Peter Madzik, Lukas Falat, Luay Jum’a, Mária Vrábliková, Dominik Zimon
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Human-centricity in Industry 5.0 – revealing of hidden research topics by unsupervised topic modeling using Latent Dirichlet Allocation
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
期刊介绍：
The subject of innovation is receiving increased interest both from companies because of their increased awareness of the impact of innovation in determining market success and also from the research community. Academics are increasingly beginning to place innovation as a priority area in their research agenda. This impetus has been partly fuelled by the Economic & Social Research Council (ESRC) who have designated innovation as one of nine research areas in their research initiative schemes.
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