Assessing cybersecurity awareness among bank employees: A multi-stage analytical approach using PLS-SEM, ANN, and fsQCA in a developing country context

Abstract

The financial sector is a prime target for cybercriminals which increases the need for banks to enhance employee cybersecurity awareness. This study examines the critical factors that enhance cybersecurity awareness among bank employees in the context of developing countries, focusing on Bangladesh. By collecting 355 valid responses through purposive sampling from bank employees across major districts, the research employs a multi-stage analytical approach that integrates Partial Least Squares Structural Equation Modeling (PLS-SEM), Artificial Neural Networks (ANN), and Fuzzy-set Qualitative Comparative Analysis (fsQCA). Findings reveal a positive correlation between response cost, information security awareness, knowledge of cyber threats, and employees' perceived threat and vulnerability, indicating their significance in shaping cybersecurity awareness. The study's methodological novelty lies in its combined use of linear and non-linear analytical techniques which optimize prediction accuracy and contribute to the robustness of cybersecurity awareness research. Its implications are vital for developing nations where technological dependence for safeguarding IT resources is critical. The outcomes highlight the need for an informed approach to cyber threat management and the promotion of cybersecurity awareness among bank employees as a shield against social engineering and other cyberattacks.