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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

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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.

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Data Availability

The preregistrations of Studies 1 and 2 contain all study material and analysis plans: https://osf.io/v3ta6/?view_only=ae04072ae3eb4f809cb5f929c5a92a2a. The de-identified, individual-level datasets for Studies 1 and 2 will be made available upon request.

Code Availability

The analysis code is in the preregistration.

Notes

  1. Situational specificity may also help avoid stereotyping by specifying whether a situation currently applies to a person, rather than assuming it applies or does not apply based on the person’s age, gender, ethnicity, etc.

  2. To obtain an omnibus test of fit (which is not possible using Baron and Kenny’s method, since each component regression model is saturated with all possible parameters estimated), we performed path modeling using maximum likelihood estimation with lavaan in R, regressing age, gender and education on fatigue and regressing fatigue on acceptance. The likelihood ratio test indicated a significant deviation from a good fit of the sample, χ2(3) = 13, p = 0.004, with fit indices CFI = 0.79, TLI = 0.51 and RMSEA = 0.14. Looking at modification indices, we added education regressed on acceptance and removed education regressed on fatigue. This resulted in a fit that was not significantly different from a good fit, χ2(3) = 3.8, p = 0.28, with fit indices CFI and TLI > 0.96 and RMSEA = 0.04.

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Acknowledgements

The authors thank Karen Penaranda Valdivia for assistance with the filming, Jeremy Bittick for assistance with the filming and constructing the inner tail structure, Professor Farrokh Janabi-Sharifi for feedback on the animations and Toronto Metropolitan University and the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants (RGPIN-2021-03139) for funding.

Funding

This research was funded by a Toronto Metropolitan University start-up grant and a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2021-03139.

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  1. Department of Mechanical and Industrial Engineering, Toronto Metropolitan University, 350 Victoria St, Toronto, ON, M5B 2K3, Canada

    Jamy Li & Mohsen Ensafjoo

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Study conception, design, material preparation, data collection and analysis, writing and registrations were performed by Jamy Li. Writing and reviewing were performed by Mohsen Ensafjoo.

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Correspondence to Jamy Li.

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Li, J., Ensafjoo, M. 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. Int J of Soc Robotics 16, 227–243 (2024). https://doi.org/10.1007/s12369-023-01072-3

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