Working With Robots: Human Resource Development Considerations in Human–Robot Interaction

Kim, Sehoon

doi:10.1177/15344843211068810

Cited by 46 publications

(26 citation statements)

References 101 publications

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“…, 2021). Also, some studies focused on analyzing this topic in tourism (Aleksander, 2017; Fleming, 2019; Kim, 2022). Clearly, with many hotels introducing service robots to assist hotel workers, employees working with service robots require more knowledge and innovation skills.…”

Section: Results Of Bibliometric Analysismentioning

confidence: 99%

“…This cluster is strongly connected to information and communication technologies (ICT) and social media and intelligent technologies that require a certain level of knowledge and innovation (Andreeva and Kianto, 2012;Nguyen and Malik, 2021;Sowa et al, 2021). Also, some studies focused on analyzing this topic in tourism (Aleksander, 2017;Fleming, 2019;Kim, 2022). Clearly, with Humanmachine collaboration in organizations many hotels introducing service robots to assist hotel workers, employees working with service robots require more knowledge and innovation skills.…”

Section: Humanmachine Collaboration In Organizationsmentioning

confidence: 99%

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Systematic literature review of human–machine collaboration in organizations using bibliometric analysis

et al. 2023

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PurposeThis study aims to systematically map the state of work on human–machine collaboration in organizations using bibliometric analysis.Design/methodology/approachThe 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.FindingsThe 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).Originality/valueThis work is the first exploratory piece to assess the extent and depth of research on human–machine collaboration.

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Section: Results Of Bibliometric Analysismentioning

confidence: 99%

Section: Humanmachine Collaboration In Organizationsmentioning

confidence: 99%

Systematic literature review of human–machine collaboration in organizations using bibliometric analysis

et al. 2023

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“…Therefore, as noted by stakeholders, many manufacturing organisations were turning to in-house training solutions, including job rotation, on-the-job training and formalised “buddy” or mentor systems. Having training models that recognise tacit experiential knowledge alongside more formal education (Machado et al. , 2021), was considered especially necessary in leadership to help SMEs transition to Advanced Manufacturing.…”

Section: Resultsmentioning

confidence: 99%

Education and training for industry 4.0: a case study of a manufacturing ecosystem

Hearn,

Williams,

Rodrigues

et al. 2023

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PurposeThe purpose of this paper is to explore the approaches to education and training adopted by manufacturing organisations to identify and develop a set of learning principles for the successful transition to Industry 4.0.Design/methodology/approachA case study of a manufacturing ecosystem in Queensland, Australia was undertaken, that included semi-structured interviews with a total sample of 22 manufacturing industry representatives, an analysis of secondary data including organisational documents and government reports, and embedded cases of two manufacturing organisations.FindingsManufacturers successfully transitioning to Industry 4.0 are distinguished by a culture which values learning, management development to understand and lead innovation, experimental learning on the job and strong links to education and training providers through internships and upskilling pathways. These four principles inform approaches to creating tailored training solutions that respond to the unique needs of diverse manufacturing organisations.Research limitations/implicationsThe two case studies describe exemplary high performing companies only and not companies at earlier stages of adopting Industry 4.0. Therefore, future research could include a broader spectrum of companies across the adoption spectrum. Nevertheless, considered as a study of a total manufacturing ecosystem, there is strong alignment of views of government, industry, union and education stakeholders regarding the key factors of transition to Industry 4.0.Practical implicationsThere is a strong need for leaders of manufacturing organisations to enable a broad strategy of capability development beyond simple acquisition of new technologies. Detailed consideration and resourcing of on-the-job training and experimentation, talent attraction through innovation workplace cultures and strong relationships with education providers are important.Social implicationsGiven that Industry 4.0 technologies such as robotics and AI are now rapidly diffusing into other industry sectors, the research has broader implications for education and training for the future of work. These technologies could produce stark differences between efficiency versus innovation-oriented adoption strategies. Whilst the former could displace workers, the latter can open pathways for upskilling, product and process innovation and cross sector employment.Originality/valueThrough the ecosystem level case approach, multiple stakeholder perspectives provide triangulated insights into advanced manufacturer's education, skills and training strategies, uncovering four learning principles that underpin the approach of manufacturers successfully transitioning to Industry 4.0. The findings have practical implications for policy makers and industry bodies supporting the transition to advanced manufacturing and provide manufacturing managers with insights into successful education and skill strategies that can be adapted to specific organisational needs.

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“…It's also encouraged to hire a particular team to send and test automation specifications in all stages. It's also encouraged to hire a particular team to send and test automation specifications in all stages of projects 37 .…”

Section: Robotics Implementations Pose the Most Riskmentioning

confidence: 99%

Untitled

2023

IJPSR

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The rapid development of artificial intelligence (AI) has far-reaching consequences for the global economy. Computers are helping with human input, decisionmaking, and data provision in medicine more than ever before. AI, algorithms, robotics, and big data are all used in today's healthcare sector and the medical profession to derive inferences for monitoring large-scale medical trends and detecting and measuring individual risks and chances based on data-driven estimations. Healthcare is a knowledge-intensive industry that relies heavily on data and analytics to advance treatments and procedures. There has been a significant expansion in the types of medical data gathered in recent years. This includes clinical, genetic, behavioural and environmental information. Large volumes of data from various sources are generated daily by healthcare providers, biomedical researchers, and patients. Technology advancements, such as AI, allow for the analysis of human thought processes. The pharmaceutical business saw significant transformation as a result of the application of machine learning, AI, and automation processes. Various types of analysis have contributed to a general decline in death rates. The use of AI techniques aids in diagnosing the most serious illnesses. To properly diagnose and treat a patient, it is essential to first identify any potential genetic disputes. Technological advancements have made it easier for pharmaceutical companies to detect dosage mistakes. Machine learning relies on monetary analysis, regular spectral analysis and the feature attraction process. The creation of an intelligent computer program is crucial for modelling mechanical systems. In this review, we have glanced at how technology plays a part in the pharmaceutical industry and at the potential outcomes that robotics and AI could have. INTRODUCTION:Robotic systems with artificial intelligence (AI) can learn, make decisions, and solve problems in a manner comparable to that of a person, but they are not yet QUICK RESPONSE CODE

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Working With Robots: Human Resource Development Considerations in Human–Robot Interaction

Cited by 46 publications

References 101 publications

Systematic literature review of human–machine collaboration in organizations using bibliometric analysis

Systematic literature review of human–machine collaboration in organizations using bibliometric analysis

Education and training for industry 4.0: a case study of a manufacturing ecosystem

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