Different dimensions of anthropomorphic design cues: How visual appearance and conversational style influence users’ information disclosure tendency towards chatbots

Text-based chatbots are widely used to deliver personalized services by leveraging user-provided information, and anthropomorphic design is crucial for their effectiveness. However, most earlier studies investigated the effects of anthropomorphic design of chatbots while manipulating only one dimension of anthropomorphic cues. The current research investigated how different dimensions of anthropomorphic design cues affect users' information disclosure tendency towards chatbots. That is, the present study examined the effects of visual appearance (high anthropomorphism vs. low anthropomorphism), manipulating the visual cues dimension, and conversational style (human-like vs. mechanical), manipulating the verbal cues dimension, on users' information disclosure tendency towards chatbots. Results showed positive effects of human-like conversational style on users' information disclosure tendency. Of particular significance, an interaction effect between visual appearance and conversational style on users' information disclosure tendency was found. Users reported a higher information disclosure tendency when the chatbot was designed with anthropomorphic cues consistent over dimensions. This finding suggested that an expectancy violation effect occurs when a chatbot exhibits inconsistent anthropomorphic design cues on two different dimensions. Besides, perceived security was identified as a positive mediating factor in the relationship between conversational style and users' information disclosure tendency. This study advances research on users' information disclosure tendency towards anthropomorphic chatbots and highlights the importance of different dimensions of anthropomorphic cues in chatbot design. Additionally, practical guidance for chatbot designers was also provided.