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The purpose of this study is to analyze the robot trends of the next generation.

This paper is divided into two sections: the key modern technology on which Europe's robotics industry has built its foundation is described. Then, the next key megatrends were analyzed.

Artificial intelligence (AI) and robotics are technologies of major importance for the development of humanity. This time is mature for the evolution of industrial and service robots. The perception of robot use has changed from threading to aiding. The cost of mass production of technological devices is decreasing, while a rich set of enabling technologies is under development. Soft mechanisms, 5G and AI have enabled us to address a wide range of new problems. Ethics should guide human behavior in addressing this newly available powerful technology in the right direction.

The paper describes the impact of new technology, such as AI and soft robotics. The world of work must react quickly to these epochal changes to enjoy their full benefits.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

This study is dedicated to critically analysing research addressing human resource management (HRM) and the adoption of artificial intelligence (AI) with the purpose of driving development in the field of human resources (HR) at the strategic and managerial level.

A systematic literature review (SLR) was conducted using the Scopus database, which gathered 61 articles between 2002 and 2022. The SLR process has the potential to, in addition to generating knowledge and theories, support and guide policy development and practice in many disciplines.

The results of this study allowed the author to identify three main conclusions: (a) there are four thematic clusters – (i) Strategic HR and AI, (ii) Recruitment and AI, (iii) Training and AI and (iv) Future of work; (b) there is a growing academic interest in studying the implementation of AI to develop the HR sector and (c) the application of AI stands out in the strategic HR and AI cluster as a means of achieving profit maximisation and the overall development of the organisation.

This study is the first SLR to present a strategic and managerial view on AI applications associated with specific HRM dimensions. The study is also the first SLR to identify key trends in the literature, drivers and obstacles to the development of AI in HRM and then place them within the landscape of positive and negative approaches in a framework. Also, as a contribution, the study has practical implications for HR managers and practitioners in adopting AI as a decision support in the area's processes.

There is limited research on the behavior of different categories of space tourists as identified by different types of space tourism. To address this deficiency, the purpose of this study is to examine what factors make consumers participate in orbital and/or suborbital space tourism, along with three dimensions of motivation, constraint and artificial intelligence. To achieve this study’s goals, a comprehensive research model was developed that included three dimensions of intrinsic and extrinsic motivation, intrapersonal and interpersonal constraint and awareness of and trust in artificial intelligence, in comparing orbital and suborbital space tourism groups.

A questionnaire was carried out with respondents who wanted to participate in orbital (n = 332) and suborbital (n = 332) space tourism in the future. Partial least squares-structural equation modeling, fuzzy-set qualitative comparative analysis, multi-group analysis and deep learning were used to understand potential space tourist behavior.

Extrinsic motivation has the greatest positive impact on behavioral intention, followed by awareness of and trust in artificial intelligence, while intrapersonal constraint strongly negatively affects behavioral intention. Surprisingly, interpersonal constraint is insignificant by partial least squares-structural equation modeling but is still one of sufficient causal configurations by fuzzy-set qualitative comparative analysis. Interestingly, the two types of space tourism have very distinct characteristics.

This study created a comprehensive integrated research model with three dimensions of motivation, constraint and artificial intelligence, along with potential orbital and suborbital space tourist groups, to identify future consumer behavior. Importantly, this study used multi-analysis methods using four different approaches to better shed light on potential orbital and suborbital space tourists.

对不同类型太空旅游所识别的不同类别太空游客行为的研究有限。 为了解决这一缺陷, 这项工作研究了哪些因素使消费者参与轨道和/或亚轨道太空旅游, 以及动机、约束和人工智能三个维度。 为了实现研究目标, 在比较轨道和亚轨道太空旅游群体时, 开发了一个综合研究模型, 包括内在和外在动机、内在和人际约束以及对人工智能的认识和信任三个维度。

对希望在未来参与轨道 (n = 332) 和亚轨道 (n = 332) 太空旅游的受访者进行了问卷调查。 利用偏最小二乘法 (PLS)-结构方程模型 (SEM)、模糊集定性比较分析 (fsQCA)、多组分析和深度学习来了解潜在的太空游客行为。

外在动机对行为意图的积极影响最大, 其次是对人工智能的认识和信任, 而内在约束对行为意图有强烈的负面影响。 令人惊讶的是, 人际约束对于 PLS-SEM 来说是微不足道的, 但对于 fsQCA 来说仍然是充分的因果配置之一。 有趣的是, 这两类太空旅游具有非常鲜明的特点。

这项工作创建了一个全面的综合研究模型, 具有动机、约束和人工智能三个维度, 以及潜在的轨道和亚轨道太空旅游群体, 以确定未来的消费者行为。 重要的是, 这项研究采用了多种分析方法, 使用四种不同的方法来更好地揭示潜在的轨道和亚轨道太空游客。

existe una investigación limitada sobre el comportamiento de las diferentes categorías de turistas espaciales identificados por diferentes tipos de turismo espacial. Para abordar esta deficiencia, este trabajo examina qué factores hacen que los consumidores participen en el turismo espacial orbital y/o suborbital, junto con tres dimensiones de motivación, restricción e inteligencia artificial. Para lograr los objetivos del estudio, se desarrolló un modelo de investigación integral que incluía tres dimensiones de motivación intrínseca y extrínseca, restricción intrapersonal e interpersonal, y conocimiento y confianza en la inteligencia artificial, al comparar grupos de turismo espacial orbital y suborbital.

se realizó un cuestionario con los encuestados que querían participar en el turismo espacial orbital (n = 332) y suborbital (n = 332) en el futuro. Se utilizaron modelos de ecuaciones estructurales (SEM) de mínimos cuadrados parciales (PLS), análisis comparativo cualitativo de conjuntos borrosos (fsQCA), análisis multigrupo y aprendizaje profundo para comprender el comportamiento potencial del turista espacial.

la motivación extrínseca tiene el mayor impacto positivo en la intención de comportamiento, seguida de la conciencia y la confianza en la inteligencia artificial, mientras que la restricción intrapersonal afecta negativamente la intención de comportamiento. Sorprendentemente, la restricción interpersonal es insignificante por PLS-SEM, pero sigue siendo una de las configuraciones causales suficientes por fsQCA. Curiosamente, los dos tipos de turismo espacial tienen características muy distintas.

este trabajo creó un modelo de investigación integral integral con tres dimensiones de motivación, restricción e inteligencia artificial, junto con posibles grupos de turistas espaciales orbitales y suborbitales para identificar el comportamiento futuro del consumidor. Es importante destacar que este estudio empleó métodos de análisis múltiple utilizando cuatro enfoques diferentes para arrojar mejor luz sobre los posibles turistas espaciales orbitales y suborbitales.

Retailers increasingly endeavour to implement artificial intelligence (AI) innovations, such as humanoid social robots (HSRs), to enhance customer experience. This paper investigates the interactive effect of HSR intelligence and consumers' speciesism on their perceptions of retail robots as sales assistants.

Three online experiments testing the effects of HSRs' intellectual intelligence on individuals' perceived competence and, consequently, their decision to shop at a retail store that uses HSRs as sales assistants are reported. Furthermore, the authors examine whether speciesism attenuates these effects such that a mediation effect is likely to be observed for individuals low in speciesism but not for those with high levels of speciesism. Data for all studies were collected on Prolific and analysed with SPSS to perform a logistic regression and PROCESS 4.0 (Hayes, 2022) for the mediation and moderated-mediation analysis.

The findings show that the level of speciesism moderates the relationship between HSR intellectual intelligence and perceived competence such that an effect is found for low but not for high HSR intelligence. When HSR intellectual intelligence is low, individuals with higher levels of speciesism (vs low) rate the HSR as less competent and display lower HSR acceptance (i.e. customers' decision to shop using retail robots as sales assistants).

This research responds to calls in research to adopt a human-like perspective to understand the compatibility between humans and robots and determine how personality traits, such as a person's level of speciesism, may affect the acceptance of AI technologies replicating human characteristics (Schmitt, 2019). To the best of the authors' knowledge, the present research is the first to examine the moderating role of speciesism on customer perceptions of non-human retail assistants (i.e. human-like and intelligent service robots). This study is the first to showcase that speciesism, normally considered a negative social behaviour, can positively influence individuals' decisions to engage with HSRs.

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.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”

The purpose of this study is to analyze state-of-the-art knowledge of artificial intelligence (AI) research in hospitality.

This study adopts the theory-context-methods framework to systematically review 100 AI-related articles recently published (i.e. from 2021 to April 2023) in three top-tier hospitality journals, namely, the International Journal of Contemporary Hospitality Management, International Journal of Hospitality Management and Journal of Hospitality Marketing and Management.

Findings suggest that studies of AI applications in hospitality are mostly theory-driven, whereas most AI methods research adopts a data-driven approach. State-of-the-art AI applications research exhibits the most interest in service robots. In AI methods research, little attention was paid to the amid-service/experience.

This study reveals inadequacies in theory, context and methods in contemporary AI research. More research from hospitality suppliers’ perspectives and research on generative AI applications are advocated in response to the unveiled research gaps and recent AI developments.

This study classifies the most recent AI research in hospitality into two main streams – AI applications research and AI methods research – and discusses the gaps in each research stream and latest AI developments. The paper then suggests future research directions to guide researchers in advancing AI research in hospitality.

This paper aims to explore the impact of emerging technologies, such as virtual reality, voice search, artificial intelligence, robotics and the Metaverse on hotel sales, marketing and revenue optimization.

This paper uses a combination of articles published in Journal of Hospitality and Tourism Technology and industry case studies to examine the integration of innovative technologies in hotel sales, marketing and revenue optimization strategies, plus the role of fundamental practices in ensuring long-term success.

The analysis demonstrates that innovative technologies can significantly enhance customer engagement, streamline booking processes and unlock new revenue streams. However, this paper also highlights the importance of fundamental marketing practices, such as user-friendly websites, fast and reliable/mobile-friendly websites, search engine optimization, social media engagement, content marketing and data-driven revenue management, in maintaining a hotel’s competitive advantage in the dynamic world of hospitality.

The findings suggest that hoteliers need to strike a balance between embracing emerging technologies and maintaining traditional marketing fundamentals to remain competitive and drive revenue growth. This integrated approach ensures long-term success in the ever-evolving hospitality landscape.

This paper bridges the gap between academia and industry practitioners by providing practical insights and implications that can be applied directly to hotels’ marketing and operational practices. The paper highlights the importance of balancing innovation with fundamental marketing strategies, emphasizing the need for an integrated approach to ensure long-term success in the ever-evolving world of hotel sales, marketing and revenue optimization – as well as those same tools in a plethora of hospitality and tourism venues working alongside the accommodations sector.

探讨新兴技术（如虚拟现实、语音搜索、人工智能、机器人技术和元宇宙）对酒店销售、营销和收入优化的影响。

本文结合发表在《酒店旅游科技杂志》（JHTT）的文章和行业案例, 研究了创新技术在酒店销售、营销和收入优化策略中的整合, 以及基础实践在确保长期成功方面的作用。

分析表明, 创新技术可以显著增强客户参与度, 简化预订流程, 并开拓新的收入来源。此外, 文章强调了基本营销实践, 如用户友好的网站、快速可靠/移动友好的网站、搜索引擎优化、社交媒体参与、内容营销和数据驱动的收入管理, 在保证酒店在充满变化的行业竞争中维持优势的重要性。

研究结果表明, 酒店经营者需要在使用新兴技术和保持传统营销基本原则之间取得平衡, 以保持竞争力并推动收入增长。这种综合方法确保在不断发展的酒店销售、营销和收入优化领域取得长期成功。

本文通过提供可直接应用于酒店营销和运营实践的实际见解和影响, 弥合了学术界与行业从业者之间的鸿沟。文章强调了创新与基本营销策略的平衡的重要性, 强调了综合方法的必要性, 以确保在不断发展的酒店销售、营销和收入优化领域取得长期成功, 以及这些工具在众多酒店和旅游场所与住宿部门一起工作的多样性领域中的应用。

This study aims to identify articles examining human-robot interaction and the effects of robotic systems on employment.

In this research, electronic searches were performed for articles published between 2000 and 2022 in Emerald, Springer, PubMed, Science Direct, Wiley and Google Scholar. In the searches of robotic systems with keywords such as “motivation, job satisfaction, job loss, performance, job giving,” 5 quantitative and 5 qualitative studies were included in the systematic review. The selected research was conducted using the Johanna Briggs Analytical Cross-Sectional Studies Checklist from the Joanna Briggs Institute (JBI) critical evaluation lists and the JBI Critical Appraisal Checklist for Qualitative Research, depending on their type. The included studies are mostly on employee-robot collaboration.

Although the majority of the articles examined in this study are included in keywords or titles, it is determined that there is a gap in descriptive quantitative studies in the literature on the effects of employee-robot collaboration, robotic systems and robotic systems on variables such as motivation, job satisfaction, job loss, performance and employment, although they do not mention a framework that directly investigates human-robot interaction and the effects of robotic systems on employment.

There are several limitations in this study. One of them is that, although the databases are comprehensively scanned, only studies published in English between 2000 and 2022 are included in the systematic review. Another limitation is the heterogeneity between studies.

As a result of the authors’ findings, the practical effects of the research are reflected as follows: It serves as a guide for future studies to fill the gap in the field, especially for academics and researchers working in the field of social sciences on robotic systems and intelligent automations. In addition to the qualitative studies on this subject, there is a need for the use of robotic systems in the field of human resources and management and quantitative studies with more sample sizes, especially at the corporate (firms) and individual (employees) level. Considering that the number of studies on this subject is very insufficient, this research is important in terms of shedding light on future studies.

The authors believe that the impact of robotic systems on employment is one of the few conceptual articles that systematically examines 6 dimensions (job satisfaction, performance, job loss, employment, motivation, employment).