How Service Robots Facilitate User Self-Disclosure: The Roles of Personality, Animacy, and Automated Social Presence

Abstract

This study investigates whether an extroverted personality of service robots can facilitate user self-disclosure and enhance human–robot intimacy. An experiment of 2 2 between-subjects factorial design was conducted to examine the effects of robot personality (extroverted and introverted) and human personality (extroverted and introverted) on user self-disclosure and intimacy. Sixty-four participants from a university campus were included in this study. We simulated a financial investment scenario in which the participants were required to disclose personal information to a robot acting as an investment advisor. Functional near-infrared spectroscopy (fNIRS) was used to capture participants’ prefrontal cortex (PFC) activity. The results revealed a positive effect of robot extroversion on user self-disclosure and intimacy, and this impact was stronger among extroverted participants than among introverted ones. Additionally, perceived robot extroversion enabled users to regard the robot as more animate, promoting automated social presence and ultimately enhancing self-disclosure and intimacy. According to the fNIRS results, the participants who interacted with an extroverted robot had stronger activation in the medial PFC than those who interacted with an introverted robot. The self-reported and fNIRS results were consistently aligned, demonstrating the positive role of extroversion in service robots. These findings provide theoretical and practical insights into the design of service robots in contexts requiring user disclosure.

Keywords:

• Self-disclosure
• robot personality
• social presence
• service robot
• human–robot interaction

Disclosure statement

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

Additional information

Funding

This work was supported by Natural Science Foundation of Zhejiang Province [LQ24G010005] and Humanities and Social Sciences Enhancement Programme – University Pre-research Fund [GZ23261320041].

Notes on contributors

Xin Lei

Xin Lei is a lecturer in School of Management at Zhejiang University of Technology. She received her PhD from Tsinghua University. Her research primarily focuses on human–robot interaction.

Fengyuan Liu

Fengyuan Liu is a master’s student in School of Management at Zhejiang University of Technology. He received his bachelor’s degree from Zhejiang University of Technology. His research interest lies in human–robot interaction.