Smart Tech, Scared Users: A Behavioral Analysis of AI-Powered Solutions for Cyberthreat-Induced Customer Complaints in Low-Income Countries

Abstract

In the face of rising cyber incidents in digital banking, artificial intelligence (AI) has emerged as a critical tool for automating threat detection, enhancing response speed, and improving complaint resolution. However, the success of such technological interventions depends significantly on user behavior, perceptions, and willingness to use these systems. This study examines the behavioral determinants influencing the implementation of AI-powered solutions for cyberthreat-induced customer complaints for banks in low-income countries. Guided by the protection motivation theory (PMT), the study adopted a quantitative, cross-sectional survey design involving 350 respondents, comprising 315 bank customers and 35 frontline bank staff, across seven Nigerian banks with international authorization. PMT constructs were used to develop the Likert-based questionnaire. Data were analyzed using Ordinal Logistic Regression (OLR) model. The findings reveal that perceived severity (? = 0.455, p < 0.05), perceived vulnerability (? = 0.387, p < 0.05), response efficacy (? = 0.658, p < 0.05), and self-efficacy (? = 0.587, p < 0.05) have positive and significant effects on AI-powered solutions for cyberthreat-induced customer complaints. However, response cost (? = -0.405, p < 0.05) has negative and significant effects on AI-powered solutions for cyberthreat-induced customer complaints. This study contributes to the growing field of AI solutions for cyber related customer complaints in banks by offering a behaviorally grounded framework for understanding how threat appraisals and coping appraisals drive support for AI-powered cyber complaint solutions. The study recommends that banks in low-income countries should actively communicate the effectiveness and success rates of AI-powered tools such as chatbots, anomaly detection systems, and automated complaint resolution platforms to demonstrate how these systems resolve issues faster, more securely, and more accurately so as to build trust among users.