Dual humanness and trust in conversational AI: A person-centered approach

Conversational Artificial Intelligence (AI) is digital agents that interact with users by natural language. To advance the understanding of trust in conversational AI, this study focused on two humanness factors manifested AI: speaking and listening. First, we explored users' heterogeneous perception patterns based factors. Next, examined how heterogeneity relates to AI. A two-stage survey was conducted collect data. Latent profile analysis revealed three distinct patterns: para-human perception, para-machine asymmetric perception. Finite mixture modeling demonstrated benefit humanizing AI's voice for competence-related can evaporate once language perceived as poor. Interestingly, asymmetry between perceptions listening impede morality-related trust. By adopting a person-centered approach address relationship dual user trust, contributes literature AI practice trust-inducing design.