1. 1
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.

2. 2
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.