Abstract
The use of humanoid robots has surged in recent decades. However, the nonverbal features in shaping robot personalities remain underexplored. This study investigates how nonverbal cues (including textual and gestural elements) can generate a spectrum of robot personality traits (introvert, ambivert, and extrovert) and evaluates their impact on users’ cognitive perceptions. Textual manipulations involved three iterations, adjusting word count, information structure, and visual effects. Gestural designs underwent two iterations, altering movement frequency, speed, and size. Multiple empirical studies were conducted to assess the development of robot personality traits and their effects. The results confirm the effectiveness of these nonverbal approaches in characterizing diverse robot personalities and significantly influencing users’ cognitive framing. This research provides valuable design guidelines for leveraging a humanoid robot’s nonverbal features to create a variety of personality traits. Our findings emphasize the importance of considering a spectrum of robot personalities rather than focusing solely on extreme traits.
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No potential conflict of interest was reported by the author(s).
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Notes on contributors
Shih-Yi Chien
Shih-Yi Chien is an Associate Professor in the Department of Management Information Systems at National Chengchi University, Taiwan. His research interests include human-robot interaction, human-automation collaboration, and XAI. His research has appeared in International Journal of Human-Computer Studies, International Journal of Human-Computer Interaction, IEEE Transactions on Human-Machine Systems, and others.
Chih-Ling Chen
Chih-Ling Chen is a Master student in the Department of Management Information Systems at National Chengchi University, Taiwan. Her research interests include user experience, social robots, and human-computer interaction.
Yao-Cheng Chan
Yao-Cheng Chan is a PhD student in the School of Information at The University of Texas at Austin, USA. His current research interests include human-robot interaction, human-automation collaboration, and incidental human-robot encounter.