Matching competency frameworks with job advertisements: a data-driven analysis of its practical application in the healthcare sector

Marcel Herold and Marc Roedenbeck

Evidence-based HRM, 2024, vol. 13, issue 2, 339-356

Abstract: Purpose - Competency-based human resource management (CBHRM) is a key component of all organisations but needs to be regularly reviewed and evaluated to ensure the quality of healthcare professionals. One common taxonomy of competency domains for health professions is from Englanderet al., where this paper aims to conduct a large-scale analysis based on topic modelling to investigate the extent to which the competency framework for the healthcare sector is applied in the German job market of health professions. Design/methodology/approach - The quantitative NLP analysis of a dataset consisting of 3,362 online job advertisements of nurses and doctors was scraped from a German job portal. The data was pre-processed according to Mineret al. For the analysis, the authors applied unsupervised (e.g. HDP, LDA) and supervised (BERTopic) methods and content analysis. Based on the extracted topics a word list was created and these words were coded to existing dimensions of the competency framework of Englanderet al. or new dimensions were created. Findings - Comparing methodologies, HDP (unsupervised) and BERTopic (supervised) were the best performing while the BERTopic algorithm outperforms HDP. For the doctor dataset 46% of one main dimension was identified but with an overall coverage of 69%, for the care dataset is weaker with 30.8% but an overall coverage of 100%. Additionally, the taxonomy was enhanced with supplementary competencies of “personality/characteristics” and “leadership” as well as two facets of job description which are “place of work” and “job conditions”. Originality/value - On the one hand selected dimensions of the taxonomy could be clearly identified but on the other hand, there is a documented gap between the taxonomy and the competencies advertised. One cause may lie in the NLP algorithms but applicants may also have the same difficulties when reading the OJAs. Thus, practitioners should carefully review OJAs regarding better separating explicit competencies they are searching for. For the scientific development of new competency frameworks, our data-driven approach exemplified an extension of a given taxonomy.

Keywords: Online job advertisement; Natural language processing; Competency frameworks; Content analysis; (un)supervised learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ebhrmp:ebhrm-07-2023-0181

DOI: 10.1108/EBHRM-07-2023-0181

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