Enhancing User’s Self-Disclosure through Chatbot’s Co-Activity and Conversation Atmosphere Visualization

Abstract

Fueled by the power of AI, chatbots are becoming more personal. Prior research showed that a chatbot has great potential to elicit its user’s self-disclosure because it does not judge the user. However, the chatbot’s features beyond the conversational characteristics in eliciting a user’s self-disclosure are not as well researched. In this study, we have developed a chatbot and implemented two non-conversation features: (1) co-activity (COA), conducting an activity together, and (2) conversation atmosphere visualization (CAV), visually displaying the emotional feelings conveyed in the conversation, to examine their effects on self-disclosure and user experience. We conducted a field study involving 87 participants who were randomly assigned to one of the four experimental conditions (control, COA only, CAV only, CAV + COA) and asked to use the assigned chatbot for 10 days in their natural life setting. Our results from this field study show that both the COA and CAV features have significant effects on a user’s self-disclosure. In addition, interaction effects between COA and CAV have been found to affect a user’s intention to use. Based on the findings, we provide design implications for a user’s self-disclosure and trusting relationship development with a chatbot.