Does AI listening encourage openness? Investigating users' self-disclosure of emotional information to artificial vs. human listeners

Abstract

This study investigates whether users experience distinct psychological concerns in AI-mediated interactions compared to human interactions. Drawing on self-presentation theory, it examines whether AI chatbots can mitigate users' self-presentation concerns when disclosing different types of information (emotional vs. factual). A 2 (listener type: AI vs. human) × 2 (information type: emotional vs. factual) between-subjects online experiment was conducted with 465 participants. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to test the main and interaction effects of these variables on users' self-disclosure intentions and subsequent platform usage intentions. The results reveal that emotional information significantly increases self-presentation concerns compared to factual information. The effect of AI listeners in reducing these concerns is only marginally significant in the emotional information condition. Furthermore, self-presentation concern mediates the relationship between information type and self-disclosure. Trust not only directly promotes self-disclosure but also buffers the negative impact of self-presentation concerns on disclosure behavior. This study is among the first to systematically compare AI and human listeners across emotional and factual contexts, clarifying the underlying psychological and behavioral differences. By identifying the boundary conditions and mechanisms through which AI reduces user concerns, this research provides theoretical insights and practical implications for the design and operation of AI-based emotional support products.