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Picking with a robot colleague: A systematic literature review and evaluation of technology acceptance in human–robot collaborative warehouses
Computers & Industrial Engineering ; 180:109262, 2023.
Article Dans Anglais | ScienceDirect | ID: covidwho-2309041
ABSTRACT
Just-in-time delivery, shorter product life cycles, demographic changes, and the Covid-19 pandemic have driven the industrial application of collaborative robots in warehouses. These robots work alongside humans, increasing their productivity and relieving them of repetitive or strenuous tasks. However, human workers can be reluctant to collaborate with robots owing to certain fears;for example, they may be concerned about job loss, stress, expected effort, or risk to physical integrity. These concerns can negatively impact the acceptance of human–robot collaboration (HRC). As the literature on this topic is fragmented, this study analyzes HRC acceptance in warehouses based on the Unified Theory of Acceptance and Use of Technology. We identify, classify, and analyze studies examining HRC acceptance in warehouses using a systematic literature review methodology. A framework is established to guide the analysis of performance expectancy, effort expectancy, (perceived) occupational safety, psychosocial, and legal and privacy factors. The results indicate the importance of corporate infrastructure and consideration of cognitive factors in particular. The findings of this study will support future research on HRC in warehouses and provide guidance for managers regarding HRC applicability.
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Texte intégral: Disponible Collection: Bases de données des oragnisations internationales Base de données: ScienceDirect Type d'étude: Études expérimentales / Révision / Examen systématique/Méta-analyse langue: Anglais Revue: Computers & Industrial Engineering Année: 2023 Type de document: Article

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Texte intégral: Disponible Collection: Bases de données des oragnisations internationales Base de données: ScienceDirect Type d'étude: Études expérimentales / Révision / Examen systématique/Méta-analyse langue: Anglais Revue: Computers & Industrial Engineering Année: 2023 Type de document: Article