The role of psychological comfort with service robot reminders: a dyadic field study

Quincy Merx, Mark Steins, Gaby Odekerken

Journal of Services Marketing

ISSN: 0887-6045

Open Access. Article publication date: 7 January 2025

369

Abstract

Purpose

This study aims to propose a service robot option to address shortages of human frontline employees (FLEs) in long-term care (LTC) service settings. With a field study, the authors investigate the effect of psychological comfort with robot reminders of LTC residents and human FLEs on acceptance and attentive engagement, ultimately resulting in effort and willingness to interact with the service robot. The outcomes provide valuable insights into human-robot interaction in the LTC sector.

Design/methodology/approach

The 45 residents and 49 human FLEs who participated in the field study completed a survey measuring various perceptual variables after deploying a service robot.

Findings

Both the residents’ sample and the FLE sample demonstrate that psychological comfort with robot reminders increases robot acceptance. This increased acceptance evokes greater attentive engagement, ultimately leading to a higher willingness to exert effort to interact with the service robots.

Research limitations/implications

This study highlights service robots with well-received reminder functions and the ability to prompt efforts by both residents and employees during their implementation at LTC services. The findings suggest further research avenues for designing service robots that can be effectively integrated.

Originality/value

This study leverages a service robot in a field study involving LTC residents and human FLEs rather than hypothetical scenarios, which is rather limited in current studies. The findings are both timely and relevant, considering the gradual implementation of service robots into LTC services.

Keywords

Citation

Merx, Q., Steins, M. and Odekerken, G. (2025), "The role of psychological comfort with service robot reminders: a dyadic field study", Journal of Services Marketing, Vol. 39 No. 10, pp. 1-14. https://doi.org/10.1108/JSM-12-2023-0476

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Quincy Merx, Mark Steins and Gaby Odekerken.

License

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode


Introduction

Service robots are gradually being implemented in long-term care (LTC) services within Western Europe, potentially benefiting both frontline employees (FLEs) and LTC residents (). Modern robots can interact, deliver and communicate in ways that can enhance customer satisfaction (), though the introduction of these novel tools also prompts drastic changes to service delivery, which might diminish the desirability of their use (; ). Still, considering how service robots have enhanced value in other service industries, such as retail () and hospitality () and their potential for performing tasks that would enable older adults to age in place (), with greater autonomy and vitality, their promise in LTC services seems considerable. Service robots might also increase older adults’ well-being in other ways, such as by enhancing independence and quality of life ().

Interacting with a service robot can be perceived as threatening and frightening (; ); therefore, finding ways to make customers feel more comfortable during these service encounters is fundamental for continued interactions, such as in LTC services where people live. In the absence of comfort, people are less likely to use the service robot and devote the effort needed to work with the robot (). Studies empirically investigating the outcomes of psychological comfort in human-robot interactions are nascent (). Moreover, it is essential in LTC services, where continued usage offers great potential.

A recent study by highlights the limitations of service robots in emotionally intense contexts. It suggests that future research should focus on how robots can complement human caregivers effectively. This research gap encouraged us to determine whether and how service robots offer care in LTC services. In an attempt to identify the influence of service robots in an LTC service and in response to recent calls for insights on the determinants of long-term usage of service robots () and on when people feel comfortable replacing a human for a service robot for a specific task (), this article reports on a dyadic field study. The field study involves both LTC residents and human FLE and assesses their psychological comfort with robot reminders. Psychological comfort impacts service robot acceptance and attentive engagement with service robots. Attentive engagement ultimately affects the willingness of both LTC residents and FLEs to make efforts, which seems crucial in the long-term usage of these LTC services.

The central research question guiding this study reads as follows: What effect does psychological comfort with service robot reminders have on the effort willingness of long-term care residents and human frontline employees?

In addition, the central research question comprises three sub questions:

Q1.

Does psychological comfort with service robot reminders influence acceptance by LTC residents and human FLEs?

Q2.

How does service robots’ acceptance among LTC residents and human FLEs relate to attentive engagement with the service robot?

Q3.

To what extent does acceptance of and attentive engagement with a service robot after an experience lead to effort willingness by LTC residents and FLEs?

This study offers two theoretical contributions. Our first contribution relates to an increased understanding of the role of psychological comfort with service robot reminders during human-robot interaction (HRI). The article goes beyond the current literature on technology acceptance models (TAM) and service robot acceptance models (sRAM); the sRAM model looks at the acceptance of technology and, more specifically, at the social presence of the service robot (). We posit that psychological comfort with the service robot is an essential driver of acceptance, attentive engagement and, ultimately, effort willingness, which indicates the long-term usage of the robot (). In the current services marketing literature, the perspective of long-term technology usage is missing (). This is particularly crucial in the current research setting, nursing homes, which are long-term facilities providing services for their permanent residents (). In other settings, such as retail services, individuals can choose whether to use the robot or switch to another service provider. However, this is not as feasible in a nursing home, where people live for extended periods, and service robots become integrated into their daily routines. Even if an LTC resident expresses a desire not to have service robots in their private room, they will still encounter them in hallways or common rooms.

Second, in contrast to nascent research in the field of service robots, this research adopts a dyadic perspective by investigating the perceptions of both LTC residents and human FLEs regarding the role of psychological comfort with service robot reminders. The importance of a dyadic perspective has been highlighted in several other studies (; ; ; ). As LTC services are typically cocreated by human FLEs and LTC residents (), it is crucial to assess both perspectives to understand the continued usage of service robots.

Implementing service robots influences both the daily routines of LTC residents as well as the job of human FLEs (). This study also offers important practical contributions by demonstrating that the psychological comfort with robot reminders of LTC residents and human FLEs is pivotal to their willingness to make efforts and, thus, the likelihood that the service robot will be widely adopted in the organization. Paying attention to psychological comfort with service robot reminders is crucial before LTC service providers attempt to implement service robots.

The literature review in the next section outlines existing research and conceptualization of our key constructs: service robots, psychological comfort (with service robot reminders), acceptance, attentive engagement and effort willingness. After detailing the field study’s survey design and research methodology, this article continues with the data analysis and test results. Next, the discussion section offers critical interpretations and insights. Finally, the article concludes with a summary of key findings, implications and limitations.

Literature review and conceptual model

A service robot is a system-based autonomous and adaptable interface that interacts, communicates and delivers a service to an organization’s customers (). In particular, interactive service robots can improve older adults’ well-being by offering service, reliability, constant presence () and medication and agenda reminders. A medication reminder, based on available medical information, prompts users to take their medication according to a previously established schedule, such that the service robot complements the efforts of human FLEs who distribute the medication. The agenda reminder monitors users’ daily routines and sends reminders to prevent them from forgetting daily responsibilities (e.g. eating). In this case, the robot can substitute for human FLEs.

Psychological comfort with service robot reminders is a driver that potentially increases robot acceptance (; ). Psychological comfort can be defined as a sense of ease that facilitates calm and worry-free feelings that arise from interactions (; ). Being worry-free is a crucial aspect of psychological comfort in relation to acceptance, as worries like anxiety or stress negatively influence acceptance (; ). Existing literature did show a positive relationship between psychological comfort and satisfaction (; ; ; ; ; ). However, the link between psychological comfort and acceptance is largely lacking in common technology acceptance models such as TAM and sRAM, the latter acknowledging the social presence of robots (), despite generic frameworks of user experience, which encompasses emotional and cognitive responses to technology. Studies in the field of human-computer interaction show that users’ emotional reactions, including feelings of comfort and anxiety, significantly impact their willingness to accept and use technology (). Acceptance is conceptualized as “The degree to which an individual in his/her behavior manifests the intention to use a system” (, p. 5). According to TAM, perceived usefulness and perceived ease of use are expected to lead to higher intention to use, which subsequently translates into actual usage of the system (). However, while usefulness and ease of use may be predictors of comfort when using technology, it does not necessarily follow that a service robot – despite being useful and easy to use – will automatically be accepted or lead to increased intention to use. This is particularly true because human emotions significantly influence consumer decision-making (). We posit that enhancing the customers’ psychological comfort will increase acceptance of the service robot, as the users of the service robot have a worry-free HRI and are, hence, more likely to accept the robot. For example, robots’ reminder functions might comfort human FLEs, who do not need to remember to visit LTC residents’ rooms to encourage them to perform a specific task while occupied with a different job. The LTC residents may also receive psychological comfort with robot reminders by decreasing their concerns about burdening others for a simple reminder (; ). Therefore, we posit the following hypothesis:

H1.

Psychological comfort prompted by a robotic reminder positively affects service robot acceptance among (a) LTC residents and (b) human FLEs.

Once users accept service robots, they will likely increase their engagement (). Attentive engagement is the degree of interest someone has or would like to have while interacting with the focus of such engagement (). To reflect the underlying meaning of the variable, this article refers to “attentive engagement.” In the specific context of LTC services, customers refer to LTC residents who receive the service from the service robot and to the human FLEs who use the service robot.

Existing studies on customer engagement focused on the relationship between satisfaction and engagement to the neglect of acceptance (), while acceptance fosters a positive attitude toward the robot, reducing resistance and increasing the likelihood of meaningful interactions (). Moreover, most previous studies on customer engagement were lab studies, and the participants did not experience the service robot in their own environment (; ). Experiencing the service robot in one’s own environment is essential as the reaction of someone might be different (more negative) in the personal space of a participant (). Furthermore, due to a heightened realism in a real service environment, a field study is preferred over an imagined or an experimental scenario in a lab setting. Therefore, the current study assesses whether service robot acceptance positively affects attentive engagement in a field experience immediately after the HRI, resulting in the following hypothesis:

H2.

A service robot’s acceptance positively affects the attentive engagement of (a) LTC residents and (b) human FLEs.

Finally, effort willingness refers to the extent to which individuals are prepared to invest effort into working with social robots (). It encapsulates a person’s readiness to exert the necessary effort to collaborate effectively with social robots, reflecting their motivation and commitment level (). Effort is generally considered aversive; when a choice is available, people usually prefer the option with the least effort (). Because effort is expensive, people often compare the work required to obtain an outcome against its worth or their preference, along with the likelihood of attaining it (; ). It is crucial to assess to what extent engagement encourages LTC residents and human FLEs to willingly devote the necessary effort to work with service robots in the future, even if it is hard for them (). Effort willingness is a crucial factor in the long-term usage of service robots, showing a willingness to try to work with the robot beyond the field experiment (). Although essential in LTC services, long-term usage is an underdeveloped aspect of service robots (). To test whether attentive engagement positively influences effort willingness, the current study addresses the following hypothesis:

H3.

Engagement with a service robot positively affects effort willingness among (a) LTC residents and (b) human FLEs.

The conceptual model in depicts the predicted links among these hypotheses.

Method

James, the service robot

James is an advanced social robot equipped with a tablet interface as its facial component. It can convey a range of facial expressions and offers flexible programming (see ), which can be altered by human FLEs and relatives of LTC residents (). In particular, James supports older adults with LTC requirements by providing structure, issuing reminders for tasks such as medication intake and scheduled appointments and suggesting engaging activities and events. The ease of programming James results from a user-friendly mobile application accessible from anywhere.

Procedure

For the field study, James entered an LTC services context. The dyadic test of the conceptual model includes both LTC residents and human FLEs, with customized procedures that produce separate measures of the key variables. During the field study, James had been functioning within the LTC service context for three days (though each respondent encountered it for one day each). As required by the ethical review committee that approved the proposed research protocol, all LTC residents signed an informed consent form before participating in the field study. All human FLEs also indicated their consent at the beginning of the survey.

The study procedure for LTC residents included multiple steps, starting with consent to participate. Thus, the data were obtained through voluntary response sampling (). The LTC residents received informational letters distributed to their rooms, at the reception, and in an on-site restaurant, which offered them the possibility to sign up to participate. Then, once the actual field study started, the service robot visited LTC residents in their private rooms, stopping within half a meter of the LTC residents and facing them. The specific field study steps proceeded according to the depiction in , after which the LTC residents were asked to complete a paper survey; the provision of a paper version reflects a reasonable assumption that LTC residents might lack familiarity with digital interfaces.

The first step for the human FLEs entailed inviting them to participate in the survey through their work e-mails and weekly messages. The request indicated they should complete the survey only after seeing the robot. James was introduced to the human FLEs during the field study (), after which they had opportunities to ask questions. The service robot then visited the LTC residents, during which the human FLEs observed their interactions. The human FLEs also received another e-mail with a link to the digital survey (on Qualtrics) and could complete it at their convenience; if any of the human FLEs did not want to complete it digitally, they could receive a paper version of the same questionnaire, though none of them requested this option.

Sample and participants

The LTC facility in which this study was conducted is located in Western Europe. Its residents experience various health issues ranging from mild cognitive impairments to physical impairments to dementia. Any LTC residents with dementia were excluded from this study.

Among the LTC residents, 45 valid responses were received (77.6% of the population of residents in the LTC facility), which meets the minimum sample size requirement of 30 participants according to the central limit theorem (). Of the 98 human FLEs working at the facility, 62 participated. After excluding 13 respondents due to missing values from the question related to the frequency of interaction with the robot (Question 2), the final sample consisted of 49 human FLE respondents (50%). Two human FLEs did not provide demographic information, but their responses still were included in the data set.

In the LTC residents’ survey, 26 respondents were women and 19 were men. Their average age was 85.2 years. Concerning their health-care indications, 28 participants indicated that their housing situation was residential, six noted that they received nursing home care, seven indicated somatics, three received psychogeriatric care and one chose a different form of care. Among the human FLEs, 40 respondents were women and seven were men. Their average age was 47.6 years. In the job descriptions, five participants indicated nurse, 11 selected caregiver individual care, seven chose caregiver, 13 respondents were health-care assistants and eight indicated a different job description. One LTC resident saw the video instead of the robotic experience because the participant stated they did not want the service robot to enter their personal room. Subsequently, the participant completed the survey, providing feedback based on their viewing experience.

Measures

lists the study measures, their sources and the Cronbach’s alphas they attained in this study. All measures used the original scales, but the wording of some questions was slightly adapted to fit the focal service robot scenario. Acceptance was measured using a 1–5 point scale, where one represents disagree and five represents agree. Psychological comfort, attentive engagement and effort willingness are on a 1–7 point scale; here, one means completely disagree and seven means completely agree.

Results

The data analysis evaluated the model fit with structural equation modeling partial least squares in SmartPLS. The paired t-tests, designed to check for differences in the means between LTC residents and human FLEs, were conducted in SPSS. shows that surveys administered to LTC residents and human FLEs exhibited high internal consistency and reliability scores. According to a generally accepted heuristic, the loadings should be at least 0.7 for individual item reliability (). All items exceeded this threshold. For construct reliability, the composite reliability and Cronbach’s alpha values should exceed 0.7 (). Thus, indicates good construct reliability because the strong composite reliability values range from 0.85 to 0.99, and the Cronbach’s alpha values range from 0.83 to 0.99. The average variance extracted (AVE) values also exceed the 0.50 threshold (), indicating convergent validity (; ). To ensure discriminant validity, according to the Fornell–Larcker criterion, all constructs should exhibit a higher square root for their AVE compared with their correlations with other constructs (). This condition is met, as can be seen in . The square root of the AVE also should not be lower than 0.7 (). affirms that this condition is met. Finally, multicollinearity was not an issue; no variance inflation factor (VIF) exceeded 5 (). Instead, the estimations indicate that the highest VIFs were 1.000 (human FLE sample) and 1.000 (LTC resident sample). Regarding predictive relevance, another evaluation pertained to the effect sizes and variance of the endogenous constructs. shows that the endogenous constructs’ R-squared values ranged from 0.29 to 0.78, above the commonly accepted thresholds (; ). These high R-squared values indicate that most of the model’s variability can be explained and should lead to accurate predictions. Furthermore, the f-squared effect sizes () ranged from 0.42 to 3.62 for supported hypotheses and varied from small to large effects. All these results support the model’s predictive relevance.

The SEM estimated for the hypothesized model in indicated support for all the predicted relationships involving LTC residents and human FLEs (see ). In more detail, psychological comfort with the robot reminders enhanced acceptance of the robot, in support of H1 (LTC residents: β = 0.54, p < 0.000, human FLEs: β = 0.88, p < 0.000). Robot acceptance, in turn, increased attentive engagement with the robot among both groups, as predicted in H2 (LTC residents: β = 0.77, p < 0.000, human FLEs: β = 0.89, p < 0.000). Finally, attentive engagement with the robot improved effort willingness (LTC residents: β = 0.70, p < 0.000, human FLEs: β = 0.88, p < 0.000) supporting H3. provides an overview of these findings. A paired t-test also showed whether variable means between the two groups (LTC residents and human FLEs) differed significantly. This was not found for any of the variables. All the results of the paired t-test are available in . Furthermore, we performed an independent samples t-test to see whether there was a difference between the participants who scored the service robot low on psychological comfort (4.9 or lower) vs high on psychological comfort (five or higher). The median was used to differentiate between high and low levels of psychological comfort. Responses ranging from 1 to 3 were categorized as “low psychological comfort,” a response of four was labeled as “neutral,” and responses from 5 to 7 were classified as “high psychological comfort” as can be seen in the survey in , Questions 9 and 10. To perform the median split, we divided the data at the midpoint of the Likert scale, which includes three negatively worded responses, one neutral response and three positively worded responses. The neutral response was combined with the negative responses to balance the sizes of the high and low groups. This method was selected because individuals who indicated disagreement with psychological comfort (responses ranging from 1 to 3) could not accurately be categorized as having high psychological comfort. For the LTC residents, this led to a significant difference between the groups for acceptance (p = 0.001) of the service robot. Here, the LTC residents who scored higher on the psychological comfort of the service robot also scored higher on acceptance of the service robot. For the human FLEs, this led to a significant result on effort willingness (p = 0.05). Thus, the human FLEs that scored psychological comfort with the service robot higher also scored higher on willingness to make an effort. All the results of the paired t-test are available in .

The models were controlled for age as a control variable, but this produced results similar to those without its inclusion, confirming the significant relationships in additional support for the proposed model. In addition, most of the effects of the age control variable on the focal variables were insignificant. Although older LTC residents tend to exhibit somewhat less attentive engagement with the robot, this effect is significant, with a p-value of 0.003, relative to a 0.05 confidence interval. Additionally, tests were performed to see the differences between genders. However, these did not indicate significant differences, suggesting that the model is robust over differences. The answers to the open question (Question 9 in the survey) revealed an interesting insight. LTC residents indicated that, typically, FLEs provide agenda reminders, which sometimes feel somewhat rushed, potentially causing a threat to the reminder process.

We also assessed the model fit using SmartPLS. The model did not meet the recommended threshold values for the LTC residents. The SRMR was 0.105, exceeding the acceptable threshold of 0.08. In contrast, for the human FLEs, the SRMR was 0.053, indicating a good model fit. In conclusion, the model fit assessment revealed contrasting outcomes between the two samples. These findings suggest that the model performs better for the human FLEs, while further refinement, or larger sample sizes, may be needed for the LTC resident sample to improve fit and ensure accurate interpretation of the relationships in this context.

Discussion

Societal priorities shift toward a greater focus on healthy aging (), and service robots represent a potentially critical resource for people to remain independent for a more extended period (). conducted a study that brings to light service robots’ challenges in emotionally charged situations such as LTC. They recommend further exploration into how robots can best work alongside human caregivers. This gap in research inspired us to investigate the role of service robots in providing care in LTC services. Service robots can reduce work pressures in LTC settings and can enhance the independence experienced by LTC residents. However, to realize these benefits, it is necessary to ensure that LTC residents and human FLEs are willing to put effort into working with the service robot, even if this may be hard sometimes. The willingness to invest effort in working with the service robot necessitates a multifaceted approach. This includes the need for individuals to cultivate a sense of psychological comfort when interacting with robot reminders. Moreover, it is crucial to maintain a high level of attentive engagement with the service robot and fully accept the robot. This interplay underscores the complex dynamics influencing individuals’ willingness to exert effort, making it a critical area for further exploration and analysis in this study (; ; ; ). With a field study involving an actual service robot in real LTC services setting, the current research tests these variables and their meaningful connections to effort willingness. The findings related to the research hypotheses are presented below.

First, concerning psychological comfort with robot reminders, the findings reveal that acceptance of the service robot increases with more psychological comfort with robot reminders (H1). This provides evidence that psychological comfort with robotic reminders can be an addition to the sRAM for this sample. Notably, previous studies often rely on relatively young students as participants; the current study establishes that a service robot can yield psychological comfort with robot reminders for relatively older LTC residents who did not grow up with computers. Furthermore, the study occurred in these residents’ residences, making their psychological comfort with robot reminders even more striking. Participants did not have to imagine James being at their home because it was physically present. Their acceptance of this service robot depended on their psychological comfort with robot reminders.

Second, we found a relationship between acceptance and attentive engagement in an LTC context when LTC residents and human FLEs experience the service robot in their regular living and working environment and when tested immediately after the HRI (H2). If customers accept a service robot but do not attentively engage with it, the results may not be sufficient for health-care organizations. However, the positive relationship between acceptance of the service robot and attentive engagement suggests that acceptance likely leads to greater attentive engagement.

Third, the relationship between attentive engagement with the service robot and effort willingness among LTC residents and human FLEs emerged after they had experienced the robot for just one day each, which extends prior research (; ; ). That is, attentive engagement with the service robot increased the effort willingness of both LTC residents and human FLEs (H3) in an actual LTC setting, tested with a field experiment, rather than a demonstration and imagined usage scenarios. After the LTC residents and human FLEs experienced the service robot for one day, they stated that when they engaged attentively with the robot, they were more willing to put effort into the HRI, even if difficulties arose. These findings emphasize that acceptance and attentive engagement with the service robot lead to positive effort willingness.

In this study, we tested models for both LTC residents and human FLEs. As shown in the paired t-test results in , the means of the variables did not differ significantly between the two groups. However, the model fit for LTC residents was lower compared to human FLEs, suggesting that factors beyond those examined in our research may influence LTC residents’ willingness to exert effort. These factors could include prior experiences with technology or other unexamined variables.

Theoretical contributions

This study offers several significant theoretical contributions to service robot literature. Whereas previous studies tend to prioritize customers’ reactions, the present study focuses on both LTC residents and human FLEs and thereby addresses some limitations identified by existing research (; ; ; ) regarding the need to investigate the responses of multiple stakeholders involved in robot-embedded services. This study enriches the service robot literature by highlighting the multidimensional nature of customer perspectives in LTC settings. The key stakeholders include LTC residents, human FLEs, and health-care organizations.

The second contribution to services marketing literature is a more comprehensive understanding of service robot acceptance that goes beyond existing acceptance models such as TAM and sRAM (; ). We argue that in addition to more functional elements characterizing service robots, such as ease of use, usefulness and social presence, it is important to focus on psychological comfort with the functions of the service robot (e.g. reminders). The rationale underlying this assumption is that long-term use will only develop if users experience psychological comfort ().

However, argue that research on how individuals experiencing vulnerabilities envision integrating service robots into emotionally intense service settings such as LTC. Therefore, our third contribution to services marketing literature also focuses on the long-term use of the service robot in LTC services expressed by our dependent variable of effort willingness. Effort willingness goes beyond acceptance as it refers to users’ willingness to make an effort to work with the service robot, even if it would be hard for them. In the service context of LTC in nursing homes, it is essential to safeguard continued rather than ad hoc usage (). Adding to literature on long-term usage of service robots, we observed that acceptance and attentive engagement with the service robot are associated with increased willingness to exert effort. This may be a crucial factor for sustained usage. Finally, this research adds to current applications of various technology or robot acceptance models such as TAM and sRAM (; ), which measure the acceptance of service robots. Until now, the acceptance models have been about the perceived ease of use, usefulness and social presence that influence the acceptance of a service robot. This does not include the psychological comfort that a person perceives while receiving a robotic reminder. The current study delved into this psychological comfort and found that it influences the acceptance of a service robot. Thus, the comfort can be a potential addition to the current sRAM. Knowing that psychological comfort is important in long-term robotic use in a LTC setting, could potentially lead to retesting the sRAM with psychological comfort added. If this turns out significant, then psychological comfort should be added to the model.

Managerial implications

The results offer some relevant criteria for health-care managers to decide which service robot to implement. The robot should be capable of issuing reminders because this task enhances psychological comfort. The choice of a specific service robot should be based on the psychological comfort expressed by both human FLEs and LTC residents. Their psychological comfort increases their acceptance, heightens their attentive engagement and perhaps most importantly, leads to effort willingness by both groups of key stakeholders. By prioritizing users’ psychological comfort, health-care managers can improve their effort willingness and potentially long-term usage for working with a service robot, which is essential. In addition, training provided by the organization might influence effort willingness among human FLEs by reducing the overall effort they need to devote to the service robot adoption process.

Psychological comfort with robot reminders issued by service robots positively affects customer acceptance, which implies that LTC residents and human FLEs are likely receptive to using service robots for reminder tasks. More broadly, the results highlight the importance of incorporating psychological comfort-enabling elements in robot design efforts (). To minimize discomfort, the service robot should function as expected, without service failures (). To increase psychological comfort with robot reminders, a service robot with a reminder function appears particularly effective. In general, providing reminders offers comfort by ensuring LTC residents to remember their important appointments. The fact that a robot delivers these reminders – unlike FLEs who might be in a rush, as open Question 9 shows – further enhances this sense of comfort. This is evidenced by the high average scores of 5.54 and 5.74 on a seven-point scale. Beyond reminders, other factors might enhance psychological comfort, too, as they still need to be further explored in research (; ; ).

Attentive engagement increases with greater robot acceptance, consistent with prior research (; ). Accordingly, LTC service providers should actively strive to increase the acceptance of service robots. LTC residents and human FLEs are unlikely to engage with service robots without acceptance. Before implementing service robots, organizations should test the acceptance of the specific robot they plan to use.

From the three managerial implications discussed, it is clear that thorough testing is essential before implementing a service robot to ensure its suitability for the specific context. This testing should encompass both LTC residents and human FLEs. For LTC residents, a combination of questionnaires and observations of HRI would provide valuable insights into their perceptions and usage of the robot. Focus groups and questionnaires are recommended for human FLEs. Engaging multiple FLEs in discussions about the service robot could also be a valuable exploratory exercise. Such testing not only helps assess potential (long-term) usage but also assists in determining the most appropriate service robot for the organization. By piloting various service robots, organizations can evaluate which features promote psychological comfort and, consequently, enhance effort willingness. Based on these findings, the organization can either select the most effective robot or, if resources permit, develop a customized robot tailored to the specific needs of both LTC residents and human FLEs.

Limitations and further research

The sample for the present study includes residents from just one LTC facility that provides residential, somatic and nursing home care. Therefore, the results likely apply to similar settings, but continued research should diversify the sample by including LTC residents in different locations and care settings (e.g. psychogeriatric care and home care). A larger sample size could also improve the data’s reliability and precision by avoiding outliers and ensuring normal distributions (). Increasing the sample size would increase confidence in the estimates and decrease uncertainty surrounding the findings, too ().

The voluntary response sampling method also represents a limitation in that LTC residents who hold opposing views of or fear robots may be less likely to participate (). To increase the reliability of the sample, further research could adopt alternative sampling methods, such as stratified sampling, that selects participants from different subgroups (e.g, resident group) and thus helps ensure representation of all categories (). A more representative sample also would increase the generalizability of the findings.

Although this field study is notable because it allows for actual HRI, the LTC residents and human FLEs only interacted with James, the service robot, for one day. Research that will enable users to experience the service robot for multiple days and interact with it even more would be beneficial. Such research could include researchers’ observations of the HRIs as study data. Although the researchers were present during all the interactions for this study, the observations were too narrow to leverage as data. Another option would be to allow the HRI to take place outside the presence of researchers, which might lead to different reactions, especially among the LTC residents. In a related sense, further research might investigate the potential influences of others on the customer journey undertaken by LTC residents. For example, human FLEs’ effort willingness might influence the effort willingness of the LTC residents. Furthermore, future research could add qualitative interviews on the advantages and disadvantages customers see while working with a service robot. An additional indication for future research would be to examine whether human FLEs actually gain time by using reminders given by service robots and if this additional time is used for more person-centered care. If this is the case, research can be conducted to determine if this leads to a higher quality of life for LTC residents. Finally, the level of compliance with the robot’s instructions and reminders remains to be seen (). Examining the consistency of LTC residents’ adherence to robot requests could shed light on the effectiveness and practicality of implementing service robots in health-care settings.

Figures

Conceptual model

Figure 1

Conceptual model

James

Figure 2

James

Study procedure

Figure 3

Study procedure

Structural equation model results

Figure 4

Structural equation model results

Scales for construct measurement

Construct (source) Items Measurement scale Standardized loadings LTC residents* Standardized loadings human FLE
Psychological comfort with the robot reminder () (1) Feel calm 1–7 0.99 0.99
(2) Feel severe 0.99 0.99
Acceptance (Schmid, 2021) (1) I think that the robot is pleasant 1–5 0.85 0.93
(2) I think that the robot is effective 0.81 0.97
(3) I think that the robot is assisting 0.80 0.97
(4) I think that the robot is desirable 0.78 0.97
Attentive Engagement (; So et al., 2016) (1) I would concentrate on the robot if I would be working with it 1–7 0.89 0.90
(2) Anything related to this robot would grab my attention 0.75 0.91
(3) I would be heavily into this robot 0.92 0.94
(4) I trust that the robot executes the agenda service accurately 0.85 0.94
Effort willingness () (1) I’m willing to put an effort to work with this robot 1–7 0.94 0.99
(2) I would try to work with this robot even if it was hard for me 0.94 0.99
Source:

Authors’ own work

Means, standard deviations, correlations and reliability estimates

Mean SD* AVE** CR *** A **** 1 2 3 4
LTC residents
Construct
1. Psychological comfort with robot reminders 5.28 2.17 0.98 0.98 0.98 0.99
2. Acceptance 3.90 1.03 0.66 0.85 0.83 0.54 0.81
3. Attentive engagement 5.39 1.56 0.73 0.89 0.87 0.62 0.77 0.85
4. Effort willingness 5.21 1.98 0.89 0.87 0.87 0.57 0.54 0.70 0.94
Human FLE
Construct Mean SD AVE CR A 1 2 3 4
1. Psychological comfort with robot reminders 5.78 1.47 0.99 0.99 0.99 0.99
2. Acceptance 4.02 0.94 0.92 0.97 0.97 0.88 0.96
3. Attentive engagement 5.61 1.49 0.85 0.94 0.94 0.91 0.89 0.92
4. Effort willingness 5.93 1.44 0.97 0.97 0.97 0.95 0.88 0.88 0.99
Notes:

*Standard deviation;

**average variance extracted; threshold value should exceed 0.5; *** construct reliability; threshold value should exceed 0.7; ****cronbach’s alpha; threshold value should exceed 0.7

Results of hypotheses testing and explained variance

Hypothesized relationships Standardized path coefficientSupported or not supported R square construct
LTC residents
H1 Psychological comfort → Acceptance 0.54*** Supported 0.29
H2 Acceptance → Attentive engagement 0.77*** Supported 0.59
H3 Attentive engagement → Effort willingness 0.70*** Supported 0.49
Human FLE
H1 Psychological comfort → Acceptance 0.88*** Supported 0.77
H2 Acceptance → Attentive engagement 0.89*** Supported 0.78
H3 Attentive engagement → Effort willingness 0.88*** Supported 0.78
Notes:

*p < 0.05; ***p < 0.001; NS = not significant

Source: Authors’ own work

Results of paired samples T-test

Paired differences
Paired sample (LTC residents – human FLE’s) Mean SDSig. (two-tailed)Sig. (two-tailed)
Psychological comfort with robot reminder (−)0.17 2.40 0.64 NS
Acceptance of the robot (−)0.01 1.38 0.97 NS
Attentive engagement with the robot (−)0.11 2.20 0.74 NS
Effort willingness (−)0.69 2.33 0.054 NS
Notes:

*p < 0.05; ***p < 0.001; NS = not significant

Source: Authors’ own work

Results of independent samples T-test

Independent sample t-test LTC residents N Paired differencesMean SDSig. (two-tailed)
Acceptance of the robot (low psychological comfort) 14 3.37 1.36 0.001
Acceptance of the robot (high psychological comfort) 31 4.27 0.62
Attentive engagement with the robot (low psychological comfort) 14 4.86 1.77 0.113
Attentive engagement with the robot (high psychological comfort) 31 5.99 1.38
Effort willingness (low psychological comfort) 14 4.14 2.20 0.116
Effort willingness (high psychological comfort) 31 5.69 1.71
Acceptance of the robot (low psychological comfort) 6 2.14 0.43 0.114
Acceptance of the robot (high psychological comfort) 41 4.29 0.57
Attentive engagement with the robot (low psychological comfort) 6 2.45 0.81 0.839
Attentive engagement with the robot (high psychological comfort) 41 6.26 0.65
Effort willingness (low psychological comfort) 6 3.58 1.02 0.050
Effort willingness (high psychological comfort) 41 6.42 0.58
Notes:

*p < 0.05; ***p < 0.001; NS = not significant

Source: Authors’ own work

Appendix. Surveys (Survey employees, LTC residents)

The survey was conducted in Dutch, the native language of the country where the research was conducted:

  1. Did you experience the robot? Yes/ No

  2. How often did you see the robot in action? 1,2,3 or more (FLE)

  3. Which reminders did the service robot give you? Agenda, medication

    • How many of these announcements did you get from the robot? 1,2, 3 or more (LTC residents)

  4. If the respondent answered no to the question with the experience, the next block showed a video of the robot.

  5. When thinking about the medication service you saw through the robot today, who does this reminder usually? In other words, what does this reminder look like when no robot is involved? I do it myself, family, health care employees, other (LTC residents)

  6. When thinking about the agenda service you saw through the robot today, who does this reminder usually? In other words, what does this reminder look like when no robot is involved? I do it myself, family, health care employees, other (LTC residents)

  7. Do you have the feeling that you are burdening this person? (1–5 scale) (LTC residents)

  8. How would you feel if this person were no longer part of this service? Open question (LTC residents)

  9. Looking back on the reminders that the robot can provide. Imagine that there would be a robot in the elderly care center. Could you indicate what you think about the statement below? If I were in the robot James procession, I would feel […] let robot James provide medication reminders. Scale 1–7 (psychological comfort for robotic medication reminder):

    • a.

      At ease

    • b.

      Relaxed

    • c.

      Secure

    • d.

      Worry-free

    • e.

      Calm

    • f.

      Severe

    • g.

      Comfortable

  10. Looking back on the reminders that the robot can provide. Imagine that there would be a robot in the elderly care center. Could you indicate what you think about the statement below? If I were in the robot James procession, I would feel […] let robot James provide agenda reminders. Scale 1–7 (psychological comfort for robotic agenda reminder):

    • a.

      At ease

    • b.

      Relaxed

    • c.

      Secure

    • d.

      Worry-free

    • e.

      Calm

    • f.

      Severe

    • g.

      Comfortable

  11. Please rate the following statements from 1 to 7 (1 = I completely disagree, 7 = I completely agree) (feelings robot):

    • a.

      I’m willing to put an effort to work with this robot

    • b.

      I would try to work with this robot even if it was hard for me

    • c.

      I would feel satisfied while working with a robot

    • d.

      I would feel frustrated while working with a robot

    • e.

      I would feel relieved while working with a robot

    • f.

      I would feel delighted while working with a robot

  12. Please rate the following statements from 1 to 7 (1I completely disagree, 7I completely agree) (customer engagement):

    • a.

      I would concentrate on the robot if I would be working with it

    • b.

      Anything related to this robot would grab my attention

    • c.

      I would like to learn more about this robot

    • d.

      I would be heavily into this robot

    • e.

      I would be passionate about this robot

    • f.

      I trust that the robot executes the medication service accurately

    • g.

      I trust that the robot executes the agenda service accurately

    • h.

      If the robot does the agenda service accurately, could this reduce the work pressure? (FLE)

    • i.

      If the robot does the medication service accurately, could this reduce the work pressure? (FLE)

    • j.

      If the robot does the agenda service accurately, I feel I burden others less. (LTC residents)

    • k.

      If the robot does the medication service accurately, I have the feeling that I burden others less (LTC residents)

  13. Would you be willing to use the robot in your (residents) daily life? Scale 1–5 (acceptance)

  14. Please rate your impression of the robot on a 1–5 scale (acceptance):

    • a.

      Useful

    • b.

      Pleasant

    • c.

      Nice

    • d.

      Effective

    • e.

      Assisting

    • f.

      Desirable

    • g.

      Raising alertness

  15. What is your gender? Male/female/other/ prefer not to say

  16. What is your age? Open question

  17. Which area of health care do you work in? (FLE)

  18. What is your current living state in the LTC facility? (LTC residents)

We want to thank you for participating in this questionnaire; this is the end of the survey. Please click once more on the arrow below to finish the study.

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Corresponding author

Quincy Merx is the corresponding author and can be contacted at: q.merx@maastrichtuniversity.nl

About the authors

Quincy Merx is a PhD candidate in the Department of Health Services Research at the Faculty of Health, Medicine and Life Sciences at Maastricht University. Her research is about Vital communities for people who require long-term care, including people with dementia. The research is centered at Maastricht University in partnership with the Limburg Living Lab of Ageing and Long-Term Care. Her educational background is an MSc in International Business at Maastricht University with a specialization in strategic marketing and organizations, management, change and consultancy.

Mark Steins is a PhD candidate in the Department of Marketing and Supply Chain Management at the School of Business and Economics, Maastricht University and a founding member of the Maastricht Center for Robots. He is also a PhD candidate at the QUT Business School – School of Advertising, Marketing and Public Relations and a member of the Center for Behavioral Economics, Society and Technology at QUT. His research focuses on service robots, service ecosystems and service technology in transformative settings like education and health care. He has published in the Journal of Service Research, Journal of Product Innovation Management, Journal of Business Research and the Journal of Service Management.

Prof. Gaby Odekerken is a full professor of customer-centric service science at Maastricht University, the Netherlands. Her main research interests are service innovation, service robots, health care services, relationship management, customer loyalty and service failure and recovery. She is one of the cofounders of Maastricht University’s Service Science Factory, as she loves to bridge theory and practice. Her research has been published in the Journal of Marketing, MISQ, Journal of Retailing, Journal of Service Research, International Journal of Social Robotics, Journal of the American Medical Doctors Association, Journal of Service Management, Journal of Services Marketing, Journal of Business Research, and many more.

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