Service Robots: Emotions of Older Adults in Different Situations

Esther Ruf, Stephanie Lehmann

and Sabina Misoch

Institute for Ageing Research, FHS St. Gallen University of Applied Sciences, Rosenbergstrasse 59,

9001 St. Gallen, Switzerland

Keywords: Robot, Emotions, Older Adults.

Abstract: Against the background of demographic change and an expected shortage of skilled nursing staff,

consideration is being given to whether robots will play a greater role to assist older adults in daily life

activities and care personnel. Many models of Technology Acceptance do not focus on emotions of older

adults triggered by service-type robots that support daily activities or care activities. The present simulated

robot study investigated emotions of 142 older adults towards different robots in different situations to

contribute to a deeper understanding of the acceptance of robots. The situation in which a robot interacts with

a human affected the emotions of the older participants differently: in the service situation, less negative

emotions were expressed than in the care situation. These results should be considered when developing

service robots for older adults. The results should be validated with existing robots in real life.

1 INTRODUCTION

The rising amount of technical innovations being

developed to support older adults at home and nursing

staff in care institutions can be attributed to several

trends in industrial societies. Amongst these are

demographic changes (Vaupel, 2000), an expected

shortage of skilled nursing staff (World Health

Organization, 2015), and the fact older adults wish to

live independently at home for as long as possible

(Marek and Rantz, 2000), with positive effects on

their quality of life (Sixsmith and Gutmann, 2013).

Against this background it is assumed that robots will

play an increasingly important role in the area of

service and care for older adults, maintaining their

independence and well-being (Ray, Mondada and

Siegwart, 2008; Wu et al., 2014). A robot is a

programmable machine that can take over tasks

(semi-)autonomously (Savela, Turja and Oksanen,

2018).

In their review, Agnihotri and Gaur (2016) report

promising applications of assistive robots for social

and daily healthcare of older adults. If robots which

support tasks usually performed by humans are to be

used, the consideration of user acceptance is essential

as in Europe, low acceptance rates for robots by older

adults are assumed (Payr, Werner and Werner, 2015).

https://orcid.org/0000-0002-1086-3075

Many models of Technology Acceptance are

characterized by behavioral or technology-oriented

approaches. They focus on cognitive (especially

evaluative) and social factors such as attitudes and

previous experiences (e.g. Theory of Reasoned

Action (TRA); Fishbein and Ajzen, 1975), the

perceived/experienced usefulness or simplicity of use

(e.g. Technology Acceptance Model (TAM); Davis,

Bagozzi and Warshaw, 1989) and other social factors

(e.g. Unified Theory of Acceptance and Use of

Technology Model (UTAUT), Venkatesh, Morris,

Davis and Davis, 2003).

In the overall construct of Technology Acceptance,

emotions triggered by robots that support daily

activities are considered marginally, globally (e.g. as

"fear" dimension in Wu et al., 2014) and unspecifically

(e.g. as "emotional involvement" and "potential threat"

in Mollenkopf and Kaspar, 2004).

It is questionable whether utilitarian factors can

sufficiently explain robot acceptance of older adults.

Goher, Mansouri and Fadlallah (2017) claim that the

two primary factors that influence adoption of

technology by older adults are ease of use and

usefulness. In contrast, in a laboratory-based user

interaction study De Graaf and Allouch (2013) found

that only “enjoyment”, and not utility, explained the

actual use of a robot. This shows that it is important

Ruf, E., Lehmann, S. and Misoch, S.

Service Robots: Emotions of Older Adults in Different Situations.

DOI: 10.5220/0009324500150025

In Proceedings of the 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2020), pages 15-25

ISBN: 978-989-758-420-6

to pay attention to emotions of older adults interacting

with robots, as these emotions and attitudes influence

their reactions (Broadbent, Stafford and MacDonald,

2009a). While emotions and their integration into

robotic systems are receiving a great deal of attention,

the investigation of people’s emotional reactions

towards robots are largely neglected (Rosenthal-von

der Pütten, Krämer, Hoffmann, Sobieraj and Eimler,

2013. Emotions of users are often studied in terms of

empathy with a robot (Rosenthal-von der Pütten et al.,

2014) rather than emotional reactions towards robots.

In the field of robotics, negative emotions towards

communication robots, which can lead to an

avoidance of communication, were investigated

(Nomura, Kanda, Suzuki and Kato, 2004; 2008).

Reactions and emotions of older adults towards

companion robots, animal-like robots such as the seal

PARO (e.g. Abbott et al., 2019; Hung et al., 2019;

McGlynn, Kemple, Mitzner, King and Rogers, 2017),

that are made to evoke explicitly positive emotions

and are used as aids in therapy, are well studied.

Emotions of older adults towards service-type robots,

which should support tasks of daily life, have hardly

been considered so far.

When considering the acceptance of robots,

important factors that promote and inhibit acceptance,

such as appearance and form, are discussed

(Broadbent et al., 2009a; Flandorfer, 2012). In

industrial contexts humanoid robots are built, with the

idea that positive human-robot interaction is

increased by human resemblance (e.g. Kiesler and

Hinds, 2004). Also, in social settings,

anthropomorphic robots seem more accepted, on the

one hand because human-like shape and behaviour

have advantages when a close interaction between a

robot and a human is necessary, and on the other hand

because people suppose that the more a robot

resembles a human, the better it performs human-like

tasks (Hwang, Park and Hwang, 2013). However, too

much human similarity can be counterproductive, a

phenomenon known as the "Uncanny Valley" effect

or "acceptance gap" (Mori, 1970). This is the

seemingly paradoxical effect that the acceptance of

artificial figures does not increase linearly with

anthropomorphism but suffers a severe slump within

the increase in human similarity: the more human-like

the figure is, the more people accept the artificial

figure presented – however, acceptance falls above a

certain degree of anthropomorphism. Today, mixed

findings concerning both the existence of the uncanny

valley and its explanations are discussed in literature

(Ho and MacDorman, 2017; Kätsyri, Förger,

Mäkäräinen and Takala, 2015; MacDorman and

Chattopadhyay, 2016; Miklósi, Korondi, Matellán

and Gácsi, 2017; Strait et al., 2017).

Studies show that in addition to the appearance of

the robot, the situation in which an interaction takes

place has a decisive influence on acceptance (Decker,

2010; Eftring and Frennert, 2016; Gaul et al., 2010;

Misoch, Pauli and Ruf, 2016). Situations in which

service robots perform tasks that are usually

performed by humans are likely to evoke emotions,

especially when the service robot comes into direct

contact with humans in an intimate task. In assistive

situations, the use of robots can lead to unclear

expectations (Compagna and Marquardt, 2015) and

unrealistic ideas (Baisch et al., 2018). Equally,

embedding robots in new everyday situations can be

emotionally challenging.

Research on human-robot interaction (HRI),

human-robot proxemics (HRP), and human-robot

spatial interaction (HRSI) shows that personal space

is a major issue in human-robot interaction (for

summary see Lauckner, Kobiela and Manzey, 2014).

Most research has shown that robots should stay

outside of a person’s intimate zone and within their

personal or social zone (Kessler, Schroeter and Gross,

2011, Walters et al., 2009a). The spatial area in which

a robot moves during interaction is determined by the

situation and its tasks. If a robot supports tasks in

everyday life, it is usually further away from the

person than if it supports care activities. A robot

interacting in a care situation in the intimate zone

could evoke more negative feelings and therefore it is

assumed that interaction with a robot in a service

situation triggers less negative feelings.

Nursing activities usually include touching the

patient and, in their survey, Parviainen, Turja and

Van Aerschot (2018) found that care workers were

reserved towards the idea of using autonomous

robots in tasks that typically involve human touch.

This shows that different situations might cause

different levels of positive or negative emotions,

depending on the appearance of the robot and the

situation it is used in.

So far, research on Technology Acceptance has

hardly considered the emotions of older adults

towards a service type robot depending on a specific

situation.

1.1 Aim

The aim of the present study was to investigate the

emotions of older adults towards different robots in

different situations to contribute to a better

understanding of the acceptance of robots.

ICT4AWE 2020 - 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health

2 METHODS

The present study is a simulated robot study,

conducted as a vignette study. A hypothetical

situation is constructed based on vignettes, and the

participants put themselves into the situation

displayed (Atzmüller and Steiner, 2010).

The study refers exclusively to service-type

robots whose main function is to support daily

activities, according to the categorization of assistive

robots for older adults by Broekens, Heerink and

Rosendal (2009, p. 95). Companion robots (e.g. pet-

like robots), whose main function is to enhance health

and psychological well-being, or other robot types,

were not considered in this study.

2.1 Material

2.1.1 Videos

Two videos with different situations showing human-

robot interactions were selected and tested

beforehand in a feasibility test (September 2017). The

service situation shows an older woman in a

retirement home sitting at a table with other older

women. The robot Care-O-bot 3 moves towards the

woman with a cup of water and then invites her to

drink, which she does.

The care situation shows a middle-aged bedridden

woman, her arms and legs being washed by the robot

Cody without other people visible. The videos were

cut to a minute in length, to accurately illustrate the

relevant interaction and were shown without sound in

order avoid distractions through verbal descriptions.

2.1.2 Pictures

For the visual stimuli, pictures of robots with varying

degrees of human appearance were selected based on

the most used classification of different authors:

machine-like, mechanical-human-like, human-like,

and android (DiSalvo, Gemperle, Forlizzi and

Kiesler, 2002; MacDorman and Ishiguro, 2006;

Walters, Koay, Syrdal, Dautenhahn and Te

Boekhorst, 2009b). The aim was to have images of

high quality of the robots, and images that depicted

meaningful representations in the context of nursing

care for older adults. The following images were

selected: for machine-like appearance: Lio (F&P

PersonalRobotics, 2019); for mechanical-human-like

appearance: Kompai (TelepresenceRobots, 2019); for

human-like appearance: Romeo (Automation and

Control Institute, 2019) and for android appearance

Otonaroid (Miraikan, 2019). The pictures of the

robots were shown without product names.

2.1.3 Questionnaire

To develop the questionnaire, various existing

emotional scales were compiled based on a literature

search. Items from eight scales and the basic emotions

of 14 authors were considered: the German version of

positive and negative affect schedule (Breyer and

Bluemeke, 2016), the State-Trait-Anxiety Inventory

(according to Spielberger in the long version of

Grimm, 2009), the SEK-ES – questionnaire for

emotion-specific self-assessment of emotional

competencies (Ebert, Christ and Berking, 2013), the

Jennifer Monathan «liking» questionnaire

(Monathan, 1998), the Emotional reactions to

domestic robots (Scopelliti, Giuliani and Fornara,

2005), the Property list at the subscale level (Janke

and Debus, 1978), the Feeling scale - Revised version

(Bf-SR) (Von Zerssen, 2011), the Multidimensional

state questionnaire (MDBF) (Steyer,

Schwenkmezger, Notz and Eid, 1997), and the Basic

Emotions (Arnold, 1960; Ekman, Friesen and

Ellsworth, 1982; Frijda, 1986; Gray, 1982; Izard,

1971; James, 1884; McDougall, 1926; Mowrer, 1960;

Oatley and Johnson-Laird, 1987; Panksepp, 1982;

Plutchik, 1980; Tomkins, 1984; Watson, 1930;

Weiner and Graham, 1984 (overview in Ortony and

Turner, 1990)). Single emotions mentioned by older

adults and the research team during a feasibility test

and workshop were added. This resulted in 79

positive, 12 neutral and 116 negative emotion items.

Items were then selected separately by two

researchers based on the following criteria: (1)

deletion of the category "neutral" because it was too

unspecific; (2) ensuring comparability with other

studies; (3) avoidance of doubled / too similar items;

(4) the same number of positive and negative items;

(5) focus on "real" emotions and not “attitudes” or

“evaluations”; (6) state emotions instead of trait

emotions; (7) comprehensibility; (8) frequently

occurring items. The results were discussed and a list

of 34 positive and 45 negative items was compiled.

Subsequently, further individual items were sorted

out based on content considerations. Four positive

items were sorted out because their content did not fit

(e.g. "in love"). 15 negative items were sorted out

because their content did not fit, because they were

already well covered by other items or because they

were judged too vague. The resulting 30 positive and

30 negative emotion items were converted into

adjectives, even if the basic emotion was a noun. The

resulting 30 positive and 30 negative emotion items

Service Robots: Emotions of Older Adults in Different Situations

were displayed in random order with the answer

selection "rather yes" or "rather no” on two pages of

the questionnaire. The dichotomous answer format

was chosen because frequencies were to be

determined and so that participants can rapidly and

easily treat the emotion lists.

Further items which were described in the

literature in the context of robots were integrated into

the questionnaire: acceptance, technology

experience, prior experience with technology and

robots, attitudes, and willingness to interact. The

following scales were considered: Robot familiarity

and use questionnaire (Mitzner et al., 2011), Robot

Attitude Scale (RAS) (Broadbent, Tamagawa, Kerse,

Knock, Patience and MacDonald, 2009b; Nomura et

al., 2008, p. 3), God-Speed questionnaire (Bartneck,

Kulic, Croft and Zoghbi, 2009), Robot-acceptance

questionnaire (Wu et al., 2014), Negative Attitude

Towards Robots Scale (NARS) (Nomura, Kanda,

Suzuki and Kato, 2006). NARS and RAS were often

used in other studies. However, these questions relate

strongly to emotional robots or to communication

with a robot. Therefore, they proved inappropriate for

the present study. The robot-acceptance questionnaire

in the version by Heerink, Kröse, Evers and Wielinga

(2010), which is based on the Unified theory of

acceptance and use of technology (UTAUT) model

fit. Six of the 41 items were selected, which proved to

be predictive for acceptance in studies or fit into the

context of the present study.

The review by Flandorfer (2012) and previous

questionnaires of the authors served as a basis for

sociodemographic items. In addition, the item "Have

you ever dealt directly with a robot" by Nitto,

Taniyama and Inagaki (2017) was added to be able to

compare the Swiss population with the German and

Japanese population.

The question formulations and answer categories

of the questionnaire were age-appropriate (according

to the recommendations of Lang, 2014). In total, the

questionnaire was four pages long and could be

completed in about 15 minutes. The questionnaire

was pre-tested (with four men and five women 60+)

and was then finalised for the study.

2.1.4 Recruiting

German-speaking older adults aged over 60 were

recruited in Eastern Switzerland. Possible

participants were asked via different existing

networks of the Institute for Ageing Research (IAF),

FHS St.Gallen, University of Applied Sciences, and

were offered several study dates. Finally, 11 study

dates took place in the period of September to

December 2018 in three different Swiss cantons

(St.Gallen, Graubünden, Lucerne).

2.1.5 Study Procedure

Several participants took part in each study

appointment. Each participant filled in the

questionnaire alone and in silence in a classroom. No

joint discussion or audible comments were allowed

during the study. The participants first saw a short

video sequence of a service situation (S1) without

sound, in which a robot reminds a resident to drink

and brings the respective resident water. Since

assistance robots in the service and care sector cover

a broad spectrum of different designs from very

technical to very human-like (Decker, 2010), the

participants were shown one of the four images

(machine-like robot (1), mechanical-human-like

robot (2), human-like robot (3) and android robot (4))

after the video sequence. The emotions caused by the

situation were then recorded with the self-constructed

questionnaire on emotions. After completing the first

questionnaire, the participants received a different

picture of the robot for the same service situation and

were again asked about their emotions. This process

was repeated with the video sequence of a care

situation (S2). The sequence of the pictures varied

randomly for each participant. Through this

procedure each participant processed four randomly

distributed vignettes, whereby the order of the

displayed vignettes varied according to

predetermined scheme, which ensures that all

possible combinations occurred equally (see table 1).

Table 1: Variation of the vignettes.

Appearance A Situation S

(A1-A4)

Service situation

(S1)

Care situation

(S2)

A1: machine-like A1 x S1 A1 x S2

A2: mechanical-

human-like

A2 x S1 A2 x S2

A3: human-like A3 x S1 A3 x S2

A4: android A4 x S1 G4 x S2

2.1.6 Analyses

The data from the questionnaires were manually

entered in a SPSS data mask. A 5% check of the

sample was carried out. After quality control and data

cleansing, the data were evaluated with the IBM

SPSS Statistics 26 program. The results are presented

descriptively: M for mean value, SD for standard

deviation or n for sample size and % for frequencies,

according to the scale level. For differences in the

ICT4AWE 2020 - 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health

scores of positive and negative emotions for different

situations t-tests were calculated. In order to

investigate the influence of different situations and

different appearance of the robot on positive and

negative emotions a one-way analysis of variance

(ANOVA) was calculated in each case.

3 RESULTS

3.1 Participants

A total of 142 older adults participated with an

average age of 73.2 years (SD=6.1, range 58 to 87).

54.2% of the participants were female. 71.1% lived

with a partner (married/living with partner). Except

for three persons (from Germany, Great Britain, no

details), all participants were Swiss. 65.5% of the

participants had completed tertiary education, 23.2%

upper secondary education, 8.5% compulsory

schooling, and 0.7% had not completed any

schooling. Except for two persons (in retirement

homes), all participants lived in a private household

(98.6%), which consisted predominantly of two

persons (64.8%). The current residential area was

reported to be more rural by 53.5%, and more urban

by 46.5%. 50.0% rated themselves as interested in

technology, 21.8% as very interested, 26.8% as rather

not interested and 1.4% as not interested at all.

76.1% already reported some experience with the

use of technology in their professional lives, which

included not only robots but also computers or

machines. When asked whether they had ever been

involved with a robot, 69.0% said they had no

experience with a robot, 6.3% did not know, and those

who said they had been involved with a robot before

(24.5%) indicated a place at home, at work, in a

workshop, at a trade fair, in a garage, at university, at a

company, in continuing education courses, on a cruise

ship, in the neighbourhood, in a lecture and more.

3.2 Emotions

30 positive emotions and 30 negative emotions were

collected for each situation and each appearance of

the robot. Overall, the three most frequently

mentioned positive emotions were "awake" (75.9%),

"attentive" (74.8%) and "interested" (71.9%). The

three most frequently mentioned negative emotions

were "tense" (49.4%), "unwell" (46.2%) and

"dissatisfied" (45.0%).

The mean value of the sum score for positive

emotions was M=12.72 (SD=9.70) and the mean

value of the sum score for negative emotions was

M=10.31 (SD=9.84). If the group of men and women

is considered separately, men (M=15.15, SD=9.70)

reported on average more positive emotions than

women (M=10.72, SD=9.25). This difference is

significant t(529.19)=5.50, p<.001. Women reported

more negative emotions (M=12.24, SD=9.99) than

men (M=7.97, SD=9.15). This difference is also

significant t(553.84)=-5.27, p<.001.

Table 2: Positive emotions for situation and appearance.

Robot appe-

arance

Service

situation (S1)

Care situation

(S2)

Both situations

M=13.80

(SD=10.22)

M=08.30

(SD=07.72)

M=11.03

(SD=09.43)

M=15.23

(SD=10.68)

M=10.29

(SD=09.13)

M=12.76

(SD=10.20)

M=15.77

(SD=08.86)

M=10.19

(SD=09.24)

M=12.92

(SD=09.44)

M=14.51

(SD=09.08)

M=13.90

(SD=10.03)

M=14.20

(SD=09.55)

Total A1-

M=14.82

(SD=09.72)

M=10.67

(SD=09.25)

M=12.72

(SD=09.70)

A1: machine-like, A2: mechanical-human-like, A3:

human-like, A4: android, M: mean value, SD: standard

deviation.

Table 3: Negative emotions for situation and appearance.

Robot appe-

arance

Service

situation (S1)

Care situation

(S2)

Both situations

M=08.96

(SD=09.72)

M=14.55

(SD=10.45)

M=11.77

(SD=10.44)

M=07.91

(SD=09.22)

M=12.79

(SD=09.87)

M=10.35

(SD=09.83)

M=07.71

(SD=08.81)

M=11.89

(SD=10.05)

M=09.84

(SD=09.66)

M=08.71

(SD=09.68)

M=09.80

(SD=09.04)

M=09.26

(SD=09.34)

Total A1-

M=08.32

(SD=09.33)

M=12.25

(SD=09.96)

M=10.31

(SD=09.84)

A1: machine-like, A2: mechanical-human-like, A3:

human-like, A4: android. M: mean value, SD: standard

deviation.

Regarding the two different situations,

participants expressed more positive emotions in the

service situation (M=14.82, SD=9.72) than in the care

situation (M=10.67, SD=9.25) (table 2). This

difference is significant t(557.18)=5.19, p<.001. And,

more negative emotions were expressed in the care

situation (M=12.25, SD=9.96) than in the service

Service Robots: Emotions of Older Adults in Different Situations

situation (M=8.32, SD=9.33) (table 3). This

difference is significant t(560)=-4.83, p<.001.

Regarding robot appearance, participants on

average expressed increasing positive emotions from

machine-like (M=11.03, SD=9.43), mechanical-

human-like (M=12.76, SD=10.20), human-like

(M=12.92, SD=9.44), to android (M=14.20, SD=9.26)

appearance of the robot (table 2). Participants on

average expressed decreasing negative emotions from

machine-like (M=11.77, SD=10.44), mechanical-

human-like (M10.35, SD=9.83), human-like (M=9.84,

SD=9.66), to android (M=9.26, SD=9.34) appearance

of the robot (table 3).

For emotions we each calculated a one-way

ANOVA with gender as covariate to assess the effects

of the situation in which the robot was shown and the

appearance of the robot on levels of positive and

negative emotions (as measured by the questionnaire).

The situation in which interaction with the robot was

shown had two categories (service situation, care

situation) and appearance of four categories (machine-

like, mechanical-human-like, human-like, android).

The level of positive emotions differed statistically

significant for the different situations, F(1,553) =

29.84, p<.001, and the different appearances of the

robot, F(3, 553) = 2.88, p=.036. The level of negative

emotions differed statistically significantly for the

different situations, F(1, 553) = 25.35, p<.001. There

was no statistically significant difference in scores of

negative emotions for the different appearances of the

robot, F(3, 553) = 1.86, p=.135.

4 DISCUSSION

The present study intended to investigate the emotions

of older adults towards robots of different appearances

in different situations. Slightly more women than men

took part in the study, which corresponds to the gender

distribution among Swiss older adults (BFS, 2019).

Several Swiss cantons could be covered, with slightly

more participants coming from rural regions.

However, the sample is made up of well-educated

older adults and therefore does not represent the

general population of Switzerland. The high

percentage of well-trained study participants in studies

with older adults and use of technology is a

phenomenon that is quite often encountered (e.g.

Dahms and Haesner, 2018; Steinert, Haesner, Tetley

and Steinhagen-Thiessen, 2015). The older adults lived

almost exclusively in private households, which was

intended but must be considered when interpreting the

results. Most participants were interested in

technology, which is in line with other studies (e.g.

Mies, 2011; Stadelhofer, 2000) and reflects the

increasing innovation orientation in older adults

(Höpflinger, 2009). In contrast to other studies (e.g.

Mollenkopf, 2006; Mollenkopf & Kaspar, 2004), more

participants assessed themselves as experienced in the

use of technology. However, caution is required when

interpreting this result, as the high percentage could be

due to the formulation of the question ("Have you

gained experience with the use of technology

(computers, machines, robots) in your professional

life"). The omnipresence computers at the workplace

and the focus on the professional context could have

led to distorted results. For example, "gaining

experience" can refer to the fact that a computer was

present, and, in contrast, housewives may have negated

the question because their technical experience did not

take place in a professional context. The proportion of

24.5% of older adults in this study who stated that they

had ever had anything to do with a robot is within the

27% stated by Nitto et al. (2017) (persons in Germany,

aged 16-69, Internet survey). The percentage of older

adults who stated that they had never had anything to

do with a robot (69%) is also comparable with the

figures from Nitto et al. (2017), where it was 73% for

Germany. Data from the Eurobarometer 2012 (from 27

EU Member States) report that 87% of EU citizens

have never had a robot in their life, 12% have had

experience with a robot (6% at home, 6% at work)

(European Commission, 2012). In the present study,

men reported significantly more positive emotions

than women, and women reported significantly more

negative emotions towards robots. Kuo et al. (2009)

found a significant gender effect, with males having a

more positive attitude toward robots in healthcare than

females. In a review Broadbent et al. (2009a) report

that gender has an impact on how people react to

robots.

In view of the two different situations in which

people interact with a robot, in the service situation,

fewer negative emotions were expressed than in the

care situation. A possible reason for this could be that

different distances of the robot (i.e. the spatial

proximity between robot and person) are accepted

differently in different situations. Koay, Dautenhahn,

Woods and Walters (2006) showed that people’s

levels of comfort varied across different distances

from a robot, and that people displayed more comfort

with the robot at the intermediate distance than they

did at close or far distances. In the videos shown in

the present study, it could be that the robot in the

service situation interacts in the intermediate

distance, which is more comfortable itself, and the

robot in the care situation interacts at close distance,

what is experienced as less pleasant. This is also

ICT4AWE 2020 - 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health

shown in further studies (e.g. Seibt, Nørskov and

Schack Andersen, 2016). The use of a robot is

accepted, but not in personal hygiene and under the

maintenance of a certain distance between robot and

human being. In their systematic literature review,

Savela et al. (2018) summarize that the attitudes of

older adults towards robots are more often positive

than negative. Other studies report that older adults

showed a more positive attitude towards robots than

other groups like health personnel, caregivers,

relatives (Broadbent et al., 2012).

Regarding the appearance of the robot, the

android robot didn’t evoke more negative emotions

than the machine-like, the mechanical-human-like or

the human-like robot. The android robot, in fact,

evoked the most positive emotions. This result seems

initially surprising, regarding the “Uncanny Valley”

hypothesis. However, findings concerning both the

existence of the “Uncanny Valley” and its

explanations are mixed (for discussion see Broadbent,

2017). As Prakash and Rogers (2015) pointed out,

familiarity with the human appearance is a primary

reason for why human-looking robots might be

favoured over mechanical appearance. This could be

especially true for older adults, particularly regarding

tasks at home that typically are performed by humans

(Blow, Dautenhahn, Appleby, Nehaniv and Lee,

2006). In addition, the findings must also be

reconciled with Korchut et al. (2017), who postulate

a preference for anthropomorphic appearances.

Our findings could have various reasons. The first

question to ask is whether the android robot was

recognized as a robot. Since it was repeatedly pointed

out in the study instructions and during the study, that

all images are robots and not humans, this

explanatory approach can be rejected. Another reason

could be the resemblance of the android robot to

female person. All other robots cannot be clearly

assigned to gender. This could have evoked more

positive emotions, since more women are employed

in nursing homes (Mercay and Grünig, 2016), and

this could have resulted in a congruent picture for the

study participants. In the study of Prakash and Rogers

(2015), some of their participants opinions about

female human-looking faces were linked to notions of

care or nursing.

The fact must also be considered that at the

beginning the participants did not have all four

possible appearance options in front of them at the

same time and were not asked which robot they would

rather use, but each robot had to be assessed

individually. However, the authors see an advantage

in this study design, which is that emotions had to be

assessed independently for each vignette.

In conclusion, the situation in which a robot

interacts with a human can be an important factor for

the emotions that older adults have, which in turn can

be important for robot acceptance. Appearance seems

to play a role in the sense that it can evoke positive

emotions. This should be considered when

developing service robots for older adults.

4.1 Limitations

In robot research, simulated robot studies and real-

world robot studies are carried out. According to

Broadbent (2017), both types of studies contribute to

knowledge. The advantages of simulating studies are

their high degree of control over study manipulations,

and that they are quicker. The disadvantages when

using simulated designs are that people are under

artificial conditions, and therefore the results may not

be transferable to real robots and real-world conditions.

In the present study, only pictures of robots were

used as not all robots shown in the study were

available in Switzerland at the time. Therefore, a

limitation of the present study is that results might

have differed if the robot had been seen in real life by

respondents. Although images were chosen that show

the four robot types as similarly as possible, image

elements might have influenced the preferences

independently of the robot. In case of the android

robot, one can assume the greatest difference between

the picture and the actual experience because the gap

between appearance and movements as described for

the “Uncanny Valley” effect could be decisive. In

future, the aim is to carry out investigations with

robots in real life.

Another fact to consider is that the robots and

persons showed in the two video conditions were not

the same. Thus, there could have been effects due to

these differences: The persons shown in the videos did

not have the same age. Both were women, but in the

service situation, the woman was clearly aged over 60,

older than the woman in the care situation. This might

have affected the responses, as the person in the service

situation was more in line with the age of the study

participants, so that they might identify more strongly

with her. Also, the robots in the videos differed. The

shape of both robots had no heads, but the appearance

of the robot in the service situation could be perceived

as more machine-like, and in the care situation more

mechanical-human-like. Although the participants

were instructed to imagine the robot shown on the

picture, there could be an effect that the appearance of

the robot in the service video was more pleasant than

the robot in the care video and this might have led to

more positive emotions.

Service Robots: Emotions of Older Adults in Different Situations

Priming effects by the video shown cannot be

excluded. While the sequence of the pictures varied,

all participants first saw the video with the service

situation. This might have led to a priming to more

positive feelings in this situation.

In the present study, only one example for each of

the four categories of humanness was used. When

choosing other or varying examples, the results might

change, especially if the robot had been shown in a

female and male form.

While the order of the presentation of the robots

was mixed across the study, the order of the questions

in the questionnaires remained the same. This may

have particularly affected the responses to questions

at the end of the questionnaire. In addition, as the

participants saw two videos and had to complete four

questionnaires, fatigue effects cannot be ruled out,

even though they might be partly absorbed by the

variation of the vignettes.

Finally, the composition of the study population is

not representative of the general population.

Although other studies report positive attitudes

towards robots, a different picture may emerge if

more members of less well-educated or less

technology-oriented people are included. In addition,

persons already in need of care who receive help with

personal hygiene, might have answered differently

and might find the robot conducive to their privacy.

Starting from this basic analysis, future studies should

increasingly pay attention to characteristics of the

study sample and analyse the specific needs of older

adults in real settings.

ACKNOWLEDGEMENTS

We would like to thank the Stiftung Suzanne und

Hans Biäsch zur Förderung der Angewandten

Psychologie for their project funding and the people

who took part in the survey.

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