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The service industry is facing the huge impact of digital transformation, in which artificial intelligence (AI) plays one of the most important roles. This study aims to expand the understanding of the AI acceptance framework and confirm whether consumers’ digital skills have a moderating effect on the research model.

Hypotheses were tested using a data set of 1,641 individuals. Partial least squares structural equation modeling and multi-group analysis were used to estimate the model.

The results indicate that antecedent factors influence consumers’ willingness to use AI devices in services. The two groups of different digitally savvy respondents differ because the influence of anthropomorphism, social influence and hedonic motivation on respondents’ perceived efforts to use AI devices in service delivery depends on respondents’ digital skills.

The novel contribution of this study is reflected in a comprehensive model that explains the moderating effect of individual digital skills on willingness to use AI devices. The attitudes of experienced and digitally skilled consumers are valuable and highlight some important theoretical, practical implications and future lines of research.

This study aims to investigate tourists’ intentions to use hotel service robots with a focus on safety and hygiene. It examines the impact of perceived safety, health awareness and service assurance on consumer engagement and robot usage.

Survey data from 275 participants with experience in robotic service were analyzed using structural equation modeling (SEM). The study used purposive sampling and collected data via the Prolific platform, using SEM and SmartPLS Ver. 3.0 for analysis.

Results indicate customers prioritize safety and hygiene, valuing effective service responses and cleanliness. Perceived robotic safety and service assurance positively influence personal engagement, with a preference for service robots among female guests.

While emphasizing the importance of safety and service assurance in hotel robotics, the study acknowledges limitations in personalization and conclusive use of service robots.

This research contributes to understanding the role of perceived safety in service robot usage, highlighting the significance of user trust and comfort in human–robot interactions. It also explores the novel connection between service assurance and service robots, offering insights into robotic performance reliability in user-centric contexts.

The gastronomy industry is one of the industries in which consumers use technology most frequently, such as robotics, artificial intelligence and automation systems. Service robots are generally defined as online programs that can act independently or under the control of a human that can carry human characteristics such as speaking, understanding and acting. This study aims to examine and evaluate the studies on service robots in the gastronomy industry regarding quantity or quality, to understand the phenomenon more comprehensively, and to determine a holistic perspective. To this end, a qualitative research approach was adopted. Document technique was used to collect data within a systematic literature review framework, and the data were analysed by descriptive analysis. According to the findings, the papers on service robots in the gastronomy industry are still in their infancy, and these studies have increasingly gained speed in the last five years.

The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.

This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.

The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.

This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.

This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.

This study aims to identify, review and synthesize existing literature on key theories, drivers and barriers affecting consumer adoption or resistance to artificial intelligence (AI) in the hospitality sector.

This study aims to conduct a complete literature review of the accrued knowledge generated so far on AI in the hospitality sector. To attain the overall objectives of this study, we used the systematic literature review (SLR) method. This method systematically handles the diversity of knowledge in a specific topic to answer precise research questions. It also generates new visions through a synthesis of the literature, to identify the knowledge gaps, set the new directions for the future researcher and provide sufficient guidance to inform the policy and practice.

The findings of this study are presented in three sections, as follows: descriptive analysis, content analysis and synthesized framework. The findings highlighted the state-of-the-art mapping of the existing research in terms of publication frequency over time and across publication outlets, key theories, methods and geographies. In addition, literature on consumer adoption (or resistance) of AI in hospitality is content analyzed to highlight key drivers and barriers. Moreover, this review critically evaluates extant literature and sets future agendas by postulating specific research questions for further knowledge development in this field of study.

The SLR focused on consumer adoption or resistance to use AI in hospitality literature. The future researcher may include additional streams to get better results.

The study findings will help multiple stakeholders to understand the underlying causes of customer resistance or barriers to the intention to use/adopt AI services in the hotel sector. Furthermore, study results will allow them to better analyze the relationship between customer barriers, intents or consumer decision behaviors.

First, this study provides a comprehensive synthesis of the literature on the consumer adoption or resistance of AI in hospitality. This study categorizes the existing diversified literature in two main themes – drivers and barriers – to present a simplistic picture of the existing literature. Second, the review highlights the gaps and limitations in existing research and provides guidance for future scholars. Third, the key contribution of this review is the development of a unified framework on the consumer adoption or resistance of AI in the hospitality sector. That is, this study puts forward the behavioral reasoning theory framework and suggests that future research using this lens will immensely contribute to existing literature. Finally, this study facilitates the practitioners to understand the key motivating and hindering factors affecting the adoption and resistance behavior.

The purpose of this paper is to provide a detailed insight into the global military robot industry with an emphasis on products and their applications.

Following an introduction which includes a brief historical account, this provides an industry overview, including various market dimensions and a discussion of the geopolitical and technological factors driving market development. The three following sections provide details of land, airborne and marine robots, their capabilities and deployments in recent conflicts. Finally, brief conclusions are drawn.

Military robots which operate on land, in the air and at sea constitute a multi-billion dollar industry which is growing rapidly. It is being driven by geopolitical tensions, notably the military-technology arms race between China and the USA and the conflict in Ukraine, together with technological progress, particularly in AI. Many robots possess multi-functional capabilities, and the leading application is presently intelligence, surveillance and reconnaissance. An increasing number of heavily armed robots are being developed, and AI has the potential to impart these with the capacity to deliver lethal force without human intervention. Although heavily criticised in some quarters, this capability has probably already been deployed on the battlefield. With ever-growing military budgets, escalating political tensions and technological innovations, robots will play an increasingly significant role in future conflicts.

This provides a detail account of military robots and their role in modern warfare.

The purpose of this paper is to propose an new online path planning method for porcine belly cutting. With the proliferation in demand for the automatic systems of pork production, the development of efficient and robust meat cutting algorithms are hot issues. The uncertain and dynamic nature of the online porcine belly cutting imposes a challenge for the robot to identify and cut efficiently and accurately. Based on the above challenges, an online porcine belly cutting method using 3D laser point cloud is proposed.

The robotic cutting system is composed of an industrial robotic manipulator, customized tools, a laser sensor and a PC.

Analysis of experimental results shows that by comparing with machine vision, laser sensor-based robot cutting has more advantages, and it can handle different carcass sizes.

An image pyramid method is used for dimensionality reduction of the 3D laser point cloud. From a detailed analysis of the outward and inward cutting errors, the outward cutting error is the limiting condition for reducing the segments by segmentation algorithm.

The advancement of technology and increased sales of service robots have led to discussions about their usage. Recently, the use of service robots in accommodation businesses has become a topic of examination. This study aims to explore the behavioural intentions of accommodation business managers towards using service robots from the perspective of the technology acceptance model. As part of this project-supported study, the ‘Sanbot Elf’ service robot was rented for 14 days and demonstrated in six accommodation businesses in Kastamonu. Semi-structured interviews were conducted with managers and unit supervisors after the demonstration, and the process was also monitored through participant observation. The data were analysed using content analysis and participant observation methods. It was determined that participants' opinions on service robots were generally positive. Participants stated that personalised service presentations could be provided with service robots and that these robots could be used in different departments and marketing processes. The study concluded that service robots could positively impact service quality, are an exciting technology for accommodation businesses and the use of service robots could increase in the future. Participants' attitudes towards using service robots in accommodation businesses were positive in this context.

This paper aims to provide insight into the evolving relationship between humans and machines, understanding its multifaceted impact on our lifestyle and landscape in the past as well as in the present, with implications for the near future. It uses bibliometric analysis combined with a systematic literature review to identify themes, trace historical developments and offer a direction for future human–machine interactions (HMIs).

To provide thorough coverage of publications from the previous four decades, the first section presents a text-based cluster bibliometric analysis based on 305 articles from 2,293 initial papers in the Scopus and Web of Science databases produced between 1984 and 2022. The authors used VOS viewer software to identify the most prominent themes through cluster identification. This paper presents a systematic literature review of 63 qualified papers using the PRISMA framework.

Next, the systematic literature review and bibliometric analysis revealed four major historical themes and future directions. The results highlight four major research themes for the future: from Taylorism to advanced technologies; machine learning and innovation; Industry 4.0, Society 5.0 and cyber–physical system; and psychology and emotions.

There is growing anxiety among humankind that in the future, machines will overtake humans to replace them in various roles. The current study investigates the evolution of HMIs from their historical roots to Society 5.0, which is understood to be a human-centred society. It balances economic advancement with the resolution of social problems through a system that radically integrates cyberspace and physical space. This paper contributes to research and current limited knowledge by identifying relevant themes and offering scope for future research directions. A close look at the analysis posits that humans and machines complement each other in various roles. Machines reduce the mechanical work of human beings, bringing the elements of humanism and compassion to mechanical tasks. However, in the future, smart innovations may yield machines with unmatched dexterity and capability unthinkable today.

This paper attempts to explore the ambiguous and dynamic relationships between humans and machines. The present study combines systematic review and bibliometric analysis to identify prominent trends and themes. This provides a more robust and systematic encapsulation of this evolution and interaction, from Taylorism to Society 5.0. The principles of Taylorism are extended and redefined in the context of HMIs, especially advanced technologies.

Artificial intelligence (AI) and big data analysis may further enhance the automated and smart features of tourism and hospitality services. However, it also poses new challenges to human resource management. This study aims to explore the direct and indirect effects of employees’ AI perception on career resilience and informal learning as well as the mediating effect of career resilience.

This paper proposed a theoretical model of AI perception, career resilience and informal learning with perceived AI as the antecedent variable, career resilience as the mediate variable and informal learning as the endogenous variable. Targeting the employees working with AI, a total of 472 valid data were collected. Data were analyzed using structural equation modeling with AMOS software.

Findings indicated that employees’ perception of AI positively contributes to career resilience and informal learning. Apart from the direct effect on informal learning, career resilience also mediates the relationship between AI perception and informal learning.

Research findings provide both theoretical and practical implications by revealing the impact of AI perception on employees’ career development, leaning activities, explaining how AI transforms the nature of work and career development and shedding lights on human resource management in the tourism and hospitality field.

本文提出了人工智能感知为前因变量、职业弹性为中介变量、非正式学习为内生变量的理论模型。以旅游业AI工作环境中的员工为研究对象, 本课题共收集了472份来自中国的有效数据, 并通过结构方程建模（SEM）来进行相关模型检验。

人工智能和大数据分析可能会使旅游和酒店服务更加自动化和智能化, 但这也对人力资源管理提出了新的挑战。本研究旨在探讨员工对人工智能（AI）的感知对职业弹性和非正式学习的直接和间接影响, 以及职业弹性的中介作用。

研究结果显示, 员工对人工智能的感知对职业弹性和非正式学习有积极影响。除了对非正式学习的直接影响外, 职业弹性在人工智能 (A I) 感知和非正式学习之间起中介作用。

本研究在以下几个方面具有重要的理论和实践意义：解释了人工智能感知对员工职业发展和学习行为的影响, 以及它是如何改变工作性质和员工职业发展的; 研究发现对旅游和酒店行业的人力资源管理具有实践指导意义。

La IA y el análisis de big data pueden potenciar aún más las características automatizadas e inteligentes de los servicios de turismo y hostelería. Sin embargo, también plantea nuevos retos a la gestión de los recursos humanos. Este estudio pretende explorar los efectos directos e indirectos de la percepción de la IA por parte de los empleados sobre la resiliencia profesional y el aprendizaje informal, así como el efecto mediador de la resiliencia profesional.

En este trabajo se propone un modelo teórico de percepción de la IA, resiliencia profesional y aprendizaje informal con la IA percibida como variable antecedente, la resiliencia profesional como variable mediadora y el aprendizaje informal como variable endógena. Dirigidos a los empleados que trabajan con IA, se recogieron un total de 472 datos válidos. Los datos se analizaron mediante un modelo de ecuaciones estructurales (SEM) con el software AMOS.

Los Resultados indicaron que la percepción de la IA por parte de los empleados contribuye positivamente a la resiliencia profesional y al aprendizaje informal. Aparte del efecto directo sobre el aprendizaje informal, la resiliencia profesional también media en la relación entre la percepción de la IA y el aprendizaje informal.

Los Resultados de la investigación proporcionan implicaciones tanto teóricas como prácticas al revelar el impacto de la percepción de la IA en el desarrollo profesional de los empleados, las actividades de aprendizaje, explicar cómo la IA transforma la naturaleza del trabajo y el desarrollo profesional, y arrojar luz sobre la gestión de los recursos humanos en el ámbito del turismo y la hostelería.