Introducing service robotics in inpatient geriatric care—a qualitative systematic review from a human resources perspective

Transferred to the current application case, the central question with regard to HRM policy choices is whether the introduction of a service robot changes the human resources flow and the task allocation, because certain tasks are then taken over by the robotic system. Another important policy aspect which might be subsumed under the term “work systems” is the aspect of competency management and training: The introduction of service robotics in a geriatric nursing home setting might require more technical expertise among the employees; a competency gap of this kind can either be closed through training the existing staff or recruiting new employees.

The introduction of robotics in the work context of a geriatric nursing home leads to new HRM policy choices with regard to the human resources flow as well as competency management and training.

The central situational factor in the current case is task technology: In our applied context, it refers to the different kinds of service robots that can be implemented in geriatric nursing homes. But other situational factors might be of equal interest. For the health-care sector, the workforce characteristics are special in so far as the sector’s workforce is known to be predominantly female and and to have a non-technical educational background. Also, laws and societal values might play an important role in health-care due to high ethical demands and personal responsibilities of each employee (Zwijsen et al. 2011).

The situational factors technology, laws and societal values have an impact on HR policy choices in the work context of a geriatric nursing home that uses robots.

Of pivotal importance for the implementation of robotic devices in healthcare are obviously the interests of their primary users, the employees. However, since investment decisions are taken by the management, the attitude of the management board (and possible that of a supervisory board) might also have a significant impact on the implementation of service robots in geriatric nursing homes and what follows from it with regard to HRM.

The interests of the employees and the management have an impact on HR policy choices in the work context of a geriatric nursing home that uses robots.

In the logic of the Harvard model, adequate HR policies lead to positive consequences on the side of the employee. In the current case, the main question is whether changes can be perceived with regard to the HR consequences after a service robot has been introduced to the work system of a geriatric nursing home.

The introduction of robotics in the work context of a geriatric nursing home leads to changes in HR consequences mediated by specific HRM policy choices.

The ultimate goal of HRM efforts is to satisfy both organisationsal needs through achieving organisational effectiveness as well as individual needs by establishing individual (and, on the long run) societal well-being. This goal is coherent with the intentions of implementing service robots in the health-care sector (Decker 2008), as was described above.

The introduction of robotics in the work context of a geriatric nursing home has long-term consequences mediated by specific HRM policy choices and HR consequences.

4 out of the 22 publications reviewed contain information on consequences of introducing the robotic device on the workflow: Robinson et al. (2016) asked the nursing staff who had been exposed to the robot Paro whether the robot did affect their job; 15 out of 21 members of staff answered “not at all”—the example comment reported by Robinson et al. (2016, p. 109) is: “Not much at all (too busy to use the robot).” In Wright’s (2018) study on the usage of a robotic lifting device, the time needed to fetch and install the robot for usage is commented negatively by several employees: “Transfer robots are big and it is time-consuming to prepare it every time you use it. We can’t use it in our current work routine.” (Wright 2018, p. 32). In contrast, in the study of Bemelmans et al. (2016), the participating nursing staff used the robot Paro in 35 different care situations (e.g., wheelchair transport, activation); in 22 out of the 35 situations, the employees reported that the robot “added value” to the execution of the respective work task. What is special about this study is that each intervention was preplanned: Based on a work schedule planning, individually assigned interventions were conducted with pre-selected patients (Bemelmans et al. 2016, p. 156). Birks et al. (2016) report a case where a recreational therapist was confronted with a resident who was constantly and unnecessarily calling for help; she changed her usual work routines by letting the robotic seal Paro calm down the resident instead of doing this by herself.

As Table 1 shows, there is only one technological device which is used in multiple studies—this is the robotic seal Paro. Paro is a soft toy which is socially responsive; it is a market-ready product since 2003. The soft toy is mainly used as a therapeutic tool especially for elderly people suffering from dementia; since Paro is used to calm elderly people—for example during transports in wheelchairs—or simply for entertainment: the animal-like sounds that the seal makes as soon as it is stroked or spoken to stimulate dementia sufferers to spend more time with Paro (Takayanagi et al. 2014). JustoCat and PaPeRo are comparable products, all belonging to the category of social robots whose main purpose is to interact with humans, hence taking over interactive activities from the caregiver. Far less studied robots are those that support physical activities like lifting, walking, bathing. As an important side note, Table 1 contains the duration of implementation which ranges from 3 “visits” of the robotic device in the nursing home to about a year of constant usage. In none of the 22 published studies, workforce characteristics like gender bias in caring professions or the skill mix of the employees have been discussed as situational factor. However, Gerling et al. (2016, p. 293) touch upon the issue by pointing out that a participatory design approach in robotic development is necessary in order to capture the needs of the health-care workforce. Three studies were found to mention legal and ethical issues as situational factors: Peronard (2013, p. 422) points out the vulnerability of nursing home residents and the risk of manipulating them; Wright (2018), whose study was published in an anthropological journal, describes in some detail legal and cultural factors that shape the appreciation of a lifting robot in Japan (which he found out to be limited, since staff rejects the lifting robot as it is considered disrespectful); Klein and Schlömer (2018) refer to the vulnerability of the elderly as a central situational factor and consequently put the ethical evaluation of the studied robotic shower system to the forefront of their research. Summarising these findings, the assumption can be supported that situational factors have an impact on the work context of a geriatric nursing home that uses robots; since HR policy choices are only explicitely addressed in 4 out of 22 studies, no conclusion can be drawn with regard to the direct impact of situational factors on HR policy choices in this research context.

The interests of geriatric nursing home staff are treated in the majority of studies alike: The usual line of argumentation is that the introduction of service robots in geriatric care carries the chance to alleviate the shortage of skilled labour (e.g., Koceski and Koceska 2016; Peronard 2013; Moyle et al. 2016; Robinson et al. 2016, 2013; Bemelmans et al. 2013, 2016; Gerling et al. 2016; Erebak and Turgut 2018; Beedholm et al. 2015; Gustafsson et al. 2015; Bäck et al. 2013; Hebesberger et al. 2017). A more in-depth analysis of the interests and motives of the nursing home employees is only to be found in the antrophologically-oriented study by Wright (2018): He pinpoints that the Japanese geriatric nurses in his study have an interest in creating a peaceful, relaxing atmosphere during the caring activities; they also have an interest in reducing the risk of suffering from backpain due to heavy lifting. The study of Beedholm et al. (2015) included the management’s interest: They report that the manager who took the investment decision for a robot bathtub justifies the investment (despite the fact that the robot bathtub turned out not work properly) with a more general orientation towards innovation and business development (Beedholm et al. 2015, p. 282). The interests of other stakeholders like shareholders, or union representatives are not covered by any of the publications under scrutiny. Hence, it can be concluded that the interests of only some of the stakeholders (mainly the employees) have an impact on the work context, and to a very limited degree, of the HR policy choices of a geriatric nursing home that uses robots.

None of the studies covered measures commitment of staff as an consequence variable. However, the following attitudes are measured which might be regarded as prerequisites for commitment: acceptance of the robot, trust in the robot, the ethic evaluation of the robot, excitement and enthusiasm regarding the robot, general job satisfaction and morale as well as general quality of life. Out of the 15 effects in this consequence cluster, 6 are positive, 2 are negative and 7 are neutral or mixed. The subjective ratings of the usability/functionality of the robots can be regarded as an indicator for the second HR consequence variable in the Harvard model, competence: If the user perceives the robot as easy to handle, he/she most likely has the competence to handle the robot (and vice versa). Out of 12 effects in this consequence cluster, 5 are positive, 3 are neutral and 4 are negative. Another important consequence cluster is the perceived therapeutic usefulness of the robot as an indicator for the congruence between the task itself and the tools used to perform the task. 14 effects could be categorized as belonging to this cluster; 12 out of these are positive, one is neutral as it covers change recommendations, and one is negative. The last consequence variable mentioned in the Harvard HR model is cost-effectiveness. Three studies contain information on the effectiveness of implementing robots in geriatric care: Whilst Shin et al. (2016) report that staff expect the robot to be potentially time-saving, both Wright (2018) and Beedholm et al. (2015) report negative subjective ratings from staff regarding the effectiveness of using a robot.

The 22 studies in focus do not report results with regard to the long-term individual well-being of the employees, the societal well-being or long-term organisational effectiveness. Hence the assumption cannot be supported that introducing robotics in geriatric nursing homes has recognizable long-term consequences.

Assess the consequences the introduction of a service robot will have on different HR policies like workplanning, workflow management and staff training; develop and communicate respective policy changes well in advance. As has been outlined earlier, introducing robotic devices in inpatient geriatric care is a major undertaking which needs proper planning in order to be successful. However, the 22 studies considered here rarely report activities in the area of HR policies. As an exception from this general finding, Bemelmans et al. (2016, p. 156) propose an 11-step implementation procedure organised in three phases (training of care staff, selection of participants, conducting interventions) which might be taken as a starting point. In addition, as Cowan (2017) shows, the usage of change communication instruments might help to prepare employees for the expected changes in their work environment.

Take the decision for or against the implementation of a robotic device by balancing strategic, ethical and human-oriented aspects. As part of the final decision, an analysis of the ethical implications of using a certain robotic device should be undertaken. Klein and Schlömer (2018) use the MEESTAR analytical model for this purpose; the MEESTAR model integrates three dimensions: On the x axis seven ethical aspects (care, autonomy, safety, justice, privacy, participation, self-conception), on the y axis four ethical judgement stages (from harmless to harmful), and on the z axis three perspectives (individual, organisational, social; Manzeschke et al. 2015). With regard to the human-oriented aspects, not only the characteristics of the residents should be taken into account, but also the workforce, team and organisational characteristics. Possible questions to ask include: Does the team possess the competence/technical skills necessary to operate the robotic device? Does the organisation have enough resources (in terms of manpower and execution time) in order to train staff to use the robotic device both in theory and practice?

Systematically explore stakeholder interests and integrate them into the implementation strategy. As the above discussion of stakeholder interests has indicated, different stakeholder groups might pursue different interests in this field. A stakeholder analysis (Savage et al. 1991) will help to understand where to find supporters and opponents for the implementation of service robotics in inpatient geriatric care. If the stakeholder analysis includes stakeholder interviews, this provides the opportunity to uncover unrealistic hopes or fears (robots will close the shortage of skilled labour in nursing/will take away all nursing jobs).

Evaluate changes in the HR consequences systematically and holistically. As Table 1 shows, evaluative efforts most often only cover some few HR consequences. The four dimensions of the Harvard HR framework provide a meaningful basis for this evaluation but can be enriched by other aspects/dimensions, for example the Technology Acceptance Model (Venkatesh and Bala 2008). Since the evaluation of organisational interventions is a science in itself, get an external expert/scientist in to take over this piece of the implementation. As evaluation efforts have to be pre-planned and are partly designed as accompanying measure (Boulmetis and Dutwin 2011), kick-off the short-term and long-term evaluation planning before the implementation starts.

Monitor long-term effects of the robotic device usage on the individual staff member, the work team and the organisation. As discussed before, long-term effects have not been studied in any of the 22 studies considered here. Longitudinal assessments are needed in order to uncover changes on the individual, the team and the organisational level. On the one hand, both residents and staff might need a considerable amount of time to accomodate the use of a robotic device. On the other hand, negative experiences in using robotic devices might sour the enthusiasm of the users and lead to less usage and negative reactions on the long run. These (and other) effects cannot be foreseen right from the beginning and therefore require close monitoring. Again, involving an evaluation expert/scientist might help to deploy a sound longitudinal evaluation design.