Misunderstanding and User Dissatisfaction in AI-Driven Chatbot Interactions with Consumers

Purpose: This study examines the key factors contributing to misunderstanding and user dissatisfaction in AI-driven chatbot interactions, with a focus on intent misinterpretation, emotional responsiveness, personalization, and privacy concerns across multiple service sectors. Methodology: A qualitative research design was adopted using semi-structured interviews with twelve participants from e-commerce, banking, education, and customer service sectors. Thematic analysis was applied to identify recurring patterns and user perceptions related to chatbot performance and experience. Findings: Findings revealed four dominant sources of dissatisfaction: misunderstanding user queries, inaccurate or incomplete responses, lack of emotional intelligence and personalization, and concerns regarding data security and privacy. While chatbots were effective for routine inquiries, they were perceived as inadequate for complex or emotionally sensitive interactions. Unique Contribution to Theory, Practice, and Policy: The study contributes theoretically by extending human–AI interaction literature through a user-centered, qualitative perspective. Practically, it offers actionable design recommendations for improving chatbot empathy, accuracy, and trustworthiness. From a policy perspective, it highlights the importance of transparent data governance and regulatory compliance in AI-driven service systems.