Xiangtian Bai
Papers
1
Total Citations
14
H-Index
1
About
Xiangtian Bai explores the intersection of human-robot interaction and privacy ethics, with a focus on how social robot design influences user trust and data-sharing behaviors. Their most-cited work, "Exploring the Impact of Social Robot Design Characteristics on Users’ Privacy Concerns," employs advanced analytical methods—PLS-SEM and fsQCA—to reveal how factors like robot appearance and transparency shape privacy perceptions. This research, already garnering 14 citations since its 2024 publication, offers critical insights for designing socially acceptable robots in healthcare, service, and domestic settings. Bai’s contributions bridge robotics engineering and behavioral science, providing evidence-based guidelines for developers to mitigate user anxiety without sacrificing functionality. Their work is particularly notable for using mixed-method approaches to untangle complex, non-linear relationships between design features and user responses. As privacy concerns increasingly dictate technology adoption, Bai’s findings are vital for creating robots that are both effective and ethically responsible. Their research continues to inform debates on trust, autonomy, and data security in human-robot interactions.
Research Focus
Key Achievements
Top Papers
- 1