Facial Trustworthiness In Robots: Exploring Key Facial Features Across Varying Appearance Anthropomorphism

Robot facial design shapes initial trust, thus acting as a crucial determinant of user perception and enabling effective human-robot interaction. However, extant research on robot facial trustworthiness yields limited and mixed findings. Besides, the prevailing perspective of anthropomorphism cannot explain differences in user perception of robot facial trustworthiness. To address these gaps, we conducted a qualitative investigation to identify specific robot facial features that influence facial trustworthiness across low, middle, and high facial anthropomorphism of robots. This paper presents our findings based on semi-structured interviews (N=13). Our research aims to construct a comprehensive framework that elucidates how specific robot facial features influence the overall facial trustworthiness across varying anthropomorphic appearance designs of robot faces. By contributing a novel theoretical perspective, we expect to enrich the discourse on AI robots within the IS community. Practically, our findings will facilitate informed design decisions for trustworthy robot appearance.