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Human-Centered Reflections on Care Robots: A Comparative Study of Caregiver Perspectives

Laura Londoño, Klaus Baumann, Abhinav Valada, Markus Langer

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Abstract

Abstract:Care robots are increasingly being introduced into healthcare settings, raising important questions about their acceptance and ethical implementation. To better understand these challenges, this study investigates caregivers' perceptions of four categories of care robots: delivering supplies, helping patients into bed, monitoring vital signs, and assisting with mobility. We conducted a mixed-methods study employing a mixed-factorial design in which 298 caregivers from the United States, Mexico, and Chile evaluated all four robot categories. Quantitative measures integrated constructs from the Unified Theory of Acceptance and Use of Technology, the Cognitive-Affective-Normative model, and overall acceptance ratings. Qualitative data were collected through open-ended questions and analyzed using a literature-informed ethical framework. The results indicate that participants across countries generally evaluated care robots positively, particularly for logistical and physically demanding tasks rather than those requiring intensive interpersonal interaction. The qualitative findings provide further insight into stakeholders' views of the ethical implications of care robot use. Participants emphasized potential benefits such as reduced workload, lower risk, and greater patient autonomy, while also expressing concerns about dependability, the need for human oversight, and potential job displacement. Although many ethical concerns were shared across countries, participants differed in how they interpreted and prioritized them. These findings advance a context-sensitive and socially informed understanding of responsible design and implementation of care robots.

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Human-Centered Reflections on Care Robots: A Comparative Study of Caregiver Perspectives Laura Londo ̃no 1,2 , Klaus Baumann 3 , Abhinav Valada 2 , Markus Langer 1 1 Department of Psychology, University of Freiburg, Germany. 2 Department of Computer Science, University of Freiburg, Germany. 3 Department of Theology, University of Freiburg, Germany. Corresponding author e-mail: londono@cs.uni-freiburg.de; Abstract Care robots are increasingly being introduced into healthcare settings, raising important questions about their acceptance and ethical implementation. To better understand these challenges, this study investigates caregivers’ perceptions of four categories of care robots: delivering supplies, helping patients into bed, monitoring vital signs, and assisting with mobility. We conducted a mixed-methods study employing a mixed-factorial design in which 298 caregivers from the United States, Mexico, and Chile evaluated all four robot categories. Quantitative measures integrated constructs from the Unified Theory of Acceptance and Use of Technology, the Cognitive–Affective–Normative model, and overall acceptance ratings. Qualitative data were collected through open-ended questions and analyzed using a literature-informed ethical framework. The results indicate that participants across countries generally evaluated care robots positively, particularly for logistical and physically demanding tasks rather than those requiring intensive interpersonal interaction. The qualitative findings provide further insight into stakeholders’ views of the ethical implications of care robot use. Participants emphasized potential benefits such as reduced workload, lower risk, and greater patient autonomy, while also expressing concerns about dependability, the need for human oversight, and potential job displacement. Although many ethical concerns were shared across countries, participants differed in how they interpreted and prioritized them. These findings advance a context-sensitive and socially informed understanding of responsible design and implementation of care robots. Keywords: Care Robots, Technology Acceptance, Human-Robot Interaction, Cross-Cultural Comparison, Robot Ethics, Responsible Robotics 1 Introduction The use of robots has expanded beyond industrial applications into a range of diverse social domains, including healthcare and long-term care [1–4]. As robots become increasingly integrated into those settings characterized by vulnerability and depen- dence, their presence raises critical questions about how they should interact with caregivers, support patients, and participate in practices traditionally grounded in human relationships [5–8]. Address- ing these questions requires more than improving robots’ technical performance and usability. It also demands careful consideration of the ethical, social, cultural, and emotional dimensions of care. As part of broader efforts to promote the responsible development of care robots and fos- ter their acceptance, scholars have emphasized 1 arXiv:2608.02411v1 [cs.RO] 3 Aug 2026 the importance of incorporating the perspectives of stakeholders directly involved in the design, deployment, and use [9–13]. For example, [8,14] argues that meaningful acceptance requires a thor- ough understanding of why end users accept or reject care robots in social contexts. They argue that responsible and context-sensitive care robotics should align robots’ technical capabilities and forms of agency with the goals, values, and needs of professional care practices. Despite growing interest in the ethical design and acceptance of care robots, stakeholders from countries outside the so-called “developed world” remain underrepresented in the literature [15–17]. This imbalance may introduce systematic biases into the development of care technologies by privi- leging culturally narrow assumptions about care practices, technological expectations, and user needs. Moreover, acceptance and ethical concerns may depend not only on sociocultural context but also on the specific function assigned to a robot. A robot that transports supplies, for example, may be evaluated according to different expectations and ethical criteria than one that physically assists or monitors a patient. To address this gap, this study investigates (1) caregivers’ perceptions of four categories of care robots: robots that deliver supplies, help patients into bed, monitor vital signs, and assist with mobil- ity. The study compares stakeholders from the United States, Mexico, and Chile, three countries with distinct socioeconomic, cultural, institutional, and technological contexts; and (2) it examines both the acceptance of these robot categories and the ethical considerations that stakeholders regard as important for their responsible design and deployment. Our analysis is guided by three premises: (1) acceptance cannot be understood independently of ethical considerations, as perceptions of a robot’s benefits, risks, and impact on care shape users’ willingness to adopt it; (2) a cross-country perspective is needed to understand how social, cultural, and institutional contexts influence these perceptions; and (3) acceptance is task-specific, meaning evaluations depend on the robot’s func- tion and care context and cannot be readily generalized across applications. Accordingly, this study pursues two main objectives (see Figure 1): Fig. 1: Overview of the comparative mixed-methods design. Quantitative acceptance analyses (UTAUT and CAN) and qualitative ethical evaluations were conducted in parallel and subsequently integrated to develop a comprehensive under- standing of the acceptance of four types of care robots across sociocultural contexts. • Aim 1: To experimentally evaluate caregivers’ acceptance of four categories of healthcare robots across three countries (the United States, Mexico, and Chile) with distinct socioe- conomic and technological contexts. Accep- tance is assessed using constructs derived from the Unified Theory of Acceptance and Use of Technology (UTAUT) [18] and Cog- nitive–Affective–Normative (CAN) [19] model, together with an overall acceptance rating. The analysis identifies factors associated with accep- tance and compares acceptance profiles across robot categories and countries. • Aim 2: To develop and apply a literature- informed ethical framework for examining the considerations that caregivers regard as impor- tant for the responsible design and deployment of the four categories of care robots across different sociocultural contexts. To address the objectives, we employ a mixed- methods study that combined quantitative and qualitative approaches within a mixed-factorial framework. Robot category served as the within- subjects factor, as each participant evaluated all four categories of care robots. Sociocultural con- text served as the between-subjects factor and was operationalized through participants’ country of residence and language. Participants completed an online survey in which the four robot cate- gories, each representing distinct clinical functions, were presented in randomized order. The quanti- tative component collected standardized measures of acceptance for each category. The qualitative 2 component used open-ended questions to elicit participants’ views on the ethical considerations relevant to the design and deployment of care robots. These responses enabled us to identify recurring ethical themes that standardized quanti- tative measures might not capture. Integrating the quantitative and qualitative findings allowed us to examine not only whether stakeholders accepted different types of care robots, but also how they understood the ethical considerations associated with care robots. Accordingly, this study makes three contribu- tions. First, it provides a within-participant com- parison of caregivers’ evaluations of four care-robot scenarios representing distinct clinical functions. Second, it integrates quantitative acceptance mea- sures with qualitative ethical analysis to identify how concerns relating to safety, autonomy, rela- tional care, accountability, and accessibility shape and qualify participants’ evaluations of these tech- nologies. Third, it offers comparative evidence on similarities and differences between the sampled groups, comprising English-language participants in the United States and Spanish-language partici- pants in Mexico and Chile. 2 Related Work This section provides a focused review of the liter- ature on technology acceptance. It also examines key findings on ethical evaluations of robot design, as well as research highlighting the importance of robot type and task characteristics in shaping user acceptance. In addition, the section consid- ers cross-cultural variations in the adoption and acceptance of robotics technology. Finally, it ana- lyzes the healthcare contexts of the United States, Mexico, and Chile to situate the study’s findings within their respective sociocultural and structural conditions. 2.1 Technology Acceptance Models Technology acceptance has been extensively stud- ied across multiple disciplines, leading to the devel- opment of numerous theoretical frameworks and standardized measurement instruments. Among the most influential is the Unified Theory of Accep- tance and Use of Technology (UTAUT) [18], which integrates and extends the Technology Acceptance Model (TAM) [20] and other earlier frameworks. UTAUT provides a comprehensive account of technology adoption by incorporating social, orga- nizational, and contextual factors that influence both behavioral intention and actual technology use. It proposes four core constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. This model recognizes that their influence varies according to demographic and situational factors such as age, gender, prior experience, and voluntariness of use [21]. In addition to technology acceptance models, other frameworks can contribute to the evaluation of emerging technologies by capturing dimensions beyond instrumental acceptance. One example is the Cognitive–Affective–Normative (CAN) model proposed by Reinares-Lara et al.[19], which incor- porates ethical considerations by assessing users’ perceptions of fairness, morality, and other norma- tive aspects of technology evaluation. While the CAN model enables the measurement of ethical perceptions, it offers limited insight into the under- lying values and forms of ethical reasoning that shape these judgments. This limitation motivates the use of complementary qualitative approaches to better understand stakeholders’ ethical evaluations of emerging technologies. 2.2 Ethical Evaluation of Care Robots While ethical reflection surrounding care robots has received increasing attention within the fields of responsible robotics, much of the existing literature has focused on developing normative frameworks, ethical principles, and design guide- lines [22–25]. Comparatively little attention has been devoted to understanding how these nor- mative insights can be integrated with empirical investigations or how ethical considerations shape the acceptance, rejection, and evaluation of care robots in practice [26,27,27,28]. As a result, a per- sistent divide remains between normative ethical theorizing and empirical research [29]. In response, there has been growing recogni- tion of the need for research approaches that move beyond this traditional separation [14,21,24]. Among the most influential contributions in this area is the work of Van Wynsberghe[27], who develops an ethical framework for evaluating the design, implementation, and use of care robots. 3 Starting from the question of how robots can sup- port care without undermining the ethical values that make care meaningful, the author argues that care should not be reduced to the automation of care-related tasks. Drawing on the ethics of care tradition developed by Gilligan[30]and Tronto [31], she argues that care is fundamentally a rela- tional moral practice rather than a specific activity or set of tasks. Consequently, the ethical evalua- tion of care robots must consider not only their effectiveness and precision in performing care func- tions but also their impact on the relationships, responsibilities, and values that constitute caring practices. While Van Wynsberghe[27]focus primarily on normative guidelines for the ethical design of care robots, Vandemeulebroucke et al.[29]argue that ethical evaluations must also consider the socio-historical, organizational, and institutional contexts in which these technologies are imple- mented. From this perspective, care robots should be evaluated not only in terms of their techni- cal capabilities but also in relation to the broader care systems and social conditions in which they operate. Building on this work, the present study proposes a theoretical framework that integrates multiple ethical perspectives and applies them to the analysis of empirical data, thereby respond- ing to calls for approaches that bridge normative ethics and empirical research. 2.2.1 Ethical Foundations of the Analytical Framework The theoretical framework proposed by Van Wyns- berghe[27]provides an important foundation for understanding the relational dimensions of care through the lens of care ethics. However, in devel- oping the ethical framework guiding the analysis in the present study, we considered that relying solely on this perspective would be insufficient to capture the broader range of ethical concerns that participants might articulate. To address this limitation, we incorporated additional ethical the- ories to enable a more comprehensive analysis of the diverse values that shape perceptions of care robots. Following, we present a short description of the ethical theories that guide our framework: • Care Ethics: Originally developed by Gilligan [30]as a critique of justice-based moral theories, the ethics of care emphasizes the moral signif- icance of relationships, interdependence, and contextual responsibilities. From this perspec- tive, ethical judgment arises from attentiveness to concrete relational contexts and the needs of others, rather than from the application of abstract universal rules. Core values associated with this perspective include care, responsibil- ity, relationality, empathy, interdependence, and sensitivity to context. • Capability Approach: Developed by Nuss- baum[32], this framework emphasizes enabling individuals to achieve fundamental capabili- ties, such as health, bodily integrity, autonomy, emotional development, and control over their environment and decisions. Ensuring these capa- bilities is considered an ethical obligation, as they constitute the minimum conditions required for a dignified human life. Key values asso- ciated with this perspective include human dignity, agency, freedom of choice, equality of capabilities, attention to diversity, and social justice. • Consequentialism: Consequentialist theories evaluate the moral rightness of actions primar- ily in terms of their outcomes [33,34]. From this perspective, actions are assessed according to the extent to which they produce desirable consequences or minimize harm. Core values associated with this perspective include the moral primacy of consequences, the maximiza- tion of overall good, impartiality, aggregation of outcomes, forward-looking moral reasoning, and sensitivity to cost–benefit considerations. The theoretical framework was developed by drawing on these ethical theories to address three important limitations in the existing literature. First, while technology acceptance models are effective in capturing perceptions of usefulness, attitudes, and adoption intentions, they provide only limited insight into the ethical reasoning that underlies these evaluations. Second, many normative approaches to care robotics rely on a single ethical perspective, thereby overlooking the diversity of values, principles, and concerns that stakeholders may express. Third, there is a need for an ethical framework that can be applied to 4 the analysis of empirical data, enabling a more sys- tematic integration of normative ethical analysis with empirical research. 2.3 Robot Type and Task Characteristics In the context of social robots, acceptance also depends on robot-related characteristics, including embodiment, social capabilities, perceived agency, and emotional expressiveness [35]. In addition, both the type of robot and the tasks it performs influence how it is perceived and accepted across contexts [36–39]. Consistent with this, Chatzoglou et al.[36]and de Graaf and Allouch[40]found that robot-related characteristics, such as phys- ical attractiveness and task importance, exert a stronger influence on acceptance than socio- cultural factors. Their findings further suggest that users evaluate robots according to the tasks they perform and the value these tasks provide in everyday life. Similar evidence has been reported in the healthcare robotics literature. In their review of healthcare robots for older adults, Broadbent et al. [11]identified the robot’s intended role and func- tion as important determinants of acceptance. The authors argued that users may evaluate robots dif- ferently depending on the tasks they are designed to perform, ranging from health monitoring and reminders to physical assistance and social com- panionship. Although these conclusions were based on a literature review rather than direct empir- ical comparisons across robot types, the review provided early evidence that task characteristics influence robot acceptance and highlighted the need for systematic empirical research. Taken together, this body of research suggests that social robot acceptance is task-dependent. Users evaluate robots according to the functions they perform and the value they provide in a given context. Consequently, findings from one robotic application cannot necessarily be generalized to others, highlighting the need to examine multiple robot types performing different care-related tasks. 2.4 Cross-Cultural Differences in Care Robot Adoption and Implementation Several scholars argue that, despite the transfor- mative impact of digital technologies, many people, particularly in countries with limited participa- tion in technological development, remain excluded from the debates shaping digital societies, a phe- nomenon referred to as digital coloniality [15,41]. Because digital societies reflect the social struc- tures in which they are embedded, they often reproduce existing historical inequalities [42]. As digital innovation is concentrated in the Global North, the priorities, values, and assumptions embedded in digital technologies predominantly reflect the perspectives of these regions, reinforc- ing structural imbalances and marginalizing voices from elsewhere. One way to address this challenge is to design robots that can adapt to diverse socio-cultural contexts. Research in human–robot interaction has shown that robots developed within specific sociocultural settings often embed implicit assump- tions about interpersonal distance, communication styles, and social roles [43–46]. When treated as universal interaction norms, these assumptions may conflict with the social practices of other cul- tural contexts [44,47]. Moreover, Lawrence[47] argue that social norms in human–robot interac- tion should be understood not only at the societal level but also at the individual level, where per- sonal expectations shape how robot behavior is interpreted and evaluated. Consistent with this view, Marchesi et al.[48]found that individual cul- tural values, rather than nationality, better predict the social inclusion of robots. In contrast, Li et al.[49]found that expecta- tions regarding appropriate robot behavior vary across cultural contexts. In their cross-cultural experiment with participants from the United States and China, preferences differed with respect to robot autonomy: while participants in some contexts preferred more autonomous and proac- tive robots, others expected robots to behave more passively. Together, these findings highlight the importance of examining social robot acceptance across different cultural contexts, as acceptance is a key prerequisite for the successful deployment of robots and their contribution to stakeholder well-being. 5 2.5 Healthcare and Sociocultural Contexts in the United States, Mexico, and Chile Healthcare environments across nations are shaped by a range of structural factors that influence how innovations are developed, adopted, and integrated [50]. Given that the acceptance and implementa- tion of healthcare robots are likely to be influenced by these contextual conditions, this study focuses on three distinct national settings—the United States, Mexico, and Chile. The following outlines key characteristics of these healthcare environ- ments, with particular attention to technology adoption and implementation. 2.5.1 Technology Adoption in the United States Healthcare System In recent decades, healthcare systems in the United States have been characterized by a strong orienta- tion toward technological innovation [51]. Empir- ical evidence indicates sustained growth in the deployment of surgical robots and artificial intel- ligence–based systems across hospitals, supported by regulatory approvals and institutional adoption patterns that facilitate their incorporation [52]. By 2022, nearly one-fifth of U.S. hospitals had adopted some form of artificial intelligence [53, 54]. As a result, contemporary hospital environ- ments increasingly involve the coexistence of care- givers, patients, and automated or semi-automated technological systems. This growing technologi- cal density not only reshapes clinical practices and organizational processes but also contributes to evolving expectations regarding efficiency, pre- cision, and the role of technology in medical decision-making. However, despite the rapid imple- mentation of advanced technologies in healthcare settings in the U.S., the adoption of AI remains uneven across institutions [53]. The implementation of robotic systems in healthcare and care settings has also reshaped how caregivers conceptualize work organization, care relationships, and patient safety [55]. While these technologies are often promoted as tools for improv- ing efficiency, reducing workload, and optimizing care processes, their integration has also gener- ated significant ethical and professional concerns. In particular, scholars have questioned whether an increasing reliance on technological solutions may contribute to the reduction of care to a set of technical tasks, potentially undermining the rela- tional and emotional dimensions that are central to caregiving [55]. 2.5.2 Technology Adoption in Mexican and Chilean Healthcare Systems Mexico and Chile were grouped to represent a broader Latin American context characterized by comparatively lower levels of healthcare robot implementation, greater resource constraints, and sociocultural values surrounding care that may differ from those commonly described in the US. These countries also remain largely dependent on technological innovations developed in countries like the US [56,57]. This situation is primarily driven by comparatively low levels of investment in technological development, limited scientific infras- tructure, and a continued reliance on imported technologies [58]. Such asymmetries in techno- logical production reinforce existing patterns of dependency. Within this structural context, inequality remains a defining feature of the region, with wealth and social resources concentrated among a small segment of the population [59]. These dispar- ities contribute to stratified healthcare systems in which access to infrastructure, specialized person- nel, and advanced medical technologies is unevenly distributed across social classes and geographic regions, systematically disadvantaging rural and marginalized communities. Moreover, institutional arrangements, financing mechanisms, and patterns of technological diffusion often reinforce these inequalities by favoring already well-resourced sec- tors [59]. Consequently, technological innovation can have ambivalent effects: while new medi- cal technologies may improve healthcare access, efficiency, and reduce costs, they may also repro- duce or exacerbate existing inequalities when not adapted to local socio-economic and institutional conditions [57]. Research examining caregivers’ perceptions of social robots in Latin America remains limited. One notable contribution in the region is the study by Churata et al.[60], which investigated factors shaping the acceptance of social robots among Brazilian caregivers involved in older-adult care. The findings indicate that caregivers gener- ally perceived social robots as valuable tools for 6 supporting instrumental care tasks, particularly those related to safety monitoring and medica- tion management. At the same time, participants expressed concerns regarding technical reliability, maintenance requirements, and data security. Inter- estingly, acceptance was more strongly associated with caregivers’ perceived workload than with prior caregiving experience or familiarity with social robots. 2.5.3 Research Gaps and Questions The related literature provides the theoretical and empirical foundations for addressing the research questions that guide this study. In particular, it reveals two interrelated gaps. First, technol- ogy acceptance research has largely emphasized instrumental and functional determinants of adop- tion, while paying comparatively limited attention to how stakeholders interpret and evaluate the ethical implications of robotic design and imple- mentation. Second, despite the growing body of research on care robotics, comparative studies examining caregivers’ perceptions of care robots across diverse sociocultural contexts remain lim- ited. This is especially evident in comparisons involving stakeholders from highly technologized healthcare environments, such as the United States, and stakeholders from Latin American contexts, such as Mexico and Chile, which differ in terms of technological development. • RQ1: To what extent do acceptance-related per- ceptions of four categories of healthcare robots, measured using UTAUT, CAN constructs, and an overall rating, vary across three countries with distinct socioeconomic and technological contexts (the United States, Mexico, and Chile)? • RQ2: What similarities and differences emerge in stakeholders’ understandings of the ethical design and deployment of care robots when analyzed through the proposed literature-based ethical framework, and how can these variations be interpreted in relation to ethical reasoning and values-based considerations? 3 Methods This section describes the study design, data collection procedures, and analytical approach. 3.1 Participants A total of 298 participants from the United States (N = 152), Mexico, and Chile (N = 146 ), took part in this study. Participants were recruited through the Prolific platform using a purposive sampling strategy. Specific inclusion filters were applied to target individuals residing in the USA, Mexico, or Chile, who were fluent in English (in the case of the United States) or Spanish (in the case of Mexico and Chile), and who reported professional experience or affiliation within the healthcare sector. This sampling approach enabled the deliber- ate selection of participants relevant to the study’s objectives, ensuring that the sample reflected the sociocultural and professional characteristics necessary to examine variations in technology acceptance and ethical reasoning across socio- cultural contexts. The study lasted approximately 27 minutes, and participants were compensated with£10.23. 3.2 Experimental Setup To address the research questions, we employed a mixed-methods design that integrated both quantitative and qualitative analytical approaches within a mixed factorial structure, consisting of one within-subjects factor and one between- subjects factor. The within-subjects factor was robot type (Robots Delivering Supplies, Robots Helping Patients into Bed, Robots Monitoring Vital Signs, and Robots Assisting with Mobility), as all participants saw videos of each robot type and then evaluated each through questionnaires. The between-subjects factor was sociocultural con- text, operationalized through participants’ coun- try of residence and language, and subsequently grouped into two broader groups: English group (EN) and Mexico/Chile group (SP). At the beginning of the study, all participants received a general introduction to the concept of care robots. They then received a standardized written description of each of the four categories of the health robots included in the experiment (see Table 1, Figure 2), followed by a short video clip illustrating the operational functionality of the respective robots. The selected robot types were chosen for two main reasons: (1) they repre- sent distinct and sufficiently differentiated robot 7 (a) Robot Delivering Supplies(b) Robot Helping Patients into Bed (c) Robot Monitoring Vital Signs (d) Robot Assisting with Mobil- ity Fig. 2: Representative examples of the four healthcare robot categories included in the study: (a) autonomous delivery robot (TUG) [61], (b) patient-assistive robot (Robear) designed to support patient transfer and lifting tasks [62], (c) monitoring robot (Florence) used for patient assessment and vital-sign monitoring [63], and (d) rehabilitation exoskeleton designed to support mobility and physical recovery [64]. functions, allowing for the examination of how vari- ations in robot tasks and decision-making influence human acceptance; and (2) they involve different levels of potential risk and forms of human–robot interaction, thereby enabling an analysis of accep- tance across a continuum of ethical and practical concerns. After viewing each video, participants com- pleted a questionnaire consisting of quantitative measures based on the UTAUT and CAN scales, as well as an overall rating. In addition, after each video, participants responded to a set of open- ended questions designed to capture their ethical reflections. Figure 3 presents a schematic overview of the experimental procedure. Each participant evaluated all videos presented in randomized order, ensuring balanced exposure across conditions and enabling within-subject comparisons. Two parallel versions of the experimental materials were devel- oped: one in English and one in Spanish. Content consistency across both versions was verified by two researchers, one a native Spanish speaker and the other a native English speaker. Each version was accessed through a separate study link. 3.3 Measures For the analysis, two primary outcome variables were analyzed: 1) Acceptance of care robots, measured and compared across countries using con- structs derived from UTAUT, CAN, and an overall rating; and 2) Ethical and normative considera- tions examined through a qualitative-theoretical analysis to identify dimensions of ethical rea- soning and values-based judgments influencing acceptance. Table 1 Description of the four robot categories used as experimental stimuli. Robot TypeDescription Delivering Supplies (DS) Mobile robots that transport medica- tions, sterile equipment, and laboratory samples, reducing nursing workload and improving logistical efficiency, espe- cially under high-pressure conditions or isolation protocols. Helping Patients into Bed (HPB) Robots that assist caregivers in lifting and transferring patients with limited mobility, helping to prevent injuries and physical strain while improving safety and efficiency in patient-handling tasks. Monitoring Vital Signs Robots equipped with sensors to autonomously measure heart rate, blood pressure, respiratory rate, and oxygen saturation, improving accuracy and reducing human error in routine monitoring. Assisting with Mobility Exoskeleton-based or mobility-assistive robots that support rehabilitation by helping patients walk and stand, moni- toring posture, balance, and gait, and reducing caregiver workload and risk of falls. 3.4 Survey Instrument and Procedure The questionnaire was composed of two sections designed to capture participants’ perceptions, evaluations, and reflections. The structure and rationale of each component are described below. 3.4.1 Quantitative Measures: Selection and Adaptation of Scales The questionnaire combined constructs from the UTAUT model [18] and the CAN model [65]. UTAUT captures key determinants of technology acceptance, including Performance Expectancy, Attitudes Toward Using Technology, Self-Efficacy, and Behavioral Intention, whereas the CAN model complements these constructs by incorporating 8 Welcome Study Informed Consent Initial Scenario Test Robot 1 Description + Video UTAUT/CAN/Overall Rating Open-ended Questions Robot 2 Description + Video UTAUT/CAN/Overall Rating Open-ended Questions Robot 3 Description + Video UTAUT/CAN/Overall Rating Open-ended Questions Robot 4 Description + Video UTAUT/CAN/Overall Rating Open-ended Questions End of Study Fig. 3: Schematic representation of the experimental procedure, including participant onboarding, scenario assessment, and repeated evaluations of the four care robots. normative dimensions. Given the relational and ethically sensitive nature of care robots, integrat- ing both models enabled a more comprehensive assessment of acceptance. We also included an overall rating. Because the original UTAUT items were devel- oped for broader technology contexts, minor wording adaptations were introduced to improve ecological validity in healthcare settings. For exam- ple, references to ”technology” were replaced with ”the robot.” This adaptation approach is consis- tent with previous acceptance research [66,67] and preserves the theoretical meaning of the original scales. Table 2 summarizes the rationale and items included for each construct. 3.4.2 Open-Ended Questions on Ethical Considerations The online questionnaire included a set of optional open-ended questions for each robot type pre- sented. These questions explored stakeholders’ perspectives on the ethical implementation of healthcare robots and complemented the quanti- tative findings by providing richer insights into acceptance and ethical considerations. The follow- ing were the open-ended questions: • What are your main reasons for accepting or rejecting the use of this robot in caregiving tasks? You may refer to specific situations, feelings, or expectations you have. • Do you see any risks or ethical concerns in using this robot in healthcare? Please explain. • Based on your professional experience, what eth- ical issues do you think should be considered when developing or implementing this robot? 3.5 Analysis Strategy This subsection describes in detail the data analysis procedures employed in the study. 3.5.1 Quantitative Analysis Prior to conducting the statistical analyses, the internal consistency of all multi-item scales was assessed to ensure the reliability of the mea- surement instruments. Following established psy- chometric practice, composite scale scores were computed only after acceptable levels of internal consistency had been confirmed. Specifically, par- ticipants’ responses were aggregated across the items within each scale to obtain scale-level scores. Descriptive statistics (means and standard devi- ations) were then computed for each construct and robot type to provide an initial overview of participants’ evaluations and to inspect the distri- butional properties of the data. These descriptive results served as the basis for subsequent infer- ential analyses addressing the study’s research questions. To address RQ1 and examine differ- ences in participants’ evaluations across robot types, repeated-measures analyses of variance (RM-ANOVA) were conducted, with robot type specified as a within-subject factor and language as a between-subject factor. Separate models were estimated for each dependent variable, including performance expectancy, attitude toward using the robot, behavioral intention to use, ethical judgment, and overall acceptance. Interaction effects between robot type and lan- guage were tested to assess whether evaluations of different robot types varied across cultural con- texts. When omnibus effects were significant, post 9 Table 2 Overview of the scales used in the user study. For each item, participants indicated their agreement on a 7-point Likert scale. DimensionRationaleItems used in the study Performance Expectancy [68] Assesses the perceived usefulness of care robots for supporting clinical work and improving job performance. 1. I would find the robot useful in my job. 2. Using the robot would enable me to accomplish tasks more quickly. 3. Using the robot would increase my productivity. 4. If I used the robot, I would have more chances of getting a raise. Attitude Toward Using Technology [68] Assesses participants’ affective evaluations of interacting with care robots, including perceived enjoyment, interest, and satisfaction. 1. My interaction with the robot would be clear and understandable. 2. The system would make work more interesting. 3. Working with the system would be fun. 4. I would like to work with the robot. Self-Efficacy [68]Assesses caregivers’ confidence in their ability to use care robots effectively under different support conditions. I could complete a job or a task using the robot... 1. If there was no one around to tell me what to do as I go. 2. If I could call someone for help if I got stuck. 3. If I had a lot of time to complete the job for which the robot was provided. 4. If I only had the built-in help facility for assistance. Behavioral Intention to Use the System [68] Assesses caregivers’ willingness to use care robots in their future clinical practice. 1. I would intend to use the robot in the next 12 months. 2. I predict I would use the robot in the next 24 months. 3. I would plan to use the robot in the next 24 months. Ethical Judgments (CAN model) [19] Assesses ethical evaluations of care robots, including fairness, morality, and cultural acceptability. How would you rate the robot’s behavior in the video shown? 1. Unethical/Ethical; 2. Unfair/Fair; 3. Not morally right/Morally right; 4. Not acceptable to my family/Acceptable to my family; 5. Culturally unacceptable/Culturally acceptable; 6. Not personally satisfying/Personally satisfying; 7. Violates an unwritten contract/Does not violate an unwritten contract. Overall Rating (own)Helps evaluate the acceptance of human–robot collaboration. 1. Overall, I would rate the robot positively. 2. In general, I would support the integration of this robot in healthcare settings. hoc pairwise comparisons were conducted using Bonferroni-adjusted tests to control for multiple comparisons. Effect sizes (η2 or partialη2) were reported alongside p-values. 3.5.2 Directed Qualitative Content Analysis The analysis followed a directed qualitative content analysis approach [69]. A literature-informed ethi- cal framework, drawing on the work of [27], was developed to establish deductive analytical cate- gories while allowing for the inductive refinement of themes emerging from the data. To capture the diversity of ethical concerns expressed by participants, the framework integrated three com- plementary ethical perspectives: care ethics, the capability approach, and consequentialism (see eth- ical framework in Section 2.2.1). This combination enabled the analysis to remain grounded in estab- lished ethical theory while remaining sensitive to themes that emerged from the empirical data (see categories in Figure 4). The coding process was conducted collabora- tively by the first author and a research assistant 10 Fig. 4: Ethical framework for the evaluation of care robots. The framework combines concepts derived from Consequentialism, Care Ethics, and the Capabilities Approach with empirically derived concerns emerging from participants’ responses to open- ended questions. The circular structure reflects the interrelated and non-hierarchical nature of these perspectives in informing ethical evaluations of care robots. using QCAmap software. In the first stage, both researchers independently coded 20 percent of the data and subsequently compared and dis- cussed their coding decisions to establish a shared understanding of the coding scheme and refine it where necessary. In the second stage, the first author coded the remaining data and conducted the content analysis. All coding decisions, category assignments, and final interpretations remained the responsibility of the researchers. Through an iter- ative process of discussion and interpretation, the first and last authors reviewed, refined, named, and defined the themes. Any questions, ambiguities, or disagreements were discussed and resolved through joint meetings until consensus was reached. Open-ended responses were originally pro- vided in English and Spanish. To standardize the dataset for qualitative analysis, all Spanish- language responses were translated into English using a single machine-translation system via an API-based scripted workflow in RStudio. Original- language responses were preserved in a separate column for auditability. To enhance translation trustworthiness, translated entries were reviewed by a bilingual researcher. Figure 5 illustrates the qualitative analysis process that was carried out. 4 Results In this section, we present the key findings derived from both the quantitative and qualitative com- ponents of the study. Quantitative analyses were conducted using RStudio (version 2026.04.0) and 1. Research question, theoretical background 2. Definition of the category system from theory (Ethics of Care, Capability Approach, Consequentialism) 3. Definition of the coding guideline, containing, for all categories: definitions, anchor examples and coding rules 4. Material run-through, preliminary codings. Complementary anchor examples, coding rules 5. Revision of the categories and coding guideline after 20% of the material, compar- ison coding between researchers 6. Final coding and review of the material 7. Analysis, category frequencies and contingencies interpretation Fig. 5: Steps involved in the qualitative content analysis. JASP (version 0.19.3). Qualitative data were ana- lyzed using the web-based application QCAmap, in combination with RStudio. 4.1 Descriptive Statistics Table 3 presents the descriptive statistics (means and standard deviations) for all study constructs by robot type and language group. All scales demonstrated acceptable to very good internal con- sistency across robot types and language versions (Cronbach’sαranging from .80 to .96). Across con- structs and robot types, mean scores were generally above the midpoint of the scale, meaning that, in general, robots were positively rated. Mean values ranged from 3.55 to 6.59, with the lowest scores observed for behavioral intention and the highest scores observed for ethical judgment and overall ratings. Descriptive statistics are reported for par- ticipants with complete data and correspond to the sample included in the repeated-measures analyses. 4.2 Quantitative Results To examine differences in participants’ evaluations across robot types and to assess whether these eval- uations varied as a function of language group, a series of RM-ANOVA were conducted. Robot type (Delivery of Supplies, Helping Patients into Bed, Monitoring Vital Signs, and Assisting with Mobil- ity) was specified as a within-subject factor, and country group (English vs. Spanish) as a between- subject factor. Separate models were estimated for each dependent variable. Specifically, this analysis 11 Table 3 Descriptive statistics (Means and Standard Deviations) by robot type and language. ConstructRobot Type EN M EN SD EN α SP M SP SD SP α Performance Expectancy DS5.0381.558.875.3891.192.87 BED4.6781.661.905.1521.481.89 MVS5.0031.553.875.4451.212.87 AM4.8311.655.895.2771.368.89 Attitudes Toward Using Robot DS5.4111.339.895.8771.107.87 BED5.1151.559.915.8031.218.87 MVS5.3601.506.905.9931.084.86 AM5.4571.361.896.0451.025.87 Self-Efficacy DS5.3371.249.845.2671.221.80 BED5.1331.364.855.4591.222.85 MVS5.2881.356.845.4911.188.84 AM5.0231.320.845.2621.311.84 Behavioral Intention DS4.8511.912.963.6672.015.95 BED4.3492.010.963.7442.040.95 MVS4.5242.058.953.7791.969.92 AM4.3362.007.953.5462.016.93 Ethical Judgment (CAN) DS6.1091.103.956.2101.002.91 BED5.8161.346.956.1470.969.86 MVS5.8471.272.956.0701.200.91 AM6.1601.067.966.4500.814.88 Overall Rating DS5.8421.464.886.2091.210.89 BED5.4611.698.886.1031.390.89 MVS5.7341.467.886.0921.354.89 AM6.0691.270.886.5860.862.89 Note. Values are scale means; EN = English, SP = Spanish; α = Cronbach’s alpha. Table 4 Repeated-measures ANOVA results across dependent variables. VariableEffectFdf n ,df d pη 2 Performance ExpectancyRobot Type5.7143, 888 < .001.007 Language10.2971, 296.001.021 Robot Type× Language0.1923, 888.9022.445× 10 −4 Attitudes Toward UseRobot Type5.1593.002.006 Language23.1231< .001.049 Robot Type× Language0.6713.5707.339× 10 −4 Behavioral IntentionRobot Type4.0093.008.003 Language17.3061< .001.041 Robot Type× Language3.306 ∗ 3.020 ∗ .003 ∗ Self-EfficacyRobot Type4.3393, 888.005.005 Language2.0651, 296.152.005 Robot Type× Language3.005 ∗ 3, 888.030 ∗ .003 ∗ Ethical Judgment (CAN)Robot Type15.3673< .001.016 Language4.9101.027.011 Robot Type× Language1.4653.222.002 Note. RM-ANOVA = repeated-measures analysis of variance. Effect sizes are reported asη 2 . Greenhouse–Geisser corrections were applied where the assumption of sphericity was violated (Performance Expectancy, Self-Efficacy, and Ethical Judgment). ∗ Indicates statistically significant interaction effects between Robot Type and Language. aims to assess acceptance-related perceptions of four categories of care robots. The RM-ANOVA results (see Table 4) indicate that robot type had a consistent and statistically significant effect across all dependent variables, suggesting that participants’ evaluations system- atically varied according to the type of care robot [36,37]. Language also showed significant main 12 effects for most outcomes, with the exception of self-efficacy, indicating overall differences between language groups in the perceived acceptance and evaluation of care robots. Overall, both language groups tended to eval- uate the different robot types in relatively similar ways, as reflected by the largely non-significant interaction effects between Robot Type and Lan- guage. These findings point to a general cross- cultural consistency in perceptions of healthcare robots, suggesting that participants from different cultural contexts shared comparable evaluations of the various robotic applications. Moreover, although several effects reached statistical signifi- cance, their small effect sizes indicate that the prac- tical magnitude of these differences was limited. An exception to this pattern emerged for behav- ioral intention and self-efficacy, where significant interaction effects between robot type and lan- guage were observed. These findings suggest that, while attitudes toward care robots were gener- ally positive across groups, participants differed in their willingness to use these technologies and in their perceived ability to do so. Specifically, par- ticipants in the English-language group reported higher behavioral intentions to use care robots than participants in the Spanish-language group. Given the composition of the sample, these dif- ferences may reflect broader distinctions between the U.S. context and the Latin American contexts included in the study. Taken together, the results suggest that sociocultural context may play a more prominent role in shaping anticipated adoption and perceived competence in using care robots than in influencing broader attitudinal evaluations. Bonferroni-adjusted post hoc comparisons (see Table 5) revealed that, in the performance expectancy dimension, robots helping patients into bed (BED) received less favorable evaluations than robots delivering supplies (DS) and monitoring vital signs (MVS). In the ethical judgment dimen- sion (CAN model), assisting with mobility (AM) robots received more favorable evaluations than BED and MVS robots, while DS robots were also evaluated more positively than BED and MVS robots. Although descriptive trends (see Table 3) suggested lower evaluations for BED robots in atti- tudes toward using robots, Bonferroni-adjusted pairwise comparisons did not reveal consistent significant differences across all robot categories. Table 5 Significant Bonferroni-adjusted post hoc comparisons. OutcomeComparison MD p bonf Performance Expectancy DS > BED0.298 .004 BED < MVS−0.309 .002 Ethical Judgment (CAN) DS > BED0.178 .029 DS > MVS0.201 .004 DS < AM−0.145 .041 BED < AM−0.323 < .001 MVS < AM−0.346 < .001 Note. Only statistically significant Bonferroni-adjusted pair- wise comparisons are presented. Mean differences retain the original comparison direction reported in the post hoc anal- yses. Symbols indicate the direction of comparatively more favorable evaluations between robot categories. DS = Deliv- ering Supplies; BED = Helping Patients into Bed; MVS = Monitoring Vital Signs; AM = Assisting with Mobility. Overall, these comparisons suggest that health- care workers differentiated between forms of physical assistance, evaluating robots involved in direct patient handling less favorably than robots performing logistical or monitoring functions. Like- wise, this distinction suggests that not all phys- ically assistive robots are perceived equally, and that the acceptability of robotic care may depend on the specific nature of the caregiving task being performed. These findings support prior research suggesting that acceptance of care robots is task- dependent and shaped by the specific caregiving functions performed by the technology [36–39]. 4.3 Qualitative Results In the following section, we present the most salient qualitative findings of the study. The analysis is organized into three parts. First, we examine par- ticipants’ ethical perceptions of care robots at a general level. Second, we explore how ethical evaluations varied according to the specific char- acteristics, functions, and tasks performed by the different robot categories. Finally, we analyze cross- cultural similarities and differences in participants’ ethical assessments, highlighting how sociocultural contexts shaped the interpretation, prioritization, and negotiation of ethical concerns surrounding care robot implementation. 4.3.1 Ethical Themes in Participants’ Responses Table 6 provides an overview of the ethical con- siderations identified in participants’ responses 13 regarding the acceptance and potential rejection of care robot technologies. To analyze these responses, we draw on the categories derived from the theoret- ical framework described in section 3.5.2. Table 6 therefore identifies both the theoretical origins of each category and those that emerged inductively during the coding process. Importantly, the interpretation of these cate- gories does not rely on a simple distinction between positive and negative evaluations. Participants fre- quently invoked the same ethical considerations to both support and criticize the use of care robots, highlighting the complex and often ambiva- lent nature of ethical reasoning surrounding these technologies. Overall, the theoretically derived categories captured a substantial proportion of par- ticipants’ responses, suggesting that the proposed framework provides a useful lens for understanding stakeholders’ ethical evaluations of care robots. In the following sections, we elaborate on how these ethical considerations emerged across the different analytical categories. 4.3.2 Cross-Cultural Comparison of Ethical Evaluations Based on the qualitative findings, some socio- cultural differences emerged in participants’ ethical perceptions regarding the use of care robots. How- ever, these differences were relatively limited, suggesting that participants across cultural con- texts generally identified similar ethical benefits and risks associated with robotic care technolo- gies. Nevertheless, some interpretative differences became evident across specific thematic categories. For instance, among participants from Mexico and Chile, the concept of impartial evaluation was pre- dominantly associated with the economic capacity to acquire or access robotic systems, reflecting concerns related to inequality and accessibility in healthcare technologies. In contrast, participants from the US tended to associate impartial eval- uation with concerns regarding algorithmic bias, discriminatory outcomes, and biases embedded within datasets used to train robotic systems. Similarly, when discussing data privacy, par- ticipants from the US consistently referred to compliance with the Health Insurance Portabil- ity and Accountability Act of 1996 (HIPAA), emphasizing the importance of adhering to federal regulations designed to protect the privacy, secu- rity, and integrity of Protected Health Information. Although participants from Mexico and Chile also raised concerns regarding data privacy and con- fidentiality, they generally did not connect these concerns to specific regulatory frameworks or legal standards. Additional contextual differences were observed in the way participants framed ethical concerns surrounding robotic systems. Responses from the US more frequently emphasized issues related to harm reduction, safety, accountability, and respon- sibility for foreseeable consequences, suggesting a stronger focus on efficiency, governance, and regulation. In contrast, participants from Mexico and Chile more commonly referred to contextual adaptation, capability enhancement or deprivation, and the relational dimensions of care, emphasiz- ing the importance of understanding the specific social and emotional realities in which robots are implemented. One particularly salient finding concerned the use of robots in psychiatric and pediatric care settings. Across both cultural contexts, par- ticipants frequently expressed skepticism about implementing robots in these environments. Many emphasized that patients in such settings may have difficulty understanding what robots are or how they operate, potentially resulting in fear, emo- tional distress, mistrust, or even accidental harm. 1 Overall, the findings suggest that ethical percep- tions regarding care robots are shaped not only by universal concerns surrounding healthcare tech- nologies, but also by the social, economic, and regulatory contexts in which these technologies are interpreted and evaluated. 4.3.3 Ethical Evaluations by Robot Type Regarding the comparison between different robotic systems, certain ethical categories were more frequently associated with specific types of robots, suggesting that participants did not evalu- ate robotic technologies as homogeneous systems, but rather interpreted their ethical implications 1 This interpretation is further supported by an exploratory part of the study in which participants were asked to iden- tify the healthcare settings in which care robots would be most useful. Across both cultural contexts, psychiatric settings were consistently evaluated less favorably than other medical environments (see Figure 6 in the supplementary material). 14 Table 6 Overview of ethical categories, analytical interpretations, and ambivalent evaluations across participants’ responses. Category Definition Analytical interpretation Positive/Neutral quote Critical/Neutral quote Welfare max-imization (Consequential- ism) Ethical argumentsemphasizing that carerobots are perceivedas desirable when theyenhance efficiency,improve well-being,and reduce workload,and as undesirable when they fail to con- tribute meaningfullyto these aspects. This category was among the most fre-quently invoked across both culturalcontexts. Participants commonly framedrobots as tools for improving efficiency andreducing workload by streamlining routinetasks. However, they also questioned whetherthese anticipated benefits would be realizedin practice or whether robot adoption mightinstead introduce new forms of workplace dis-ruption. “It would free up a person to workon something else”. (Robots Deliver- ing Supplies) “I consider that this type of robot is extremely necessary and beneficialin the health area. It could help alot of patients. In addition, it wouldalso benefit caregivers who usuallyrely on their own physical strengthfor these tasks.” Robots Assisting with Mobility). “Although the robot helps me tomake the movement and to load thepatient, it still has to be watched atthe time of performing the action, soI can not be so productive because Ihave to be watching that the move-ment is done correctly and that itdoes it a little slow, so I could losetime if it is done in a place withmany patients.” (Robot Helping Patients into Bed) “I feel that taking vital signs myself is faster than using the robot. It wouldalso only be usable with patients whocan actively participate in the pro-cess.” (Robots Taking Vital Signs) . Harm reductionand safety (Con- sequentialism) Ethical considerationsemphasizing that carerobots are morally jus-tified insofar as theyreduce risk, preventharm, or minimizenegative outcomes.Conversely, they areunjustified if theycompromise individ-uals’ well-being bycausing accidents orharm. Safety, alongside welfare maximization,emerged as one of the most salient moralconcerns. Participants viewed care robotspositively when they were perceived as reduc-ing errors, physical strain, burnout, or harmto patients. Conversely, robots were also seenas potential sources of accidents, malfunc-tions, or loss of control, particularly duringphysically intimate care tasks. Participantsfurther stressed that safety depends not onlyon technical performance but also on care-givers’ responsible use of these systems. “I believe it would facilitate thetransfer of overweight patients andprevent accidents/injuries to nurses,orderlies and physiotherapists.” (Robots Assisting with Mobility) . “I think it is a wonderful advance- ment in technology. It is incrediblyhard to support the weight of a per-son and assist in mobility and itis also riskier for injuries to bothclient and caregiver without adevice like this. (Robots Assisting with Mobility) . “I would only be afraid of it hurting,dropping someone, or malfunctioning.” (Robots Helping Patients into Bed) “Robots need thorough testing to prevent malfunctions that mightdelay or misdeliver important itemslike medications or lab samples .” (Robots Delivering Supplies) Impartialevaluation (Con- sequentialism) Ethical argumentsemphasizing fairness,equal considera-tion, and balancedevaluation acrossstakeholders. This category captured responses in whichparticipants evaluated care robots in termsof their consequences for multiple stakehold-ers. Participants expressed concerns aboutdistributive justice, questioning whetherrobotic technologies would reinforce orreduce existing inequalities. In particular,they highlighted unequal access to robottechnologies. Participants also raised con-cerns about algorithmic bias, emphasizingthat AI systems trained on biased data mayproduce discriminatory outcomes. “I don’t see how the robot can poseany ethical problems as long as ittreats each person equally.” (Robots Delivering Supplies) “I think as long as the robot remains neutral and it doesn’tassess the patient based on anythingthat would be considered as beingprejudiced or biased, there would beno problems with ethics” (Robots Helping Patients into Bed) . “Equity and accessibility should beaddressed. The technology should notonly be available to well-funded hos-pitals but also accessible to smallerfacilities and diverse patient popula-tions.” (Robots Helping Patients into Bed) . “Fairness. Developers must ensure that the robot does not reinforcebiases or discriminate against anygroup of patients.” (Robots Deliver- ing Supplies) . Note. Categories identified inductively from the data are indicated with ∗ . 15 Category Definition Analytical interpretation Positive/Neutral quote Critical/Neutral quote Responsibilityfor foreseeableconsequences (Care Ethics) Ethical argumentsemphasizing anticipa-tion of future risks,unintended effects,and responsibility forforeseeable outcomes. Participants used this category to expressa future-oriented ethical stance centered onpreventing foreseeable risks associated withcare robots. They emphasized that hospi-tals, designers, and healthcare institutionsshould establish clear protocols and responseprocedures before implementation to miti-gate potential harms. Participants also raisedconcerns about accountability in complexsocio-technical systems, particularly whenresponsibility for errors or adverse outcomesmay become diffuse or unclear. “Hospitals need systems in placeto track deliveries accurately andassign responsibility if errors occur,such as delivering the wrong medica-tion.” (Robots Delivering Supplies) “Accountability: Is the manufac- turer, the hospital, the managingtherapist, or the robot’s program-mer legally and morally liable if asystem malfunctions and a child ishurt? Prior to implementation, it isnecessary to establish clear lines ofaccountability.” (Robots Assisting with Mobility) “Accountability matters if a robotmalfunctions during a transfer who isresponsible? The nurse?The hospitalquestionmark? the manufacturerquestionmark? ethical use demandscrystal clear accountability and fail-safes because one error could meana serious injury” (Robots Helping Patients in Bed) “What if the robot supplies the wrong drugs to the wrong room. Who takesthe blame?” (Robots Delivering Supplies) Relational careand humanoversight (Care Ethics) Ethical argumentsemphasizing humanrelationships, emo-tional connection,interpersonal pres-ence, and appropriatehuman oversight. Participants frequently emphasized thatrobots should support rather than replacehuman caregivers. Responses reflected a ten-sion between the efficiency of robotic systemsand the ethical importance of empathy, emo-tional warmth, and human presence. Whilesome viewed robots as valuable complementsto care, they stressed that meaningful humanoversight must remain central and thatresponsibility for decisions affecting patients’health should always rest with caregivers. “Human oversight should alwaysbe maintained, ensuring staff canintervene if needed” Robots Helping Patients into Bed . “The robot should be designed to support patient dignity, motivation,and emotional well-being withoutreplacing essential human interac-tion.” Robots Helping with Mobility “Over-reliance on automated mon-itoring may reduce direct humaninteraction, which is important forpatient comfort, trust, and emotionalsupport.” Robots Monitoring Vital Signs “Patients may feel a loss of personal dignity or autonomy if they aremoved by a machine instead of ahuman caregiver.” (Robots Helping Patients into Bed) ∗ Robots literacy Ethical considera-tions emphasizingeducation, training,and understanding ofrobotic systems. Participants emphasized that the successfulimplementation of care robots depends onproviding patients, caregivers, and healthcarestakeholders with clear guidance on how therobot operates and what it is designed to do.Such explanations were considered essentialfor reducing uncertainty, facilitating appro-priate interaction, and establishing realisticexpectations. Conversely, insufficient under-standing of robotic systems was associated with confusion, misuse, and unrealistic expec- tations about the robot’s capabilities. “As long as a person is trained,there should not be risks”. Robots Assisting with Mobility “Just mak- ing sure the patient has a goodunderstanding of how the robot willoperate in assisting staff with theircare.” Robots Helping Patients into Bed “I can see where some patients maybe fearful of the robot-assisted medcart moving. Educate the patientsthat this is what is going to be hap-pening.” Robots Delivering Supplies “Risk of lack of staff training.” Robots Assisting with Mobility Capabilityenhancementand depriva-tion (Capability Approach) Ethical argumentsaddressing theimpact of robots onautonomy, agency,independence,dependence, andopportunities formeaningful participa-tion. This category captured the ambivalence sur-rounding robots as both capability-enhancingand capability-restricting technologies. Someparticipants viewed robots as tools thatcould expand patient autonomy, mobility,independence, and participation in health-care. Others, however, expressed concernthat excessive reliance on robotic systemscould reduce healthcare workers’ activeinvolvement, weaken professional expertise,or limit human decision-making. “It seems to me that any tool thatis capable of providing a patient with a greater degree of auton- omy is a tool that should not beoverlooked and should be consid-ered when evaluating the qualityof medical care and quality of lifeof the patient.” (Robots Assisting with Mobility) “I am in favor of its use to support mobility therapies,help people to walk again and givethem a more dignified life.” (Robots Assisting with Mobility) “None, they will only make traineesand nurses ”lazier”, since in thefuture they may stop teaching howto take vital signs.” (Robots Taking Vital Signs) “Over-reliance on automation: Employees may grow unduly relianton the robot and fail to notice criticalclinical cues that call for human judg-ment.” (Robots Taking Vital Signs) Note. Categories identified inductively from the data are indicated with ∗ . 16 Category Definition Analytical interpretation Positive/Neutral quote Critical/Neutral quote ∗ Data privacy, security, andinformed con-sent (Capability Approach) Ethical considerationsrelated to sensitiveinformation, data pro-tection, cybersecurity,and consent to inter-act with robots. Participants associated robotic implemen-tation with risks related to data collection,storage, surveillance, and institutional trust. While some viewed the storage of personal data as enhancing safety and security, othersperceived it as increasing surveillance andreducing privacy. Participants emphasizedthat acceptable implementation depends oninformed consent and individuals’ freedom todecide whether to use robotic systems. Par-ticular concerns were raised about obtaininginformed consent from psychiatric patientsand individuals with mental illnesses. “It can help to have a more accu-rate record of patient data withouthaving to annotate, and reduces therisk of human error.” (Robots Moni- toring Vital Signs) “None at all. I fully recommend these robots. Of course, we wouldneed to obtain the patient’s consentor the family member of the patientif the patient has cognitive impair-ments such as dementia/Alzheimer.” (Robots Helping Patients in Bed) “Data protection and confidential-ity are major concerns. These robotsoften rely on sensors, cameras, or AIsystems that record sensitive healthinformation, so developers mustensure strict data encryption andcompliance with healthcare privacyregulations.” Robots Helping Patients into Bed “Data privacy and security is also important, as these robots oftenoperate through centralized systemsthat track deliveries and item details.” (Robots Delivering Supplies) Contextualization (Capability Approach) Ethical argumentsemphasizing adap-tation to individualneeds, abilities, cir-cumstances, and carecontexts. Participants rejected one-size-fits-allapproaches to robotic implementation,emphasizing that the appropriateness ofrobots depends on patients, tasks, and carecontexts. They expressed particular concernabout psychiatric and pediatric settings, where patients may not fully understand or feel comfortable interacting with roboticsystems, increasing the risk of distress. Par-ticipants also stressed that robots shouldrespect patients’ wishes and preferences andbe adapted to the architectural and spatialconditions of hospitals. “It is only a robot that assists inmobilization and rehabilitation, sothere are not really many ethicalissues to comment on, only thatthe robot is well adapted to eachperson’s mobility needs.” (Robots Assisting with Mobility) “When implementing this in hos- pitals, they should test differentpopulations within a hospital. That way they could see what type of population receives this type ofrobot more appropriately and, onthe other hand, be able to adapt dif-ferent types of robots for differentpopulations.” (Robots Monitoring Vital Signs) “Care should be adapted to eachperson, which may be difficult forstandardized technology.” (Robots Delivering Supplies) “I believe that in specific cases, such as pediatric, geriatric or psychiatricpatients, it could become a problem,especially for this public to under-stand the way in which they are used.” (Robots Monitoring Vital Signs) ∗ Job loss Ethical concernsrelated to replace-ment of human laborand socio-economicconsequences ofautomation. This category was among the least frequentlymentioned, and all responses expressed neg-ative views. Participants raised concernsabout job displacement and the impactof robotic technologies on the healthcare workforce. Consistent with the category of relational care and human oversight, manyemphasized that robots should supportrather than replace caregivers, warning thateconomic and efficiency-driven incentivescould ultimately favor robotic over humancare. None “As a matter of ethics, people losingtheir jobs as they are replaced byrobots or AIs.” (Robots Delivering Supplies) “This model can do many things, and I would be a bit concerned about joblosses.” (Robots Monitoring Vital Signs) Note. Categories identified inductively from the data are indicated with ∗ . 17 according to the specific forms of care, task, interaction, and vulnerability involved in each context. Overall, participants appeared to dif- ferentiate between robotic systems that support bodily assistance and patient autonomy, and those that mediate clinical judgment, monitoring, or interpersonal interaction. For example, the category ”capability enhance- ment and deprivation” was particularly promi- nent in discussions concerning robots assisting with mobility and robots monitoring vital signs; however, the connotations associated with this category differed substantially between the two sys- tems. In the case of robots assisting with mobility, participants generally used this category positively, frequently describing the robot as a tool capable of supporting patient autonomy, improving mobility, and facilitating rehabilitation, particularly in situ- ations involving physical dependence or reduced movement capacity. In contrast, discussions surrounding robots monitoring vital signs reflected a predominantly negative interpretation of the same category. Par- ticipants frequently associated these systems with the potential loss or deterioration of caregivers’ clinical skills, particularly regarding the man- ual assessment and monitoring of vital signs. Rather than viewing automation as enhancing, participants often perceived these systems as poten- tially replacing human competencies and reducing direct clinical engagement. These findings sug- gest that participants distinguished between forms of automation perceived as augmenting human care and those perceived as substituting essential human skills and interactions. Some illustrative quotes related to these findings can be found in Table 6. Notably, robots assisting with mobility and those helping patients into bed were more fre- quently associated with promoting dignified care. Participants often emphasized that assistance with mobility and bodily support could enhance patients’ dignity by reducing situations in which they feel exposed, dependent, or physically vul- nerable in the presence of others. Interestingly, these findings suggest that participants did not necessarily associate robotic intervention with dehumanization; rather, in certain physically sen- sitive situations, robotic assistance was perceived as potentially less invasive and more dignity- preserving than human intervention alone. At the same time, robots assisting patients into bed were also strongly associated with the cat- egory ”harm reduction and safety”. Participants frequently expressed concerns regarding the possi- bility of technical malfunction, mechanical errors, or physical accidents that could directly harm vul- nerable patients during bodily transfers. These findings reveal an important ethical tension in par- ticipants’ perceptions: while robotic systems were recognized as capable of preserving dignity and reducing physical dependency, they were simul- taneously perceived as introducing new forms of physical risk in highly vulnerable care situations. Similarly, robots monitoring vital signs and delivering supplies were more frequently associated with the category of relational care and human oversight. Participants emphasized that activities such as monitoring vital signs are not merely tech- nical procedures but also important opportunities for interpersonal connection, emotional support, and human presence. Although these robots were generally viewed positively, participants consis- tently stressed that their use should remain subject to human and clinical oversight, expressing greater concern about fully autonomous care systems lacking human validation and accountability. Overall, the qualitative findings regarding the robot types indicate that participants’ ethical evaluations of care robots were strongly shaped by the type of human relationship affected by the technology. Robotic systems were generally perceived more positively when they supported patient autonomy, reduced physical vulnerability, or preserved dignity, whereas greater skepticism emerged when robots were perceived as replac- ing human interaction, reducing relational care, or eroding clinical competencies. 5 Discussion The goal of this study was to investigate caregivers’ acceptance of four categories of care robots across the United States, Mexico, and Chile; and second, to evaluate a literature-informed ethical framework for analyzing stakeholders’ ethical perceptions of these technologies. Three key findings emerged from our analyses. First, participants generally expressed more favorable attitudes toward robots performing logistical and physically demanding tasks than toward robots operating in contexts requiring close interpersonal interaction. Second, 18 although participants from Mexico and Chile reported positive evaluations across most accep- tance dimensions, they expressed lower behavioral intentions to use these technologies, suggesting greater uncertainty regarding their practical imple- mentation in local healthcare systems. Finally, the qualitative findings revealed both shared and context-specific ethical concerns related to the design, deployment, and governance of care robots, highlighting how sociocultural values and robot type shape their acceptance. For the discussion, we focus on integrating the quantitative and qualitative findings to provide a comprehensive understanding of participants’ perceptions, evaluations, and ethical reflections regarding care robots across different sociocultural contexts, as well as to discuss some of the most latent ethical. 5.1 Task-Dependent Acceptance: Relational and Contextual Dimensions Some of the qualitative data provided comple- mentary insights that helped explain participants’ quantitative evaluations of care robots. Consistent with previous research [11,35–37,40], acceptance varied across the four robot categories, although the overall evaluation patterns remained broadly consistent across cultural contexts. One of the clearest findings was that robots designed to help patients into bed received less favorable evaluations than the other robot types. Qualitative responses suggested that, although participants frequently associated these robots with greater patient auton- omy, enhanced dignity, and reduced dependence on caregivers for mobility-related or potentially embarrassing situations, they also perceived them as particularly high-risk technologies. Concerns about accidents, physical harm, loss of control, and technological malfunction appeared to outweigh sometimes their perceived benefits [70]. Another prominent quantitative finding was that, within the ethical judgment dimension (CAN model), robots assisting with mobility received more favorable evaluations than robots help- ing patients into bed or monitoring vital signs. Although all three involve direct patient inter- action, mobility-assistance robots are typically perceived as wearable technologies that support, rather than independently perform, caregiving tasks [35]. This interpretation was reinforced by the qualitative findings, in which mobility-assistance robots were frequently associated with positive ethical outcomes, particularly in relation with increasing autonomy and enhancing quality of life (capability enhancement and deprivation cat- egory), whereas robots monitoring vital signs were more often linked to negative ethical evalua- tions. Together, these findings suggest that ethical evaluations of care robots depend not only on the presence of physical interaction but also on how agency, control, vulnerability, and assistance are embodied within the caregiving relationship. Technologies perceived as supporting patients’ autonomy, rather than replacing caregivers, appear to be viewed as more ethically acceptable [38]. Similarly, the quantitative analyses revealed generally limited interaction effects between robot type and language, although behavioral intention varied across language groups for some robot cate- gories [47–49]. Participants from Mexico and Chile consistently reported lower behavioral intention scores than those from the United States. One pos- sible explanation for this pattern emerges from the qualitative findings, as participants from Mex- ico and Chile frequently referred (coded under the category of contextualization) to the high costs of care robots and unequal access to technological resources. These concerns may reflect the chal- lenges faced by countries that rely primarily on imported healthcare technologies, where acqui- sition, maintenance, and implementation costs constitute substantial barriers to adoption, par- ticularly for healthcare institutions with limited financial and infrastructural resources [56–59,71]. 5.2 Beyond Instrumental Acceptance: Moral Tensions in Care Robotics The qualitative analysis, guided by the theoret- ical framework proposed in this study, showed that users’ ethical perceptions of care robots are shaped by both the sociocultural context and the robots’ characteristics and functions. Rather than being stable or purely instrumental, ethical evalua- tions emerged as relational and context-dependent negotiations between competing moral values [29]. Participants frequently evaluated the same robotic systems from multiple, and sometimes conflict- ing, ethical perspectives, highlighting the moral 19 complexity of technologies that mediate practices of care [38]. What some participants perceived as ethically beneficial, others interpreted as eth- ically problematic or potentially harmful. Next, we discuss some of the most important ethical tensions. 5.2.1 Welfare Maximization Within the category of welfare maximization, par- ticipants acknowledged that robots designed to help patients into bed or monitor vital signs could reduce the physical burden associated with patient handling and continuous supervision, thereby pro- moting overall well-being. However, they also emphasized that introducing these technologies into clinical workflows may generate new cognitive, technical, and organizational demands. In particu- lar, caregivers would need to learn not only how to operate the robots but also how to coordinate their use within everyday care practices. In this regard, this category was linked to Robots Literacy. Conse- quently, the anticipated gains in efficiency were not perceived as automatic, as the additional workload associated with implementation could partially off- set the robots’ practical benefits [22,72]. These findings illustrate how values commonly associated with technological innovation, such as efficiency and welfare maximization, become ethically and practically contingent when embedded within the organizational and relational realities of care work [27, 73]. 5.2.2 Harm Reduction and Safety The category of harm reduction and safety revealed important ethical tensions regarding the role of automation in caregiving environments. Partici- pants frequently emphasized that care robots could reduce risks and physical strain for healthcare personnel, for example by minimizing medication delivery errors when robots deliver supplies or by preventing back injuries and physical exhaustion when robots help patients into bed. Nevertheless, participants repeatedly expressed concerns regard- ing the possibility of mechanical failures, malfunc- tions, or loss of control, which could ultimately generate even greater forms of harm [28]. These findings suggest that participants viewed robotic technologies as shifting, rather than elimi- nating, risks within the caregiving process. While robots were perceived as reducing certain physical and operational risks, they were also seen as intro- ducing technological vulnerabilities that require continuous human oversight, clear operational pro- tocols, and well-defined accountability structures. Accordingly, concerns about harm reduction and safety were closely intertwined with the categories of responsibility for foreseeable consequences and relational care and human oversight [27]. 5.2.3 Capability Enhancement and Deprivation The category Capability enhancement and depri- vation revealed important tensions in how partici- pants understood autonomy in relation to the use of care robots. On the one hand, some robotic tech- nologies were frequently associated with increased independence, improved mobility, and enhanced support for both patients and caregivers. On the other hand, participants also expressed con- cerns that excessive reliance on robotic systems could foster new forms of technological dependency that might ultimately undermine autonomy itself [39, 74]. Importantly, participants suggested that the increasing delegation of caregiving tasks to robots could contribute not only to the erosion of prac- tical and interpersonal caregiving competencies [75], but also to broader forms of social isolation, loneliness, and the weakening of human interac- tion within caregiving environments [55,76,77]. In this sense, autonomy was not understood as a sta- ble condition, but rather as something relationally constructed and potentially transformed through human–robot interactions. This category was con- sistently connected to relational care and human oversight, suggesting that participants perceived the ethical integration of care robots as dependent not only on technological functionality, but also on the preservation of meaningful human relation- ships and on users’ ability to critically understand, supervise, and appropriately engage with these systems. 5.3 Implications for Care Robot Design and Implementation Based on the findings of the present study, several practical implications can be derived for the design and implementation of care robots. 20 First, although participants across the exam- ined cultural contexts generally expressed positive attitudes toward care robots, subtle differences in perceptions and concerns may influence their accep- tance in diverse sociocultural settings. These differ- ences were particularly evident for robots expected to operate in situations involving close interper- sonal interaction and physical proximity, such as those monitoring patients’ vital signs or assisting patients into bed. The findings therefore highlight the importance of moving beyond purely techni- cal considerations and adopting a user-centered, culturally sensitive design approach that carefully considers how a robot’s appearance, behavior, com- munication style, and decision-making processes shape users’ trust, comfort, and sense of control. Second, the findings highlight the importance of considering the socioeconomic contexts in which care robots are expected to be deployed. While research on care robotics often emphasizes techni- cal performance and usability, the present study suggests that economic accessibility is also a key determinant of public acceptance. Developers should therefore design care robots with afford- ability and accessibility in mind, adapting both the technology and its implementation to the real- ities of different healthcare systems. Importantly, affordability should be regarded not only as a mar- ket consideration but also as an ethical design principle, as technologies developed primarily for high-income healthcare systems may exacerbate existing inequalities in access to care. Third, the findings raise important concerns regarding the use of care robots in mental health and psychiatric settings. Although robotic tech- nologies are increasingly being developed to sup- port individuals with mental health conditions [78– 80],participants feared that robots could increase stress, anxiety, confusion, or emotional distress among vulnerable patients. These findings sug- gest that deploying care robots in mental health settings should be approached with caution, sup- ported by rigorous risk–benefit assessments and continuous evaluation to ensure patient well-being, particularly for populations that may be dispro- portionately affected by technological failures or inappropriate interactions [81]. Finally, the findings of this study suggest that the ethical and practical factors influencing the acceptance of care robots should not be under- stood as inherently positive or negative. Rather, participants frequently perceived the same robot system as both beneficial and potentially prob- lematic, revealing a fundamental ambivalence in how these technologies are evaluated. This high- lights the importance of recognizing and addressing such ambivalence throughout the design process. Instead of focusing exclusively on maximizing func- tionality or usability, developers should adopt a more holistic and human-centered approach that considers the complex and sometimes conflicting expectations users hold toward care robots. 6 Limitations Several limitations of this study should be acknowl- edged. First, although statistically significant dif- ferences were identified, most quantitative effects were small in magnitude, suggesting that the find- ings should be interpreted as general tendencies rather than strong predictive relationships. Sec- ond, the study focused on participants from the United States, Mexico, and Chile; therefore, the findings cannot be generalized to all sociocultural or healthcare contexts. Additionally, participants evaluated hypothetical care robot scenarios rather than interacting directly with robotic systems in real clinical settings, which may differ from actual experiences of use and implementation. We also only evaluated four types of care robots, however, the interpretation might change with those types. Finally, while the qualitative analysis provided important interpretive insights, the proposed eth- ical categories remain theoretically situated and interpretive in nature, meaning that alternative analytical perspectives and frameworks could lead to different interpretations of the data. 7 Conclusion Overall, the present analyses demonstrate that caregivers’ acceptance of care robots is shaped by a wide range of interconnected factors, includ- ing the specific type of robot, the sociocultural context in which it is introduced, and the charac- teristics, expectations, and concerns of the different stakeholders involved. The findings further suggest that care robots are not evaluated solely accord- ing to their technical functionality or efficiency, but also according to the particular forms of care- giving relationships they mediate. In this sense, participants’ evaluations appeared to be strongly 21 influenced by how different robotic systems con- figured autonomy, bodily vulnerability, control, intimacy, assistance, and human oversight within caregiving interactions. Moreover, the empirical findings revealed the ethical complexity involved in evaluating care robots, as participants frequently articulated con- tradictory moral evaluations, competing ethical priorities, and tensions between different values. Rather than relying on isolated ethical princi- ples, participants often evaluated these technolo- gies through interconnected ethical values that attempted to account for caregiving practices in a more holistic, relational, and context-sensitive manner. Ultimately, the findings highlight the impor- tance of developing care robotic technologies that are not only functional and efficient, but also socially meaningful, culturally responsive, and aligned with the values, needs, and lived realities of the communities in which they are intended to operate. More broadly, the study demonstrates that understanding care robot acceptance requires moving beyond instrumental evaluations of technol- ogy to a more complex ethical discussion in order to examine how robotic systems transform the social, relational, and ethical dimensions through which care itself is understood and practiced. Funding. This work was funded by the Carl Zeiss Foundation with the ReScaLe project and the German Research Foundation (DFG) Emmy Noether Program grant number 468878300. Data Availability. The complete dataset and coding materials have been deposited in a pub- lic repository and will be made available upon reasonable request. 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This question was included to quanti- tatively examine whether different types of robots were perceived as more suitable for particular healthcare contexts than others (see Figure 6). The results revealed a remarkably similar pattern across cultures. In both language groups, robots were consistently perceived as less appropriate for psychiatric settings than for any other healthcare context. This finding complements the qualitative results, in which participants repeatedly expressed concerns that robot deployment in psychiatric envi- ronments could increase stress, anxiety, confusion, or emotional distress among vulnerable patients. 27

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