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.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government [21ZR1100, A Study of Hyper-Connected Thinking Internet Technology by autonomous connecting, controlling and evolving ways].

Notes on contributors

Rafikatiwi Nur Pujiarti

Rafikatiwi Nur Pujiarti earned her B.S. in Computer Science from the University of Indonesia and M.S. in Knowledge Service Engineering from Korea Advanced Institute of Science and Technology (KAIST). Her research interests include user experience and user interaction with conversational agents.

Bumho Lee

Bumho Lee is a doctoral student in the Graduate School of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology (KAIST). His research interests are in human-computer interaction and user behavior modeling, with a particular focus on AI-based interaction. He investigates how people perceive, evaluate, and respond to agent-based technology.

Mun Yong Yi

Mun Yong Yi is Professor at Korea Advanced Institute of Science and Technology (KAIST). Before joining KAIST, he was Associate Professor at University of South Carolina. His current research interests include user behavior modeling and human-AI interaction. He received his Ph.D. in Information Systems from University of Maryland, College Park.