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The purpose of this paper is to develop an automated disassembly cell that is flexible and robust to the physical variations of a product. In this way it is capable of dealing with any model of product, regardless of the level of detail in the supplied information.

The concept of cognitive robotics is used to replicate human level expertise in terms of perception and decision making. As a result, difficulties with respect to the uncertainties and variations of the product in the disassembly process are resolved.

Cognitive functions, namely reasoning and execution monitoring, can be used in basic behaviour control to address problems in variations of the disassembly process due to variations in the product's structure particularly across different models of the product.

The paper provides a practical approach to formulating the disassembly domain and behaviour control of the cognitive robotic agent via a high‐level logical programming language that combines domain‐specific heuristic knowledge with search to deal with variations in products and uncertainties that arise during the disassembly process.

Full disassembly automation that is flexible and robust to the uncertainties that may arise potentially replaces human labour in a difficult and hazardous task. Consequently, the disassembly process will be more economically feasible, especially in developed countries.

The paper provides a practical approach to the basic cognitive functions that replicate the human expert's behaviour to the disassembly cell.

The working hypothesis, on which this paper is built, is that it is advantageous to look at protocols of robot rehabilitation in the general context of human-robot interaction in haptic dyads. The purpose of this paper is to propose a new method to detect and evaluate an index of active participation (AC index), underlying the performance of robot-assisted movements. This is important for avoiding the slacking phenomenon that affects robot therapy.

The evaluation of the AC index is based on a novel technique of assistance which does not use constant or elastic forces but trains of small force impulses, with amplitude adapted to the level of impairment and a frequency of 2 Hz, which is suggested by recent results in the field of intermittent motor control. A preliminary feasibility test of the proposed method was carried out during a haptic reaching task in the absence of visual feedback, for a group of five stroke patients and an equal group of healthy subjects.

The AC index appears to be stable and sensitive to training in both populations of subjects.

The main original element of this study is the proposal of the new AC index of voluntary control associated with the new method of pulsed haptic interaction.

The study seeks to understand the possible opportunities that Industry 5.0 might offer for various aspects of inclusive sustainability. The study aims to discuss existing perspectives on the classification of Industry 5.0 technologies and their underlying role in materializing the sustainability values of this agenda.

The study systematically reviewed Industry 5.0 literature based on the PRISMA protocol. The study further employed a detailed content-centric review of eligible documents and conducted evidence mapping to fulfill the research objectives.

The advancement of Industry 5.0 is currently underway, with noteworthy initial contributions enriching its knowledge base. Although a unanimous definition remains lacking, diverse viewpoints emerge concerning the recognition of fundamental technologies and the potential for yielding sustainable outcomes. The expected contribution of Industry 5.0 to sustainability varies significantly depending on the context and the nature of underlying technologies.

Industry 5.0 holds the potential for advancing sustainability at both the firm and supply chain levels. It is envisioned to contribute proportionately to the three sustainability dimensions. However, the current discourse primarily dwells in theoretical and conceptual domains, lacking empirical exploration of its practical implications.

This study comprehensively explores diverse perspectives on Industry 5.0 technologies and their potential contributions to economic, environmental and social sustainability. Despite its promise, the practical evidence supporting the effectiveness of Industry 5.0 remains limited. Certain conditions are necessary to realize the benefits of Industry 5.0 fully, yet the mechanisms behind these conditions require further investigation. In this regard, the study suggests several potential areas for future research.

This study aims to apply the predictive processing theory to examine the influence of artificial intelligence (AI)-driven robotic performers on audience emotions and the audience’s resulting electronic word-of-mouth (eWOM) behaviors during tourism service encounters.

Using a quantitative research methodology, survey responses from 339 regular customers of performing arts in tourism destinations were analyzed. The respondents were recruited through Prolific, a professional data collection platform. SPSS 23.0 was used for the preliminary analysis, from which a research model to achieve the aim was proposed. SmartPLS 3 was used for partial least squares structural equation modeling to test the model.

Interactive and novel robotic performances significantly encouraged the consumers to share their experiences online, thereby enhancing eWOM. However, melodic resonance had no significant impact on eWOM intentions. The consumers’ emotional responses fully mediated the relationship of the novelty and interactivity of the performances to the consumers’ eWOM intentions but did not mediate the relationship of the musical elements to their eWOM intentions.

This study enriches the understanding of how AI-driven performances impact consumers’ emotional engagement and sharing behaviors. It extends the application of the predictive processing theory to the domain of consumer behavior, offering valuable insights for enhancing audience engagement in performances through technological innovation.

本研究旨在运用预测处理理论, 考察人工智能（AI）驱动的机器人表演对观众情感及其在旅游服务接触中的电子口碑（eWOM）行为的影响。。

采用定量研究方法, 分析了339名经常观看旅游景点表演艺术的常客的调查问卷。受访者通过专业数据收集平台Prolific招募。初步分析使用SPSS 23.0进行, 从中提出了实现研究目标的研究模型。使用SmartPLS 3进行偏最小二乘结构方程模型测试该模型。

互动性和新颖性的机器人表演显著鼓励消费者在线分享他们的体验, 从而增强电子口碑。然而, 旋律共鸣对电子口碑意图没有显著影响。消费者的情感反应完全中介了表演的新颖性和互动性与消费者电子口碑意图之间的关系, 但没有中介音乐元素与电子口碑意图之间的关系。

本研究丰富了对AI驱动表演如何影响消费者情感参与和分享行为的理解。将预测处理理论的应用扩展到消费者行为领域, 为通过技术创新增强观众参与度提供了宝贵的见解。

This article aims to provide a brief overview of the field now known as “evolutionary developmental robotics (evo-devo-robo)”, which is based on the concept and principles of evolutionary and development principles such as evolutionary developmental psychology, evolutionary developmental biology (evo-devo) and evolutionary cognitive neuroscience.

Evo-devo-robo is a new field bringing together developmental robotics and evolutionary robotics to form a new research area. Basic concepts and the origins of the field are described, and then some basic principles of evo-devo-robo that have been developed so far are discussed.

Finally, some misunderstand concepts and the most promising future research developments in this area are discussed.

Basic concepts and the origins of the field are described, and then some basic principles of evo-devo-robo that have been developed so far are discussed. Finally, some misunderstood concepts and the most promising future research developments in this area are discussed.

The purpose of this paper is to report on experiences and reflect on challenges in transdisciplinary technology foresight as exemplified by cognition and robotics research.

The study was conducted as a broad transdisciplinary process involving users and producers of robot technology solutions as well as scientists and other experts in cognition and robotics. Transdisciplinarity is understood as the transcendence of disciplinary modes together with the involvement and participation of non‐scientists in problem formulation and knowledge provision. The study focuses on the possibilities for innovation at the crossroads where robotics and cognition meet.

The paper reflects on the following methodological issues: medium‐ and long‐term research and innovation possibilities and barriers in a transdisciplinary context; the classification and framing of transdisciplinary fields; the facilitation of technology foresight processes; and the trustworthiness of the foresight process and its recommendations.

The results have been disseminated among relevant advisory and grant‐awarding bodies within research and innovation, relevant knowledge institutions and universities, and companies on both the development and user sides of the technologies.

The paper contributes to European experiences on national‐level foresight exercises. The conceptual findings of the case study are of value to science and innovation policy makers, foresight practitioners and scholars within the field.

This paper reviews existing research to understand when, how and with what results robotic process automation (RPA) is implemented by organizations.

The authors have identified 84 sources across eight databases and have analyzed them through the lens of a context–intervention–mechanism–outcomes framework (CIMO). The CIMO analysis maps the contextual drivers, intervention approaches and value related outcomes associated with RPA implementation.

The result of the analysis is the identification of four mechanisms explaining the approach organizations take to implement RPA: digitizing business processes, performing knowledge work together with humans, replacing outsourcing with RPA robots and developing a new business model. Therefore, in this paper, in order to reduce RPA literature fragmentation, the authors take into account the digital transformation (DT) perspective, by considering RPA as one example of digital technology.

This study sensitize organizational adopters to the different mechanisms they can deploy to conduct RPA implementations to achieve different desired outcomes in response to different drivers. Moreover, having a clear picture of the key enablers and associated barriers to the realization of these alternative paths serve as a useful map to guide the implementation process.

The findings contribute to DT research by conceptualizing these mechanisms through which organizations deploy automation tools—such as RPA.

Purpose – The purpose of this chapter is to conduct a critical literature review that examines the origins and development of research on service robots in organizations, as well as the key emotional and cognitive issues between service employees, customers, and robots. This review provides a foundation for future research that leverages the emotional connection between service robots and humans.

Design/Methodology/Approach – A critical literature review that examines robotics, artificial intelligence, emotions, approach/avoid behavior, and cognitive biases is conducted.

Findings – This research provides six key themes that emerge from the current state of research in the field of service robotics with 14 accompanying research questions forming the basis of a research agenda. The themes presented are as follows: Theme 1: Employees have a forgotten “dual role”; Theme 2: The influence of groups is neglected; Theme 3: Opposing emotions lead to uncertain outcomes; Theme 4: We know how robots influence engagement, but not experience; Theme 5: Trust is necessary but poorly understood; and Theme 6: Bias is contagious: if the human mind is irrational…so too are robot minds.

Practical Implications – Practically, this research provides guidance for researchers and practitioners alike regarding the current state of research, gaps, and future directions. Importantly for practitioners, it sheds light on themes in the use of AI and robotics in services, highlighting opportunities to consider the dual role of the employee, examines how incorporating a service robot influences all levels of the organization, addresses motivational conflicts for employees and customers, explores how service robots influence the whole customer experience and how trust is formed, and how we are (often inadvertently) creating biased robots.