Warmth or Competence:Examining Chinese Users' Attitudes to In-car Voice Assistant

This study investigates how the warmth and competence perceptions of in-car AI voice assistants, brought about by voice type (human-like voice/synthetic voice) and communication style (task-oriented/social-oriented), affect Chinese users’ attitudes. Based on the stereotype content model (SCM), the study argues that users' perceptions of the warmth (trustworthiness, friendliness and kindness) and competence (capability, intelligence, effectiveness) of voice assistants will affect overall evaluation of the system. A 2 (voice type: human-like vs. synthetic) × 2 (interaction style: social vs. taskoriented) between-subjects experiment was conducted among 184 Chinese participants. The results show that Chinese consumers have no obvious preference for in-car AI voice assistants with different voices and communication styles, and we further explain the reasons for this result considering China's cultural background. The results provide theoretical support for human-computer interaction (HCI) and SCM research and provide practical suggestions for designing more attractive and effective in-car voice assistants.