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Ph.D. Abstract

Dr. Rebecca Stafford

The number of people aged over 65 years is increasing worldwide, and this is placing

increased demand on healthcare services. Engineers have proposed that eldercare robots

may be able to meet the increasing healthcare needs of the aging population; however

eldercare robots have not yet been widely adopted. Reasons for this are likely multifaceted,

but one reason may be insufficient attention to the psychological aspects of the human robot

interaction (HRI) in eldercare.

Technology acceptance models indicate that people’s perceptions of technology attributes

(particularly perceived usefulness) predict technology acceptance more strongly than more

objective design parameters. However, little research to date has investigated the

importance of perceptions to the acceptance of eldercare robots. The central thesis of this

PhD is that older people’s perceptions will influence their acceptance of healthcare robots.

Specifically, three main perceptions are studied - older people’s perceptions of their own

unmet needs, their attitudes towards robots in general, and their perceptions of the robot’s

mind. It is proposed that more positive attitudes and perceptions of robots will predict better

acceptance of healthcare robots.

This thesis contains four peer reviewed publications. One is a discussion paper on the

importance of assessing the unmet needs of eldercare stakeholders in order to develop

more useful and acceptable robots. Three publications present the results of three different

Human Robot Interaction (HRI) trials conducted with prototype healthcare robots. All three

studies employed autonomous service-type robots and older participants, and two of the

three HRI trials were conducted within real-world eldercare environments.

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The key findings of the HRI studies were that people’s perceptions of robots and ‘robot mind’

predicted robot acceptance. In all three studies, participants’ ‘pre-interaction’ generic robot

attitudes predicted acceptance of specific robots. This suggests that even people who have

never used robots before can hold mental models of robots that influence robot acceptance.

Additionally, people’s robot attitudes improved after interacting with the robot, and these

changes also predicted robot acceptance. This suggests that a positive HRI is important for

robot acceptance. Compared with people who perceived robots as possessing more mind,

people who perceived robots as having less mind were more likely to use a robot.

Furthermore, despite robot-users perceiving less robot-mind at baseline, they perceived the

robot to have even less mind after interacting with it. While this result suggests that people

may hold unrealistically high perceptions of a robot’s mind which may be a barrier to

acceptance, it also suggests that these perceptions are revised downwards after actually

experiencing a robot’s capabilities.

In conclusion, older people’s perceptions and attitudes towards robots do predict eldercare

robot acceptance. Future implications of this work are that building robots that meet the

specific unmet needs of older people and paying more attention to users’ perceptions of

robots may increase the acceptance of eldercare robots. Future research should investigate

whether interventions designed to promote realistic and adaptive perceptions of robots in

older people can increase the acceptance of eldercare robots.