Cybersecurity behavioural model for students in the tertiary institutions / Fatokun Faith Boluwatife - Fatokun Faith , Boluwatife (2020)

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Humans are majorly identified as the weakest link in cybersecurity. Tertiary institution student’s face lot of cybersecurity issues due to their increased Internet exposure, however cybersecurity behavioural studies focusing on tertiary students is limited. This study focused on investigating tertiary institutions students’ cybersecurity behaviour, via validated cybersecurity factors, Perceived Vulnerability (PV); Perceived Barriers (PBr); Perceived Severity (PS); Security Self-Efficacy (SSE); Response Efficacy (RE); Cues to Action (CA); Peer Behaviour (PBhv); Computer Skills (CS); Internet Skills (IS); Prior Experience with Computer Security Practices (PE); Perceived Benefits (PBnf); and a newly added factor, Familiarity with Cyber-Threats (FCT), to explore the factors relationship with the students’ Cybersecurity Behaviours (CSB). The research also explored if age, gender and educational level had any moderating effect on the cybersecurity behaviour factors. The new construct of Familiarity with Cyber-Threat performed excellently well. The research investigations resulted into a model tagged: Cybersecurity Behavioural Model for Tertiary Institutions Students (CBM-TIS). A crosssectional online survey was used to gather data from 450 undergraduate and postgraduate students from tertiary institutions within Klang Valley, Malaysia. Series of Structural Equation Modelling techniques was employed for the model’s evaluation, and SPSS version 25 was used as the tool for data analysis. Results from regression analysis indicated that the influencing factors of the student’s cybersecurity behaviours were their SSE (t = 4.325, P<0.001), RE (t = 2.167, P = 0.031), PE (t = 5.281, P<0.001) and PBnf (t = 1.978, P = 0.04). Also, from the point biserial correlation analysis, Age had effect only on PBr (r = 0.101, p = 0.036), while gender had effects on PS (r = -0.132, p = 0.006), SSE (r = 0.362, p<0.001), CS (r = 0.233, p<0.001), IS (r = 0.115, p = 0.016), PE (r = 0.123, p = 0.010), and CSB (r = 0.150, p = 0.002); however Educational level had effects on CS (r = 0.155, p = 0.001), IS (r = 0.120, p = 0.012), FCT (r = 0.106, p = 0.026), and CSB (r = 0.110, p = 0.022). From the Pearson Correlation analysis conducted, PV (R2 = 0.377, p = 0.003), PBr (R2 = 0.332, p = 0.002), SSE (R2 = 0.670, p < 0.001), RE (R2 = 0.495, p < 0.001), CA (R2 = 0.471, p < 0.001), PBhv (R2 = 0.436, p < 0.001), CS (R2 = 0.594, p < 0.001), IS (R2 = 0.428, p < 0.001), PE (R2 = 0.667, p < 0.001), PBnf (R2 = 0.511, p < 0.001), and FCT (R2 = 0.540, p < 0.001) were all significantly related to the student’s cybersecurity behaviours, except PS. Practically, the study instigates the need for more cybersecurity training and practices in the tertiary institutions. The factor of Prior Experiences with Computer Security Practices had the highest influence on the student’s cybersecurity behaviour, hence if appropriate security practices are being upheld by tertiary institutions, it would help in maintaining good cybersecurity assurance in the entire institution.