This study selected two labor-intensive processes in harsh environments among domestic food production processes. It analyzed their improvement effectiveness using 3-dimensional (3D) simulation. The selected processes were the “frozen storage source transfer and dismantling process” (Case 1) and the “heavily loaded box transfer process” (Case 2). The layout, process sequence, man-hours, and output of each process were measured during a visit to a real food manufacturing factory. Based on the data measured, the 3D simulation model was visually analyzed to evaluate the operational processes. The number of workers, work rate, and throughput were also used as comparison and verification indicators before and after the improvement. The throughput of Case 1 and Case 2 increased by 44.8% and 69.7%, respectively, compared to the previous one, while the utilization rate showed high values despite the decrease, confirming that the actual selected process alone is a high-fatigue and high-risk process for workers. As a result of this study, it was determined that 3D simulation can provide a visual comparison to assess whether the actual process improvement has been accurately designed and implemented. Additionally, it was confirmed that preliminary verification of the process improvement is achievable.

Purpose: To determine whether artificial -intelligence quarantine robots can positively contribute to environmental management in medical institutions. We conducted in-depth interviews to understand the experiences of hospitalized patients receiving environmental management from such robots. Methods: Data were collected during March 20~May 20, 2023. individual in-depth interviews were conducted with 15 hospitalized patients, who could provide specific details of their experience of receiving environmental management from a quarantine robot. Results: Qualitative content analysis identified four themes, including “First encounter with an amazing and new quarantine robot,” “Efficiently performs non-face-to-face quarantine work in hospitals,” “Problems with quarantine robots,” and “Recommendations for improving the functionality of quarantine robots in medical institutions so quarantine robots and humans can coexist.” Conclusion: Currently, quarantine robots have limited functionality, and additional research is needed to identify various other functions in consideration of future infectious disease situations and the level of quarantine appropriate in a hospital.

Recently, in the case of the root industry, although it is a basic industry that forms the basis of manufacturing competitiveness, there continues to be a shortage of manpower due to reasons such as dangerous working environments, industrial economic difficulties, and low wage systems. In addition, the demand for automation of production lines using robots is increasing due to a shrinking labor market due to a decrease in the working population due to aging, higher wages, shorter working hours, and limitations of foreign workers. In this study, a system was developed to automate the injection molding process for producing ball valves for automobiles by applying robot system. The applied process flow consists of alignment and insertion of insert parts, and removal, transfer, and loading of the product after injection molding, which is currently performed manually. Through the application of the developed robot automation system, the cycle time was improved by more than 30% and the defect rate was reduced by more than 70%.

As the importance of artificial intelligence grows rapidly and emerges as a leader in technology, it is becoming an important variable in the next-generation industrial system along with the robot industry. In this study, a safety system was developed using deep learning technology to provide worker safety in a robot workplace environment. The implemented safety system has multiple cameras installed with various viewing directions to avoid blind spots caused by interference. Workers in various scenario situations were detected, and appropriate robot response scenarios were implemented according to the worker's risk level through IO communication. For human detection, the YOLO algorithm, which is widely used in object detection, was used, and a separate robot class was added and learned to compensate for the problem of misrecognizing the robot as a human. The performance of the implemented system was evaluated by operator detection performance by applying various operator scenarios, and it was confirmed that the safety system operated stably.

Technological breakthroughs, combined with the demographic challenges of an aging population and the aftermath of the COVID-19 pandemic, spur new business opportunities in the service robot industry. From a management perspective, these technologies are positively evaluated in terms of increasing productivity, new business opportunities, and financial benefits (Belanche et al., 2020). However, although automation and robotics have already gained attention in the tourism and hospitality industry, research on their use in restaurants and the customer's attitude toward these new service solutions is still limited (Berezina et al., 2019; Ivanov et al., 2019; Kuo et al., 2017).

In the hospitality and tourism sectors, service robots have become increasingly adopted by companies to facilitate frontline service to reduce human labor, improve efficiency and provide better customer experience. In addition, the COVID-19 pandemic has intensified consumers’ demand for service robots to reduce human contact. To better understand the impact of service robots in the service sector, researchers have been examining user behavior of different technologies. While prior empirical studies have mainly focused on utilitarian-based acceptance models, other perspectives, such as the interpersonal relations, have been largely overlooked. With the ubiquitous of social media, people become connected but alone, leading to negative effects on interpersonal relations.

AI technology has been increasingly integrated into a wider range of industries – from small-sized home appliances robots to service robots in the hospitality or education sectors. Subsequently, researchers are increasingly interested in understanding how consumers respond to AI robots. Adding to such stream of studies, this research delves into how consumer responses differ depending on the characteristics of AI robots, specifically, the degree of appearance resemblance to a real-life object and the benefit type that the robot is designed to provide. Particularly, this research focuses on the role of perceived fit in shaping consumers’ intention to adopt AI robots as the underlying mechanism.

The proliferation of service robots has led to a growing concern about the impact of these technological advancements on privacy. Despite the development, quantitative research on the influence mechanism of privacy concerns on service robot adoption intentions is still limited. This research explores the influencing mechanism and boundary conditions of the interaction effect between customer privacy concerns and service type on customers’ service robot adoption intentions.

Coffee has become one of the most popular beverages since the 1960s. These days, coffee is more about pleasure, experience, success, lifestyle, high concentration, and social status. Bhumiratana et al. (2014) state that individuals consume coffee with the intention of eliciting positive high-energy emotions, which can help in reducing fatigue, increasing alertness, improving work performance, and promoting a focused mental state, such as feeling motivated, productive, in-control, or clear-minded. Aguirre (2016) and Bhumiratana et al. (2014) also suggest that people drink coffee to stay awake. As coffee has popularized, café has become important as a place for people meetings. However, customers may tend to evaluate coffee as a service product depending on what service encounter they faced. They might perceive the taste and quality of coffee differently by following their mindset and stereotypes about perfect coffee. Customers pay attention to many factors in consuming coffee depending on service encounters. They may pay less for coffee prepared by robots and evaluate coffee prepared by humans as more precious. Therefore, this study proposes a specific factor theoretical framework of customers’ mindset about coffee prepared by human baristas and robot-barista. The study aims to determine how customers evaluate coffee prepared by baristas and robot-barista. Specifically, to which characteristics they pay attention in barista or robot-barista cases. Then, the study examines relationship between expected and perceived coffee characteristics and intentions to revisit café. Finally, the study compares customers' expectations about coffee depending on contact (barista) or untact (robot-barista) service.

An increasing number of companies are using service robots, such as physical robots and virtual chatbots, to interact with their customers. Service robots are autonomous agents with the purpose of providing services to customers by performing a variety of physical and nonphysical tasks (Joerling et al., 2019). Conceptual work has suggested that social robots will increasingly be used in the front line of service encounters (Gonzalez-Jimenez, 2018; Van Doorn et al., 2017). Real examples of the inclusion of these robots in retail settings already exist. For instance, some retailers use Softbank´s robot Pepper to greet and inform customers. Social robots such as Pepper can use their sensors and cameras to interpret customer reactions and adapt accordingly. Moreover, the robot can even make product recommendations based on the assessed customer´s mood, age, gender and, if available, purchase history (McKenna, 2018). Not surprisingly, there is a vast market potential associated with these robots, which is expected to grow to 87 billion by 2025 (BCG, 2017).

Integrating both the Computers-Are-Social-Actors Theory and Social Exchange Theory, this study investigates human-robot interactions in a frontline service setting. Data was collected via an intercept survey of 282 Gen Z visitors at a polytechnic library in Singapore. The data was analyzed using structural equation modelling with partial least squares. The results demonstrate the significant positive effects of robot anthropomorphism attributes, namely perceived warmth, perceived coolness, appearance, social capability, and personalized service on perceived trust as well as the significant positive effects of perceived coolness and social capability on positive emotion. Both trust and emotion affected consumer co-creation while only emotion impacted consumer wellbeing. The results add to the service robot literature and offer practical guidance for service managers to leverage trust in designing and personalizing inclusive service experiences for successful human-robot interactions.

The rapid development of artificial intelligence technology has accelerated the promotion and application of service robot in the market. Although technology has provided service robot with increasingly autonomous functions, more research is needed on how service robot with different levels of autonomy affects customer satisfaction in the hospitality industry. Guided by affordance theory, the study examines whether service robot operational and decisional autonomy would have effects on customer satisfaction and explored explanatory mechanism. Adopting an experimental vignette method (EVM), the study reveals that direct effect of service robot operational autonomy and indirect effect of decisional autonomy on customer satisfaction, and functional affordance played a positive mediating role in the impact of service robot autonomy on customer satisfaction. This research extends and enriches the relevant literature on human-machine interaction and customer satisfaction research. Further, the study also provides marketing insights for enterprises to improve autonomous robot design and enhance customer relationships.

Last-mile delivery is the quintessential example of a logistics sector that is ripe for innovation, owing to a powerful confluence in the advancement of technologies and consumption models. Compared to the conventional delivery mode, the delivery robot offers countless advantages, including maintaining social distancing, improving health safety, protecting the environment, and reducing shipping costs and delivery times. Considering the high investments associated with developing and introducing delivery robots, a better understanding is needed of the consumers’ perceptions and willingness to guide practitioners on whether it is worth starting/continuing investing in such innovation. For this reason, this study addresses the following research questions: 1) What are the relevant characteristics of robot delivery and how do consumers cognitively and affectively respond to these characteristics? 2) What are the processes that form consumers’ willingness to reuse the delivery robot and spread the word about it (i.e., WOM)? Drawing on the technology-task fit model, the present study provides three interesting theoretical insights in relation to the post-adoption behaviour on delivery robots. In the first part, the findings enrich the literature by revealing that consumers’ evaluation of the perceived fit of using delivery robots are based on a combination of internal, external and conditional factors. This study found that hedonic motivations, perceived anthropomorphism and robotic service quality exhibited a positive influence on perceived task-technology fit. This indicates that, being able to experience a sense of fun or enjoyment when using novel technologies (e.g., robot services) will encourage future behaviour and push individuals to continue exploring the benefits of this innovation. Also, consumers tend to attribute the human-like robots as more capable and reliable as it gives the feeling that they are interacting with a social entity. The findings also value to current scarcity research and demonstrate the positive influence of service quality in matching the performance and functionality of delivery robots. In the second part, the empirical findings confirm the positive influence of perceived task-technology fit on trust and value-in-use in the emerging context of delivery robots. In the last part, this research goes further, revealing the indirect link between perceived task-technology fit and post-adoption behaviour. In line with consumer behaviour literature that suggests the individual will undergo a psychological state (or mental evaluation) in goal-oriented behavior, this research deepens our understanding of the mechanisms involved in this relationship. It reveals that trust and value-in-use acted as the missing links in bridging the associations through which perceived task-technology fit influences service reuse likelihood and positive word-of-mouth.

In order to solve the problem of improper thrust distribution of each thruster of underwater vehicle, the PSO optimization algorithm is used to solve the problem of thrust distribution. According to the spatial layout of the thruster, the algorithm model of the underwater vehicle propulsion system is established. The thrust input is carried out under the broken line search trajectory, and the simulation verifies the thrust allocation results of the PSO algorithm and the traditional pseudo-inverse method. The simulation results show that compared with the traditional algorithm. First of all, the PSO algorithm can set the physical threshold for each thruster to prevent the thruster from having too much thrust. Secondly, it can ensure that the thruster can turn with a reasonable torque to prevent the robot from drifting due to the large thrust gap. This paper provides a theoretical reference for thrust distribution of underwater salvage robot, and has practical engineering significance.

본 논문은 한국 현대음악 역사상 최초로 발표된 작곡가 이돈응의 드로봇이 21세기의 창작음악 에 시사하는 바에 대해 고찰한 연구이다. 이 글에서 필자는 드로봇이 21세기의 기술적 시류에 편 승하려는 목적에서 등장한 것이 아니라, 이돈응이 1980년대부터 약 40년간 추구해온 ‘인간적인 전 자음악’이라는 미학적 모토가 궁극적으로 집약된 산물이라는 것을 주장하고자 하였다. 이를 위해 이 글은 먼저 드로봇의 음악을 가능케 한 이돈응의 작품세계를 자세히 들여다보고, 드로봇이 현 시대에 갖는 독자적 의의를 파악하기 위해 18세기부터 현재까지 연주하는 로봇의 역사를 면밀히 살펴보았다. 이후 드로봇의 제작과정과 예술적 원리, 대표작을 상세히 짚어보면서 기술의 주체적 인 사용과 고도의 수공예적 테크네를 통해 완성도 있는 소리를 만들어나가는 드로봇의 음악적 의 의에 대해 논의하였다.

Radioactive Oxide is formed on the surface of the coolant pipe of the nuclear power plant. In order to remove the oxide film that is formed on the surfaces of the coolant pipe, chemical and physical decontamination technologies are used. The disadvantage of traditional technologies is that they produce secondary radioactive wastes. Therefore, in this study, the short-pulsed laser eco-friendly technology was used in order to reduce the production of secondary radioactive wastes. It was also used to minimize the damage that was caused to the base material and to remove the contaminated oxide film. The study was carried out using a Stainless steel 304 specimen that was coated with nickel-ferrite particles. Additionally, a transport robot was 3D modeled and manufactured in order to efficiently remove the oxide film from the coolant pipe of the nuclear power plant. The transport robot has a fixed laser head to move inside the horizontal and vertical pipes. The rotating laser head removes the contaminated oxide film on the inner surface of the coolant pipe. In the future, as a condition of the 1064nm short-pulsed laser ablation technique determined by basic analysis, we plan to analyze whether the transport robot is applicable to the radiation contamination site of the nuclear power plant.