The Robotic Social Attributes Scale (RoSAS) Development and Validation

• Published in: 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI pp. 254 - 262
• Main Authors: Carpinella, Colleen M., Wyman, Alisa B., Perez, Michael A., Stroessner, Steven J.
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 06.03.2017
• Series: ACM Conferences
• Subjects: Anthropomorphism; Applied computing; Applied computing > Law, social and behavioral sciences > Psychology; Computing methodologies; Computing methodologies > Artificial intelligence > Philosophical/theoretical foundations of artificial intelligence; Eigenvalues and eigenfunctions; Human Factors; Human-centered computing; Human-centered computing > Human computer interaction (HCI) > HCI design and evaluation methods; Human-Robot Interaction; Loading; Measurement; Perception; Personality Perception; Psychology; Psychometric Scale; Reliability; Robotics; Robots; Safety; Social Perception; Social Robotics; Social Robots; human-robot interaction; human factors; psychometric scale; social robots; anthropomorphism; robotics; social robotics; robots; measurement; perception; personality perception; social perception
• ISBN: 9781450343367; 1450343368
• ISSN: 2167-2148
• DOI: 10.1145/2909824.3020208

Accurately measuring perceptions of robots has become increasingly important as technological progress permits more frequent and extensive interaction between people and robots. Across four studies, we develop and validate a scale to measure social perception of robots. Drawing from the Godspeed Scale and from the psychological literature on social perception, we develop an 18-item scale (The Robotic Social Attribute Scale; RoSAS) to measure people's judgments of the social attributes of robots. Factor analyses reveal three underlying scale dimensions-warmth, competence, and discomfort. We then validate the RoSAS and show that the discomfort dimension does not reflect a concern with unfamiliarity. Using images of robots that systematically vary in their machineness and gender-typicality, we show that the application of these social attributes to robots varies based on their appearance.