Archives of Gerontology and Geriatrics 116 (2024) 105137

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Archives of Gerontology and Geriatrics
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Developing a model to explain users’ ethical perceptions regarding the useof care robots in home care: A cross-sectional study in Ireland, Finland,
and Japan
Hiroo Ide a, Sayuri Suwa b, *, Yumi Akuta c, Naonori Kodate d, Mayuko Tsujimura e,
Mina Ishimaru b, Atsuko Shimamura f, Helli Kitinoja g, Sarah Donnelly d, Jaakko Hallila g,Marika Toivonen g, Camilla Bergman-Kärpijoki h, Erika Takahashi i, Wenwei Yu jaInstitute for Future Initiatives, The University of Tokyo
Department of Community Health Nursing, Division of Innovative Nursing for Life Course, Graduate School of Nursing, Chiba UniversitycDivision of Nursing, Faculty of Healthcare, Tokyo Healthcare Universityd
UCD School of Social Policy, Social Work and Social Justice, University College DublineDivision of Visiting Nursing, School of Nursing, Shiga University of Medical Sciencef
Division of Community Health Nursing, Department of Nursing, Faculty of Health Science, Toho Universityg
Seinäjoki University of Applied ScienceshHumana, Finlandi
Graduate School of Humanities, Chiba UniversityjCenter for Frontier Medical Engineering, Chiba Universityb

H I G H L I G H T S• Surveys for home-care robots were conducted in Japan, Ireland, and Finland.
• The study built a model of ethical perceptions regarding the use of home-care robots.
• Our final model had explanatory power across countries with different backgrounds.
• This model can also be applied to explain ethical perceptions by attributes.

A R T I C L E I N F O

A B S T R A C T

Keywords:
Ethical issues
Caregivers
Robotics
Assistive technology
Psychometric proprieties
Validation

To date, research on ethical issues regarding care robots for older adults, family caregivers, and care workers has
not progressed sufficiently. This study aimed to build a model that universally explains the relationship between
the use of care robots and ethical awareness, such as regarding personal information and privacy protection inhome care. We examined data obtained from cross-sectional surveys conducted in Japan (n=528), Ireland(n=296), and Finland (n=180). We performed a confirmatory factor analysis by using responses to 11 items
related to the ethical use of care robots. We evaluated the model based on the chi-square to degrees of freedom
ratio, the comparative fit index, and the root mean square error of approximation. Subsequently, we compared
the model with the Akaike’s information criterion. Ten items were adopted in the final model. There were 4
factors in the model: ‘acquisition of personal information’, ‘use of personal information for medical and longterm care’, ‘secondary use of personal information’, and ‘participation in research and development’. All fac­
tor loadings of the final model ranged between 0.63 and 0.92, which were greater than 0.6, showing that thefactors had a high influence on the model. The final model was applied to each country; the fit was relatively
good in Finland and poor in Ireland. Although the three countries have different geographies, cultures, de­mographics, and systems, this study showed that the impact of ethical issues regarding the use of care robots inhome care can be universally explained by the same model.

* Corresponding author at: Department of Community Health Nursing, Division of Innovative Nursing for Life Course, Graduate School of Nursing, Chiba Uni­
versity, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8672, Japan.
E-mail address: suwa-sayuri@faculty.chiba-u.jp (S. Suwa).
https://doi.org/10.1016/j.archger.2023.105137
Received 9 December 2022; Received in revised form 20 July 2023; Accepted 22 July 2023
Available online 25 July 2023
0167-4943/© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Archives of Gerontology and Geriatrics 116 (2024) 105137

robots has emphasised the importance of raising awareness regarding
ethical issues (Rantanen et al., 2018; Stahl & Coeckelbergh, 2016;
Vandemeulebroucke et al., 2018). The following models have alreadybeen constructed: the Almere model for assessing older adults’ accep­
tance of assistive social agent technology (Heerink et al., 2010), a robot
acceptance model for care (RAM-care model) focusing on care workers’
individual context-dependent values behind care robot acceptance(Turja et al., 2020), and Alaiad and Zhou’s (2014) structural model ofdeterminants (SMD) involved in the introduction of care robots.
Suwa et al. (2020a) reported that care workers’ ethical perceptions
influenced their willingness to utilise care robots. Other possible factorsthat influence users’ intention to use care robots include their culturalcontext, ageing rate, health care policy, and the level of development oftechnology and its social implementation in each country. The existingfindings on these factors are conflicting. Some studies demonstrated thatemotions towards robots vary by country (Coco et al., 2018; Suwa et al.,2020b), whereas another study suggested that the situations in Europe
and Japan may not vary significantly (Nakada et al., 2021). Hence, there
is no universal structure to explain the ethical perceptions that affect thewillingness to use care robots across countries.
Therefore, this study aimed to build a model that explains the rela­tionship between the use of care robots and ethical awareness, such as
regarding personal information protection and privacy protection inhome care. With the aim to construct a universal model that can uni­versally explain the impact of ethical issues on the use of home-carerobots beyond jurisdictions, we examined data obtained from surveys
conducted in Japan, Ireland, and Finland, which have different geog­
raphies, cultures, demographics, and systems.

1. Introduction
The population of people aged 65 and over worldwide is projected toincrease to more than 1.5 billion by 2050. Although the rate of ageing
differs between countries, the population is ageing faster particularly indeveloped countries (Department of Economic and Social Affairs, 2019).
Societal ageing causes problems such as an increase in the number ofolder adults with dementia and increase in the care burden of family
caregivers and care staff (European Commission & Department of Eco­nomic and Financial Affairs, 2012; Fazio et al., 2020; United Nations
Population Fund & HelpAge International, 2012). Japan, for example, iscurrently facing both a declining birth rate and an ageing population,
making it difficult to secure caregivers (Ide et al., 2021).
In addition, it is estimated that by 2040, Japan will face a shortage of
690,000 caregivers (Ministry of Health, Labour and Welfare, 2021). As
one of the solutions to the limited caregivers problem, the use of tech­
nologies such as robotics, artificial intelligence, and information andcommunication technology (ICT) is expected to support the older adult
population by providing high-quality care. The Japanese government
has prioritised caregiving, including assistance with long-term carefunctions such as transfers, mobility problems, toileting, patient moni­
toring and communications, bathing, and the collection and aggregationof data associated with those functions for care robots (Ministry of
Health, Labour and Welfare, 2017).
Against this background, robots that provide care services are being
developed (Abdi et al., 2018; Casey et al., 2020; Jordan, 2016; Inoueet al., 2021; Ismail & Lokman, 2017; Moyle et al., 2020; Wang et al.,2017). There are high expectations regarding the development and so­cial implementation of assistive devices such as care robots that utilise
ICT and sensing technologies (Fares et al., 2021; Japan Economic
Research Institute, 2020; Qiu et al., 2021). However, thus far, the social
implementation of care robots has not been sufficiently achieved(Granja et al., 2018; Schreiweis et al., 2019). The reason why this idea
has not been socially implemented is related to not only the function andeconomic efficiency of care robots but also the presence of ethical issues.
For example, when a robot takes care of a person, the person being cared
for may experience a lack of warmth that accompanies a human care­giver (Coghlan, 2021; Pirni et al., 2021). Additionally, there is anargument that care robots will take over providing care, which has been
performed by humans thus far, consequently taking jobs away frompeople. There are also problems that exist from the perspectives of pri­vacy protection and security (Coeckelbergh, 2015; Palmerini et al.,2014; Sharkey & Sharkey, 2012; Stahl & Coeckelbergh, 2016).
These important ethical issues have been considered from the per­spectives of both developers and users. One early example is therecommendation provided by the Danish Council of Ethics (2010)
regarding the use of robots. The council intended that policy makers andstakeholders be aware of the recommendation when they apply regu­
lations regarding protection of privacy and personal information,
advertisement, and safety, and develop robots. Furthermore, the Insti­
tute of Electrical and Electronics Engineers (IEEE) has formulated aconcept termed Ethically Aligned Design (IEEE Standards Association,2018), which mainly deals with the ethical behaviours of researchersand developers of autonomous and intelligent systems. Ethically Aligned
Design uses the concept of Responsible Research and Innovation (RRI) to
promote innovation in research and development while addressing
ethical issues. RRI has also been adopted by HORIZON 2020, a large
research funding programme of the European Union (Horizon 2020,2013).
While there has been a shift in focus towards co-design and coproduction of care robots (Koutentakis et al. 2020), little progress has
been made in research on ethical issues regarding care robots for older
adults, family caregivers, and care workers. Regarding social imple­
mentation, multiple users’ (not only older people themselves but also
caregivers and family members) perspectives on ethical considerations
should be assessed. Thus far, research on acceptance models for care

2. Methods
2.1. Survey
We developed a questionnaire examining the ethical issues that
could potentially determine the willingness to use a home-care robot by
older adults, family caregivers, and care workers. The questionnaire
contained questions about respondents’ sex, age group, attribute (older
adults, family caregivers, and/or care workers), attitude towards
homecare robots (I am open to the use of home-care robots; ‘yes’ or ‘no’),and willingness to use home-care robots (Would you like to use a homecare robot at home when providing care for your family? Would you liketo use a home-care robot at home when receiving care yourself?; ‘yes’,‘yes, to some extent’, ‘not really’ or ‘never’); additionally, there were 11
items related to the ethical use of care robots, such as personal infor­
mation protection, privacy protection, and participation in research anddevelopment, wherein risks to the individual are assumed (Table 1). The
respondents were asked to rate the 11 questionnaire items on a 4-point
Likert scale (e.g. To what extent and from whom do you agree to obtainor use information about home-care robots? Scores ranged from 1 =‘disagree’ to 4 = ‘agree’).
Data from the three countries were obtained between November
2018 and February 2019. In Japan, we systematically sampled thenames of home-care offices from the list obtained from the government’s
Long-Term Care Insurance service information disclosure system. The
questionnaires were distributed to older people, family caregivers, andhome-care staffs through home-care offices. In Ireland, the respondents
to the questionnaire were chosen based on the following criteria: 1)
people aged 65 years or older who were using or may use health or social
care services, 2) family caregivers of people aged 65 years or older who
were using or may use services related to nursing care, and 3) homecare/health and social care professionals including nurses and careproviders. In Finland, the respondents were potential users of home-carerobots including adults aged 65 years or older, family caregivers ofpeople aged 65 years or older, and home-care professionals. In all three
countries, the respondents could send the answers either by postal mailor via the Internet. Details of the survey are provided in another report2

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Archives of Gerontology and Geriatrics 116 (2024) 105137

Table 1
Factors and items in the ethical perception model regarding the use of care robots in home care.
Questionnaire

FactorItem

1)

Acquisition of personal information

Identifiable photo and video

2)
3)
4)
5)
6)
7)
8)
9)
10)
11)

Home-care robots should be allowed to take photos or record videos that can identify the user,
with their permission.
Home-care robots should be allowed to take photos or record videos as long as the individual
cannot be identified (e.g. by blurring images so that only silhouettes are shown or by converting
images to text).
Health care professionals should be allowed to use photos and videos recorded by home-carerobots for clinical care and monitoring.
Health care professionals should be allowed to receive information on vital signs obtained by ahome-care robot (e.g. blood pressure, body temperature, respiration, and pulse).
Health care professionals should be allowed to use verbal information obtained by a home-carerobot from the user.
Health care professionals should be allowed to use information about the user’s location
obtained by a home-care robot.
The person whom you can trust (non-family member), if agreed by both parties, should beallowed to use information obtained by a home-care robot.
Health care professionals should be allowed to use secondary information (e.g., blurred images
and analysed data) collected by a home-care robot.
Researchers should be allowed to use secondary information (e.g., blurred images and analyseddata) collected by a home-care robot.
I want to help other people and society by participating in the research and development ofhome-care robots.
I am open to using a home-care robot even during the research and development stage if it would
benefit me.

Unidentifiable photo and video
Use of personal information for
medical and long-term care

Photos and videos for providing
care and monitoring
Notifying vital signs information
Notifying voice information
Notifying location information

Secondary use of personal
information
Participation in research anddevelopment

Use of information by non-family
members
Secondary use of information byprofessionals
Secondary use of information byresearchers
Proactive participation in
research and development
Use during research anddevelopmentto 7), ‘secondary use of personal information’ (Items 8 and 9), and‘participation in research and development’ (Items 10 and 11). Modelevaluation was based on the chi-square to degrees of freedom ratio (χ2/df) ratio, the comparative fit index (CFI), and the root mean square errorof approximation (RMSEA). Comparison between the models was per­formed using the Akaike’s information criterion (AIC) (Burnham &Anderson, 2004; Finch, 2020). As a rule, around 2–5 is considered areasonable value for the χ2/df ratio. A CFI of 0.95 or higher is preferred,
and a CFI of 0.9 or higher is acceptable. An RMSEA of less than 0.05 isgood and less than 0.08 is acceptable. When we improved the model, we
decided to focus on the χ2/df ratio, the CFI, and the RMSEA criteria andchose a model with a smaller AIC. First, using data from all three
countries, the basic model was evaluated according to the criteria, andthen iteratively improved. For the final model, Pearson’s correlation
coefficient was calculated for the correlation between the factors.
Additionally, Cronbach’s alpha reliability coefficient was calculated toascertain internal consistency between the items within the factors. We
have confirmed that the χ2/df ratio, CFI, and RMSEA of the final model
meet the standards of Japan, Ireland, and Finland. Finally, we summed
the scores of the sub-items for each factor obtained by confirmatory
factor analysis and compared the descriptive statistics of the three
countries using analysis of variance and multiple comparisons by Tukey
method. SPSS version 28 and AMOS version 28 were used for the ana­lyses. The statistical significance was set at 5%.
This study underwent an ethical review by the Chiba University
Graduate School of Nursing Ethics Review Committee (No. 30-19) inJapan and the University College Dublin’s Human Research Ethics
Committee – Humanities (HS-18-81-KODATE) in Ireland, and wasapproved by both bodies. For Finland, two separate ethical approvals
were obtained for this study. Specifically, we obtained the ethicalapproval necessary to conduct the study from the city of Seinäjoki, towhich the participants belonged, and the Joint Municipal Authority of
Ilmajoki and Kurikka (JIK), which provides social and medical services
to both municipalities.

(Suwa et al., 2020b).
The Japanese definition of a robot is ‘an intelligent machine thatcombines sensing, thinking/controlling, and acting technologies’(Robot Policy Study Group, 2006). Home-care robots employ robot
technology aimed at helping users remain independent and reduce theburden on caregivers. Japan has been officially promoting the devel­
opment and implementation of care robots (Ministry of Economy, Trade,and Industry, 2018). We considered potential respondents to not haveseen or dealt with home-care robots before, and therefore, provided
them with the following definition of home-care robots: ‘Home-carerobots come in many forms. The term “home-care robot” used in thissurvey refers to devices and systems that perform functions such as
monitoring of older people and their surroundings and provide support
for older people and their caregivers (including communication thatenables interactive conversation, assistance with activities of dailyliving, and managing medications)’. Additionally, images and illustra­tions of home-care robots were included in the questionnaire form
(Fig. 1).
A total of 1,004 respondents completed the questionnaire, with 528
from Japan, 296 from Ireland, and 180 from Finland. The respondents
comprised not only people with a single attribute but also those withmultiple attributes, such as being both an older adult and family care­giver. Data of respondents with multiple attributes were treated sepa­
rately for each attribute. We excluded 118 answers from Ireland for the
analysis because they did not answer the required number of questions.
As a result, the total number of respondents whose data were analysed
included 664 from Japan, 208 from Ireland, and 260 from Finland, with
1,132 people in total.
2.2. Analysis
We analysed the respondents’ characteristics by country. As there
were some missing responses, the imputation of missing values was
performed based on a linear regression approach (SPSS, Inc., 2021).
Descriptive statistics, such as mean values for each item, were confirmed
before and after the imputation of missing data, and it was confirmedthat imputation did not change the mean value and reduce the standard
deviation.
Thereafter, a confirmatory factor analysis was performed. The re­sponses to the 11 items were divided into four factors based on thefollowing constructs: ‘acquisition of personal information’ (Items 1 and2), ‘use of personal information for medical and long-term care’ (Items 3

3. Results
3.1. Characteristics of the respondents by country
The descriptive statistics of the survey by country are included in the
Appendix. Although we conducted the survey with potential users ofhome-care robots in each country, the proportions of the answers to each3

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Archives of Gerontology and Geriatrics 116 (2024) 105137

Fig. 1. Illustrations of home-care robots provided in the questionnaire form.

question varied widely. Japanese (77.1%) and Irish (70.3%) re­
spondents were more open to using home-care robots than their Finnish(52.8%) counterparts. The proportions of answers that indicated re­spondents’ positive willingness to use home-care robots was the highest
in Japan, followed by Finland and Ireland.

within an acceptable range. Ultimately, 10 items were adopted in the
final model: Items 1 to 3 for ‘acquisition of personal information’, Items
4 to 6 for ‘use of personal information for medical and long-term care’,
Items 8 and 9 for ‘secondary use of personal information’, and Items 10
and 11 for ‘participation in research and development’.
The factor loadings of the final model ranged between 0.63 and 0.92,
and all factor loadings were greater than 0.6, showing a high influenceof the factors upon the model (Fig. 2). Inter-factor correlations ranged
from 0.46 to 0.79. The correlation between the factors ‘use of personal
information for medical and long-term care’ and ‘secondary use of
personal information’ was the highest, and the correlations between thefactors ‘participation in research and development’ and the other factors
were generally low (Table 3). The internal consistency among the items
ranked below each factor was high, ranging from 0.755 to 0.887 for eachfactor, and 0.886 overall (Table 4).
When the final model was fitted to the Japanese data, a χ2/df ratio
was 4.892, a CFI was 0.966, and an RMSEA was 0.077 (90% CI: 0.064,
0.089; p < 0.05), and all factor loadings were greater than 0.6. For
Ireland, a χ2/df ratio was 2.630, a CFI was 0.963, and an RMSEA was
0.089 (90% CI: 0.065, 0.113; p < 0.05), and all factor loadings weregreater than or equal to 0.6. For Finland, a χ2/df ratio was 1.795, a CFI
was 0.985, and an RMSEA was 0.055 (90% CI: 0.030, 0.079; p = 0.332),
and all factor loadings were greater than 0.6, excluding the item ‘Homecare robots should be allowed to take photos or record videos as long as
the individual cannot be identified’. Among the three countries, the fit
was relatively good in Finland and poor in Ireland (Fig. 3).
Additionally, we checked the final model fit for all respondents
without considering multiple attributes. For Japan (n=528), a χ2/df
ratio was 3.613, a CFI was 0.970, and an RMSEA was 0.070 (90% CI:
0.056, 0.085; p < 0.05). For Ireland (n=178), a χ2/df ratio was 2.020, a
CFI was 0.973, and an RMSEA was 0.076 (90% CI: 0.047, 0.104; p =0.065). For Finland (n=180), a χ2/df ratio was 1.656, a CFI was 0.981,
and an RMSEA was 0.061 (90% CI: 0.027, 0.090; p = 0.264).
We further tested our final model by respondents’ attributes—older
adults, family caregivers and care workers. For older adults (n=396), aχ2/df ratio was 2.401, a CFI was 0.984, and an RMSEA was 0.060 (90%
CI: 0.042, 0.078; p < 0.177). For family caregivers (n=292), a χ2/df
ratio was 2.075, a CFI was 0.981, and an RMSEA was 0.061 (90% CI:
0.039, 0.082; p = 0.192). For care worker (n=442), a χ2/df ratio was

3.2. Data preparation
The number of participants for whom missing values were assigned
ranged from 7 to 13 in Japan, 15 to 26 in Ireland, and 13 to 20 inFinland. The number of respondents whose data were used in theanalysis was 664 in Japan, 208 in Ireland, and 260 in Finland; therefore,
the ratios of the number of people were 1.1–2% in Japan, 7.2–12.5% in
Ireland, and 5–7.7% in Finland. Even after imputing the missing values,
the average score of each item remained almost unchanged. Ireland andFinland demonstrated smaller standard deviations (Table 2).
3.3. Model verification
A confirmatory factor analysis resulted in a χ2/df ratio of 8.629, a CFI
of 0.954, an RMSEA of 0.082 (90% confidence interval [90% CI]: 0.074,
0.090; p < 0.05), and an AIC of 383.911, indicating room for
improvement in the RMSEA. Thus, Item 3 (Health care professionals
should be allowed to use photos and videos recorded by home-care ro­bots for clinical care and monitoring) was moved from the factor ‘use of
personal information for medical and long-term care’ to the factor‘aquisition of personal information’. As a result, a χ2/df ratio of 6.032, a
CFI of 0.97, an RMSEA of 0.067 (90% CI: 0.059, 0.075; p < 0.05), and an
AIC of 285.219 were obtained. The χ2/df ratio, the RMSEA and the AIC
were smaller than those of the original model. However, the RMSEA
value still exceeded the goodness threshold of 0.05. Therefore, wecreated a model in which Item 7 (The person whom you can trust [nonfamily member], if agreed by both parties, should be allowed to use
information obtained by a home-care robot) was deleted from the factor‘use of personal information for medical and long-term care’. This
yielded a model with a χ2/df ratio of 5.158, a CFI of 0.98, an RMSEA of
0.061 (90% CI: 0.051, 0.070; p < 0.05), and an AIC of 201.595, all ofwhich demonstrated an improvement from the second model. Although
the RMSEA values remained slightly above the good standard, they were4

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Archives of Gerontology and Geriatrics 116 (2024) 105137

Table 2
Descriptive statistics before and after imputation of missing values.

Identifiable photo and video

Unidentifiable photo and video

Photos and videos for providing care and monitoring

Notifying vital signs information

Notifying voice information

Notifying location information

Use of information by non-family members

Secondary use of information by professionals

Secondary use of information by researchers

Proactive participation in research and development

Use during research and development

Japan
IrelandFinland
Total
Japan
IrelandFinland
Total
Japan
IrelandFinland
Total
Japan
IrelandFinland
Total
Japan
IrelandFinland
Total
Japan
IrelandFinland
Total
Japan
IrelandFinland
Total
Japan
IrelandFinland
Total
Japan
IrelandFinland
Total
Japan
IrelandFinland
Total
Japan
IrelandFinland
Total

Before imputation
n
mean

SD

After imputation
n
mean

SD

654
189
247
1090
654
190
245
1089
656
192
244
1092
657
193
243
1093
657
188
245
1090
651
188
240
1079
657
188
245
1090
657
185
243
1085
656
182
243
1081
655
186
246
1087
656
187
246
1089

1.02
1.08
1.05
1.05
0.96
1.02
0.99
0.98
0.93
1.02
1.01
0.97
0.74
0.87
0.88
0.8
0.91
0.9
1.02
0.94
0.89
0.95
0.99
0.92
0.95
0.95
1.01
0.96
0.94
1.02
1.02
0.97
0.93
1.05
1.04
0.98
0.95
0.97
1.02
0.97
0.98
1.05
1.11
1.03

664
208
260
1132
664
208
260
1132
664
208
260
1132
664
208
260
1132
664
208
260
1132
664
208
260
1132
664
208
260
1132
664
208
260
1132
664
208
260
1132
664
208
260
1132
664
208
260
1132

1.02
1.03
1.03
1.03
0.96
0.98
0.96
0.96
0.93
0.98
0.98
0.95
0.73
0.84
0.85
0.78
0.91
0.85
0.99
0.92
0.88
0.9
0.95
0.9
0.94
0.91
0.98
0.94
0.93
0.96
0.98
0.95
0.92
0.99
1.01
0.96
0.94
0.92
0.99
0.95
0.98
1.00
1.08
1.01

2.67
2.32
2.36
2.54
2.66
2.43
2.54
2.59
2.78
2.56
2.78
2.74
3.45
3.25
3.45
3.41
3.04
2.96
2.85
2.98
3.08
3.03
3.14
3.08
2.81
2.94
2.81
2.83
2.79
2.62
2.97
2.80
2.80
2.52
2.81
2.76
2.92
3.13
2.95
2.96
2.84
3.04
2.72
2.84

2.67
2.34
2.37
2.54
2.66
2.44
2.54
2.59
2.78
2.57
2.78
2.74
3.45
3.26
3.45
3.41
3.04
2.96
2.86
2.98
3.08
3.03
3.14
3.08
2.81
2.93
2.81
2.83
2.79
2.64
2.96
2.80
2.80
2.55
2.81
2.76
2.92
3.12
2.95
2.96
2.84
3.02
2.72
2.84

Notes: SD = standard deviation

2.539, a CFI was 0.975, and an RMSEA was 0.059 (90% CI: 0.042, 0.076;
p = 0.174).

participation in development research. As the country model demon­
strated the characteristics of each country, it is worth discussing the datafrom the three countries in turn.

3.4. Comparison of the total score between the three countries

4.1. Finland

When the total scores for each factor among the three countries werecompared, significant differences were found among the three factors
other than the factor ‘use of personal information for medical and longterm care’. In Japan, the score for the factor ‘acquisition of personal
information’ was higher than in the other two countries. In Ireland,
compared to the scores of the other two countries, the score for thefactor ‘secondary use of personal information’ was lower and the score
for the factor ‘participation in research and development’ was higher (p< 0.05) (Fig. 4).

The data from Finland stand out in that the item ‘identifiable photoand video’ in the factor ‘acquisition of personal information’ was rated
higher at 0.74 than in the other two countries. Additionally, the item
‘notifing vital signs information’ in the factor loadings for ‘use of per­sonal information for medical and long-term care’ was high at 0.81.
These results reflect the current situation of Finland. The use of homecare is already common, and trusted medical and nursing staff use dataof older adults through ICT (i.e. Kanta). Therefore, it is possible that
Finns are less concerned about sharing their personal information withcare robots so that older adults can receive better care (Haverinen et al.,2022; Kujala et al., 2022). Since 2013, when Kanta was introduced,
digital health strategies have been implemented by leveraging thefoundations of public medical and nursing care provided in local com­munities. Therefore, it is possible that the well-developed infrastructure
and trust in the system design are positively promoting the use of
medical information (e.g. ‘eHealth strategy: the Finnish Nurses Associ­ation 2015–2020’, etc.) (Finnish Nurses Association’s digital social and

4. Discussion
This study, which was conducted in Ireland, Finland, and Japan,
found that the impact of ethical issues regarding the use of care robots inhome care can be explained by the same model. Two of the four factors
included in the confirmatory factor analysis were related to personal
information, and the other two were related to research and develop­
ment; namely, the secondary use of personal information and5

H. Ide et al.

Archives of Gerontology and Geriatrics 116 (2024) 105137

Fig. 2. The ethical perception model regarding the use of care robots in home care (Final model).
Notes: df, degrees of freedom; CFI, comparative fit index; RMSEA, root mean square error of approximation; AIC, Akaike’s information criterion

health services strategy, 2021; Finnish Nurses Association ehealthexpert working group, 2015).

Table 3
Inter-factor correlations among the factors in the final model.
Factor1
Factor 1: acquisition of personal information
Factor 2: use of personal information for medical
and long-term care
Factor 3: secondary use of personal information
Factor 4: participation in research anddevelopment

Factor2

Factor3

4.2. JapanThe average scores for the three items related to ‘acquisition of
personal information’ were 8.1 in Japan, 7.7 in Finland, and 7.4 inIreland. The score in Japan was significantly higher than that in theother two countries. In the Japanese model, the factor loading of theitem ‘photos and videos for providing care and monitoring’ related to
the factor ‘acquisition of personal information’ was 0.74, which was
lower than that for Ireland (0.90) and Finland (0.91). Based on thesefindings, we concluded that although the Japanese are tolerant of theacquisition of personal information, it is difficult for them to imagine
specific applications for such data collection, such as in providing careand watching over people in a super-aged society. A data collection
system called the Long-term care Information system For Evidence(LIFE) was introduced within the long-term care insurance system in
2021, followed by a movement to build evidence for care (Ministry of
Health, Labour and Welfare, 2021). Considering this point, the gov­
ernment should address the fact that secondary use of personal

0.76
0.69
0.46

0.79
0.52

0.51

Table 4
Internal consistency within the factors and the final model.
Cronbach αOverallacquisition of personal information
use of personal information for medical and long-term caresecondary use of personal information
participation in research and development

0.886
0.755
0.832
0.887
0.844

6

H. Ide et al.

Archives of Gerontology and Geriatrics 116 (2024) 105137

Fig. 3. Application of the final model for each country.
Notes: df, degrees of freedom; CFI, comparative fit index; RMSEA, root mean square error of approximation; AIC, Akaike’s information criterion

Fig. 4. Results of multiple comparisons of total scores of each factor among Japan, Ireland, and Finland.

information will contribute to improving care quality. To this end, it isalso necessary to develop technologies that protect personal informationand privacy and to provide users with peace of mind and ease of use. If
this progress makes such technology more familiar to people, it ispossible that social implementation of home-care robots will become
more prevalent.

personal information is evident. This is supported by the fact that theinter-factor correlation between the factors ‘secondary use of personal
information’ and ‘participation in research and development’ is low.
Although secondary use of personal information is necessary to develop
care robots, there is an apparent reluctance among the Irish respondents.
This can be attributed to the fact that the readiness for digitalisation and
ICT infrastructure is lacking in Ireland, compared to the other two
countries (Lolich et al., 2022). To address this issue, one positive mea­
sure may be to explain the significance of secondary use of information
as a response to the Irish people’s willingness to contribute to researchand development.

4.3. Ireland
On items related to the factor ‘participation in research and devel­
opment’, the average scores of Irish respondents were significantly
higher than those of the other two countries. A positive attitude wasshown towards the development of care robots that contribute to publicwelfare. In contrast, the average score of ‘secondary use of personal
information’ was significantly lower in Ireland than those of the othertwo countries. Thus, a cautious attitude towards secondary use of

4.4. Model improvement
In this study, we improved the model based on the χ2/df ratio, the
CFI, and the RMSEA and selected a model with a smaller AIC. Although7

H. Ide et al.

Archives of Gerontology and Geriatrics 116 (2024) 105137

we assumed the four factor model, we improved the model incremen­tally by balancing these indices. As a result, Item 3 (Health care pro­fessionals should be allowed to use photos and videos recorded by homecare robots for clinical care and monitoring) was moved from the factor‘use of personal information for medical and long-term care’ to ‘acqui­sition of personal information’. Because the other items under ‘acqui­sition of personal information’ were related to photo and video
evidence, even though the users were professional, it was more appro­
priate to group them as a type of information. Moreover, image infor­
mation may be uniquely important to people these days. Finally, Item 7(The person whom you can trust [non-family member], if agreed by bothparties, should be allowed to use information obtained by a home-carerobot) was omitted from the final model. We were aware that the ab­solute covariate value of this item was smaller than those of other itemsin the previous step; therefore, we considered it appropriate to remove
the unnecessary items from the improvement process.

advocated by the IEEE.
Suggestions can be drawn from this study regarding the relationshipbetween developers and users for future development in other fields. A
technique called agile development is generally used for software devel­
opment. Based on the Agile Software Development Declaration (Mani­
festo for Agile Software Development, 2001), this technique involves
developers and users co-creating and repeating the development process.
Unlike traditional waterfall development, which is a one-way develop­
ment based on specifications, agile development involves users using thetechnology and providing feedback. Until now, the mainstream method
for the development of devices such as care robots has been based onwaterfall development. However, it is possible to introduce agile devel­
opment from an early stage of the development of care robots and focus onplanning, improvement, and evaluation while receiving feedback frompotential users. Involving users in the development process is also sig­
nificant from an efficient development perspective.
It is worth noting that developers of care robots should not interpretthe models developed in this research to their own advantage. For
example, regarding privacy-protected images, it is extremely simplistic
to interpret that social implementation of home-care robots will be
realised if everyone agrees to the collection of personal data or if users
are convinced to be enthusiastic about research and development. Sta­tistical analysis to develop the universal model shows the average of
people’s tendency of the use of care robots; additionally, we also
recognise that how decisions concerning the use of care robots are madevaries from person to person in practice, depending on their circum­
stances and family situation, and that nursing care should prioritise therelational aspects and interactions between caregivers and care re­cipients (Lolich et al., 2022). Accordingly, we must prioritise thedevelopment and social implementation of care robots (Pleschberger,
2007; ROSE Consortium, 2017). Sharkey and Sharkey (2012) raised
ethical concerns about the use of care robots for older adults, such as thepotential reduction in the amount of human contact and loss of privacy.
Hence, they argued for the need for guidelines regarding care robot use.
Therefore, to identify the differences in attitudes between users anddevelopers is necessary, and researchers and developers of care robots
should determine how to realise or implement the RRI while considering
the users’ intention and willingness. In this study, our purpose waslimited to elucidating the ethical issues related to users’ willingness touse home-care robots. In future studies, the broader elements related tothe intention to use home-care robots should be surveyed. Additionally,
continuity across surveys should be considered to ascertain any possible
changes to the results.

4.5. Why a universal model works
Although the extent of the impact on the ethical issues related to theuse of care robots is greatly influenced by each country’s culture, rate ofageing, health care policies, and status of technology development andlevel of social implementation, this study showed that a universal model
can explain this impact. The model can be explained due to threepossible reasons. First, unlike previous surveys of care robots andnursing care, this survey focused on people who are familiar with care,such as older adults receiving care, family caregivers, and care workers.
It is possible that more specific intentions towards care were expressed
because the participants had shared their experience of care. Second,
these three countries have implemented national measures against de­mentia (Department of Health, 2014; Ministry of Health, Labour andWelfare, 2019; Ministry of Social Affairs and Health, 2013). Therefore,
the participants may have been able to answer the survey by specificallyenvisioning the time when care robots are implemented. Third, tech­
nologies such as smartphones and smart speakers are commonly used indaily life, and large amounts of personal information are collected today
in countries worldwide (Insider Intelligence, 2022). It is possible thatsuch changes in the social environment are reducing the differences
between countries regarding the ethical issues related to the use of carerobots.
At the same time, it must be noted that satisfactory values for fit
indices were not obtained in some cases. The reason for this is not only
due to an insufficient number of respondents but also because of animbalance between the numbers of respondents from the three coun­tries, which may have caused statistical variance, thereby leading to a
relatively low CFI, and a relatively high and statistically non-significant
RMSEA. Although potential users of care robots were analysed bycountry, it is possible that the differences in their attributes may haveinfluenced the model’s fit. Therefore, it is necessary to collect a morebalanced and larger sample for validation in each country.

4.7. Limitations
Data collected from three countries between November 2018 andFebruary 2019 were analysed, and it was determined that the use of carerobots was not common in the surveyed countries. Therefore, it is pre­dicted that the willingness to use care robots may change due to the
influence of social conditions. For example, Turja et al. (2022) con­ducted a longitudinal survey of Finnish care workers and demonstrated
that their expectations from care robots changed positively in 2020
compared to 2016. They attributed these changes to social norms to­wards care robots, the threat of technological unemployment, and stresscaused by COVID-19.
Furthermore, the number of responses received from each country
was small and may not be sufficiently representative of each country’s
population. We could not check our final model for responses to each
attribute in all countries. Additionally, as discussed previously, the fit
indices used in this study have not always demonstrated reasonable
results. A possible reason is the insufficient number of respondents.
Although we were able to build a unified model for the three countries
targeted in this study, whether this model can be applied to othercountries, such as developing countries in which the burden of nursing
care will increase in the future, remains unknown.

4.6. Users’ participation in research and development
Ethically Aligned Design, formulated by the IEEE, demands ethicalbehaviour based on principles derived by researchers and developers ofautonomous and intelligent systems. In this study, we included ‘partic­
ipation in research and development’ by older adult users, family
caregivers, and care workers as one of the factors. The inter-factor
correlation between this factor and others was lower than that be­
tween the remaining factors. Therefore, ‘participation in research anddevelopment’ can be considered a relatively independent factor. Thus,
apart from the appropriate collection and utilisation of personal infor­
mation by care robots, providing opportunities for users to participate in
the development stage of care robots and to make decisions about theiruse and social implementation can be considered important. The resultsof this study empirically support the concept of Ethically Aligned Design8H. Ide et al.

Archives of Gerontology and Geriatrics 116 (2024) 105137

Appendix Table
Descriptive statistics of the responses by country.

Sex

FemaleMale
Not specified
No answer
39 or younger
40s
50s
60s
70s
80 or older
No answer
Older adults
Family caregivers
Care workers
No answer
Yes
No
No answer
Yes
Yes, to some extent
Not really
Never
No answer
Yes
Yes, to some extent
Not really
Never
No answer

Age group

Attribute (Multiple answers)

I am open to using home-care robots
Would you like to use a home-care robot at home when providing care to your family?

Would you like to use a home-care robot at home when receiving care yourself?

5. Conclusions

Japan
n=528
n
%

Irelandn=296
n%Finland
n=180
n
%

Total
n=1,004
n
%

405
122
0
1
57
103
133
102
78
55
0
176
169
319
0
407
117
4
170
196
117
39
6
208
169
107
41
3

122
38
7
129
16
21
15
24
49
41
130
128
90
136
0
208
70
18
42
59
35
15
145
52
61
36
14
133

121
54
1
4
26
19
16
34
42
37
6
107
85
67
1
95
60
25
22
67
43
37
11
23
67
41
39
10

648
214
8
134
99
143
164
160
169
133
136
411
344
522
1
710
247
47
234
322
195
91
162
283
297
184
94
146

76.7
23.1
0.0
0.2
10.8
19.5
25.2
19.3
14.8
10.4
0.0
33.3
32.0
60.4
0.0
77.1
22.2
0.8
32.2
37.1
22.2
7.4
1.1
39.4
32.0
20.3
7.8
0.6

41.2
12.8
2.4
43.6
5.4
7.1
5.1
8.1
16.6
13.9
43.9
43.2
30.4
45.9
0.0
70.3
23.6
6.1
14.2
19.9
11.8
5.1
49.0
17.6
20.6
12.2
4.7
44.9

67.2
30.0
0.6
2.2
14.4
10.6
8.9
18.9
23.3
20.6
3.3
59.4
47.2
37.2
0.6
52.8
33.3
13.9
12.2
37.2
23.9
20.6
6.1
12.8
37.2
22.8
21.7
5.6

64.5
21.3
0.8
13.3
9.9
14.2
16.3
15.9
16.8
13.2
13.5
40.9
34.3
52.0
0.1
70.7
24.6
4.7
23.3
32.1
19.4
9.1
16.1
28.2
29.6
18.3
9.4
14.5

Hallila: Methodology, Investigation. Marika Toivonen: Methodology,
Investigation. Camilla Bergman-Kärpijoki: Investigation. Erika
Takahashi: Investigation. Wenwei Yu: Validation, Supervision.

In this study, we used data obtained from surveys conducted inJapan, Ireland, and Finland—countries with different geographies,
cultures, demographics, and systems. The survey respondents comprised
those who receive or provide care (i.e. potential users of care robots),
namely, older adults, family caregivers, and home-care staff. This study
showed that a model developed through a systematic analysis of the data
can universally explain the impact of users’ ethical perceptions related
to their willingness to use care robots. As a policy suggestion, it is firstnecessary in the future to conduct a longitudinal survey, grasp thechanges in ethical awareness, and work on the development and social
implementation of care robots that truly contribute to human wellbeing.

Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgements
The authors would like to thank all the supporting organisations andrespondents of the study.
AppendixFunding

Appendix Table

This work was supported by the Pfizer Health Research Foundation,
International Joint Research, and IAAR Next-generation Research ofSocial Value Creation Research Support Program, Chiba University,
Japan. The funding sources did not have any involvement in study
design; in the collection, analysis, and interpretation of data; in thewriting of the report; and in the decision to submit the article forpublication.

References
Abdi, J., Al-Hindawi, A., Ng, T., & Vizcaychipi, M. P. (2018). Scoping review on the useof socially assistive robot technology in elderly care. BMJ open, 8(2), 1–20. https://doi.org/10.1136/bmjopen-2017-018815
Alaiad, A., & Zhou, L. (2014). The determinants of home healthcare robots adoption: An
empirical investigation. International Journal of Medical Informatics, 83(11), 825–840.
https://doi.org/10.1016/j.ijmedinf.2014.07.003
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and
BIC in model selection. Sociological Methods and Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644
Casey, D., Barrett, E., Kovacic, T., Sancarlo, D., Ricciardi, F., Murphy, K., et al. (2020).
The perceptions of people with dementia and key stakeholders regarding the use andimpact of the social robot MARIO. International Journal of Environmental Research andPublic Health, 17(22), 8621. https://doi.org/10.3390/ijerph17228621
Coco, K., Kangasniemi, M., & Rantanen, T. (2018). Care personnel’s attitudes and fears
toward care robots in elderly care: A Comparison of data from the care personnel in
Finland and Japan. Journal of Nursing Scholarship, 50(6), 634–644. https://doi.org/
10.1111/jnu.12435
Coeckelbergh, M. (2015). Artificial agents, good care, and modernity. Theoretical
Medicine and Bioethics, 36(4), 265–277. https://doi.org/10.1007/s11017-015-9331yCRediT authorship contribution statement
Hiroo Ide: Data curation, Formal analysis, Writing – original draft.
Sayuri Suwa: Conceptualization, Methodology, Writing – original draft,
Project administration, Funding acquisition. Yumi Akuta: Formal
analysis, Writing – original draft. Naonori Kodate: Conceptualization,
Methodology, Investigation, Writing – original draft. Mayuko Tsuji­
mura: Methodology, Investigation, Data curation. Mina Ishimaru:
Methodology, Writing – review & editing. Atsuko Shimamura: Meth­odology, Writing – review & editing. Helli Kitinoja: Conceptualization,
Methodology. Sarah Donnelly: Methodology, Investigation. Jaakko
9Archives of Gerontology and Geriatrics 116 (2024) 105137

H. Ide et al.

Ministry of Health, Labour and Welfare. (2021). The long-term care information system forevidence (LIFE). https://www.mhlw.go.jp/stf/shingi2/0000198094_00037.html.
Ministry of Health, Labour, and Welfare. (2017). Robotto gijutsu no kaigoriyou niokeru
jyutenbunya no kaitei [Revision of. priority fields in long-term care]. https://www.mhlw.
go.jp/stf/houdou/0000180168.html.
Ministry of Health, Labour, and Welfare. (2021). Kaigo jinzai kakuho ni muketa torikumi[Efforts to secure nursing care personnel]. https://www.mhlw.go.jp/stf/newpage_0
2977.html.
Ministry of Social Affairs and Health. (2013). National Memory Programme 2012-2020.
Creating a “memory-friendly”. https://nordicwelfare.org/wpcontent/uploads/2018/02/Reports_2013_9_Memory_verkko.pdf.
Moyle, W., Jones, C., & Sung, B. (2020). Telepresence robots: Encouraging interactive
communication between family carers and people with dementia. Australasian
Journal on Ageing, 39(1), e127–e133. https://doi.org/10.1111/ajag.12713
Nakada, M., Kavathatzopoulos, I., & Asai, R. (2021). Robots and AI artifacts in plural
perspective(s) of Japan and the West: The cultural-ethical traditions behind people’sviews on robots and AI artifacts in the information era. Review of Socionetwork
Strategies, 15(1), 143–168. https://doi.org/10.1007/s12626-021-00067-8
Palmerini, E., Azzarri, F., Battaglia, F., Bertolini, A., Carnevale, A., Carpaneto, J., et al.
(2014). RoboLaw deliverable D6.2 Guidelines on regulating robotics. Regulating
emerging robotic technologies in europe, robotics facing law and ethics. Robolaw. htt
p://www.robolaw.eu/.
Pirni, A., Balistreri, M., Capasso, M., Umbrello, S., & Merenda, F. (2021). Robot care
ethics between autonomy and vulnerability: Coupling principles and practices inautonomous systems for care. Frontiers in Robotics and AI, 8, 184. https://doi.org/
10.3389/frobt.2021.654298
Pleschberger, S. (2007). Dignity and the challenge of dying in nursing homes: The
residents’ view. Age and Ageing, 36(2), 197–202. https://doi.org/10.1093/ageing/
afl152
Qiu, S., An, P., Kang, K., Hu, J., Han, T., & Rauterberg, M. (2021). Investigating sociallyassistive systems from system design and evaluation: A systematic review. Universal
Access in the Information Society, 1–25. https://doi.org/10.1007/s10209-021-00852w.
Rantanen, T., Lehto, P., Vuorinen, P., & Coco, K. (2018). The adoption of care robots inhome care- A survey on the attitudes of Finnish home care personnel. Journal of
Clinical Nursing, 27(9–10), 1846–1859. https://doi.org/10.1111/jocn.14355
Robot Policy Study Group. (2006). Robotto seisaku kenkyuukai houkokusho [Report by the
robot policy committee].
ROSE Consortium. (2017). Robotics in care services: A finnish roadmap. http://rosepro
ject.aalto.fi/images/publications/Roadmap-final02062017.pdf.
Schreiweis, B., Pobiruchin, M., Strotbaum, V., Suleder, J., Wiesner, M., & Bergh, B.
(2019). Barriers and facilitators to the implementation of ehealth services:
Systematic literature analysis. Journal of Medical Internet Research, 21(11), e14197.
https://doi.org/10.2196/14197
Sharkey, A., & Sharkey, N. (2012a). Granny and the robots: Ethical issues in robot carefor the elderly. Ethics and Information Technology, 14(1), 27–40. https://doi.org/
10.1007/s10676-010-9234-6
Sharkey, N., & Sharkey, A. (2012b). The eldercare factory. Gerontology, 58(3), 282–288.
https://doi.org/10.1159/000329483
Stahl, B. C., & Coeckelbergh, M. (2016). Ethics of healthcare robotics: Towards
responsible research and innovation. Robotics and Autonomous Systems, 86, 152–161.
https://doi.org/10.1016/j.robot.2016.08.018. 2016.08.018
Suwa, S., Tsujimura, M., Ide, H., Kodate, N., Ishimaru, M., Shimamura, A., et al. (2020a).
Home-care professionals’ ethical perceptions of the development and use of homecare robots for older adults in Japan. International Journal of Human-Computer
Interaction, 36(14), 1295–1303. https://doi.org/10.1080/10447318.2020.1736809
Suwa, S., Tsujimura, M., Kodate, N., Donnelly, S., Kitinoja, H., Hallila, J., et al. (2020b).
Exploring perceptions toward home-care robots for older people in Finland, Ireland,
and Japan: A comparative questionnaire study. Archives of Gerontology and Geriatrics,
91, Article 104178. https://doi.org/10.1016/j.archger.2020.104178
The Danish Council of Ethics. (2010). Recommendations concerning social robots. https://www.etiskraad.dk/.
Turja, T., Aaltonen, I., Taipale, S., & Oksanen, A. (2020). Robot acceptance model forcare (RAM-care): A principled approach to the intention to use care robots.
Information and Management, 57(5), 1–9. https://doi.org/10.1016/j.im.2019.103220
Turja, T., Taipale, S., Niemelä, M., & Oinas, T. (2022). Positive turn in elder-care
workers’ views toward telecare robots. International Journal of Social Robotics, 14(4),931–944. https://doi.org/10.1007/s12369-021-00841-2
United Nations Population Fund, & HelpAge International. (2012). Ageing in the twentyfirst century: A celebration and a challenge. United Nations. https://www.unfpa.
org/publications/ageing-twenty-first-centurY.
Vandemeulebroucke, T., Dierckx de Casterlé, B., & Gastmans, C. (2018). The use of carerobots in aged care: A systematic review of argument-based ethics literature. Archivesof Gerontology and Geriatrics, 74, 15–25. https://doi.org/10.1016/j.
archger.2017.08.014
Wang, R. H., Sudhama, A., Begum, M., Huq, R., & Mihailidis, A. (2017). Robots to assist
daily activities: Views of older adults with Alzheimer’s disease and their caregivers.
International Psychogeriatrics, 29(1), 67–79. https://doi.org/10.1017/
S1041610216001435

Coghlan, S. (2021). Robots and the possibility of humanistic care. International Journal ofSocial Robotics, 26, 1–14. https://doi.org/10.1007/s12369-021-00804-7
Department of Economic and Social Affairs. (2019). World population ageing 2019,
Highlights. United Nations.
Department of Health. (2014). The irish national dementia strategy. Department of
Health. https://assets.gov.ie/10870/3276adf5273f4a9aa67e7f3a970d9cb1.pdf.
European Commission & Department of Economic and Financial Affairs. (2012). Ageing
report: Europe needs to prepare for growing older. https://ec.europa.eu/economy_fina
nce/articles/structural_reforms/2012-05-15_ageing_report_en.htm.
Fares, N., Sherratt, R. S., & Elhajj, I. H. (2021). Directing and orienting ICT healthcare
solutions to address the needs of the aging population. Healthcare, 9(2), 147. https://doi.org/10.3390/healthcare9020147
Fazio, S., Zimmerman, S., Doyle, P. J., Shubeck, E., Carpenter, M., Coram, P., et al.
(2020). What is really needed to provide effective, person-centered care forbehavioral expressions of dementia? Guidance from the Alzheimer’s Association
dementia care provider roundtable. Journal of the American Medical Directors
Association, 21(11), 1582–1586. https://doi.org/10.1016/j.jamda.2020.05.017
Finch, W. H. (2020). Using fit statistic differences to determine the optimal number offactors to retain in an exploratory factor analysis. Educational and Psychological
Measurement, 80(2), 217–241. https://doi.org/10.1177/0013164419865769
Finnish Nurses Association ehealth expert working group. (2015). eHealth strategy of thefinnish nurses association 2015–2020. https://sairaanhoitajat.fi/wp-content/uploads/2020/01/eHealth_RAPORTTI-_ENGLANTI.pdf.
Finnish Nurses association’s digital social and health services strategy. (2021). https://sairaanhoitajat.fi/wp-content/uploads/2021/06/E-health-2021_.pdf.
Granja, C., Janssen, W., & Johansen, M. A. (2018). Factors determining the success andfailure of ehealth interventions: Systematic review of the literature. Journal of
Medical Internet Research, 20(5), e10235. https://doi.org/10.2196/10235.
PMID29716883.
Haverinen, J., Keränen, N., Tuovinen, T., Ruotanen, R., & Reponen, J. (2022, August 12).
National development and regional differences in ehealth maturity in Finnish publichealth care: Survey study. JMIR Medical Informatics, 10(8), e35612. https://doi.org/
10.2196/35612
Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010). Assessing acceptance of assistivesocial agent technology by older adults: The Almere model. International Journal ofSocial Robotics, 2(4), 361–375. https://doi.org/10.1007/s12369-010-0068-5
Horizon 2020. (2013). European commission website. http://wayback.archive-it.org/12
090/20220124075444/https://ec.europa.eu/programmes/horizon2020/en/.
Ide, H., Kodate, N., Suwa, S., Tsujimura, M., Shimamura, A., Ishimaru, M., et al. (2021).
The ageing “care crisis” in Japan: Is there a role for robotics-based solutions?
International Journal of Care and Caring, 547, 165–171.
IEEE Standards Association. (2018). ETHICALLY aligned design version 2. https://standards.ieee.org/news/2017/ead_v2/.
Inoue, K., Wada, K., & Shibata, T. (2021). Exploring the applicability of the robotic seal
PARO to support caring for older persons with dementia within the home context.
Palliative Care and Social Practice, 1–10. https://doi.org/10.1177/
26323524211030285
Insider Intelligence. (2022). Voice Assistants in 2022: Usage, growth, and future of the AI
voice assistant market. A fact-finding survey on the spread of smartphones and smart
speakers?. Ministry of internal affairs and communications white paper on informationand communications. https://www.insiderintelligence.com/insights/voice-assistants/.
Ismail, A., & Lokman, F. (2017). Redzuan: Piritual design elements as emotional therapy
for Malay Muslim elderly with Alzheimer’s disease using therapeutic robot. Journalof Theoretical and Applied Information Technology, 95, 3949–3962.
Japan Economic Research Institute. (2020). Overseas research related to the development
and standardization of robotic care equipment. https://robotcare.jp/data/outcomes/20
20/09.pdf.
Jordan, J. M. (2016). Robots. The MIT Press.
Koutentakis, D., Pilozzi, A., & Huang, X. (2020). Designing socially assistive robots for
Alzheimer’s disease and related dementia patients and their caregivers: Where weare and where we are headed. Healthcare, 8(2), . https://doi.org/10.3390/
healthcare8020073.
Kujala, S., Hörhammer, I., Väyrynen, A., Holmroos, M., Nättiaho-Rönnholm, M.,
Hägglund, M., et al. (2022). Patients’ experiences of web-based access to electronic
health records in Finland: Cross-sectional survey. Journal of Medical Internet
Research, 24(6), e37438. https://doi.org/10.2196/37438
Lolich, L., Pirhonen, J., Turja, T., & Timonen, V. (2022). Technology in the home care ofolder people: views from Finland and Ireland. Journal of Cross-Cultural Gerontology,
37(2), 181–200. https://doi.org/10.1007/s10823-022-09449-z
Manifesto for Agile Software Development. (2001). https://agilemanifesto.org/iso/ja/manifesto.html.
Ministry of Economy, Trade, and Industry (METI). (2018). Current status and future
development of robots in the care and medical fields. http://www.technoaids.or.jp/
robot/file30/forum2018_02.pdf.
Ministry of Health, Labour and Welfare. (2019). Dementia policy promotion outline.
ministry of health. Labour and Welfare. https://www.mhlw.go.jp/content/0005
22832.pdf.

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