Robot acceptance model for care (RAM-care): A principled approach to the intention to use care robots

This is the accepted manuscript of the article, which has been published in Information & Management, 2020, 57(5), 103220. https://doi.org/10.1016/j.im.2019.103220 © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license Robot acceptance model for care (RAM-care):A principled approach to the intention to use care robots AUTHORS: Tuuli Turja (corresponding author) M. Soc. Sci., Researcher Tampere University, Faculty of Social Sciences Kalevantie 5, 33014 Tampere, Finland [email protected] Iina Aaltonen Ph.D., Research Scientist VTT Technical Research Centre of Finland [email protected] Sakari Taipale Ph.D., Adjunct Professor University of Jyväskylä, Department of Social Sciences and Philosophy [email protected] Atte Oksanen Dr. Soc. Sci., Professor of social psychology Tampere University, Faculty of Social Sciences [email protected] ABSTRACT Robots are emerging in welfare services, and organizations require information on whether novel technologies are approved among staff. On the basis of technology acceptance theories, this study proposes a model that adds a principled approach to the intention to use care robots. Data of 544 professionals with care robot experience were collected. The use intention was predicted by usefulness, enjoyment, social influence, and attitude. Respondents who found robots useful and accepted by their colleagues were more likely to view robot use as consistent with their personal values. The care robot acceptance model supports consideration of the professionspecific context in robotization. Keywords: healthcare, robot acceptance model, robotization, social robotics, technological change, therapy robots 1. Introduction Promoting the robotization of everyday services has become one of the focal points of many government strategies [1–3]. However, this objective requires an in-depth understanding of the factors that influence the acceptance of robots and implementation processes in different fields of work. Robot assistance in industrial work has been commonplace for many decades. Now robotization seems to be gradually making its way to service fields. Service robots are viewed as providing possible solutions for relieving, renewing, and rearranging care work in a time of aging populations and increased needs for social and care services [4,5]. According to a common industrial definition, a robot is a programmable mechatronic device capable of moving in its environment [6]. Computers and robots are built on advanced information systems, but robots differ from computers in their ability to physically manipulate or interact with their environment. In contrast to industrial robots, which are typically used in manufacturing and other assembly applications, service robots work in the service sector, for example, in cleaning, customer service, and search and rescue. Care robots are service robots that are used in a care context. Today, care robots are almost entirely human operated, but robot autonomy is increasing with advancements in artificial intelligence (i.e., machine learning), artificial morality (i.e., coded ethics), and improvements in sensor technology [7,8]. Telepresence robots are mobile videoconference or consultation devices fully operated by, for example, a nurse interviewing a home-care customer from a remote location [9,10] or family members contacting a relative living in a residential care home [11]. So-called social robots (e.g., “Nao” or “Pepper”) are used to entertain and engage customers physically, cognitively, and emotionally [12,13]. Social robots are often humanoids, which means the robots have some physical characteristics similar to humans, such as arms, a torso, and a head with some facial features. Although these robots are seemingly interactive, the dialogue is almost always preprogrammed. Patient-lifting robots can be semi-autonomous (e.g., “RIBA bear”), which means they perform tasks independently, but only when safety is confirmed by a human operator [14]. Typically, the more autonomous the robot, the fewer functions it includes. For example, robotic animals designed for therapeutic purposes (e.g., “Paro seal”) have limited features and mobility, and hence, they have low maintenance and do not require constant management [15,16]. Theoretical and qualitative study results have implied that healthcare professionals may resist using care robots because using them would not be consistent with the way the professionals understand the principles of care work [17–20]. However, the significance and generality of this principled mindset, and its actual connection to the intention to use robots in care work, have not been investigated. Among the general population, however, fundamental concerns have been expressed about implementing robotics in social and care services. In the Eurobarometer data 2017 (N = 27,901) of adult (15+) citizens of the European Union, on average, 32% (ranging from 14% to 56% depending on the country; e.g., in Finland, 30%) would be comfortable with a robot providing services and companionship to infirm or elderly people [21]. In this study, we surveyed healthcare professionals’ acceptance of robots and identified the factors that determine the intention to use robots specifically in the context of care for the elderly. We used the outcome variable repeat use intention that reflects the subjective probability that a care professional would continue to use the same technology with which he or she had firsthand experience. That said, we do not strive to explain the intention to use robots with yet another cluster of variables but widen the investigation with a principled approach to acceptance of robots. Possible principles underlying acceptance of robots are viewed as threefold: through instrumental, interpersonal, and ethical values. Most technology acceptance models (TAMs) do not include motivational factors such as compatibility with moral or instrumental values, which lead to intention and behavior [22,23]. However, we expect that particularly moral evaluations of technology implementations in human-centered services significantly affect acceptance of robots. Virtue-ethical values of implementing new technology, in general, aim to assure people’s privacy, distribution of welfare, and social inclusion [24]. Ethical values of nursing, then, include respectfulness, compassion, partnership, trustworthiness, competence, and safety [25,26]. Extending the previously suggested robot acceptance model by including a principled approach to the intention to use care robots, we propose a new robot acceptance model for care (RAM-care) to be used in academic and workplace studies with an aim to statistically model acceptance of care robots among employees. 2. Background 2.1. Acceptance of robots Robot acceptance models are typically based on models developed for generic technology use. TAMs are applied to explain the intention to use and the actual use of a particular type of technology such as new information systems, automatons, and robots. Intention to use refers to behavioral intention as the measure of one’s motive to perform a specified behavior or action [27 p. 288]. The systematic analysis of different TAMs, and emphasis on the differences between behavioral intention to use and the actual use of technology, led to the development of the unified theory of acceptance and use of technology (UTAUT) [28]. The UTAUT explains the actual use of and the intention to use technology by four core constructs—performance expectancy, effort expectancy, social influence, and facilitating conditions— and by moderators of weaker explanatory value (e.g., gender and experiences with technology) [28]. In the Almere model, the UTAUT was further developed to measure possible end users’ (i.e., older adults) assistive social agent acceptance [29]. The Almere model explains the intention to use robots by factors of functional and social acceptance (Fig. 1), and in this model, intention to use is a strong predictor of the actual use of technology [29]. In the Almere model, social influence, attitude, perceived usefulness, perceived ease of use, perceived enjoyment, and trust have been found to predict intention to use [29,30]. Social influence is typically measured as a subjective perception of the opinions of important others [28] and understood here as parallel to interpersonal values [73]. Attitude is defined as the individual user’s dispositional, positive or negative, feelings about performing the target behavior [27 p. 216], in this case, using care robots. Perceived usefulness has consistently been found to be a strong determinant of intention to use, whereas perceived ease of use has demonstrated a less consistent effect across technology acceptance studies [31,32]. Exceptionally high robot use self-efficacy (i.e., trust in one’s ability to use robots in one’s work) of Finnish nurses [33] confirmed that the ease of use is not one of the most critical factors influencing the intention to use new technology in a professional care context. The Almere model is used as the base for the first hypothesis for a direct positive relation between the repeat intention to use care robots and favorable social influence (H1a), attitude toward use (H1b), perceived usefulness (H1c), perceived ease of use (H1d), perceived enjoyment (H1e), and trust (H1f) [29]. [FIG. 1 ABOUT HERE] 2.2. New model as an extension of the Almere model The UTAUT is used to model technology acceptance in the workplace, but the theory is very general and does not consider a particular professional context [34]. By suggesting a new robot acceptance model for care, we believe that healthcare work has a distinct principled level affecting acceptance of robots. The constructs of intention to use, social influence, attitude, ease of use, perceived usefulness, perceived enjoyment, and trust stem from a model of robot acceptance [29]. As an expansion, we drafted a new model that includes a principled view of a technological change. We added perceived compatibility between the use of care robots and personal moral values and perceived technological unemployment caused by robots (Fig. 2). [FIG. 2 ABOUT HERE] Matters of principle play a part in ethical decision making when people have, for example, the opportunity to evaluate organizational changes [39 p. 1971, 40]. The principled mindset toward a technological change can be viewed as originating from the evaluation of justice. On the basis of the study by Cropanzano and colleagues, the causes of perceived workplace justice or injustice can be traced to instrumental, interpersonal, or ethical values. Instrumental values refer to self-interest (e.g., financial gains), interpersonal values refer to the thoughts of what is socially appropriate, and ethical values refer to the commitment to personal moral standards [73]. Interpersonal values are included in the Almere model as social influence that stands for subjective perception of the opinions of important others [28]. Thus, the additions to the new model are ethical values (i.e., compatibility between use of robots and personal moral values) and instrumental values (i.e., perceived technological unemployment caused by robots). Perceived usefulness in TAMs refers to the functional value of using the technology, whereas personal moral values motivate change only if the change is compatible with existing values [37]. Steelman and Soror [38] state usefulness as an explanatory factor typically focuses on performance outcomes and less so on hedonic or affective values. Particularly in the service sector, people should carefully plan which tasks are suitable, safe, and appropriate for robotizing. The occupational ethics of nursing work emphasize this requirement. From this approach, measuring merely the usefulness of the technology falls short. Adding value-based consideration to the model acknowledges the distinctive nature of healthcare work compared to other less sensitive service work. Personal moral values stem from virtue ethics: a humane worldview and moral acts promoting people’s well-being. Virtue ethics are written in, for example, the moral values of designing new technologies, which are safety, sustainability, distribution of welfare, universal usability, and trust [24,41]. Virtue ethics are also at the center of nursing ethics, which include respectfulness, compassion, partnership, trustworthiness, competence, and safety [25,26]. In artificial morality discussions, virtue-ethical values are viewed as a good starting point when coding ethical principles in a machine [41]. Although a robot is not genuinely capable of deliberating what is safe or unsafe, with programming, the robot can predict what a human would evaluate as such [41 p. 324]. An individual worldview defined as a view on life, the world, and humanity contains personal moral values of right and wrong and regulates thoughts and actions [35]. Values are considered socioculturally shaped, and even dictated, when it comes to laws and principles of certain fields of work. Ethical standards of nursing are principles that nurses are expected to commit to as individuals, and as a community [26 p. 6]. Changes in healthcare work can be seen as invariably reflected against these internalized moral principles, which, depending on perceived compatibility, either increase or decrease the enthusiasm to use new innovations [36]. Can virtue-ethically driven values and principles be supported by care robots? In the future, respectfulness may refer to the patient’s or customer’s right to choose robot care over human care or vice versa [42], but what about compassion and partnership? Compassion is considered to originate from human empathy, but it can merely be a performance. In some occupations, more than others, we are expected to express or suppress our emotions [43]. For example, nurses are required to signal their empathetic concern regardless of their genuine feelings [44]. Intelligent robots have the potential to present as compassion-simulating, everpatient, and ever-friendly companions [45,46]. In a health coach robot study, an empathy-simulating robot was accepted as a friendly and trustworthy partner by users [47]. Trustworthiness, then, is a matter of consistency and reliability. Even though robots can be depreciated as truly compassionate actors, as reliability goes, the underlying assumption is that robots are expected to do exactly what and when they are told to do. Thus, the consistency of a robot’s behavior has the potential to increase the feeling of safety in the healthcare context. However, healthcare professionals may consider that robotization can lead to decreased quality of care. Some researchers have stressed the difficulty of implementing robots in care tasks because of the holistic nature of care work [48]. Healthcare professionals do have the right and the responsibility to define acceptable approaches in their areas of expertise, even in the midst of a technological change [49–51]. From the public perspective, to use care robots is to endorse them. The staff working at one of the first care facilities piloting the robot “Nao” in Finland received negative feedback from citizens, and instead of the management, the lower level staff had to justify the purchase and use of a care robot to the public [13]. In addition to ethical values, instrumental values may influence the acceptance of technology. Instrumental values of work include earning a living [52,53], and healthcare professionals sometimes view robotization as a threat to people’s careers, income, or future employment [33]. Technological unemployment refers to changes in employment due to technical progress, such as new methods of production. The gradual integration of robots into service fields has brought technological unemployment back to public discussions. Most (72%) of the Eurobarometer respondents believed that robots would take people’s jobs, and even a larger proportion (74%) thought that, because of robotization, more jobs would disappear than would be created [21]. Moreover, the fear of unemployment, in general, correlated with the fear of robots [54]. Deterministic views on technology state that advances in technology inevitably change societies and working life. However, more voluntaristic and dynamic views, such as Sabanovic’s concept of mutual shaping of robotics and society, have been taking over [55]. Society shapes robots, and robotization shapes the society in a dynamic interaction [55]. As a counterview for technological determinism, social determinism states that technology is not considered inevitable but as rising from social needs [56 p. 15]. Again, the technologically determined view is that robots replace human work as artificial intelligence, and sensor technology reaches the required maturity levels, but according to social determinism, people have the means to decide which technology is actually usable in which context. However, in organizations, decision making is not always shared, and this raises questions about the mandatory use of technology as a source of cognitive dissonance—a conflict between an individual’s beliefs and (expected) behavior. Compatibility with values represents an intrinsic motivation, without which people are expected to, for example, work in an environment that counters their own internal belief system [57]. An example of this cognitive dissonance [58] is a healthcare professional thinking that mandatory use of robots is not consistent with his or her personal values. Cognitive dissonance can also be viewed as a cause of technostress, which occurs when the worker is unable to adapt to using technology [59]. Overall, incompatibility with ethical or instrumental values can be a reason for rejecting new technology. In a study of information system acceptance, Karahanna et al. [60] found that compatibility with values predicted the perceived usefulness of technology, which again predicted the actual use of the technology. Following the innovation diffusion theory [36], however, the compatibility of personal values would directly explain the variation in the intention to use robots. Thus, the competing hypotheses are as follows: H2: Compatibility of personal moral values predicts a stronger intention to use care robots. For example, this hypothesis would be true if those who feel that use of robots fits their worldview were more willing to use care robots than those who feel that the use of robots does not fit their worldview. H3: Compatibility of personal moral values predicts the intention to use robots indirectly through perceived usefulness. Viewing ethical and instrumental values from an experimental motivation psychology perspective, instrumental motives (e.g., financial) causally influence the virtue-ethical motives of right and wrong [52]. In addition, it is implied that the relation between values and behaviors is mediated by social influence [61,62]. Thus, we hypothesize the following: H4: Perception of technological unemployment predicts lower compatibility between personal moral values and the use of care robots. For example, this hypothesis would be supported if those who think robots are taking jobs from people assessed the use of robots as less fitting their values than those who do not think robots are taking people’s jobs. H5: Personal moral values predict a stronger intention to use robots indirectly through social influence. 3. Method 3.1. Participants Data were collected from 544 healthcare, mostly nursing, professionals who reported firsthand experience with care robots in a larger survey of Finnish care workers (N = 3,800) between October and November 2016. The original random samples were collected in collaboration with two major trade unions in the field: the Union of Health and Social Care Professionals in Finland and the Finnish Union of Practical Nurses. Within an expected margin of error, the division between practical (64.9%) and registered (35.1%) nurses in the survey data complied with such a division of practical (64.7%) and registered (35.3%) nurses in the population [63]. The completion rate analysis did not note the differences in occupation or gender, but the respondents who dropped out were, on average, younger (M = 44.0 years) than those who completed the questionnaire (M = 47.3 years; F(01) = 61.19; p < 0.001). In the subsample used in this study, participants were aged 19–70 years (M = 46.8; SD = 11.46), and 95.0% were native speakers of Finnish. Most were practical nurses (62.4%) or registered nurses (33.9%), while the rest (3.7%) were physiotherapists, instructors, and assistants. The most common places of work were an assisted living facility (53.8%), home care (17.0%), or a hospital (15.2%). A considerable portion of the participants (80.1%) worked with patients with dementia. An online questionnaire included multiple-choice questions about personal and occupational details, experiences with care robots, and attitudes toward technology in general, and robots specifically. Participants who reported using a particular kind of care robot were directed to additional questions concerning this type, and only the type of robot with which they had experience. The four types of robots presented were 1) a telepresence robot (example picture of “Double”), 2) an entertaining or activating robot (example picture of the humanoid “Nao”), a therapy animal robot (example picture of “Paro seal”), and a patient-lifting robot (example pictures of “RIBA bear” and a robotized bed). 3.2. Measures Each robot type reported to have been used opened up seven additional questions, including the dependent variable of the intention to use the robot in the future and its six explanatory variables from the Almere model: social influence, attitude, ease of use, perceived usefulness, perceived enjoyment, and trust [29]. Responses were given on a 5-point Likert scale ranging from totally disagree to totally agree, and after the scales were standardized, a higher reading indicated a more positive view of the robot. The reliability of the six explanatory variables of the original Almere model was highly acceptable (α = 0.939). Three statements of personal moral values were modified and translated by professionals into the Finnish language from the information system acceptance questionnaire validated by Karahanna et al. [60]. The response scale ranged from 1 (totally agree) to 5 (totally disagree). Thus, the composite variable ranged from 3.0 to 15 (α = 0.929); a higher score indicated care robots’ compatibility with personal moral values. The statements about the Almere model and personal moral values are presented in the Appendix. To measure the perceived technological unemployment, we used a repeated and validated Special Eurobarometer [21] question about whether participants believed that “Robots steal people’s jobs,” with the response scale ranging from 1 (totally disagree) to 5 (totally agree). Interest in technology was used as a control variable. It was measured with a question modified from the Special Eurobarometer [21]: “Are you very interested (3), moderately interested (2), or not at all interested (1) in technology and its developments?” 3.3. Statistical analysis Preliminary analysis included percentages, means, modes, standard deviations, and correlations measured with Spearman’s rho (rS). Differences between groups were tested with chi-square ( χ 2) and t-tests. To test the theoretical model, we found that the multidimensional construct of technology acceptance and values is best modeled as a multivariate structural equation model (SEM). SEM is an extension of regression analysis involving simultaneous regression models and rendering one variable to be a dependent and an independent variable. Because some of the measures were ordinal, a generalized SEM was applied. All of the variables were observed (none latent), and they are reported as unstandardized coefficients. The McFadden’s countries of the European Union [21]. Moreover, care work culture and technology use differ between countries, and some of this variance may also be associated with employees’ ethnic background. However, the model RAM-care is not restricted to specific cultures or even professional care work but should be tested with other end users as well, such as informal caregivers and care receivers from different cultural backgrounds. A recent study, for example, showed that employees in cultures with higher power distance and masculinity values than those in Finland are more likely to experience technostress [72]. Without comparative studies, we are not able to say, for example, whether the high intention to use care robots is distinctive to Finnish care workers, who have substantial professional autonomy and relatively downplayed hierarchy [77]. 5.2. Conclusion Implementing robots and other advanced information systems in new fields of work requires an indepth understanding of the factors associated with technology acceptance among groups of professionals. A new model, RAM-care, is proposed to measure the acceptance of care robots. The intention to use robots is traditionally explained by functional and social factors stemming from TAMs. In addition, we emphasize context-dependent value-based principles behind the acceptance of robots. In the model, personal moral values predict intention to use, not directly but through social influence and perceived usefulness. Dispositional attitude and perceived enjoyment were the most significant predictors of the intention to use robots, but two original factors from the robot acceptance model, namely, ease of use and trustworthiness, did not reach statistical significance. In total, the RAM-care model explained approximately 30% of the variance in repeat intention to use care robots. 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AUTHOR BIOGRAPHIES: Tuuli Turja is a researcher (BA Psych, MSocSci) at Tampere University’s Faculty of Social Sciences and in the project Robots and the Future of Welfare Services. Her research focuses on the social psychology of robotization and occupational changes. Iina Aaltonen (DrSciTech) is a research scientist at VTT Technical Research Centre of Finland Ltd. Her research work focuses on human factors and user experience in several domains including social and industrial robotics. Sakari Taipale (DrSocSci) is a senior lecturer at the University of Jyväskylä, where he serves as a research group leader of the Centre of Excellence in Research on Ageing and Care (CoEAgeCare). His research interests relate to digital technologies and social robotics. Atte Oksanen (DrSocSci) is professor of social psychology at Tampere University, Finland. His research focuses on social media and emerging technologies. Table 2 Descriptions of the variables in the analysis. Gender Interest in technology Table 1 Intention to use the robot types (scale, 1–5). n Mean Mode Std. deviation Intention to use a telepresence robot 93 3.69 4 1.02 Intention to use a therapy animal robot 23 9 3.96 4 1.10 Intention to use a patient-lifting robot 10 0 4.37 5 0.75 Intention to use an entertaining or activating robot 11 2 3.66 4 1.10 Total 54 4 3.93 4 1.10 Variables Mean Std. deviation Range % Intention to use robots 3 .93 1.10 1–5 Robots’ compatibility with personal moral values 8.46 3.55 3–15 Perceived technology unemployment 3.14 1.13 1–5 Social influence 2.81 0.90 1–5 Attitude 3.89 1.06 1–5 Ease of use 3.71 1.13 1–5 Perceived usefulness 3.51 1.13 1–5 Perceived enjoyment 3.42 1.01 1–5 Trust 3.26 1.08 1–5 Female 94.1 Male 5.9 Very 23.8 Moderate 70.3 Not at all 5.9 _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Fig. 1. The Almere model of robot acceptance among older adults [29]. Fig. 2. Draft model of care robot acceptance. SOCIAL INFLUENCE ATTITUDE EASE OF USE PERSONAL VALUES INTENTION TO USE PERCEIVED TECHNOLOGY UNEMPLOYMENT ENJOYMENT TRUST PERCEIVED USEFULNESS Fig. 3. RAM-care model: Coefficients reported in cases of statistically significant results; the direct path from personal values to intention to use removed from the model. SOCIAL INFLUENCE ATTITUDE EASE OF USE PERSONAL VALUES INTENTION TO USE PERCEIVED TECHNOLOGY UNEMPLOYMENT ENJOYMENT TRUST * p < .05, ** p < .01, *** p < .001 ------ non-significant PERCEIVED USEFULNESS -0.64*** 0.18* 0.52*** 0.12*** 0.20** H3 H4 H1a H1b H1c (H1d) H1e (H1f) 0.15*** H5 (-0.18) (H2) 0.43*** Appendix Variable Items Intention to use robots oIf the telepresence robot were available, I would use it. oIf the robot used for entertaining or activating were available, I would use it. oIf the therapy animal robot were available, I would use it. oIf the patient-lifting robot were available, I would use it. Attitude oI think it’s a good idea to use the telepresence robot. oI think it’s a good idea to use the entertaining or activating robot. oI think it’s a good idea to use the therapy animal robot. oI think it’s a good idea to use the patient-lifting robot. Ease of use oI think I can use the telepresence robot without any help. oI think I can use the entertaining or activating robot without any help. oI think I can use the therapy animal robot without any help. oI think I can use the patient-lifting robot without any help. Perceived usefulness oI think the telepresence robot is useful in my job. oI think the entertaining/activating robot is useful in my job. oI think the therapy animal robot is useful in my job. oI think the patient-lifting robot is useful in my job. Perceived enjoyment oI enjoy doing things with the telepresence robot. oI enjoy doing things with the entertaining or activating robot. oI enjoy doing things with the therapy animal robot. oI enjoy doing things with the patient-lifting robot. Trust oI would not trust that the telepresence robot is safe. oI would not trust that the entertaining or activating robot is safe. oI would not trust that the therapy animal robot is safe. oI would not trust that the patient-lifting robot is safe. Social influence oUsing care robots is mainly considered a positive thing among my colleagues. Robots’ compatibility with personal values oUsing care robots runs counter to my own values. oUsing care robots does not fit the way I view the world. oUsing care robots is not appropriate for a person with my values when thinking about the role of robots. ___________________________________________________________________________________ ___________________________________________________________________________________ ___________________________________________________________________________________