Navigating the human element: Unveiling insights into workforce dynamics in supply chain automation through smart bibliometric analysis

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dc.contributor.authorAngielsky, Melanie
dc.contributor.authorCopus, Lukas
dc.contributor.authorMadzik, Peter
dc.contributor.authorFalat, Lukas
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2024-09-06T08:23:15Z
dc.date.available2024-09-06T08:23:15Z
dc.description.abstractThis study aims to create a scientific map of supply chain automation research focusing on human resources management, which will be applicable in practice and widen the knowledge in theory. It introduces the scientific articles, subject areas and dominant research topics related to supply chain automation, focusing on human resources management. In this study, 509 publications retrieved from the Scopus database were analyzed by a novel methodological approach – a smart bibliometric literature review using Latent Dirichlet Allocation with Gibbs sampling. The study processes scientific articles with automated tools. It uses a novel machine-learning-based methodological approach to identify latent topics from many scientific articles. This approach creates the possibility of comprehensively capturing the areas of supply chain automation focusing on human resources management and offers a science map of this rapidly developing area. This kind of smart literature review based on a machine learning approach can process a large number of documents. Simultaneously, it can find topics that a standard bibliometric analysis would not show. The authors of the study identified six topics related to supply chain automation, focusing on human resources management, specifically (1) network design, (2) sustainable performance and practices, (3) efficient production, (4) technology-based innovations and changes, (5) management of business and operations, and (6) global company strategies. The study’s results offer key insights for decision-makers, illuminating essential themes related to automation integration in the supply chain and the vital role of human resources in this transformation. The limitations of this study are the qualitative level of results provided by the machine learning approach, which does not contain manual analysis of documents and the subjectivity of the expert process to set the appropriate number of topics.en
dc.formattext
dc.identifier.doi10.15240/tul/001/2024-5-011
dc.identifier.eissn2336-5604
dc.identifier.issn1212-3609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/175285
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectAutomationen
dc.subjectsmart manufacturingen
dc.subjectIndustry 4.0en
dc.subjectsupply chainen
dc.subjectqualificationen
dc.subjectworkforceen
dc.subject.classificationM12
dc.subject.classificationJ24
dc.subject.classificationL23
dc.subject.classificationO33
dc.titleNavigating the human element: Unveiling insights into workforce dynamics in supply chain automation through smart bibliometric analysisen
dc.typeArticleen
local.accessopen
local.citation.epage87
local.citation.spage72
local.facultyFaculty of Economics
local.filenameEM_3_2024_5
local.fulltextyes
local.relation.abbreviationE+Mcs
local.relation.abbreviationE&Men
local.relation.issue3
local.relation.volume27
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