 361, July 1989, doi: 10.2307/258173.

E. D. Frauenstein and S. Flowerday, “Susceptibility to phishing on social network sites: A personality information processing model,” Comput. Secur., vol. 94, p. 101862, July 2020, doi: 10.1016/j.cose.2020.101862.

Y. Hong and S. Furnell, “Understanding cybersecurity behavioral habits: Insights from situational support,” J. Inf. Secur. Appl., vol. 57, p. 102710, Mar. 2021, doi: 10.1016/j.jisa.2020.102710.