Abstract
PurposeThe construction industry is experiencing digital transformation, which is also defined as intelligent construction. With the rise of intelligent construction, job characteristics are changing rapidly. Current knowledge about job competencies required by intelligent construction is lacking. Therefore, the aim of this paper is to explore job competencies related to intelligent construction by text mining recruitment information. It is expected to reveal the trend of talent development for the intelligent construction industry.Design/methodology/approachA total of 375 job advertisements regarding the demanding professionals and industrial workers related to intelligent construction were collected and analyzed to reveal the demands of the current labor market. Different job posts related to intelligent construction were classified into 11 categories. Job competencies were extracted and analyzed using the latent Dirichlet allocation (LDA) model, frequency–inverse document frequency (TF-IDF) algorithm and k-means cluster analysis method. The text mining results identified 10 job competencies.FindingsCurrently, there was a high demand for high-tech talents in the labor market related to intelligent construction. Those high-tech job posts, such as software engineers and R&D staff, required digital technology, R&D skills, electrical automation knowledge and programming capability. Current employees demanding for intelligent construction are expected to be capable of both using information technology and having a general knowledge of the construction industry.Originality/valueThrough text mining of current job advertisements, the overall demand for compound talents in the labor market of intelligent construction were explored. The results provide empirical reference for personnel training and talent cultivation in the development of intelligent construction. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in intelligent construction companies, would benefit from the results of our analysis.
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