Research trends analysis using text mining in construction management: 2000–2020

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dc.contributor.authorBilge, Eymen Çağatay
dc.contributor.authorYaman, Hakan
dc.date.accessioned2022-01-06T10:51:40Z
dc.date.available2022-01-06T10:51:40Z
dc.date.issued2021en_US
dc.departmentFakülteler, Mimarlık ve Tasarım Fakültesi, İç Mimarlık ve Çevre Tasarımı Bölümüen_US
dc.description.abstractPurpose – This study aims to identify the trends that have changed in the field of construction management over the last 20 years. Design/methodology/approach – In this study, 3,335 journal articles published in the years 2000–2020 were collected from the Web of Science database in construction management. The authors applied bibliometric analysis first and then detected topics with the latent Dirichlet allocation (LDA) topic detection method. Findings – In this context, 20 clusters from cluster analysis were found and the topics were extracted in clusters with the LDA topic detection method. The results show “building information modeling” and “information management” are the most studied subjects, even though they have emerged in the last 15 years “building information modeling,” “information management,” “scheduling and cost optimization,” “lean construction,” “agile approach” and “megaprojects” are the trend topics in the construction management literature. Research limitations/implications – This study uses bibliometric analysis. The authors accept that the cocitation and co-authorship relationship in the data is ethical. They accept that honorary authorship, self-citation or honorary citation do not change the pattern of the construction management research domain. Originality/value – There has been no study conducted in the last 20 years to examine research trends in construction management. Although bibliometric analysis, systematic literature reviews and text mining methods are used separately as a methodology for extracting research trends, no study has used enhanced bibliometric analysis and the LDA topic detection text mining method.en_US
dc.identifier.doi10.1108/ECAM-02-2021-0107en_US
dc.identifier.scopus2-s2.0-85111076310en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/5158
dc.identifier.urihttps://doi.org/10.1108/ECAM-02-2021-0107
dc.identifier.wosWOS:000675563100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEmerald Publishingen_US
dc.relation.ispartofEngineering, Construction and Architectural Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectConstruction managementen_US
dc.subjectResearch trendsen_US
dc.subjectText analyticsen_US
dc.subjectBibliometricen_US
dc.titleResearch trends analysis using text mining in construction management: 2000–2020en_US
dc.typeArticleen_US

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