The costs associated with law enforcement have seen a sharp increase, driven by rising personnel costs and the growing demand for policing services (Gascón, 2010; Urban Institute, 2020). Considerable discussion has arisen about how science can potentially help law enforcement “do more with less”, and some scholars have suggested introducing new crime control technologies to address this problem (e.g., Roach, 2022; Weisburd & Neyroud, 2011). With the onset of the COVID-19 pandemic, police departments around the world had additional demand, as they were made responsible for overseeing and ensuring compliance with COVID protocols. As a response, some countries (e.g., Singapore and China; Barrett, 2021) resorted to employing service robots either alongside or in place of police officers to assist with COVID-related compliance tasks.

The outbreak of COVID-19 has brought significant changes to today’s life. Human contact is regarded as a source of risk. Thus, the low-contact services provided by service robots have gained more attention in the hospitality industry. However, a relatively smaller proportion of empirical research exists about service robots on the consumer side. Moreover, technology acceptance theories were mostly used in the adoption of new technology products, but the psychological aspects of consumers were rarely explored. Therefore, the stimulus- organism-response model (SOR model) was applied to explore consumers’ acceptance of adopting service robots and to understand what factors will successfully stimulate consumers’ subsequent value and behavioral responses. Furthermore, we investigated the relationship among stimulus (coolness and affinity), organism (utilitarian value and hedonic value) and response (intention to use and word-of-mouth recommendation). This study used convenience sampling and tested the hypotheses with SPSS and Smart PLS.

The use of artificial intelligence (AI) service robots is on the rise. With service frontlines gradually shifting to human–robot interactions, the question of whether AI robots should be humanlike or machinelike has emerged. While many firms use robots that resemble humans in their appearance and actions, others use machinelike robots, assuming that very humanlike robots may lead to uncanny valley effects. There is no consensus on whether the anthropomorphism of service robots facilitates or constrains consumers’ experiences. To fill this gap, this article examines when and how service companies should use anthropomorphic AI service robots.

The extensive application of robots in hospitality and tourism service has transformed the original human-contact into contact-less, so it is necessary to understand the transformation of customers consumption behaviors under this new service mode. While studies have started investigating how service robots enhance the consumer autonomy, the impact of such technology on customers consumption behaviors remains largely unexplored and its underlying mechanism are still unclear. To address this issue, we explore how service robots shape customers autonomous behaviors in hospitality and tourism services. Drawing on the social impact theory, we presented an underlying process in terms of social discomfort, and reveal the boundary conditions.

Technologies, such as Artificial Intelligence (AI) and robotics are emerging as a new way to improve services, readjusting and impacting all business industries and relationships among people (Loureiro et al., 2021; Makridakis, 2017; Mingotto et al., 2020). The hospitality industry is no exception to this (Mingotto et al., 2020) since a quick growth in the use of robots and AI in this industry has registered a turnover of 249 million U.S. dollars (International Federation of Robots, 2021). However, very few of the existing studies highlight the customers’ perspective and sentiments on service robots (Luo et al., 2021) or the robot-human interactions/ customer service experience (Choi et al., 2021). Thus, further studies in the enhancement of human well-being through transhumanistic technologies, close relationship marketing capabilities, and the evolution of the engagement process between humans and AI-enabled machines are necessary (Loureiro et al., 2021). This research intends to understand how different types of robots influence customers’ perception of the service provided. Therefore, the following research questions are proposed; Can humans develop feelings of identification with a service robot? Can the identification that customers perceive between themselves, and service robots be strong enough to influence the creation of a close relationship between both parties? What are the features of service robots that heighten customer well-being?

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.

Building on Technology Readiness and Acceptance Model(TRAM), the study aimed to examine how technology readiness affects consumers’ perceptions of ease of use, usefulness, and risk, which in turn predict their intention to use retail service robots. Specifically, the study proposed that technology readiness motivators (optimism and innovativeness) would influence perceived ease of use and usefulness, while technology readiness inhibitors (discomfort and insecurity) would affect perceived risk. The study further examined if the perception factors (ease of use, usefulness, and risk) contribute to intention to use retail service robots. A survey method was used with data collected from Korean consumers. The final sample size was 418. The data was analyzed using structural equation modeling. Findings of the study revealed that technology readiness motivators positively affected perceived ease of use and usefulness while innovativeness had no impact on usefulness. All the inhibitors increased perceived risk. Lastly, as hypothesized, perceptions of ease of use, usefulness, and risk predicted intention to use retail service robots. This study extended the retail technology literature by applying and validating TRAM to the context of consumer acceptance of retail service robots. The study further helped marketers and retailers by highlighting the importance of technology readiness in improving consumer perceptions and responses towards retail service robots.

The human-following is one of the significant procedure in human-friendly navigation of mobile robots. There are many approaches of human-following technology. Many approaches have adopted various multiple sensors such as vision system and Laser Range Finder (LRF). In this paper, we propose detection and tracking approaches for human legs by the use of a single LRF. We extract four simple attributes of human legs. To define the boundary of extracted attributes mathematically, we used a Support Vector Data Description (SVDD) scheme. We establish an efficient leg-tracking scheme by exploiting a human walking model to achieve robust tracking under occlusions. The proposed approaches were successfully verified through various experiments.

This paper describes how a person extracts a unknown object with pointing gesture while interacting with a robot. Using a stereo vision sensor, our proposed method consists of two stages: the detection of the operators' face, the estimation of the pointing direction, and the extraction of the pointed object. The operator's face is recognized by using the Haar-like features. And then we estimate the 3D pointing direction from the shoulder-to-hand line. Finally, we segment an unknown object from 3D point clouds in estimated region of interest. On the basis of this proposed method, we implemented an object registration system with our mobile robot and obtained reliable experimental results.

The development of IT technology makes the functions and services of robots be integrated, and thus the robots become more intelligent and useful. As sophisticated usage of robots has evolved, direct communication by human language is necessary to increase the efficiency of their usage. In this paper, we propose a conversational interface platform for integrated service robots using MS Robotics Studio. The proposed platform consists of three types of components: a conversation manager to control the flows of the integrated service robots, a user interface to interact with users, and multiple service robots to perform actions or services. For a test-bed of the proposed platform, we build a schedule manager system and confirm the usability through SUS subject test by comparing the schedule manager system with MS Outlook.

Reliable functionalities for autonomous navigation and object recognition/handling are key technologies to service robots for executing useful services in human environments. A considerable amount of research has been conducted to make the service robot perform these operations with its own sensors, actuators and a knowledge database. With all heavy sensors, actuators and a database, the robot could have performed the given tasks in a limited environment or showed the limited capabilities in a natural environment. With the new paradigms on robot technologies, we attempted to apply smart environments technologies-such as RFID, sensor network and wireless network- to robot functionalities for executing reliable services. In this paper, we introduce concepts of proposed smart environments based robot navigation and object recognition/handling method and present results on robot services. Even though our methods are different from existing robot technologies, successful implementation result on real applications shows the effectiveness of our approaches. Keywords:Smart Environments, Service Robot, Navigation

This paper introduces a prototype smart home environment that is built in the research building to demonstrate the feasibility of a robot-assisted future home environment. Localization, navigation, object recognition and handling are core functionalities that an intelligent service robot should provide. A huge amount of research effort has been made to make the service robot perform these functions with its own sensors, actuators and a knowledge base. With all complicated configuration of sensors, actuators and a database, the robot could only perform the given tasks in a predefined environment or show the limited capabilities in a natural environment. We started a smart home environment for service robots for simple service robots to provide reliable services by communicating with the environment through the wireless sensor networks. In this paper, we introduce various types of smart devices that are developed for assisting the robot in the environment by providing sensor and actuator capabilities. In addition, we present how the devices are integrated to constitute the smart home environment for service robots.