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Article

Future Service Robot Scenarios in South Korea

by
Uijin Jung
1,*,
Jinseo Lee
2,
Ji-Young Choi
3,
Hyun Yim
1 and
Myoung-Jin Lee
3,*
1
Korea Institute of Science and Technology Evaluation and Planning, Chungbuk Innovation City 27740, Republic of Korea
2
Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
3
Department of Sociology, Korea University, Seoul 02841, Republic of Korea
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(22), 15679; https://doi.org/10.3390/su152215679
Submission received: 14 September 2023 / Revised: 24 October 2023 / Accepted: 5 November 2023 / Published: 7 November 2023
(This article belongs to the Section Sustainable Engineering and Science)
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Abstract

:
Advances in digital technology, periodic threats from infectious diseases, and shrinking working-age populations have increased the demand for autonomous systems. South Korea is now in crisis because its society is aging and has limited resources. The implementation of service robots is one of the possible alternative plans that has been receiving attention both for sustainable economic growth and as a solution to social problems. However, many things should be considered for service robots to be widely used in society. The aim of this study was to identify key factors that will affect the future of service robots and discuss corresponding policy measures. Four scenarios were developed using general morphology analysis (GMA). The scenarios were defined according to six key factors: technological development, infrastructure development, commercial acceptance, social acceptance, policy and regulatory environments, and technological competition. In scenario A, policy measures need to ensure that South Korea will continue as a global service robot leader. In scenario B, it is necessary to narrow the gap between South Korea and competitors in terms of service robot technology development and adoption. In scenario C-1, policies should encourage the adoption of service robot technologies both domestically and abroad. In scenario C-2, it is necessary to develop service robot technologies and promote the service robot industry.

1. Introduction

Societies around the world are aging [1]. South Korea currently has the lowest birth rate in the world [2]. By 2025, South Korea is expected to become a super-aged society, in which over 20% of the population is elderly [3]. Social aging is expected to produce various problems such as labor shortages, soaring labor costs, healthcare concerns, and skyrocketing medical expenses and pension costs [4,5]. To establish a sustainable society, service robots may be an alternative plan, alongside solving social and economic problems. Many industrial robots are already used in South Korea, but now the service robot market is also growing [6,7]. Service robots are expected to be used in many areas, such as logistics, education, agriculture, space, entertainment, the home, and healthcare [8,9,10]. In the situation of a demographic crisis, service robots can increase labor productivity and service quality [11]. They can also help elders’ cognitive training and physical activities [12,13,14,15,16].
Advances in cloud and artificial intelligence (AI) technologies and 5G cellular services have accelerated the development of robot-related technologies. Moreover, the pandemic of the infectious disease COVID-19 resulted in the promotion of non-contact service demand, including services such as delivery and hospitality [17,18]. The digital economy has been expanding as offline services move toward online and telecommuting systems [19,20]. The more digital technologies are used, the more service robots are expected to be used. Those robots have various functions, roles, and designs, reflecting the variety of tasks they execute. Much research has been conducted on service robot functions, safety, emotions, and intelligence [21,22,23,24,25]. While there are many individual studies in this particular field, there are few studies on what needs to be considered in order to increase the use of service robots.
Service robots are related to a variety of technologies, including AI, cloud-based data analysis, batteries, cameras, sensors, and communication networks, which increases their effectiveness. Nevertheless, the development of technologies such as long-lasting batteries and sophisticated operation is not enough to meet all social needs. There are still uncertainties preventing their further adoption, including safety regulations, costs, personal information leakage concerns, and concerns over the fact that they will cause some jobs to be lost. Robots’ actions should be designed to be safe [26]. There is a possibility of conflict between humans and robots [27].
The research questions in this study were “As technology is developing, the scope of use of service robots is also increasing, so what range can we call it’s a service robot?”, “What factors should be considered for the use of service robots other than technology?”, “What does the future of service robots look like?”, “What kind of policies are needed for the spread of service robots?”. To achieve these aims, we developed the following objectives:
(1)
Derive key factors that will affect the future of service robots.
(2)
Present several possible futures and discuss corresponding policy measures.
This study was conducted to identify possible scenarios for service robot development until 2035. Technological, policy, and social issues related to the use of service robots are intertwined, so it is important to develop policies that maximize the benefits of service robots and minimize their side effects. The scenarios were defined according to six key factors: technological development, infrastructure development, commercial acceptance, social acceptance, policy and regulatory environments, technological competition, and the degree of changes in these factors. By identifying these scenarios, this study’s results can help make decisions about relevant science and technology policies.

2. Methods and Materials

Service robots were first defined in 1993 by the Fraunhofer Institute for Manufacturing Engineering and Automation Laboratory as programmed devices that can perform services semi- or fully autonomously to support human activities [8,28,29]. The International Organization for Standardization defines service robots as robots that perform useful tasks for humans or interact with other kinds of machines [30]. Unlike industrial robots used in factories, service robots interact, communicate with, and help humans in homes or offices. Service robots are generally classified as either personal or professional [31]. Personal service robots offer household services, such as vacuuming and cleaning floors, while professional service robots are used to do strenuous and repetitive jobs such as logistics and medical services.
The first service robots were designed to complete simple, repetitive tasks, but with technological advances, they are now being designed to complete more complex, analytical, and emotion-related tasks [32,33]. Various telecommunications and security companies are developing service robots for the healthcare, education, agriculture, and leisure industries [31]. Humanoid robot technology is also developing, allowing telepresence robot users to see, hear, and communicate through robots in remote locations [34,35,36,37]. Additionally, service robots have a physical form, but they also exist virtually, such as Alexa. With the recent convergence of AI-based technologies and the diversification of devices, the term “service robot” is also being widely used in software algorithms that use digital task automation software [38]. Recently, increasing amounts of research have been conducted on the effects of service robots, such as robot–customer relationships and robot reliability [39,40]. Service robots can be used to meet social demands, such as aiding in non-face-to-face health monitoring and elder care [15]. Service robots are less likely to infect others than humans, do not become exhausted, and their performance does not decrease when they perform repetitive tasks [28]. Robot-based delivery systems are becoming increasingly feasible [35]. New service robot technologies are changing service models and so they are expected to play important roles in service experiences in the future [41].
In order for service robots to be well-accepted in society, many uncertain issues such as technology, the receptiveness of robots, and the global situation should be considered. We have chosen scenario planning as the methodology to deal with this uncertainty. Scenario planning is an effective tool for strategic decision-making [42]. Scenario planning can reduce risks to policymakers in uncertain situations [43]. It allows us to recognize opportunities and threats using predictions about the future and to come up with appropriate options. Therefore, policymakers can make better decisions and use their limited resources more effectively. Numerous studies have been conducted in various fields such as climate change, healthcare, and security [43,44,45,46]. Scenarios are not developed as accurate predictions about the future but to help make better decisions today [47].
There are many scenario approaches and techniques such as literature reviews, trend extrapolation, and expert workshops [48]. Scenarios are either exploratory or normative [49], have a wide range of complexity, and can be qualitative and quantitative [42,50]. Exploratory scenarios are used to explore possible futures and are developed by analyzing present trends and dynamics. Normative scenarios are developed to describe desired futures [42]. There is no single perfect scenario planning method, and both qualitative and quantitative methods can be appropriate [42,51]. There are a variety of scenario development techniques [52]. In general, many scenarios define issues and identify key drivers, trends, and stakeholders, and they are organized systematically by influence and uncertainty [42]. To build scenarios, it is necessary to identify what needs to be dealt with and what can be ignored [53,54].
Scenarios can present coherent, systematic, comprehensive, and plausible stories by weaving complex elements together [42]. They are used to draw logical conclusions, so scenario development techniques often include discussions among participants who can identify relevant factors.
We conducted social, technological, economic, environmental, and political (STEEP) analysis to gather information that was used to identify factors. STEEP analysis identifies and categorizes factors as social, technological, economic, environmental, or political [54]. STEEP analysis can identify factors that are relevant to the situation in question [55,56]. After such factors were identified, experts from various fields discussed which factors were the most relevant to the future of service robots.
After identifying these key factors, we used general morphological analysis (GMA) because there were many key factors that were not quantifiable, and they had unclear social, policy, and cognitive causal relationships. GMA was developed by Fritz Zwicky, a Bulgarian-born professor of physics at the California Institute of Technology in 1966 [57,58]. GMA ensures scenario consistency and relevance [59]. Non-quantifiable problem complexes were structured and contained in multidimensions [57,59,60,61]. Conventional scenario development techniques, such as the interactive future simulation technique, previously known as BASICS, can provide insights but rely on cause-and-effect relationships [42]. However, causal thinking is restricted in the scenario process because sometimes unpredictable events such as political situations or human behaviors occur. In this study, it was difficult to establish simple, causal relationships between factors. The GMA technique ensures consistency under uncertain and complex situations [50]. Moreover, it has been used to reflect the opinions of numerous experts as quantitative data, not the subjective judgment of a small number of experts. GMA has been widely used in many fields from engineering design to policy analysis [62]. It has been used in crime and terrorist attack scenarios [63], scenarios for Southeast Asia and the Southwest Pacific [64], and threat scenarios in nuclear facilities [65].
The research framework of this study is presented in Figure 1. Driving forces are influencing trends [66]. Driving forces such as digital transformations, a super-aging society, and infectious disease threats can lead to significant environmental changes. Key factors, such as technology development and social acceptance, that were expected to influence the future of service robots were identified. Then, policy measures were derived based on possible scenarios.
A flow diagram of this study is presented in Figure 2. In step 1, we analyzed issues related to service robot development with STEEP analysis. Then, workshops were held with 14 experts from various fields and types of organizations, including private companies and research institutes. This number of experts was considered to be sufficient for this study. During the workshops, the experts discussed the scope of future service robot use and defined key factors. In step 2, we designed a scenario planning method and GMA analysis judged to be suitable for use. We limited the scenarios’ scope to South Korea and set the time to 2035. In step 3, we developed possible scenarios for service robots. In step 4, experts discussed corresponding policy measures for each scenario. This study developed four future scenarios for how service robots might be developed and used in South Korea and corresponding policy measures for each.

3. Results

With the development of service robot technology, service robots’ roles and functions have diversified, resulting in an expansion of the concept of what service robots are. Therefore, before identifying future scenarios for service robot use, it was necessary to define service robots. Based on the workshop’s results, offline service robots were defined as offline, online, or offline–online. Offline service robots were defined as those that use AI to offer services for people or interact with other machines, semi- or totally independently (Figure 3). Online service robots were defined as those that can be controlled with the Internet. Offline–online service robots were defined as those that have the characteristics of both offline and online service robots.
The service robot-related social issues identified were demographic changes caused by a low birth rate and an aging society, a shortage of labor due to the declining population, intergenerational conflicts, an increasing number of double-income households due to women’s entry into the workforce, and conflicts between service robots and humans. Moreover, South Korean workers are reluctant to work dangerous jobs in the manufacturing and construction industries [67]. Workers perceive service robots as both a threat and a source of support in their jobs [68]. Companies often suffer when they replace workers with technology without considering how doing so will affect the customer experience [69]. Also, a lack of familiarity with service robots could be problematic for older people who are less familiar with new technologies [70]. Service robot technology is developing as AI technology develops [71]. Battery and telecommunication technology development make existing service robots, such as telepresence and patient mobility robots, more effective [72]. People can use digital twins in virtual environments to control service robots [73]. However, there is no common robot use platform [74]. There are also concerns about how data are handled. Service robots are not cost-effective in many situations [75,76,77]. The robot-as-a-service business model is applied to cloud computing, but this robot subscription service will be extended to other fields [78,79]. Declining automation costs put downward pressure on workers’ wages, so collecting a service robot use tax to support displaced workers is being discussed [80,81]. The environmental issues identified were expanding roles for robots, such as managing water quality and firefighting. Service robots can be used to assist in dangerous work, such as dissembling electric vehicle batteries [82]. In terms of policy, the US and China are in a trade war for global technological dominance [83]. As service robot technology advances, laws, ethics, and responsibilities related to them should be discussed [84,85,86].
The expert workshop identified six key factors derived from the abovementioned issues including technological development, infrastructure development, commercial acceptance, social acceptance, policy and regulatory environments, and technological competition. Variables were developed for each factor, such as technological development being classified as accelerating or stagnating (Table 1).
Technological and infrastructure development were categorized as either accelerating or stagnating. Commercial acceptance was categorized as either accepting or rejecting. Social acceptance was categorized as either accepting, neutral, or rejecting. Policy and regulatory environments were categorized as favorable, status quo, or unfavorable. Technological competition was categorized as alleviated, status quo, or intensified.
After the workshop, the experts conducted a cross-consistency assessment (Table 2). All valuables were compared with each other in a cross-impact matrix [87]. There were numerous pairs that were inconsistent and so were eliminated. Relationships between major factors can be identified [59]. The relationships between factors were assessed using a three-point scale, with 0 being the weakest and 2 being the strongest.
In the next stage, scenarios were developed based on the level of consistency among factors as determined using the cross-consistency assessment. This value was the Euclidean distance between the coordinates, which were eigenvectors derived from the GMA results. The smaller the value, the higher the degree to which the experts agreed on the factors’ statuses for a given scenario. The consistency was 1.247 for scenario A, 1.602 for scenario B, 1.920 for scenario C-1, and 1.633 for scenario C-2. These values indicate a relatively high degree of agreement between expert judgments for scenario A and a relatively low degree of agreement for scenario C-1.
Scenario A is optimal because it includes accelerating technological and infrastructure development, commercial and social acceptance, favorable policy and regulatory environments, and alleviated technological competition. Scenario B is pessimistic because it includes stagnating technological and infrastructure development, commercial and social rejection, unfavorable policy and regulatory environments, and intensified technological competition. The distances between (d2, f2, e2) and (a1, b1, c1) and between (d2, f2, e2) and (a2, b2, c2) were similar, so scenario C was divided into two parts: C-1 and C-2 (Figure 4, Table 3). The coordinates of the spectra are shown in Appendix A. C-1 is hopeful because it includes accelerating technological and infrastructure development, commercial acceptance, neutral social acceptance, and status quo policy and regulatory environments and technological competition. Scenario C-2 is close to reality because it includes stagnating technological and infrastructure development, commercial rejection and neutral social acceptance, and status quo policy and regulatory environments and technological competition.

3.1. Scenario A: “We Can’t Go Back to the World before the Service Robots”

In 2035, South Korea is experiencing more rapid demographic changes than any other country in the world. Over 50% of people in their 30s were unmarried, so the low birth rate problem worsened. The government began to use service robots to care for the elderly, which historically was the responsibility of families. Periodic threats from infectious diseases like COVID-19 have led to increasing demand for service robots to prevent their spread.
South Korean companies are leading the global service robot-related industry and are setting international standards. Most service robot operating systems have become open-source, reducing barriers to entry into service robot-related markets. Countries leading the world in service robots and related technology production have reduced tariffs on relevant parts and technologies between each other, encouraging trade.
The Robot Ethics Act was enacted when people began raising questions about whether robots with sufficiently advanced AI systems should have rights. Laws regarding personal information, insurance, crimes, and compensation for service robot-related accidents were enacted early, and the number of lawyers specializing in these areas increased accordingly. Laws are updated as service robot technology changes.
Nevertheless, hacking and privacy violations related to service robots frequently occur, and the number of service robot-related crimes has increased. Some have become fully dependent on the judgment of service robots, so the government imposed restrictions on the level of service robots’ AI capabilities. International agreements have limited the development of military robots that are capable of autonomously killing humans.
Scenario A is ideal from the perspective of widespread service robot production and adoption (Table 4). It has conditions that are advantageous for the development of both hardware and software. Many companies and public institutes conduct research that facilitates the growth of the service robot and related markets. Service robot-related technologies have become increasingly common due to being standardized or open-source, lowering barriers to entry for startups. Data generated by service robots are used in various ways. Integrated robot control towers are installed throughout urban areas, allowing service robots to seamlessly send and receive data. Robot subscription services have emerged as a new type of business that alleviates social problems, such as labor shortages. However, increased dependence on service robots increases technological competition, making it difficult to procure the parts and materials needed to produce service robots. As the use of service robots expands, security threats, such as hacking, and personal information leaks become more problematic. The increased productivity of service robots increases the gap between the rich and the poor, and different adoption rates by age group lead to generational conflicts. Vulnerable groups who have trouble adapting to service robots are left behind in many ways.
In scenario A, the government should continue promoting service robot-related technological development and implement policies to ensure that high-value-added production and services are offered domestically to continue to maintain South Korea’s status as a global leader in the service robot market.

3.2. Scenario B: “Stagnant Market Growth Due to Worse-Than-Expected Technological Development and Antipathy from Consumers”

As a result of the continuing decline in fertility, there are not enough workers in essential fields that often involve difficult or dangerous tasks, such as agriculture, production, and nursing. The government tried to replace human workers in these jobs with service robots but did not succeed because service robots do not yet have sufficient capabilities and are more expensive than human workers, and there has been strong social resistance to their adoption. The low level of service robot technology and high prices have caused people to feel antipathy toward using service robots.
Although South Korea is developing its own service robot technologies, it is still far behind the US and China, which have poured huge amounts of resources into research and development in this area over the past decade. It is difficult to use service robots in South Korea due to regulations and resistance from stakeholders. Small- and medium-sized firms in the service robot market are struggling in the face of competition from low-priced imports.
As technological development stagnates, service robots have relatively limited capabilities, so there is not a strong drive for developing related infrastructure. Service robots are generally only used as assistants in various fields, such as healthcare, rehabilitation, and legal affairs, due to personal information protection requirements.
There are no laws regarding responsibility or compensation for damage caused by service robots. Therefore, service robot users are generally held responsible for damage caused by service robots, even if such damage is caused by an error in the robot’s judgment. Retired service robots are not recycled properly, resulting in a large amount of industrial waste. Service robots do not deliver on their promise of being energy-efficient and ecofriendly.
Scenario B is the opposite of scenario A in that every condition is unfavorable for the development and adoption of service robots (Table 5). Technology and market development and personal information security are all inferior to their scenario A counterparts. Service robots are not seen as a tool for building a new social paradigm. In this scenario, South Korea must develop strategies for developing niche markets given the competition from the US and China.
Threats to the growth of the domestic service robot market come from the influx of low-cost products from overseas, the low level of technology and infrastructure, and the social rejection of service robots. The rejection is growing due to concerns that robots will replace human workers. The government still has to solve social problems in the face of the low social and commercial acceptance of service robots. As technological competition intensifies, isolated ecosystems for the research and development of service robot technology deteriorate and the supply chain becomes unstable.
In this scenario, the government should implement policies to reduce the technological gaps between South Korea and other leaders in service robot technology and production and increase the social acceptance of service robots.

3.3. Scenario C-1: “Service Robot Technology Is Advanced but Their Wide Adoption Is Uncertain”

Service robots have emerged as a way to solve problems caused by the shrinking population, and the government strives to promote their use by developing service robot-friendly policies. Thanks to these efforts, service robots are quite common, especially in dirty or dangerous jobs, such as delivery and cleaning, because they are more cost-effective than human workers. Service robot-related infrastructure is developing rapidly. Elevators, roads, charging facilities, and ICT-based systems for service robots are common. The government has made massive investments in service robot infrastructure.
China has an advantage in the international service robot market because it has so many materials and parts available domestically and is developing state-of-the-art hardware and software. South Korea is competing with China in terms of technological development to produce robots capable of high-level functions, such as microsurgery. However, service robots are still not in common use, so most service robots are used in traditional ways or to complete tasks that humans are unable to complete.
There is no coherent legal framework for service robots, so the scope of their use is limited, and there are no clear guidelines about who is responsible for any damage they cause, so people tend to only use them when necessary. There are also regular conflicts between labor unions and companies as service robots replace human workers. The younger generation, which is relatively quick to adopt new technology, welcomes the use of robots, while older generations are reluctant to do so, resulting in generational conflicts.
In scenario C-1, technology and infrastructure are developing, and service robots are accepted commercially, but the other factors are not favorable to the development and use of service robots (Table 6). In this scenario, South Korea has developed advanced service robot technology, so they can perform various functions, are relatively inexpensive, and can solve social problems. South Korean companies export state-of-the-art technology. Threats to the growth of the service robot markets include the relocation of domestic service robot factories overseas, accidents caused by service robots without proper legal guidelines in place, and robot infrastructure only being developed in large cities. In this scenario, the government needs to create policies that expand the demand for service robots or explore overseas markets.

3.4. Scenario C-2: “Technology and Social Acceptability Are Still Far Away in the Future”

South Korea is experiencing labor shortages due to the low birth rate and the lack of widespread service robot adoption. Service robots that do exist largely cannot replace human workers. Although the markets for a few types of service robots, such as home care and education, are growing, their functionality falls short of the younger generation’s expectations. Technological development has been stagnant, so service robots are not significantly different from how they were a decade ago, and they are still largely simple serving and cleaning robots. Most service robots are manufactured in China, where new service robot products and services emerge every year thanks to the price competitiveness of its products and its AI performance. Thus, Chinese products dominate the global service robot market.
Even if South Korean companies manage to develop service robot technology, their full potential cannot be achieved due to a lack of infrastructure. Most new service robot projects are pilot projects due to performance limitations and security issues. Industrial robots have limited applications and are mostly used in factories because of concerns over personal information leakage from robot communication networks.
Scenario C-2 assumes that technology, infrastructure, and commercial acceptance are unfavorable to the development of the service robot market, while social acceptance, policy and regulatory environments, and technological competition are largely neutral (Table 7). However, technological limitations, the fact that service robot development is dominated by a few large companies, and low levels of investment in service robot infrastructure hinder widespread service robot use. Service robot accessibility may decrease due to a growing wealth gap between the wealthiest part of society and the rest. Low rates of service robot use mean that they are unable to make up for labor shortages. Technological development may stagnate if international technology exchanges stop. In scenario C-2, the government should enact policies that accelerate technological development and increase social acceptance.

4. Discussion

In this study, four scenarios about the future of service robots were created using GMA. In uncertain situations, policy measures can be prepared based on modeled scenarios. There are several managerial issues such as technology development, human and financial resource management, and standardization in implementing new technologies [88]. The policy measures should address technology and industry, human resources, and society, considering diplomatic, welfare, and ethics aspects.
In scenario A, South Korea is a leading service robot producer as a result of the rapid growth of domestic companies producing state-of-the-art technology. In this scenario, the government should support companies in the service robot industry and proactively promote sustainable development, establishing South Korean dominance throughout the value chain, including hardware and software, to protect against intensifying global competition. Furthermore, the government should support the local production of core materials and components to minimize dependence on foreign products. Infrastructure for service robots should also be developed, such as standardized facilities and integrated robot control centers for cloud-based data management. In terms of human resources, the government should support research institutes and foster the development of experts who can develop the core technologies of the next generation of service robots. Universities and technical colleges should provide service robot-related training for those whose jobs are replaced by robots, such as how to maintain service robots and how to work with them. Fundamental AI- and service robot-related education programs should be developed, and curriculums should be redesigned. In terms of society, the government should develop countermeasures against the hacking of service robots and related forensic technology. It should also offer support to increase service robot accessibility for vulnerable populations. Finally, it should identify international trends in the service robot market and support the participation of domestic organizations in resolving ethical, social, and human rights issues related to service robots. Robots can transform the nature of certain jobs and organizations because they diminish the need for managers to monitor worker activities to ensure production quality [89]. Therefore, to smoothly transition to a society in which service robots are widely adopted, the government and businesses must cooperate to develop related systems [90]. The opportunities are that service robot-related technologies can become common with open-source materials, barriers preventing startups’ entry can be lowered, and robot subscription services can be widely used. However, service robot-related technology adaptability may lead to a threat to social adaptability.
In scenario B, South Korea’s competitiveness in the service robot industry is relatively low. In terms of technology and industry, the government should enact policies that reduce technology gaps between South Korea and service robot leaders. Small and medium enterprises (SMEs) might require government financial support to begin using service robots. For example, public institutions should be required to use domestically produced service robots, and funds should be provided to related startup companies. It may also be necessary to subsidize the purchase of service robots produced by SMEs to encourage their growth. In terms of human resources, the government should provide relevant training at key universities and increase scholarships for AI- and service robot-related education programs to prevent brain drain. In terms of society, the government should engage in competitive diplomacy to support the domestic development of core technologies and related parts. Trade restrictions may also be necessary to protect the domestic market from imports. The government should establish data protection measures to increase consumer confidence in service robot use. Data leaks from foreign companies operating in South Korea should be prevented and unfair competition should be actively policed. Service robot use should be promoted and international cooperation in academia or industry should be supported to attract professionals to develop service robots domestically. The opportunities are that government support such as research funds and deregulation can be greatly strengthened. The threat is that the development of technology is not possible, which can lead to a vicious cycle of the lack of infrastructure and social acceptance. Meyer (2020) found that retail managers feel frustrated with service robots because of technical problems, a lack of relevant functionality, and the time and financial cost of maintenance. They think that it is difficult for service robots to meet the needs of both customers and employees. They also need to learn how to protect data and code to properly use service robots, but they do not have the time to learn [91]. Therefore, SMEs and stores need to educate employees about how to use service robots. It is also important to establish partnerships between service robot manufacturers, vendors, and research institutes to conduct cooperative research about how to meet users’ demands.
In scenario C-1, even though service robot technology is relatively advanced, people are divided over its adoption. Service robots might have a much more disruptive impact in non-manufacturing areas, such as agriculture, transportation, clothing production, security, and utilities [90]. In order for service robots to be accepted, people have to recognize their usefulness. In terms of technology and industry, the government can increase the demand for service robots by promoting the development of service robot subscription companies and developing overseas markets for domestic products through marketing, information collection, and sales channel procurement. Tax benefits and worker support benefits should be offered to ensure that service robot factories stay in South Korea. In terms of human resources, the government should prevent the brain drain of AI- and service robot-related professionals. The government should also increase collaboration between academia and industry over service robot development to encourage their commercial and social adoption. In terms of society, opportunities to experience service robots should be provided to the public, and infrastructure should be built that fits the characteristics of the community. It is also necessary to establish a stable international value chain in which South Korea primarily provides technology, China primarily provides parts, and the US primarily provides services. Finally, the government should develop legislation and policies regarding the ethical use of service robots. For example, AI capabilities should be limited in certain contexts. There are opportunities to occupy the global market in the service robot industry because the technology supports it. The threat is that domestic robot factories can be moved overseas. In addition, accidents can occur when robots are operated without safety regulations.
In scenario C-2, service robot technology is relatively limited, so there is little commercial and social adoption. In terms of technology and industry, the government should promote the development of service robots using existing technology such as wheel-based logistics robots instead of four-legged robots or interactive speakers instead of social robots. The development of service robots should also be targeted for specific functions such as disaster relief and construction. The government can also promote the development of the business-to-government market. The high costs of service robots should also be reduced. Public projects should be executed to promote the growth of the service robot market. The government should promote the formation of joint ventures to attract leading overseas technologies. In terms of human resources, the government should promote cooperation between domestic and foreign institutions and attract excellent foreign talent. In terms of society, the government should identify best practices in terms of laws and policies and use cases in other countries. As an opportunity, low-cost overseas robots can be used to promote social demands regarding service robots. Suitable business models can increase demand even if service robot technology is relatively undeveloped [92]. The threats are that there are technology barriers from advanced countries and robots can be developed only in major companies.
Although it is possible that one of these scenarios will be realized by 2035, they may also be realized sequentially. For example, scenario C-2 may occur, followed by scenario C-1 and scenario A. Service robot adoption is most dependent on technology development, which will help increase service robots’ commercial and social acceptance. These factors can create synergies that will help them be adopted more and more quickly. Beyond technological development and the enacting of related regulations, service robot adoption can be encouraged by ensuring that they are used ethically, such as limiting AI capabilities in certain contexts. Other factors, such as favorable tax policies, will also help pave the way for a new paradigm of service robot use in South Korea.

5. Conclusions

This study was conducted to identify various futures for service robots. South Korea is in a critical situation due to having the lowest birth rate in the world. To sustain society, service robot use is one of the alternative plans to increase productivity and care for the elderly. However, even if service robot technology does develop to the point that it can help solve social problems, it is uncertain whether it would be socially accepted for various reasons, including concerns about the cost of robots and dissatisfaction with the service of robots.
We examined the issues related to service robots using STEEP analysis. We also conducted workshops in which experts identified the six significant factors and the statuses for each: technological development, infrastructure development, commercial acceptance, social acceptance, policy and regulatory environments, and technological competition. This study analyzed four scenarios for the future of service robots in South Korea using GMA. Regarding the development and adoption of service robots, policy measures were discussed for each scenario. The results of this study may be referred to by policymakers.
Each scenario’s situation is different, so they each need different policy measures. In scenario A, measures need to lead international standardization and promote high-value-added industrial policies to continue as a global leader in service robots. In scenario B, it is necessary to promote policies to secure original technologies and expand social acceptance to narrow the competition gap related to service robots. In scenario C-1, policies to increase service robot reliability and overseas market development are needed because service robot technology is sufficiently developed. In scenario C-2, it is necessary to give people more opportunities to experience service robots to increase demand for them.
This study offers academic contributions. Most of the studies related to service robots have focused on specific technologies or functions. However, this study focused on the future of service robots in South Korea in 2035. Fourteen experts identified six key factors related to the development of service robots. In addition, policy measures were discussed based on possible scenarios. Even though it offers contributions, our study also has some limitations. The scenarios identified in this study were limited by the key factors used to define them [87,93]. However, there may be other possible futures, such as those in which service robots interact with the metaverse. In addition, although relevant policy measures for each scenario were discussed, more sophisticated policy measures may be needed in reality. The results of this study may not apply to other countries that are not in a similar situation as South Korea because this research was conducted considering South Korea’s current situation.

Author Contributions

Conceptualization, U.J., J.L. and H.Y.; methodology, U.J., J.-Y.C., H.Y. and M.-J.L.; formal analysis, U.J., J.-Y.C. and M.-J.L.; investigation, J.L. and U.J.; resources, U.J. and H.Y.; data curation, U.J., J.L., J.-Y.C. and M.-J.L.; writing—original draft preparation, J.L. and U.J.; writing—review and editing, U.J., J.L. and H.Y.; visualization, U.J. and J.L.; supervision, M.-J.L. and H.Y.; project administration, U.J.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This Research was funded by the Korea Institute of S&T Evaluation and Planning (AT22020, AT23010).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding authors.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Spectra coordinates.
Table A1. Spectra coordinates.
Coordinates of SpectraDimension
12
Technological developmentAccelerating (a1)−0.569−0.024
Stagnating (a2)0.599−0.172
Infrastructure developmentAccelerating (b1)−0.6760.071
Stagnating (b2)0.6330.028
Commercial acceptanceAccepting (c1)−0.638−0.152
Rejecting (c2)0.6180.139
Social acceptanceAccepting (d1)−0.749−0.108
Neutral (d2)−0.1110.319
Rejecting (d3)0.858−0.031
Policy and regulatory environmentFavorable (e1)−0.506−0.279
Status quo (e2)0.0970.649
Unfavorable (e3)0.796−0.206
Technological competitionAlleviated (f1)−0.447−0.484
Status quo (f2)−0.2250.768
Intensified (f3)0.320−0.518

References

  1. United Nations. World Population Prospects 2022 Summary of Results. Available online: https://www.un.org/development/desa/pd/content/World-Population-Prospects-2022 (accessed on 5 October 2022).
  2. Ne, B.; Natha, J. South Korea’s Demographic Advantage is Over: The Regional Context and the Economic and Security Implications. Pac. Forum Issues Insight Work. Pap. 2021, 21, wp10. [Google Scholar]
  3. The Lancet Regional Health—Western Pacific. South Korea’s population shift: Challenges and opportunities. Lancet Reg. Health West Pac. 2023, 36, 100865. [CrossRef]
  4. Yoon, H.-S. Korea: Balancing Economic Growth and Social Protection for Older Adults. Gerontologist 2013, 53, 361–368. [Google Scholar] [CrossRef]
  5. Kim, K.W.; Kim, O.S. Super Aging in South Korea Unstoppable but Mitigatable: A Sub-National Scale Population Projection for Best Policy Planning. Spat. Demogr. 2020, 8, 155–173. [Google Scholar] [CrossRef]
  6. Atkinson, R.D. Which Nations Really Lead in Industrial Robot Adoption? Inf. Technol. Innov. Found. 2018, 1–10. Available online: https://www2.itif.org/2018-industrial-robot-adoption.pdf (accessed on 12 July 2022).
  7. Statista. Revenue in the Service Robotics Market in South Korea. Available online: https://www.statista.com/outlook/tmo/robotics/service-robotics/south-korea (accessed on 5 October 2022).
  8. Decker, M.; Dillmann, R.; Dreier, T.; Fischer, M.; Gutmann, M.; Ott, I.; Spiecker genannt Döhmann, I. Service robotics: Do you know your new companion? Framing an interdisciplinary technology assessment. Poiesis Prax. 2011, 8, 25–44. [Google Scholar] [CrossRef] [PubMed]
  9. Karabegović, I.; Karabegović, E.; Mahmić, M.; Husak, E. The application of service robots for logistics in manufacturing processes. Adv. Prod. Eng. Manag. 2015, 10, 185–194. [Google Scholar] [CrossRef]
  10. Garcia-Haro, J.M.; Oña, E.D.; Hernandez-Vicen, J.; Martinez, S.; Balaguer, C. Service Robots in Catering Applications: A Review and Future Challenges. Electronics 2021, 10, 47. [Google Scholar] [CrossRef]
  11. Shimmura, T.; Ichikari, R.; Okuma, T.; Ito, H.; Okada, K.; Nonaka, T. Service robot introduction to a restaurant enhances both labor productivity and service quality. Procedia CIRP 2020, 88, 589–594. [Google Scholar] [CrossRef]
  12. Salichs, M.A.; Castro-González, Á.; Salichs, E.; Fernández-Rodicio, E.; Maroto-Gómez, M.; Gamboa-Montero, J.J.; Marques-Villarroya, S.; Castillo, J.C.; Alonso-Martín, F.; Malfaz, M. Mini: A New Social Robot for the Elderly. Int. J. Soc. Robot. 2020, 12, 1231–1249. [Google Scholar] [CrossRef]
  13. Shibata, T.; Wada, K. Robot Therapy: A New Approach for Mental Healthcare of the Elderly—A Mini-Review. Gerontology 2010, 57, 378–386. [Google Scholar] [CrossRef]
  14. Cifuentes, C.A.; Pinto, M.J.; Céspedes, N.; Múnera, M. Social Robots in Therapy and Care. Curr. Robot. Rep. 2020, 1, 59–74. [Google Scholar] [CrossRef]
  15. Asgharian, P.; Panchea, A.M.; Ferland, F. A Review on the Use of Mobile Service Robots in Elderly Care. Robotics 2022, 11, 127. [Google Scholar] [CrossRef]
  16. Łukasik, S.; Tobis, S.; Suwalska, J.; Łojko, D.; Napierała, M.; Proch, M.; Neumann-Podczaska, A.; Suwalska, A. The role of socially assistive robots in the care of older people: To assist in cognitive training, to remind or to accompany? Sustainability 2021, 13, 394. [Google Scholar] [CrossRef]
  17. Kim, S.; Kim, J.; Badu-Baiden, F.; Giroux, M.; Choi, Y. Preference for robot service or human service in hotels? Impacts of the COVID-19 pandemic. Int. J. Hosp. Manag. 2021, 93, 102795. [Google Scholar] [CrossRef] [PubMed]
  18. Khan, Z.H.; Siddique, A.; Lee, C.W. Robotics Utilization for Healthcare Digitization in Global COVID-19 Management. Int. J. Env. Res. Public Health. 2020, 17, 3819. [Google Scholar] [CrossRef] [PubMed]
  19. Afonasova, M.; Panfilova, E.; Galichkina, M.; Ślusarczyk, B. Digitalization in Economy and Innovation: The Effect on Social and Economic Processes. Pol. J. Manag. Stud. 2019, 19, 22–32. [Google Scholar] [CrossRef]
  20. Soava, G.; Mehedintu, A.; Sterpu, M. Analysis and Forecast of the Use of E-Commerce in Enterprises of the European Union States. Sustainability 2022, 14, 8943. [Google Scholar] [CrossRef]
  21. Tussyadiah, I.P.; Zach, F.J.; Wang, J. Do travelers trust intelligent service robots? Ann. Tour. Res. 2020, 81, 102886. [Google Scholar] [CrossRef]
  22. Haddadin, S.; Albu-Schäffer, A.; Hirzinger, G. Requirements for safe robots: Measurements, analysis and new insights. Int. J. Rob. Res. 2009, 28, 1507–1527. [Google Scholar] [CrossRef]
  23. Schepers, J.; Belanche, D.; Casaló, L.V.; Flavián, C. How smart should a service robot be? J. Serv. Res. 2022, 25, 565–582. [Google Scholar] [CrossRef]
  24. Chen, X.; Ji, J.; Jiang, J.; Jin, G.; Wang, F.; Xie, J. Developing high-level cognitive functions for service robots. AAMAS 2010, 10, 989–996. [Google Scholar]
  25. Murashov, V.; Hearl, F.; Howard, J. Working safely with robot workers: Recommendations for the new workplace. J. Occup. Environ. Hyg. 2016, 13, D61–D71. [Google Scholar] [CrossRef] [PubMed]
  26. Zacharaki, A.; Kostavelis, I.; Gasteratos, A.; Dokas, I. Safety bounds in human robot interaction: A survey. Saf. Sci. 2020, 127, 104667. [Google Scholar] [CrossRef]
  27. Babel, F.; Vogt, A.; Hock, P.; Kraus, J.; Angerer, F.; Seufert, T.; Baumann, M. Step Aside! VR-Based Evaluation of Adaptive Robot Conflict Resolution Strategies for Domestic Service Robots. Int. J. Soc. Robot. 2022, 14, 1239–1260. [Google Scholar] [CrossRef]
  28. Holland, J.; Kingston, L.; McCarthy, C.; Armstrong, E.; O’Dwyer, P.; Merz, F.; McConnell, M. Service Robots in the Healthcare Sector. Robotics 2021, 10, 47. [Google Scholar] [CrossRef]
  29. Schraft, R.D.; Degenhart, E.; Hagele, M. Service robots: The appropriate level of automation and the role of users/operators in the task execution. In Proceedings of the IEEE Systems Man and Cybernetics Conference—SMC, Le Touquet, France, 17–20 October 1993; Volume 4, pp. 163–169. [Google Scholar]
  30. IFR. Service Robots. Available online: https://ifr.org/service-robots (accessed on 7 September 2022).
  31. Gonzalez-Aguirre, J.A.; Osorio-Oliveros, R.; Rodríguez-Hernández, K.L.; Lizárraga-Iturralde, J.; Morales Menendez, R.; Ramírez-Mendoza, R.A.; Ramírez-Moreno, M.A.; Lozoya-Santos, J.D.J. Service Robots: Trends and Technology. Appl. Sci. 2021, 11, 702. [Google Scholar] [CrossRef]
  32. Lee, I. Service Robots: A Systematic Literature Review. Electronics 2021, 10, 2658. [Google Scholar] [CrossRef]
  33. Wirtz, J.; Kunz, W.H.; Paluch, S. The Service Revolution, Intelligent Automation and Service Robots. Eur. Bus. Rev. 2021, 29, 38–44. Available online: https://www.europeanbusinessreview.com/the-service-revolution-intelligent-automation-and-service-robots/ (accessed on 20 December 2022).
  34. Seo, K.H.; Lee, J.H. The Emergence of Service Robots at Restaurants: Integrating Trust, Perceived Risk, and Satisfaction. Sustainability 2021, 13, 4431. [Google Scholar] [CrossRef]
  35. Pitardi, V.; Wirtz, J.; Paluch, S.; Kunz, W.H. Service robots, agency and embarrassing service encounters. J. Serv. Manag. 2022, 33, 389–414. [Google Scholar] [CrossRef]
  36. Tuomi, A.; Tussyadiah, I.P.; Stienmetz, J. Applications and Implications of Service Robots in Hospitality. Cornell. Hosp. Q. 2020, 62, 232–247. [Google Scholar] [CrossRef]
  37. Park, S.S.; Tung, C.D.; Lee, H. The adoption of AI service robots: A comparison between credence and experience service settings. Psychol. Mark. 2021, 38, 691–703. [Google Scholar] [CrossRef]
  38. Wirtz, J.; Patterson, P.G.; Kunz, W.H.; Gruber, T.; Lu, V.N.; Paluch, S.; Martins, A. Brave new world: Service robots in the frontline. J. Serv. Manag. 2018, 29, 907–931. [Google Scholar] [CrossRef]
  39. Fuentes-Moraleda, L.; Díaz-Pérez, P.; Orea-Giner, A.; Muñoz-Mazón, A.; Villacé-Molinero, T. Interaction between hotel service robots and humans: A hotel-specific Service Robot Acceptance Model (sRAM). Tour. Manag. Perspect. 2020, 36, 100751. [Google Scholar] [CrossRef]
  40. Pozharliev, R.; De Angelis, M.; Rossi, D.; Romani, S.; Verbeke, W.; Cherubino, P. Attachment styles moderate customer responses to frontline service robots: Evidence from affective, attitudinal, and behavioral measures. Psychol. Mark. 2021, 38, 881–895. [Google Scholar] [CrossRef]
  41. Liu, Y.; Wang, X.; Wang, S. Research on service robot adoption under different service scenarios. Technol. Soc. 2022, 68, 101810. [Google Scholar] [CrossRef]
  42. Amer, M.; Daim, T.U.; Jetter, A. A review of scenario planning. Futures 2013, 46, 23–40. [Google Scholar] [CrossRef]
  43. Enserink, B.; Kwakkel, J.H.; Veenman, S. Coping with uncertainty in climate policy making: (Mis)understanding scenario studies. Futures 2013, 53, 1–12. [Google Scholar] [CrossRef]
  44. Schreuder, R.F. Health scenarios and policy making: Lessons from the Netherlands. Futures 1995, 27, 953–958. [Google Scholar] [CrossRef]
  45. Van Dijk, M.; Meijerink, G.W. A Review of Food Security Scenario Studies: Gaps and Ways forward. In The Food Puzzle: Pathways to Securing Food for All; Wegeningen UR: Wageningen, The Netherlands, 2014; pp. 30–32. Available online: https://edepot.wur.nl/349629 (accessed on 21 June 2022).
  46. Stead, D.; Banister, D. Transport policy scenario-building. Transp. Plan. Technol. 2003, 26, 513–536. [Google Scholar] [CrossRef]
  47. Schwartz, P. The Art of the Long View: Planning for the Future in an Uncertain World; Doubleday, a Division of Bantam Doubleday Dell Publishing Group: New York, NY, USA, 1991. [Google Scholar]
  48. Popper, R. How are foresight methods selected? Foresight 2008, 10, 62–89. [Google Scholar] [CrossRef]
  49. Durance, P.; Godet, M. Scenario building: Uses and abuses. Technol. Forecast. Soc. Chang. 2010, 77, 1488–1492. [Google Scholar] [CrossRef]
  50. Bishop, P.; Hines, A.; Collins, T. The current state of scenario development: An overview of techniques. Foresight 2007, 9, 5–25. [Google Scholar]
  51. Erik Karlsen, J.; Øverland, E.F.; Karlsen, H. Sociological contributions to futures’ theory building. Foresight 2020, 12, 59–72. [Google Scholar] [CrossRef]
  52. Verity, J. Scenario planning as a strategy. Eur. Bus. J. 2003, 5, 185–195. [Google Scholar]
  53. Wright, G.; Bradfield, R.; Cairns, G. Does the intuitive logics method—And its recent enhancements—Produce “effective” scenarios? Technol. Forecast. Soc. Chang. 2013, 80, 631–642. [Google Scholar] [CrossRef]
  54. Schoemaker, P. When and How to Use Scenario Planning: A Heuristic Approach with Illustration. J. Forecast. 1991, 10, 549–564. [Google Scholar] [CrossRef]
  55. Szigeti, H.; Messaadia, M.; Majumdar, A.; Eynard, B. STEEP analysis as a tool for building technology roadmaps. In Proceedings of the International Challenges E-2011 Conference, Florence, Italy, 26–28 October 2011; pp. 26–28. [Google Scholar]
  56. Sims, G.D.; Bishop, G. Foresight Study and Competitive Analysis; NPL Report MATC(A)80; National Physical Laboratory: Teddington, UK, 2001; Available online: http://eprintspublications.npl.co.uk/id/eprint/2780 (accessed on 22 September 2022).
  57. Ritchey, T. Fritz Zwicky, Morphologie and Policy Analysis. In Proceedings of the 16th EURO Conference on Operational Analysis, Brussels, Belgium, 12–15 July 1998. [Google Scholar]
  58. Zwicky, F.; California Institute of Technology (Pasadena, Calif.). The Morphological Method of Analysis and Construction; California Institute of Technology: Pasadena, CA, USA, 1948. [Google Scholar]
  59. Johansen, I. Scenario modelling with morphological analysis. Technol. Forecast Soc. Chang. 2018, 126, 116–125. [Google Scholar] [CrossRef]
  60. Ritchey, T. Modeling Alternative Futures with General Morphological Analysis. World Future Rev. 2011, 3, 83–94. [Google Scholar] [CrossRef]
  61. Ritchey, T. Principles of Cross-Consistency Assessment in Morphological Modelling. Acta Morphol. Gen. 2015, 4, 1–20. [Google Scholar]
  62. Álvarez, A.; Ritchey, T. Applications of General Morphological Analysis from Engineering Design to Policy Analysis. Acta Morphol. Gen. 2015, 4, 1–40. [Google Scholar]
  63. Tzezana, R. Scenarios for crime and terrorist attacks using the internet of things. Eur. J. Futures Res. 2016, 4, 18. [Google Scholar] [CrossRef]
  64. Coyle, R.G.; McGlone, G.R. Projecting scenarios for South-east Asia and the South-west Pacific. Futures 1995, 27, 65–79. [Google Scholar] [CrossRef]
  65. Ritchey, T. Nuclear Facilities and Sabotage: Using Morphological Analysis as a Scenario and Strategy Development Laboratory. In Proceedings of the Adaptation of a Paper Delivered to the 44th Annual Meeting of the Institute of Nuclear Materials Management, Phoenix, AZ, USA, 13–17 July 2003. [Google Scholar]
  66. Parris, T.M.; Kates, R.W. Characterizing a sustainability transition: Goals, targets, trends, and driving forces. Proc. Natl. Acad. Sci. USA 2003, 100, 8068–8073. [Google Scholar] [CrossRef]
  67. Ducanes, G.; Abella, M. Labour Shortage Responses in Japan, Korea, Singapore, Hong Kong, and Malaysia: A Review and Evaluation; Regional Office for Asia and the Pacific, International Labour Orgainzation: Tokyo, Japan, 2008; Available online: https://www.ilo.org/asia/publications/WCMS_099166/lang--en/index.htm (accessed on 23 December 2022).
  68. Meyer, P.; Jonas, J.M.; Roth, A. Frontline employees’ acceptance of and resistance to service robots in stationary retail-an exploratory interview study. SMR-J. Serv. Manag. Res. 2020, 4, 21–34. [Google Scholar] [CrossRef]
  69. Modliński, A.; Fortuna, P.; Rożnowski, B. Human–machine trans roles conflict in the organization: How sensitive are customers to intelligent robots replacing the human workforce? Int. J. Consum. Stud. 2003, 47, 100–117. [Google Scholar] [CrossRef]
  70. Suto, H. Robot literacy an approach for sharing society with intelligent robots. Int. J. Cyber Soc. Educ. 2013, 6, 139–144. [Google Scholar] [CrossRef]
  71. Billard, A.; Kragic, D. Trends and challenges in robot manipulation. Science 2019, 364, 6446. [Google Scholar] [CrossRef]
  72. Bodenhagen, L.; Suvei, S.-D.; Juel, W.K.; Brander, E.; Krüger, N. Robot technology for future welfare: Meeting upcoming societal challenges—An outlook with offset in the development in Scandinavia. Health Technol. 2019, 9, 197–218. [Google Scholar] [CrossRef]
  73. Kaarlela, T.; Pitkäaho, T.; Pieskä, S.; Padrão, P.; Bobadilla, L.; Tikanmäki, M.; Haavisto, T.; Blanco Bataller, V.; Laivuori, N.; Luimula, M. Towards Metaverse: Utilizing Extended Reality and Digital Twins to Control Robotic Systems. Actuators 2003, 12, 219. [Google Scholar] [CrossRef]
  74. Gates, B. A robot in every home. Sci. Am. 2007, 296, 58–65. [Google Scholar] [CrossRef]
  75. Schroeck, F.R.; Jacobs, B.L.; Bhayani, S.B.; Nguyen, P.L.; Penson, D.; Hu, J. Cost of New Technologies in Prostate Cancer Treatment: Systematic Review of Costs and Cost Effectiveness of Robotic-assisted Laparoscopic Prostatectomy, Intensity-modulated Radiotherapy, and Proton Beam Therapy. Eur. Urol. 2017, 72, 712–735. [Google Scholar] [CrossRef]
  76. Dobbs, R.W.; Magnan, B.P.; Abhyankar, N.; Hemal, A.K.; Challacombe, B.; Hu, J.; Dasgupta, P.; Porpiglia, F.; Crivellaro, S. Cost effectiveness and robot-assisted urologic surgery: Does it make dollars and sense? Minerva Urol. Nefrol. 2017, 69, 313–323. [Google Scholar] [CrossRef] [PubMed]
  77. Lotan, Y. Is robotic surgery cost-effective: No. Curr. Opin. Urol. 2012, 22, 66–69. [Google Scholar] [CrossRef] [PubMed]
  78. Buerkle, A.; Eaton, W.; Al-Yacoub, A.; Zimmer, M.; Kinnell, P.; Henshaw, M.; Coombes, M.; Chen, W.-H.; Lohse, N. Towards industrial robots as a service (IRaaS): Flexibility, usability, safety and business models. Robot. Comput. Integr. Manuf. 2023, 81, 102484. [Google Scholar] [CrossRef]
  79. Girija, P.; Mareena, J.; Fenny, J.; Swapna, K.; Kaewkhiaolueang, K. Amazon robotic service (ars). Eng. Technol. Manag. Stud. Proj. 2021, 2309. Available online: https://archives.pdx.edu/ds/psu/36434 (accessed on 3 July 2022).
  80. Guerreiro, J.; Rebelo, S.; Teles, P. Should Robots Be Taxed? Rev. Econ. Stud. 2022, 89, 279–311. [Google Scholar] [CrossRef]
  81. Zhang, P. Automation, wage inequality and implications of a robot tax. Int. Rev. Econ. Financ. 2019, 59, 500–509. [Google Scholar] [CrossRef]
  82. Wegener, K.; Chen, W.H.; Dietrich, F.; Dröder, K.; Kara, S. Robot Assisted Disassembly for the Recycling of Electric Vehicle Batteries. Procedia CIRP 2015, 29, 716–721. [Google Scholar] [CrossRef]
  83. Schneider-Petsinger, M.; Wang, J.; Jie, Y.; Crabtree, J. US—China Strategic Competition; The Quest for Global Technological Leadership, Chatham House, The Royal Institute of International Affairs: London, UK, 2019; Available online: https://www.chathamhouse.org/sites/default/files/CHHJ7480-US-China-Competition-RP-WEB.pdf (accessed on 5 March 2023).
  84. Lin, P.; Abney, K.; Bekey, G. Robot ethics: Mapping the issues for a mechanized world. Artif. Intell. 2011, 175, 942–949. [Google Scholar] [CrossRef]
  85. Asaro, P.M. What Should We Want from a Robot Ethic? Int. Rev. Inf. Ethics 2006, 6, 9–16. [Google Scholar] [CrossRef]
  86. Hinds, P.J.; Roberts, T.L.; Jones, H. Whose Job Is It Anyway? A Study of Human-Robot Interaction in a Collaborative Task. Hum. Comput. Interact. 2004, 19, 151–181. [Google Scholar] [CrossRef] [PubMed]
  87. Zheng, W.; Kankaanranta, J.; Suominen, A. Morphological analysis of technologies using multidimensional scaling. J. Bus. Chem. 2012, 9, 147–160. [Google Scholar]
  88. Bajic, B.; Rikalovic, A.; Suzic, N.; Piuri, V. Industry 4.0 Implementation Challenges and Opportunities: A Managerial Perspective. IEEE Syst. J. 2021, 15, 546–559. [Google Scholar] [CrossRef]
  89. Dixon, J.; Hong, B.; Wu, L. The robot revolution: Managerial and employment consequences for firms. Manag. Sci. 2001, 67, 5586–5605. [Google Scholar] [CrossRef]
  90. Kaivo-Oja, J.; Roth, S.; Westerlund, L. Futures of robotics. Human work in digital transformation. Int. J. Technol. Manag. 2017, 73, 176–205. [Google Scholar]
  91. Meyer, P.; Roth, A.; Gutknecht, K. Service robots in organisational frontlines—A retail managers’ perspective. J. Retail. Consum. Serv. 2023, 70, 103173. [Google Scholar] [CrossRef]
  92. Yun, J.J.; Won, D.; Jeong, E.; Park, K.; Yang, J.; Park, J. The relationship between technology, business model, and market in autonomous car and intelligent robot industries. Technol. Forecast. Soc. Chang. 2016, 103, 142–155. [Google Scholar] [CrossRef]
  93. Kosow, H.; Gaßner., R. Methods of Future and Scenario Analysis: Overview, Assessment, and Selection Criteria. In Social Science Open Access Repository; Deutsches Institut für Entwicklungspolitik gGmbH: Bonn, Germany, 2008; p. 133. Available online: https://nbn-resolving.org/urn:nbn:de:0168-ssoar-193660 (accessed on 20 August 2022).
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Figure 1. Research framework of this study.
Figure 1. Research framework of this study.
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Figure 2. Flow diagram of this study.
Figure 2. Flow diagram of this study.
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Figure 3. Personal and professional service robot types.
Figure 3. Personal and professional service robot types.
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Figure 4. GMA results for the future service robot scenarios factors.
Figure 4. GMA results for the future service robot scenarios factors.
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Table 1. Definition of the statuses of key factors identified in the expert workshops.
Table 1. Definition of the statuses of key factors identified in the expert workshops.
Key FactorStatus (Variable)Definition
Technological developmentAccelerating (a1)
-
Hardware: Service robots are being designed with complex manipulation and mobility technologies, such as advanced batteries, so they are able to fulfil a variety of purposes.
-
Software: Autonomous learning and adaptive AI can replace physical, intellectual, and emotional workers.
Stagnating (a2)
-
Hardware: Service robots are only used in controlled environments and so they are not widely used.
-
Software: Operations, sensor, and battery technologies have not advanced enough to meet service demands.
Infrastructure developmentAccelerating (b1)
-
Service robots have ubiquitous assets for data communication networks.
-
Elevators for robots and robot-friendly roads are standardized to simplify the configuration of robotics systems, enabling the rapid spread of service robot use.
Stagnating (b2)
-
Service robot uses are limited because of insufficient infrastructure.
Commercial acceptanceAccepting (c1)
-
Multifunctional service robots are rapidly adopted, creating a variety of benefits.
Rejecting (c2)
-
Service robots fail to generate more benefits than human workers due to an inflow of foreign workers and the limited application of service robots.
Social acceptanceAccepting (d1)
-
Service robots become essential products when everyone has at least one, like smartphones.
Neutral (d2)
-
Service robots quickly replace human workers in fields where the cost of their implementation is lower than that of hiring human workers.
Rejecting (d3)
-
Rejection of and anxiety about robots bring fierce resistance from workers, causing service robot-related companies to withdraw from South Korea.
Policy and regulationFavorable (e1)
-
Policy: Relatively large amounts of service robot research and development funding are provided by the government.
-
Regulation: The government reduces regulations to promote the service robot industry.
Status quo (e2)
-
The government adjusts service robot-related policies and regulations.
Unfavorable (e3)
-
Privacy Data Protection Act: Automatic data collection makes people reluctant to use service robots, hampering their adoption.
-
Physical safety and responsibility: Ambiguity over responsibility for accidents reduces service robot adoption.
-
Security: Punishments for crimes, such as privacy violations, are strict to reduce information exposure and security concerns.
-
Service robot tax: A service robot tax reduces the economic benefits of service robot adoption.
Technological competitionAlleviated (f1)
-
Service robot standardization reduces the price of parts and materials, leading to the spread of universal robots that can be easily afforded by anyone.
Status quo (f2)
-
The service robot industries in China and the United States (US) consume a substantial percentage of service robot-related parts and AI technologies.
Intensified (f3)
-
The US and European Union pass strong regulations against the import of Chinese service robots.
-
The Chinese service robot industry focuses on serving only Asian markets.
-
The US suppresses the growth of the Chinese service robot industry due to concerns about data privacy.
-
Due to strict restrictions on imports to and exports from China of service robot-related semiconductors, technology, and data, it becomes significantly harder for Chinese producers to procure robot parts.
Table 2. Cross-consistency assessment scores for service robot future scenarios.
Table 2. Cross-consistency assessment scores for service robot future scenarios.
Key Factor (Variable)Technological DevelopmentInfrastructure DevelopmentCommercial AcceptanceSocial AcceptabilityPolicy and Regulatory EnvironmentsTechnological Competition
Accelerating (a1)Stagnating (a2)Accelerating (b1)Stagnating (b2)Accepting (c1)Rejecting (c2)Accepting (d1)Neutral (d2)Rejecting (d3)Favorable (e1)Status quo (e2)Unfavorable (e3)Alleviated (f1)Status quo (f2)Intensified (f3)
Technological developmentAccelerating (a1)3.0
Stagnating (a2)0.0 3.0
Infrastructure developmentAccelerating (b1)2.20.13.0
Stagnating (b2)0.61.90.0 3.0
Commercial acceptanceAccepting (c1)2.10.520.13.0
Rejecting (c2)0.61.80.12.10.0 3.0
Social acceptanceAccepting (d1)2.20.31.90.42.20.23.0
Neutral (d2)1.40.91.111.30.90.0 3.0
Rejecting (d3)0.11.9020.21.9003.0
Policy and regulatory environmentFavorable (e1)2.10.42.10.31.90.41.91.10.33.0
Status quo (e2)0.910.90.80.70.811.10.80.0 3.0
Unfavorable (e3)0.12.10.22.10.21.60.20.72.10.0 0.0 3.0
Technological competitionAlleviated (f1)1.80.61.50.72.10.41.210.21.610.63.0
Status quo (f2)10.81.10.8110.80.80.3110.40.0 3.0
Intensified (f3)0.71.70.71.30.41.90.50.60.91.70.90.70.0 0.0 3.0
Table 3. Statuses of four service robot scenarios.
Table 3. Statuses of four service robot scenarios.
ScenarioTechnological DevelopmentInfrastructure DevelopmentCommercial AcceptanceSocial AcceptancePolicy and Regulatory EnvironmentTechnological Competition
AAcceleratingAcceleratingAcceptingAcceptingFavorableAlleviated
BStagnatingStagnatingRejectingRejectingUnfavorableIntensified
C-1AcceleratingAcceleratingAcceptingNeutralStatus quoStatus quo
C-2StagnatingStagnatingRejectingNeutralStatus quoStatus quo
Table 4. Scenario A factors.
Table 4. Scenario A factors.
Technological DevelopmentInfrastructure DevelopmentCommercial AcceptanceSocial AcceptancePolicy and Regulatory EnvironmentsTechnological Competition
AcceleratingAcceleratingAcceptingAcceptingFavorableAlleviated
Table 5. Scenario B factors.
Table 5. Scenario B factors.
Technological DevelopmentInfrastructure DevelopmentCommercial AcceptanceSocial AcceptancePolicy and Regulatory EnvironmentTechnological Competition
StagnatingStagnatingRejectingRejectingUnfavorableIntensified
Table 6. Scenario C-1 factors.
Table 6. Scenario C-1 factors.
Technological DevelopmentInfrastructure DevelopmentCommercial AcceptanceSocial AcceptancePolicy and Regulatory EnvironmentTechnological Competition
AcceleratingAcceleratingAcceptingNeutralStatus quoStatus quo
Table 7. Scenario C-2 factors.
Table 7. Scenario C-2 factors.
Technological DevelopmentInfrastructure DevelopmentCommercial AcceptanceSocial AcceptancePolicy and Regulatory EnvironmentsTechnological Competition
StagnatingStagnatingRejectingNeutralStatus quoStatus quo
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Jung, U.; Lee, J.; Choi, J.-Y.; Yim, H.; Lee, M.-J. Future Service Robot Scenarios in South Korea. Sustainability 2023, 15, 15679. https://doi.org/10.3390/su152215679

AMA Style

Jung U, Lee J, Choi J-Y, Yim H, Lee M-J. Future Service Robot Scenarios in South Korea. Sustainability. 2023; 15(22):15679. https://doi.org/10.3390/su152215679

Chicago/Turabian Style

Jung, Uijin, Jinseo Lee, Ji-Young Choi, Hyun Yim, and Myoung-Jin Lee. 2023. "Future Service Robot Scenarios in South Korea" Sustainability 15, no. 22: 15679. https://doi.org/10.3390/su152215679

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