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About

Shengliang Zhang is a leading researcher in the emerging field of service robot–human interaction, with a particular focus on how robotic design and deployment influence customer perceptions and service quality. His work critically examines the anthropomorphism of service robots—exploring how dimensions like appearance and personification affect consumer trust, decision-making, and overall service experience. In a landmark 2022 study (76 citations), Zhang demonstrated that the type of service principal—robot versus human staff—significantly impacts perceived service quality, mediated by the attributes customers assign to the service provider. He further dissected the multifaceted nature of robot anthropomorphism (43 citations), identifying key factors and internal relationships that shape user responses. Notably, his research on appearance personification in product recommendation contexts (18 citations) addresses a timely gap exposed by the COVID-19 pandemic, revealing how robot design can influence customer choices in contactless retail settings. By integrating these insights with the revised service quality gap model, Zhang provides a robust framework for understanding and improving robot-delivered services. His work is essential reading for scholars and practitioners aiming to design effective, customer-centric service robots.