Balancing Personalization and Privacy: A Theoretical Model for Enhancing Chatbot User Experience

As AI-driven chatbots become central to enhancing user engagement through personalization, balancing user satisfaction with privacy threats remains crucial for users, businesses, and regulators. This paper presents a theoretical model that conceptualizes the relationships among chatbot personalization, user satisfaction, perceived privacy threats, and utilization. Grounded in Privacy Calculus Theory, Reactance Theory, and the Big Five Personality Traits, we hypothesize that while personalized chatbots boost satisfaction and continued use, they also increase perceived privacy threats. We further explore how privacy controls and traits like openness to experience moderate these effects. Our model provides insights for designing chatbots that optimize satisfaction while addressing privacy threats, benefiting users and providers.