Human-centricity in Industry 5.0 – revealing of hidden research topics by unsupervised topic modeling using Latent Dirichlet Allocation
Abstract
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.
Keywords
Acknowledgements
This research was supported by grant VEGA 1/0614/23 The readiness of enterprises for the challenges associated with Industry 4.0 from the point of view of business processes and process management and by grant VEGA 1/0113/24 Emerging digital technologies and their influence on elimination of supply chain vulnerability - Building lean, agile, resilient and green supply chains in the age of unpredictable disruptive events. The funder of both grants is “ Vedecká grantová agentúra Ministerstva školstva, výskumu, vývoja a mládeže Slovenskej republiky a Slovenskej akadémie vied”.
Citation
Madzik, P., Falat, L., Jum’a, L., Vrábliková, M. and Zimon, D. (2024), "Human-centricity in Industry 5.0 – revealing of hidden research topics by unsupervised topic modeling using Latent Dirichlet Allocation", European Journal of Innovation Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EJIM-09-2023-0753
Publisher
:Emerald Publishing Limited
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