Service Robots Research Articles

Consumer resistance to service robots: a stressor-based perspective on engagement and wellbeing

Purpose Applying the Innovation Resistance Theory, this study aims to examine the effects of contextual and trait stressors on consumer engagement and wellbeing in the context of service robots. Design/methodology/approach Data were collected from 560 users who interacted with a service robot at a library and a museum. The data were analyzed using Smart PLS 4.0. Findings The findings show the significant negative effects of perceived intrusion on consumer engagement and wellbeing. In addition, technology anxiety exhibited a significant positive effect on consumer engagement and wellbeing, whereas consumer engagement predicted wellbeing. Moreover, the findings highlight the importance of consumer engagement as a key mediator between the stressors and wellbeing. Practical implications The findings equip service managers with the necessary information to effectively integrate service robots in an inclusive manner that resonates with consumer engagement and wellbeing. Originality/value This research uses field data to empirically validate the effects of contextual and trait stressors on consumer resistance to service robots.

Robot anthropomorphism at luxury hotels: a dual-congruity mechanism with luxury and task under anxiety

ABSTRACT Although service robots have been extensively studied recently, few empirical investigations have focused on the luxury setting. Targeting the setting of luxury hotels, in this study, expectation-confirmation theory and dual-congruity theory are combined to examine the impact of robot anthropomorphism on consumers’ expectations of service robots. The findings obtained from a sample of 556 respondents indicated that robot anthropomorphism increased consumers’ perceived social capability, performance expectancy, and perceived importance. Furthermore, luxury-technology fit and task-technology fit mediated the effects of social capability, performance expectancy, and perceived importance on the intention to use robotic services. Furthermore, the moderating role of anxiety toward robots in strengthening the effect of luxury-technology fit on the intention to use robotic services was revealed. The findings contribute to an enhanced understanding of consumer psychology in the context of advanced service technologies, offering valuable insights for the strategic implementation of service robots at luxury hotels.

I-DINO: High-Quality Object Detection for Indoor Scenes

Object Detection in Complex Indoor Scenes is designed to identify and categorize objects in indoor settings, with applications in areas such as smart homes, security surveillance, and home service robots. It forms the basis for advanced visual tasks including visual question answering, video description generation, and instance segmentation. Nonetheless, the task faces substantial hurdles due to background clutter, overlapping objects, and significant size differences. To tackle these challenges, this study introduces an indoor object detection approach utilizing an enhanced DINO framework. To cater to the needs of indoor object detection, an Indoor-COCO dataset was developed from the COCO object detection dataset. The model incorporates an advanced Res2net as the backbone feature extraction network, complemented by a deformable attention mechanism to better capture detailed object features. An upgraded Bi-FPN module is employed to replace the conventional feature fusion module, and SIoU loss is utilized to expedite convergence. The experimental outcomes indicate that the refined model attains an mAP of 62.3%, marking a 5.2% improvement over the baseline model. These findings illustrate that the DINO-based indoor object detection model exhibits robust generalization abilities and practical utility for multi-scale object detection in complex environments.

The impact of service robot compatibility on hospitality employee learning behaviors: a transformative learning perspective

ABSTRACT In the era of burgeoning artificial intelligence and robotics, the integration of robots within the hospitality sector has become a prevalent phenomenon. Despite this, scholarly attention has predominantly been directed toward the dynamics of robot-customer interactions, leaving a significant gap in our understanding of the impact of robots on employee learning behaviors. This study uses transformative learning theory to explore how robot compatibility influences employee learning. A PLS-SEM analysis of a survey of 347 hotel employees found that robot compatibility had a positive influence on employee explorative and exploitative learning. Both contextual intrinsic and extrinsic motivation partially mediated the effect of compatibility on employee learning behaviors. Robot awareness positively moderated the effect of compatibility on contextual motivation. The study’s conclusions provide enlightenment for hospitality organizations to achieve improved employee-robot collaboration and value co-creation.

Service robot acceptance: agenda for tourism and hospitality research

Purpose This study aims to provide insights and guidance for future research on service robot acceptance in the tourism and hospitality (T&H) domain through a comprehensive literature review. Design/methodology/approach A total of 249 journal articles, retrieved from the Scopus and Web of Science databases, were reviewed using an inductive analysis method. Findings Since its emergence in 2008, the topic of robot acceptance has gained significant attention, especially after 2019. The research assesses the current state of research, methodologies used, theoretical foundations and antecedents influencing acceptance. Five categories of antecedents affecting acceptance were systematically identified: robot design factors, individual/organisational factors, situational factors, appraisal factors and affective factors. Originality/value This study provides a comprehensive overview of the current landscape of service robot acceptance research. It offers insights and theoretical directions to advance research in the T&H domain.

Patient acceptance of medical service robots in the medical intelligence era: an empirical study based on an extended AI device use acceptance model

In the era of medical intelligence, there are still few studies focusing on medical service robots from a user experience perspective. Guided by the model of artificial intelligence (AI) device use acceptance (AIDUA), this article develops a theoretical model to explain patients’ intention to use medical service robots at hospitals. The proposed model specifically distinguished the dimensions of anthropomorphic attributes of service robots and further introduces two variables, perceived empathy and interaction experience, as a way to construct a three-stage psychological mechanism for patient acceptance of robots. 400 questionnaires from Chinese patients were collected offline and analyzed using structural equation modeling (SEM). The results revealed that the four attributes of anthropomorphism play a differential influence in performance expectations and effort expectations, but all positively contribute to empathy, which in turn positively affects interaction quality. Interaction quality, performance expectations, and effort expectations all influence patients’ emotions, thus having an impact on patients’ intentions to accept service robots. The findings from this study will assist the healthcare sector in upgrading medical service robots, which will improve patient acceptance of these robots.

The Service Robot Customer Experience (SR-CX): A Matter of AI Intelligences and Customer Service Goals

This research aims to generate a nuanced understanding of service robot customer experiences (SR-CX). Specifically, this work (1) empirically explores the impact of different AI intelligences (mechanical, thinking, and feeling AI) on SR-CX (i.e., SR-CX strength and SR-CX dimensionality) and its downstream impact on important service outcomes (i.e., overall service experience and service usage intentions), and (2) considers the moderating role of consumer service goals (hedonic versus utilitarian). Drawing on insights from two field studies and two online experiments, this research demonstrates that SR-CX—which is impacted differently by varying AI intelligences—affects service outcomes. Specifically, more sophisticated AI intelligences lead to enhanced service outcomes for customers with hedonic service goals across settings by strengthening SR-CX and triggering a more extensive set of SR-CX dimensions. This pattern, however, is less clear for customers driven by utilitarian service goals. For these customers, the role of SR-CX strength and SR-CX dimensionality varies across settings. These findings, which empirically support the importance of SR-CX, may help organizations to strategically leverage robots with different intelligence levels along service journeys of customers with different service goals.

Enhancing intention prediction and interpretability in service robots with LLM and KG

The rapid advancement of artificial intelligence has significantly expanded the role of service robots in everyday life. This expansion necessitates the accurate recognition and prediction of human intentions to provide timely and appropriate services. However, existing methods often struggle to perform effectively in complex and unstructured environments. To address this challenge, we propose the Large language model and Knowledge graph based Intention Recognition Framework (LKIRF), which combines large language model (LLM) with knowledge graphs (KG) to enhance the intention recognition capabilities of service robots. Our approach constructs an offline KG from human motion and environmental data and builds an online reasoning graph through real-time interaction, utilizing LLM for interpretation. Experimental results indicate that compared to traditional methods, LKIRF not only improves prediction accuracy across various scenarios but also enhances the transparency and interpretability of the intention reasoning process.

Commentary: The Future of Human-Robot Interactions

Service robots are poised for enormous impact helping to achieve the U.N Sustainable Development goals as well as supporting older adults and young learners. This article briefly explores these application areas in three invited commentaries written by leading scholars and an industry entrepreneur, expressing their varied perspectives on the future of service robots. The article accompanies this Journal of Service Research special issue on human-robot interactions. In their commentaries, the authors share views on areas of personal interest ranging from global grand challenges to healthy aging and educational development. Included within each commentary are managerial insights and suggestions for needed research in the highlighted area.

Role of Technological Advancements on Customer Satisfaction in Japan’s Hospitality

Purpose: The aim of the study was to analyze the role of technological advancements on customer satisfaction in Japan’s hospitality. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Technological advancements have significantly enhanced customer satisfaction in Japan’s hospitality industry by improving service efficiency, personalization, and overall guest experience. Innovations such as AI-driven customer service robots, automated check-in systems, and smart hotel rooms equipped with IoT devices offer convenience and seamless interactions for guests. These technologies reduce waiting times, streamline operations, and allow for personalized services, such as customized room settings and tailored recommendations, increasing guest satisfaction. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), service quality model (SERVQUAL) & expectancy-confirmation theory (ECT) may be used to anchor future studies on the role of technological advancements on customer satisfaction in Japan’s hospitality. Hospitality businesses should adopt integrated management systems that combine customer relationship management (CRM), property management systems (PMS), and online booking platforms. Industry regulators should promote standards for technology integration in the hospitality sector.

Societal Attitudes Toward Service Robots: Adore, Abhor, Ignore, or Unsure?

Societal or population-level attitudes are aggregated patterns of different individual attitudes, representing collective general predispositions. As service robots become ubiquitous, understanding attitudes towards them at the population (vs. individual) level enables firms to expand robot services to a broad (vs. niche) market. Targeting population-level attitudes would benefit service firms because: (1) they are more persistent, thus, stronger predictors of behavioral patterns and (2) this approach is less reliant on personal data, whereas individualized services are vulnerable to AI-related privacy risks. As for service theory, ignoring broad unobserved differences in attitudes produces biased conclusions, and our systematic review of previous research highlights a poor understanding of potential heterogeneity in attitudes toward service robots. We present five diverse studies (S1–S5), utilizing multinational and “real world” data (Ntotal = 89,541; years: 2012–2024). Results reveal a stable structure comprising four distinct attitude profiles (S1–S5): positive (“adore”), negative (“abhor”), indifferent (“ignore”), and ambivalent (“unsure”). The psychological need for interacting with service staff, and for autonomy and relatedness in technology use, function as attitude profile antecedents (S2). Importantly, the attitude profiles predict differences in post-interaction discomfort and anxiety (S3), satisfaction ratings and service evaluations (S4), and perceived sociability and uncanniness based on a robot’s humanlikeness (S5).

Challenges in service robot devices for elderly motion assistance

Abstract Motion assistance for elderly people is a field of application for service robotic systems that can be characterized by requirements and constraints of human–machine interaction and by the specificity of the user’s conditions. The main aspects of characterization and constraints are examined for the application of service systems that can be specifically conceived or adapted for elderly motion assistance by having to consider conditions of motion deficiency and muscular strength weakness as well as psychological aptitudes of users. The analysis is discussed in general terms with reference to elderly people who may not even suffer from specific pathologies. Therefore, the discussion focuses on the need for motion exercise in proper environments, including domestic ones and frame familiar to a user. The challenges of such applications oriented toward elderly users are discussed as requiring research and design of solutions in terms of specific portability, user-oriented operation, low costs, and clinical-physiotherapeutic functionality. Results of the author’s team experiences are presented as an example of problems and attempted solutions to meet the new challenges of service systems for motion assistance applications for elderly people.

I Care That You Don’t Share: Confidentiality in Student-Robot Interactions

Enabled by technological advances, robot teachers have entered educational service frontlines. Scholars and policymakers suggest that during Human-Robot Interaction (HRI), human teachers should remain “in-the-loop” (i.e., oversee interactions between students and robots). Drawing on impression management theory, we challenge this belief to argue that robot teacher confidentiality (i.e., robot teachers not sharing student interactions with the human teacher) lets students make more use of the technology. To examine this effect and provide deeper insights into multiple mechanisms and boundary conditions, we conduct six field, laboratory and online experiments that use virtual and physical robot teachers (Total N = 2,012). We first show that students indeed make more use of a confidential (vs. nonconfidential) robot teacher (both physical and virtual). In a qualitative study (Study 2), we use structural topic modeling to inductively identify relevant mediators and moderators. Studies 3 through 5 provide support for these, showing two key mediators (i.e., social judgment concern and interaction anxiety) and two moderators (i.e., student prevention focus and teacher benevolence) for the effect of robot teacher confidentiality. Collectively, the present research introduces the concept of service robot confidentiality, illustrating why and how not sharing HRI with a third actor critically impacts educational service encounters.

Service Robots in the Workplace: Fostering Sustainable Collaboration by Alleviating Perceived Burdensomeness

This study investigates the impact of job demands and perceived burdensomeness on employees’ willingness to collaborate with service robots (SRs) in the workplace, with a focus on the moderating effects of organizational support, self-esteem, and self-development. Conducted among 200 employees in South Korea, the results show that higher job demands significantly increase perceived burdensomeness, which negatively affects employees’ willingness to collaborate with SRs. Organizational support moderates the negative impact of job demands, reducing their detrimental effects, although it was less effective in buffering the reduction in social interaction. Self-esteem and self-development positively moderated the relationship between burdensomeness and collaboration. This study contributes to the understanding of sustainable workforce management by highlighting the importance of fostering personal and organizational resources to ensure that SR integration enhances both operational efficiency and employee well-being. The findings align with broader sustainability goals by promoting human–robot collaboration that optimizes resource use, enhances workforce adaptability, and maintains social and psychological balance in the workplace.

Feasibility analysis of robots in greenhouses. A case study in European Mediterranean countries

Agriculture plays a key role in the development of today's society. The population is constantly increasing, and increased production is required that is contained and consumes resources sustainably. Automation and robotization are one way to achieve this goal. Intensive agriculture, and in particular greenhouse agriculture, modifies the production environment to improve productivity while optimising the use of resources. This article studies the current feasibility of using robots in greenhouses from five perspectives: technical, economic, social, legal, and environmental. To this end, two representative applications have been selected, spraying and harvesting, in a type of greenhouse that is also representative, the Mediterranean type, which accounts for 92% of the total greenhouse area worldwide. The work can help companies and researchers make technical and economic decisions about the introduction of robotics in greenhouses. It concludes that there are currently gaps in the technical and legal aspects, and that the business model is not profitable at present, however, robotics in agriculture will be fully viable in the future, given the growing trend towards service robots, supported by a strong interest in developing a particular legislative regulation, and a technology that evolves very quickly, reducing its costs and ensuring the sustainability of the processes.

Augmenting Consumer Acceptance of Robot-Assisted Technologies in Retail Industry: An Interdisciplinary Approach

Since they have been incorporated into retail, robots have been discussed more frequently in academic publications and public debate. Customers in retail sector, as well as workers will be resistant to the rising employment of robots as their capabilities increase and they are employed to supplement or replace human labour. "Interdisciplinary" denotes a technique that amalgamates knowledge and methodologies from several academic or professional domains to attain a thorough comprehension of a particular subject or matter. As robots and AI become more common in society, this study examines the public's attitudes towards their use in retail. Consumer perceptions of retail robots are examined using consumer psychology and technology attitudes literature. To fill the research gap, 393 retailers' responses on service robot deployment in retail contexts are analyzed, with an emphasis on the East region. Service robot attitudes vary based on aspects such as robot benefits, social skills, and technology attitudes. Cluster analysis divides respondents into "high techies" and "high touches," depending on their service robot acceptance. The regression study clarifies how numerous factors affect retail robot-assisted service perceptions. Retailers and policymakers can learn from the survey that consumer opinion is significant for the future integration of robots in retail.

Design player robot badminton-based microcontroller

Robots are one of the technologies that is currently advancing quickly. Generally speaking, a robot's movement is similar to that of an automobile; it can only move forward, backward, left, and right. Because the movement is controlled by these movements, it is thought that the robot's movement is extremely restricted to the left and right directions. solely with the front wheels. As a result, a robot was developed in this study that can control sliding motions to the left and right utilizing omniwheels on its front and back wheels. Badminton is a sport involving rackets that is played by two people or two opposing pairs. Robotic badminton players are employed as a substitute for human trainers in the training process, particularly for service and drive motions. With the use of a wireless joystick, the robot's ATMega 8535 microprocessor controls both the robot's direction of motion and the movement of its racket. Using a double acting pneumatic cylinder that requires 7 bar of air pressure, the robot service arm uses the compressor's air pressure. The average time it takes for the racket to strike the ball at 7 bar of wind pressure is 00:5.2 seconds. The time it takes for the ball to fall onto the racket in the absence of wind pressure is 00:28 seconds on average. A difference value of 00:22.7 seconds is acquired, and this value will be utilized as the programming reference delay. The robot encounters a slope with an average angle change of 7º when moving forward, an average angle change of 10º when moving backward, an average angle change of 5.2º when moving right, and an average angle change of 3º when moving left. The uneven field surface causes the robot to move at a slope, which modifies the speed of the motor on the wheels

Adaptive FPGA-Based Accelerators for Human–Robot Interaction in Indoor Environments

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot’s intention to serve based on the human’s location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human–robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation.

Framework for Integrating Large Language Models with a Robotic Health Attendant for Adaptive Task Execution in Patient Care

The development of intelligent medical service robots for patient care presents significant challenges, particularly in integrating diverse knowledge sources and enabling robots to autonomously perform tasks in dynamic and unpredictable healthcare environments. This study introduces a novel framework that combines large language models with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant. Utilizing OpenAI’s ChatGPT, the system processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the robot successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the language model and predefined task specifications. The results demonstrate the potential of language models to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural language.

Application of the Extended Unified Theory of Acceptance and Use of Technology to a Robotic Golf Caddy: Health Consciousness as a Moderator

The COVID-19 pandemic not only accelerated the spread of contactless robot services but also revitalized the golf industry. These changes have expedited the adoption of robot caddies, making it necessary to conduct research on golfers’ acceptance of robot caddies. This study, thus, intended to investigate the importance of the extended unified theory of acceptance and use of technology (UTAUT) in the framework of robotic golf caddies and examined health consciousness as a moderator. An online survey was conducted with South Korean golfers, and a total of 391 responses were analyzed. The data analysis results indicated that all five dimensions of the extended UTAUT have a positive impact on attitude. Also, attitude is a significant predictor of both intentions to use and word-of-mouth intentions. Lastly, this study discovered that the moderating effect of health consciousness on the correlation between performance expectancy and attitude was significant.