It’s Not UAV, It’s Me: Demographic and Self-Other Effects in Public Acceptance of a Socially Assistive Aerial Manipulation System for Fatigue Management

Li, Jamy; Ensafjoo, Mohsen

Abstract

Modern developments in speech-enabled drones and aerial manipulation systems (AMS) enable drones to have social interactions with people, which is important for therapeutic applications involving flight and above-eye-level monitoring in people’s homes, but not everyone will accept drones into their daily lives. Consistently assessing who would accept a socially assistive drone into their home is a challenge for roboticists. An animation-based Mechanical Turk survey (N = 176) found that acceptance of a voice-enabled AMS for fatigue – i.e., physical or mental tiredness in the participant’s life – was higher among younger adults with higher education and longer symptoms of fatigue, suggesting demographics and a need for the task performed by the drone are critical factors for drone acceptance. Participants rated the drone as more acceptable for others than for themselves, demonstrating a self-other effect. A second video-based YouGov survey (N = 404) found that younger adults rated an AMS for managing the symptom of day-to-day fatigue as more acceptable than older adults. The self-other effect was reduced among participants who read a situation with specific versus general phrasing of the AMS’s imagined use, suggesting that it may be caused by an attribution bias. These results demonstrate how analyzing demographics and specifying the wording of technology use can more consistently assess to whom drones for fatigue are acceptable, which is of interest to public opinion researchers and roboticists.

Citation

Li, J., & Ensafjoo, M. (2024). It’s Not UAV, It’s Me: Demographic and Self-Other Effects in Public Acceptance of a Socially Assistive Aerial Manipulation System for Fatigue Management. International Journal of Social Robotics, 16, 227-243. https://doi.org/10.1007/s12369-023-01072-3

• Journal Article Type: Article
• Acceptance Date: Oct 16, 2023
• Online Publication Date: Nov 17, 2023
• Publication Date: 2024-01
• Deposit Date: May 7, 2024
• Journal: International Journal of Social Robotics
• Print ISSN: 1875-4791
• Electronic ISSN: 1875-4805
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Pages: 227-243
• DOI: https://doi.org/10.1007/s12369-023-01072-3
• Keywords: Unmanned Aerial Vehicles, Questionnaire, Survey, Attribution, Familiarity, Situational Specificity