The Key Strategies for Increasing Users’ Intention of Self-disclosure in Human-robot Interaction through Robotic Appearance Design

Abstract

Bridging the communication gap between humans and social robots constitutes a critical area of research in the field of human-robot interaction (HRI). However, inducing self-disclosure in HRI is a challenging task, primarily due to the unique characteristics of social robots. This study aims to address the following research question: Can the design expression dimensions that influence user disclosure depth in the HRI process be deconstructed? Do design strategies take precedence over appearance design? Firstly, we segmented the focus of appearance design into three dimensions: Artistic Expression, Featured Expression, and Identified Expression, hypothesizing their level of significance. Subsequently, we created a prototype with balanced degrees for experimentation and invited 52 users to participate in HRI, guiding them to self-disclose and record data. Bayesian statistics were utilized to model the data and obtain comprehensive user feedback regarding appearance design. Based on the experimental results, we reviewed the content and potential order of the three dimensions and proposed our design and research model. Currently, at the theoretical level, there is no significant emphasis on users’ willingness to self-disclose in HRI. Our proposed model serves as a valuable tool for researchers in the fields of human-computer interaction, communication, and user experience design to gain quick insights into this topic. At the practical level, our model has reference significance for industrial and visual designers, enabling them to better comprehend the design goals, situations, and output design methods.