From speaking like a person to being personal: The effects of personalized, regular interactions with conversational agents

Abstract

As human-AI interactions become more pervasive, conversational agents are increasingly relevant in our communication environment. While a rich body of research investigates the consequences of one-shot, single interactions with these agents, knowledge is still scarce on how these consequences evolve across regular, repeated interactions in which these agents make use of AI-enabled techniques to enable increasingly personalized conversations and recommendations. By means of a longitudinal experiment (N = 179) with an agent able to personalize a conversation, this study sheds light on how perceptions – about the agent (anthropomorphism and trust), the interaction (dialogue quality and privacy risks), and the information (relevance and credibility) – and behavior (self-disclosure and recommendation adherence) evolve across interactions. The findings highlight the role of interplay between system-initiated personalization and repeated exposure in this process, suggesting the importance of considering the role of AI in communication processes in a dynamic manner.