Predicting Cyber Security Behaviors Through Psychosocial and Demographic Conditions During COVID-19

Abstract

This research examines the influence of demographic conditions on those psychosocial conditions resulting from the pandemic to predict cyber security behaviors. Multiple linear and logistical regression models showed that addicted users who barely worked from home before the pandemic are more likely to exhibit risky cyber security behaviors, which is also similar for lonely users who barely worked from home to predict internet addiction. One interesting finding is that addicted female users were found to be more susceptible to cyberattacks. The implications and recommendations focused on therapeutic interventions, social change, awareness campaigns, and so forth. The limitation of this study is also covered, and possible future research areas are recommended.