4.6 Article

Requirements for Project Managers-What Do Job Advertisements Say?

Journal

SUSTAINABILITY
Volume 13, Issue 23, Pages -

Publisher

MDPI
DOI: 10.3390/su132312999

Keywords

text mining; requirements; project manager; Latent Dirichlet Allocation

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This study aimed to identify the most desired project manager competencies by analyzing job advertisements through text mining. The results showed that text mining of job offers can help determine project manager competencies in demand, which can be useful for organizations training future project managers to adapt curricula to labor market needs and monitor current trends in project manager requirements.
The growing number of projects and the key role of project managers in their implementation makes the competencies of managers a subject of many studies. An attempt can be made to determine the project manager competencies that employers appreciate the most through analyses of job advertisements. Due to a very large number of job advertisements, it may be difficult or even impossible to analyze their content manually. A solution may be to fetch and process job advertisements automatically. The main purpose of this paper was to identify the project manager competencies that are the most desired by employers. An analysis of job advertisements was performed to identify the project manager competencies required by employers. Job advertisements were automatically downloaded from online job boards. Fragments of job advertisements that described requirements were analyzed with text mining. The analysis included preprocessing, building of corpora of documents, construction of document-term matrices, application of traditional data mining methods, and Latent Dirichlet Allocation (LDA), which is a popular topic modeling algorithm. After the initial text processing (all characters except letters were removed, uppercase letters were converted to lowercase letters, words deemed useless were removed, and words were converted to their basic form), n-grams were built, and topics identified with LDA were generated. The most frequently used words and n-grams, along with the identified topics, were graphically represented. The meanings of the words and sentences were not analyzed in the text mining analysis of the job advertisements. The analysis did not take into account whether the words appeared side by side in the document-except for the intentional creation of n-grams (such as communication skill). The analysis, however, facilitated the identification of certain patterns and regularities in the occurrence of specific strings in the documents (fragments of advertisements describing the requirements). The interpretation of the results is based on the frequency of words and n-grams and frequency of words in topics identified by the LDA algorithm. This paper contributes to science by showing that text mining of job offers can, to some extent, help determine project manager competencies in demand. The method can be used by organizations training future project managers to modify and better adapt curricula to the needs of the labor market. It can be used to monitor the current trends in project manager requirements as well.

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