Search results

Recent years have seen a technological change, Industry 4.0, in the manufacturing industry. Human–robot cooperation, a new application, is increasing and facilitating collaboration without fences, cages or any kind of separation. The purpose of the paper is to review mainstream academic publications to evaluate the current status of human–robot cooperation and identify potential areas of further research.

A systematic literature review is offered that searches, appraises, synthetizes and analyses relevant works.

The authors report the prevailing status of human–robot collaboration, human factors, complexity/ programming, safety, collision avoidance, instructing the robot system and other aspects of human–robot collaboration.

This paper identifies new directions and potential research in practice of human–robot collaboration, such as measuring the degree of collaboration, integrating human–robot cooperation into teamwork theories, effective functional relocation of the robot and product design for human robot collaboration.

This paper will be useful for three cohorts of readers, namely, the manufacturers who require a baseline for development and deployment of robots; users of robots-seeking manufacturing advantage and researchers looking for new directions for further exploration of human–machine collaboration.

Cooperation of a pilot with an automated aircraft control and monitoring systems is a problem which should be solved designing the whole system. The method of design, which creates an assistant of a pilot, is the purpose of this study.

An analysis of human factors shows demands for working environment. An integration method for various technological systems and algorithms is searched.

It is possible to make the whole system to become a pilot assistant, which has ability to exchange information with pilot by a dialogue. Structural flexibility is obtained in multi-agent system structure.

Proposed approach is a solution of how to integrate increasing amount of aircraft systems. It is expected that new form of cooperation fits to human features. Proposed methodology solves problem of simultaneous control by two controllers and cooperative making decisions.

Dialogue between human and the system proposed in this solution will change perception of machines.

New abilities of machines and proposition of their realisation are presented. Presented solution of simultaneous control and decision-making during aircraft control is a novel approach to human–machine cooperation.

Human or machine, which one is more intelligent and powerful for performing computing and processing tasks? Over the years, researchers and scientists have spent significant amounts of money and effort to answer this question. Nonetheless, despite some outstanding achievements, replacing humans in the intellectual tasks is not yet a reality. Instead, to compensate for the weakness of machines in some (mostly cognitive) tasks, the idea of putting human in the loop has been introduced and widely accepted. In this paper, the notion of collective hybrid intelligence as a new computing framework and comprehensive.

According to the extensive acceptance and efficiency of crowdsourcing, hybrid intelligence and distributed computing concepts, the authors have come up with the (complementary) idea of collective hybrid intelligence. In this regard, besides providing a brief review of the efforts made in the related contexts, conceptual foundations and building blocks of the proposed framework are delineated. Moreover, some discussion on architectural and realization issues are presented.

The paper describes the conceptual architecture, workflow and schematic representation of a new hybrid computing concept. Moreover, by introducing three sample scenarios, its benefits, requirements, practical roadmap and architectural notes are explained.

The major contribution of this work is introducing the conceptual foundations to combine and integrate collective intelligence of humans and machines to achieve higher efficiency and (computing) performance. To the best of the authors’ knowledge, this the first study in which such a blessing integration is considered. Therefore, it is believed that the proposed computing concept could inspire researchers toward realizing such unprecedented possibilities in practical and theoretical contexts.

The purpose of this paper is to propose a knowledge extraction framework to extract knowledge, including entities and relationships between them, from unstructured texts in digital humanities (DH).

The proposed cooperative crowdsourcing framework (CCF) uses both human–computer cooperation and crowdsourcing to achieve high-quality and scalable knowledge extraction. CCF integrates active learning with a novel category-based crowdsourcing mechanism to facilitate domain experts labeling and verifying extracted knowledge.

The case study shows that CCF can effectively and efficiently extract knowledge from multi-sourced heterogeneous data in the field of Tang poetry. Specifically, CCF achieves higher accuracy of knowledge extraction than the state-of-the-art methods, the contribution of feedbacks to the training model can be maximized by the active learning mechanism and the proposed category-based crowdsourcing mechanism can scale up the effective human–computer collaboration by considering the specialization of workers in different categories of tasks.

This research proposes CCF to enable high-quality and scalable knowledge extraction in the field of Tang poetry. CCF can be generalized to other fields of DH by introducing domain knowledge and experts.

The extracted knowledge is machine-understandable and can support the research of Tang poetry and knowledge-driven intelligent applications in DH.

CCF is the first human-in-the-loop knowledge extraction framework that integrates active learning and crowdsourcing mechanisms; he human–computer cooperation method uses the feedback of domain experts through the active learning mechanism; the category-based crowdsourcing mechanism considers the matching of categories of DH data and especially of domain experts.

Exploring trust's impact on AI project success. Companies can't leverage AI without employee trust. While analytics features like speed and precision can build trust, they may also lower it during implementation, leading to paradoxes. This study identifies these paradoxes and proposes strategies to manage them.

This paper applies a grounded theory approach based on 35 interviews with senior managers, users, and implementers of analytics solutions of large European companies.

It identifies seven paradoxes, namely, knowledge substitution, task substitution, domain expert, time, error, reference, and experience paradoxes and provides some real-life examples of managing them.

The limitations of this paper include its focus on machine learning projects from the last two years, potentially overlooking longer-term trends. The study's micro-level perspective on implementation projects may limit broader insights, and the research primarily examines European contexts, potentially missing out on global perspectives. Additionally, the qualitative methodology used may limit the generalizability of findings. Finally, while the paper identifies trust paradoxes, it does not offer an exhaustive exploration of their dynamics or quantitative measurements of their strength.

Several tactics to tackle trust paradoxes in AI projects have been identified, including a change roadmap, data “load tests”, early expert involvement, model descriptions, piloting, plans for machine-human cooperation, learning time, and a backup system. Applying these can boost trust in AI, giving organizations an analytical edge.

The AI-driven digital transformation is inevitable; the only question is whether we will lead, participate, or fall behind. This paper explores how organizations can adapt to technological changes and how employees can leverage AI to enhance efficiency with minimal disruption.

This paper offers a theoretical overview of trust in analytics and analyses over 30 interviews from real-life analytics projects, contributing to a field typically dominated by statistical or anecdotal evidence. It provides practical insights with scientific rigour derived from the interviews and the author's nearly decade-long consulting career.

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.

The study aims to investigate the integration of human and machine intelligence in Industry 4.0 (I4.0), particularly in the convergence of industrial revolutions 4.0 (IR4.0) and 5.0. It seeks to identify employee competencies aligned with industry 5.0 (I5.0) and propose a framework for deep multi-level cooperation to improve human integration within the intelligence system.

This study uses bibliometric analysis to review 296 research papers retrieved from the Scopus database between 2002 and 2022. The prominence of the research is evaluated by analyzing the publication trend, sample statistics, theoretical foundation, commonly used keywords, thematic evolution, country-based contributions and top-cited documents.

The study observed that research in I5.0 has been limited in the past but has gained momentum since 2015. An analysis of research papers from 2002 to 2022 reveals a gradual shift toward human-centric practices. The literature on I4.0, the internet of things (IoT), artificial intelligence (AI), cloud manufacturing, blockchain and big data analysis has been increasingly highlighting the growing importance of digitalization in the future. An increase in the number of countries contributing to the field of study has also been observed.

This analysis offers valuable insights for managers, policymakers, information technology (IT) developers and stakeholders in understanding and implementing human-centric practices in I5.0. It emphasizes staying current with trends, embracing workforce empowerment through reskilling and upskilling, and prioritizing data privacy and security in adaptable systems. These strategies contribute to developing effective, inclusive and ethically sound approaches aligned with the principles of I5.0.

This paper examines the beliefs of human resource professionals (HRPs) regarding the impact of Industry 4.0 on organizations in terms of readiness for human resources management (HRM) transformation, the challenges of a potential new legal and financial framework, the new means on performance management and automation, and finally the decision-making process in the era of human-machine cooperation.

The authors analyzed a sample of 251 HRPs from 11 different countries divided into 4 cultural clusters to explore their attitude to incorporate new practices to the HR field because of technological development. The paper explores HRPs' beliefs in a legal and financial context, performance management issues, and the impact of automation on the decision-making process. Furthermore, the authors perform a cross-cultural comparison analysis to examine potential significant differences between cultural clusters.

HRPs are aware of how technology adoption is affecting work environment and they highlight the importance of human resources (HR) for businesses, despite the global trend of extensive machinery exploitation. Interestingly, our results suggest that overall globalization, common knowledge, and internationalized practices lead to homogeneity for most issues under study.

To the best of the authors' knowledge, there has not been any comprehensive study exploring and analyzing the effects of Industry 4.0 on HRPs perceptions in the context of a dynamic HR environment influenced by technological transformation. The study shows that HRPs' present similar perspectives for most issues addressed, irrespective of cultural characteristics of HRPs. Hence, this paper generates some important insights in an attempt to build a framework for enhancing HR in this new era.

This study aims to systematically map the state of work on human–machine collaboration in organizations using bibliometric analysis.

The authors used a systematic literature review to survey 111 articles on human–machine collaboration published in leading journals to categorize the theories used and to construct a framework of human–machine collaboration in organizations. A bibliometric analysis is applied to statistically evaluate the published materials and measure the influence of the publications using co-citation, coupling and keyword analyses.

The results inform that the research on human–machine collaboration in the organizational field is targeted at four aspects: performance, innovation, human resource management and information technology (IT).

This work is the first exploratory piece to assess the extent and depth of research on human–machine collaboration.

The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.