Decision-makers’ attitudes toward the use of care robots in welfare services

Traditionally, robots have been used in industry to assist the human workforce by performing repetitive and dull tasks (Sharkey 2008). For example, robots function well in manufacturing lines where the same task must be performed repeatedly throughout the day (Lopes et al. 2017). Currently, a wide variety of robots are used in addition to those used in industry. Robots can be divided into two categories—industrial robots and service robots [International Federation of Robotics (IFR) 2012]. Robots have also become available for private consumers, for example, robot vacuum cleaners (Forlizzi and DiSalvo 2006).

While the purposes of industrial robots are evident, the uses of service robots, to which category care robots belong (see Okamura et al. 2010), are less clearly defined. The International Organization for Standardization (ISO) defines a service robot as a robot “that performs useful tasks for humans or equipment excluding industrial automation applications” (ISO 8373 2012). Service robots can be defined as those used by service providers or individual consumers to serve people by performing various tasks, for example, vacuum cleaning. Wu et al. (2012) divide service robots into three categories: (1) monitoring robots (to help observe behavior and health), (2) assistive robots (to provide support to the individual and/or their caregiver in daily tasks), and (3) socially assistive robots (to provide companionship). Examples of these robots are telepresence robots (e.g., Double and Giraff), medicine-dispensing robots (e.g., Evondos), robotic walking aids (e.g., Lea), robotic spoons (e.g., Gyenno), exoskeletons for rehabilitation of the patient (e.g., Indego) or for supporting the care worker (e.g., Laevo, Auxivo), transportation robots for delivering meals and medicine (e.g., TUG), and social robots for therapy, entertainment, and communication (e.g., Paro, JustoCat, Zora, Pepper) (Niemelä et al. 2021; Pirhonen et al. 2019). This development from industrial robots to service robots has brought robots closer to people, with the development of robots that are able to assist people in a variety of tasks, such as indoor transportation (Severinson-Eklund et al. 2003; Summerfield et al. 2011).

Attitudes toward robots have been investigated in many service contexts, for example, at home (de Graaf et al. 2019), in elder-care facilities (Melkas et al. 2020; Tuisku et al. 2019), at school (Fridin and Belokopytov 2014), and for shopping (Doering et al. 2015). These studies have often concentrated on the acceptance of one particular robot. The question of acceptance becomes more abstract when dealing with a general level of acceptance—when the study participants’ perceptions cannot be tied to a known robot. Interestingly, the Europe-wide Eurobarometer surveys (European Commission 2012, 2015) have shown that attitudes toward robots in general are favorable, with 70% of European respondents expressing a positive attitude toward robots. Finland was found to be the fourth most positive country in Europe in relation to the use of robots, with an 85% acceptance rate; only Sweden, Denmark, and the Netherlands reported higher acceptance rates. Turja and Oksanen (2019) further investigated Eurobarometer data further from the perspective of robot acceptance in the workplace in general. They found that acceptance of robots as assistants at work depends on the individual’s experience, which means that acceptance is more related to the individual’s own principles than to the European country in which they live. A recent study by Latikka et al. (2021) concerning working life noted that the participants’ attitudes were more positive toward robots as pieces of equipment that are semi-autonomous than toward robots that functioned as autonomous co-workers in the workplace context. This is because the latter may evoke connotations of humans being replaced with robots at work, whereas equipment is more likely to support or complement human work.

The development of the appearance and gestures of robots has mainly concentrated on how robots can be made to act similarly to humans (e.g., Ghazali et al. 2018) or to look like humans (e.g., Walters et al. 2008), although a robot’s appearance depends on the particular tasks it was designed to perform. While these are important aspects of human–robot interaction (HRI), it is equally important to understand how people utilize robots in their daily lives and what new possibilities are arising due to interaction with robots. Human–robot interaction is thus not only about the adaptation to and adoption of robots, but also about developing a richer sociotechnical imagination to enable designers to create robots that better fit into particular social contexts (Šabanović and Chang 2016; see also Nickelsen 2018). The departure point for this study is the need for a broad societal understanding of human–robot interaction.

Studies focusing on participants’ experiences and attitudes toward care robots available for utilization in welfare services (Bedaf et al. 2015) include, for example, the therapy seal robot Paro (Wada and Shibata 2007), the socially assistive humanoid robot Zora (Melkas et al. 2020; Tuisku et al. 2019), telepresence robots (Cesta et al. 2016; Koceski and Koceska 2016), and exoskeletons (Sale et al. 2012). In addition to these physical robots, there exist studies of solutions such as voice assistants and chatbots that can be considered to have a social and communicative function in care (e.g., Lee et al. 2020; Skjuve et al. 2021). Acceptance and ethical issues related to socially interactive and therapeutic robots have been studied by Jenkins and Draper (2015) and by McGlynn et al. (2017). However, different types of robots may trigger different responses in terms of acceptance, depending on the target group (Robinson et al. 2013).

Concerning public opinion about care robots, while the Eurobarometer data showed favorable attitudes toward robots in general, using robots specifically in health care was seen negatively. Only 22% of the general public in Europe accepted the use of robots in health care, with an 18% acceptance rate in Finland. Literature reviews have also been conducted on the acceptance of robots in various occupational fields (e.g., Savela et al. 2017), and while the results show that attitudes toward robots are positive in many fields of work (e.g., education, business, and industry), in the case of care work, attitudes are more negative. These results are mainly explained by the fact that people consider it inhumane for a robot to take care of a person.

The general public’s attitudes toward the use of robots in the care of older people were examined in a media analysis that showed that opinions were mostly negative (Tuisku et al. 2019). It was found that the general public’s main concern was that robots would replace human-to-human contact and care. However, based on these results, it was obvious that the general public did not have sufficient knowledge about the use of robots in welfare services. For the general public, media coverage may provide the only information they receive regarding the use of care robots.

Broadbent et al. (2010) investigated general perceptions relating to the use of robots in welfare services. Interestingly, the participants saw many benefits and applications for health-care robots, including performing simple medical procedures and providing physical assistance. However, some participants were concerned about reliability, safety, and the loss of personal care. The researchers concluded that perceptions about the use of robots were influenced by the participants’ prior exposure to robots in literature or in entertainment media. Previous research has shown that exposure to robots positively affected attitudes toward them (Broadbent et al. 2010; Melkas et al. 2020), although other studies have obtained contradictory results (Kristoffersson et al. 2011).

In addition to focusing on public opinion, prior research on the acceptance of robots in welfare services has focused on the attitudes of actual users, that is, older people and care workers (Broadbent et al. 2012; Coco et al. 2018; Melkas et al. 2020; Turja et al. 2018). Older people interact with a robot in the course of receiving services from it, while care workers are often the ones who control the robot. The results concerning the attitudes of actual users—older people and their caregivers—indicated that when people had more knowledge about robots, were shown a robot, or were able to use one, their attitudes toward robots became more positive (e.g., Johansson-Pajala et al., 2019). Some negative connotations seem to be attached to the idea of care robots, along with an ambiguity in terminology, which may lead to misunderstandings or ‘colored’ (distorted) information (Pekkarinen et al. 2020a, b). Papadopoulos et al. (2018) reported that health-care workers have mixed views on the use of robots in care settings, and few health-care professionals had used or even seen a robot. A lack of knowledge creates false impressions concerning robots’ capabilities and risks (Johansson-Pajala et al. 2020). Providing hands-on experience and promoting increased awareness and education about what care robots can do are necessary to move away from preconceptions based on fiction and imaginary images of robots (Frennert et al. 2021).

Attitudes toward care robots have also been studied through surveys. Turja et al. (2018) investigated the attitudes of Finnish health-care workers toward the use of robots in welfare services and discovered that the views of the health-care workers were generally negative. However, they considered a robot acceptable for doing certain work tasks, such as heavy lifting. It seems that care workers have reservations about the use of robots in welfare services. On the other hand, Melkas et al. (2020) found that the attitudes of care workers changed when they were able to interact with a robot and witness the positive attitudes of older people. Niemelä et al. (2016) noted in their study that care workers had negative prejudices toward the use of social robots in care, but valued them for their positive social and emotional impact on older residents.

While many of the experiences reported with care robots have been positive (see Beeuwkes Buntin et al. 2011; Andtfolk et al. 2021), certain concerns persist regarding the use of robots in welfare services. Welfare services are traditionally seen as a field in which only humans can perform (Parks 2010; Sparrow and Sparrow 2006). Sharkey and Sharkey (2012) list six concerns regarding the use of robots in elderly care, including (1) a potential reduction in the amount of human contact, (2) an increased feeling of objectification and loss of control, (3) loss of privacy, (4) loss of personal liberty, (5) deception and infantilization, and (6) unclear circumstances under which older people should be allowed to control a robot. The principle fear in utilizing robots in welfare services is the loss of human contact (Sharkey and Sharkey 2010). This fear is expressed by both care personnel who may not have had any experience using a robot with older people and by the general public (Tuisku et al. 2019). However, as many studies show, social robots are not meant to substitute for care workers, but rather to complement them (Pfadenhauer and Dukat 2015), and in fact, additional caregivers may be needed when utilizing a robot with elderly clients (Melkas et al. 2020). In recent years, this fear has characterized the discussion around the utilization of robots.

In the few studies focusing on the views of decision-makers, decision-makers have been shown to have more positive attitudes toward care robots than ordinary employees. For example, Tuisku et al. (2019) found that managers who had been actively involved in the acquisition of a care robot for elderly care saw it as a positive tool. Beedholm et al. (2015) investigated the attitudes of different stakeholders (management, nursing staff, and older people) regarding the acquisition of a robot bath for a nursing home in Denmark. Their interviews revealed that attitudes were highly dependent on the respondent’s role. The managers who were responsible for acquiring the robot bath showed the most positive attitude. A study by Niemelä et al. (2016) showed that managers were interested in acquiring a lifting robot, while the caregivers were distrustful and preferred a therapy robot seal. Thus, acceptance may also depend on the robot in question, for example, its appearance and usability (Pfadenhauer and Dukat 2015; Johansson-Pajala et al. 2020).

Gallego et al. (2008) identified the importance of decision-makers’ roles in acquiring new health-care technologies. Their study showed that decision-makers, in this case, senior health service managers, nurse managers, and senior medical clinicians, were responsible for the acquisition of new health-care technologies and played an active role in acquiring such solutions. This study revealed that while the managers had to adhere to constraints imposed at a higher level, such as funding issues, they were still in a position to make decisions regarding the kind of technology to be purchased. Thus, people at the level of management and policy-making play a considerable role in the initial introduction of technologies in the care sector, and this is why their motivation and attitudes need to be studied. In addition, they play an important role in the broader stakeholder network.

An online survey was conducted in the spring of 2017 among stakeholder networks (see Pekkarinen et al. 2019) of decision-makers in Finland. The respondents were selected from the identified decision-makers in the field of care robotics, including the service actors responsible for acquiring robots in welfare services (e.g., managers within municipalities and hospital districts) and the research and development actors (i.e., members of parliament and representatives of ministries, businesses in the field of robotics, associations, and research institutes) whose tasks are related to development work in the field of care robotics. A careful screening process was conducted to identify relevant decision-makers (i.e., judgment sampling was utilized) (Marshall 1996). To be more specific, in this case, judgment sampling means that our screening was based on the notion of health care as a sociotechnical system (Pekkarinen and Melkas 2019, based on Geels 2002) consisting of the industry, infrastructure, and service structures producing health-care products and services, as well as the products (derived from both high and low technology) and services themselves. The sociotechnical system also consists of policies and people’s preferences related to the products and services they use and consume, as well as the response of the market and the public sector to those wishes and requirements (Geels 2002). On the basis of these elements of the sociotechnical system, we identified the stakeholders to include in the study.

Our screening process is also in line with the views of Camarinha-Matos et al. (2015), who noted that ambient assisted-living (AAL) ecosystems possess different value systems, namely, a business-oriented value system and a social-oriented value system, and “in this sense, an AAL ecosystem represents a form of hybrid value chain which combines and leverages the strengths of business and social actors, which although driven by different value systems, share a common goal of providing better care and assistance services and create opportunities for social innovation” (Camarinha-Matos et al. 2015, p. 618).

In Finland, where the present study was conducted, welfare services (including both social and health-care services) are organized by the public sector (i.e., municipals and hospital districts), and they provide comprehensive social and health care for all citizens. For the municipalities and hospital districts, the survey was sent to the registry offices with a letter requesting that the survey invitations be forwarded to the relevant people, such as directors of elder-care facilities, directors of health care or social services and similar people, and the members of municipal councils.¹ The questions were originally in Finnish, but have been translated into English for this paper. The study followed the ethical principles outlined in the Declaration of Helsinki of 1975, as revised in 2000 and 2008.

In the survey, care robots were defined for the respondents as follows: “Different types of care robots are used to support an older person’s daily life. The tasks that care robots can do include, but are not limited to, monitoring vital functions, providing a flexible remote connection with a relative or caregiver, and offering companionship to older people. Care robots can be of different shapes and sizes, depending on the care robot’s tasks, and not all care robots are human-like.” Welfare services were defined as “various types of public/private/third sector social and health services as well as health and welfare promotion in general.”

The survey, developed by the authors, consists of 45 questions and includes the following elements: background information, general questions about robotics and robotics issues in welfare services, and questions relating to care robots. In this paper, we will focus on analyzing responses to nine questions related to attitudes about and acceptance of robots; these questions are provided in Sect. 3.3.

A total of 176 persons (128 females, 47 males, 1 unreported) responded to all questions and were included in this study. The respondents were divided into two groups: service actors (n = 104) and research and development (R&D) actors (n = 72). The service actors included persons currently working in health care or elderly care services in municipalities, as well as municipal-level decision-makers. They had received the survey through Finnish municipalities or hospital districts. They included health care managers, elderly care managers, service managers, project managers, administrative coordinators, directors of medical services, and managing nurses. These people and their organizations were considered to represent the potential users of service robots, that is, the user side in the stakeholder network around robots. They have more hands-on expertise in welfare services than the R&D actors and act at a more local level than the R&D actors.

The R&D actors were national-level actors and consisted of members of parliament along with representatives of ministries, enterprises in the field of robotics, associations, and research institutes. The R&D actors represented a wide range of professions, including members of parliament, CEOs, executive managers, research managers, other managers, entrepreneurs, technology consultants, and researchers. While the service actors represent the user side, the R&D actors can be considered to represent the producer side of the stakeholder network as producers of technology, knowledge, or policies related to service robots. That is, they have less practical expertise related to welfare services, but they are interested or involved in welfare services through the development of policies and technologies or by having conducted research related to the field. More details are provided below in Table 1.

In this paper, we focus on analyzing the responses to nine questions related to attitudes about and acceptance of robots (see Table 2 for details).

Qualitative data from the responses to questions for which additional information was asked were analyzed using qualitative content analysis (Mayring 2000). The data were first examined many times to fully comprehend the content and then categorized based on the identified themes.

In this question, the respondents were allowed to choose as many options as they wished. On average, the service actors selected 3.99 options, and the R&D actors selected 5.85 options. Figure 1 shows the mean responses + standard error of the means (SEMs) for each option. Table 3 shows the results of the pairwise comparisons. In case the response was ‘other,’ the respondents were able clarify their response but, for this question, no clarifications were given.

In this question, the respondents were able to select only one answer. Figure 2 shows a breakdown of the responses. Pairwise t tests were not statistically significant, indicating that the two response groups were equally interested in the future of welfare services.

In this question, the respondents were able to select only one answer. Figure 3 shows the breakdown of the responses. Pairwise t tests showed that the R&D actors were more interested in technology development than were the service actors; t (174) = 2.66, p < 0.01.

In this question, the respondents were able to select only one answer: ‘yes’ or ‘no.’ If the response was ‘yes,’ the respondents were invited to clarify their answer. The breakdown of responses is shown in Fig. 4. Pairwise t tests showed that the R&D actors had used significantly more robots than had service actors; t (174) = 5.56, p < 0.001.

While the R&D actors reported having used robots more than the service actors did, the types of robots used did not differ between the user groups. The respondents reported having used the care robot Zora, NAO, the seal robot Paro, Just-O-Cat, the walking aid Lea, the remote robot Double, remote-controlled drones, software robots, industrial robots, delivery robots, robot vacuum cleaners, and robot lawnmowers. Based on these responses, it was evident that the respondents had wide experience with a range of robots, but that not all the robots they had used were related to welfare services.

In this question, the respondents were able to select only one answer. Figure 5 shows a breakdown of the responses related to the respondents’ knowledge of robotics. The pairwise t test showed that the R&D actors had a better knowledge of robotics than the service actors; t (174) = 6.25, p < 0.001.

In this question, the respondents were able to select only one answer. Figure 6 shows a breakdown of the respondents’ responses. Pairwise t tests showed that the R&D actors had a more positive view of the use of robots in welfare services than the service actors; t (174) = 3.40, p < 0.01.

In this question, the respondents were able to select only one answer. The respondents were also invited to explain the reasons for their changed attitude regarding the use of care robots. Figure 7 shows a breakdown of the responses. Pairwise t tests were not statistically significant, indicating that the participants in the two groups had similar views on their change in attitude.

The results are reported as a whole because no statistically significant differences were found between the respondent groups. The reason most often mentioned for a change toward having a more positive attitude was the respondent’s increased knowledge of and experience with robots. The respondents also mentioned that they understood that robots would be in use in the future, and this made them willing to learn more about them, which in turn, had affected their attitude. They had also seen that robots would not be replacing humans in their work tasks; rather, they would provide help to humans. Furthermore, they mentioned that robots had been more widely introduced, and this had allowed the respondents to see them ‘live,’ which had a significant influence on their change in attitude.

For those whose attitude remained unchanged, this was largely because their attitude was already positive. This might have been because these respondents had worked with robots before, either in a robot manufacturing company or in a company that sold robots. One respondent explained that they had always been positively oriented toward new things and that the use of robots in welfare services was no exception. Unfortunately, no reasons were given to explain attitudes that became more negative. Very likely, as suggested in a study by Naneva et al. (2020), increasingly negative attitudes are related to the growing anxiety that the use of robotics would lead to a significant loss of human jobs.

In this question, the respondents were able to select only one answer. The respondents were also invited to explain the reasons for their acceptance. Figure 8 shows the breakdown of the responses. The pairwise comparisons were not statistically significant, indicating that the two response groups had similar views on accepting robots as part of their care. While the pairwise comparisons were not statistically significant, they indicated that the R&D actors were more willing than the service actors to accept robots as part of their own care; t (174) = 1.88, p = 0.061.

Among the maybe responses, the robot would be accepted as a helper or as a tool to assist the human caregiver with, for example, cleaning, cooking, and lifting, so the human caregiver would have more time to perform actual care activities. The robot was also seen as a potential companion who could discuss things, read the news, and so on. It was also mentioned that while the robot could assist in care, it could not replace the human touch and social contact between humans. Those giving a no response explained that a robot cannot replace a human, and they were afraid that social contact would suffer.

In this question, the respondents were able to select multiple answers. On average, the service actors selected 3.21 and the R&D actors selected 3.72 of the five possible responses. For each option, the respondents also provided their ideas about what the care robot could do for that particular user group. Figure 9 shows the mean values + SEMs, and Table 4 shows the results of the pairwise comparisons.

For the response ‘children and youths,’ the open-ended responses given by the two groups were similar. The respondents in both groups suggested that a robot could, for example, teach language skills, mathematics, reading, gymnastics, or motor skills. They also suggested that a robot could act as a group leader or a source of information for a child (e.g., your mother is running late or your mother will be home soon) or to help the child keep in touch with relatives (e.g., as a device to call a grandmother). However, the service actors suggested more uses for robots in monitoring health issues than the R&D actors did.

For the response ‘adults,’ both response groups suggested that a robot could help with everyday chores, such as doing the laundry, carrying heavy objects, and cleaning. They also suggested that a robot could aid in health-related tasks, such as (overall) monitoring, checking the pulse, reminding someone to take medicine, and dispensing medication. The robot could also act as a walking aid. Approximately, 50% of those who gave an open-ended response to this question suggested that the robot could help with everyday chores, and 50% suggested health-related matters. Interestingly, on this question, the service actors gave more responses relating to companionship than did the R&D actors.

For the response ‘older people,’ the tasks that the respondents suggested for robots were similar to those suggested for adults; in addition, they suggested that the robot could give reminders, for example, about a person’s daily schedule. The robot could also provide different kinds of entertainment for older people, discuss things with them, read novels or the news to them, and provide general companionship. The two response groups did not differ in their responses.

For the response ‘disabled people,’ the tasks that the respondents suggested a robot could do were similar to those mentioned above. The only addition was that a robot could assist this group in moving around or could act as a personal assistant. The two response groups did not differ in their responses.

For the response ‘others,’ only a few respondents suggested additional user groups. One suggested that a robot could help immigrants learn the language skills that they needed in a new country. Another suggested that a robot could work with animals or drive a car. The two response groups did not differ in their responses.

To summarize, the respondents’ suggestions about what robots can do for the different user groups were not directly linked to care and care robots. However, the suggestions can be linked to care as they concern improving the quality of life of robot users by offering them different types of services and recreational activities.