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Purpose – This study sets out to examine the mediating role of quiet quitting in the relationship between various workplace antecedents and turnover intention, with a specific focus on generational differences across GenX, GenY, and GenZ.
Aims(s) – The primary aim is to identify the antecedents that show significant indirect generation-specific effects on turnover intention via quiet quitting. Design/methodology/approach – Utilizing a sample of 2,193 urban and suburban employees from Berlin, Germany, this study tested a mediation model featuring five independent variables linked to quiet quitting, with turnover intention as the outcome variable. The analysis employed Cronbach’s alpha, confirmatory factor analysis, multicollinearity checks, and multiple-mediation regression techniques.
Findings – The results reveal that quiet quitting partially mediated the relationship between dissatisfaction and turnover intention for GenY, as well as the relationship between negative extra-role behavior and cynicism/depersonalization for GenZ. Additionally, negative work-life balance showed partial mediation across all three generations—GenX, GenY, and GenZ. Full mediation effects were observed specifically in GenZ for both negative extra-role behavior and cynicism/depersonalization. No significant mediating effect of quiet quitting was found for disengagement. Limitations of the study – The sample is limited to Berlin and its suburbs, with no comparative data from other regions. The generational groups are represented by moderately sized subsamples. Additionally, after conducting reliability analysis and confirmatory factor analysis (CFA), most scales were reduced to just two or three items.
Originality/value – This study positions quiet quitting as a mediating factor within a network of related variables and is among the first to examine how these mediation effects differ across GenX, GenY, and GenZ.
A review of the literature on the application of artificial intelligence (AI) in the recruitment and selection process (RSP) was conducted, but no relevant studies were identified. While several reviews have focussed on AI in human resource management in general, none of these have examined the RSP in detail or employed an AI taxonomy for clustering. Consequently, we applied an AI taxonomy identified in the literature with the aim to identify the stages of the RSP in the focus of research and the algorithms mostly used. We conducted a systematic literature review underpinned by a concept matrix, complemented by a computational literature review (CLR), that employed natural language processing (NLP). The initial 4,579 studies were sourced from three databases and narrowed down to a total of 502. Our major findings indicate that the majority of studies were categorised under the stages “assessment & selection” and “processing incoming applications” in the RSP. The predominant algorithms in use pertain to the field of NLP and machine learning. The CLR emphasised the significance of ethics in AI research. While our study has expanded the general AI taxonomy by incorporating an ethical perspective and is one of the studies with the most articles used to reflect this topic, it is solely focussing on describing the past. Nevertheless, this article helps to align research on exploring and testing alternative approaches with those most frequently used.
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 Englander et 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 Miner et 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 Englander et 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.
Cultural values and the P-O fit: comparative NLP analysis of German online job advertisements
(2025)
Purpose
Within the Person-Organization fit framework and Signalling Theory, this study investigates the performance of word dictionaries detecting cultural values in online job advertisements as one form of external communication of an organization. Based upon a merge of the dictionaries, a corporate value analysis of Germany is conducted.
Design/methodology/approach
The study builds on a dataset (n > 151 k) of online job advertisements which were scraped from a German job portal. It was pre-processed according to natural language processing standards. For analysing the values of an organization a dictionary based word count was applied. Therefore, the current state-of-the-art dictionaries were tested, and an enhanced dictionary was developed and translated from English to German. Finally, a cluster analysis was conducted.
Findings
This study supports the possibility of measuring cultural values in texts where the enhanced dictionary based on Ponitzovskiy shows the best results. It thereby supports the use of the Universal Value Structure model (Schwartz, 1992) as well as the Signalling Theory (Guest et al., 2021), that values spread across 10 core or 4 aggregated dimensions are communicated via online job advertisements. Finally, the study offers a profile of the German corporate culture average as well as 4 cultural clusters and separate organizations, all with different profiles.
Originality/value
This study develops an enhanced dictionary based on a large dataset of online job advertisements for analysing the external communication of values or culture of an organization for improving the Person-Organization fit.
Die Analyse von Unternehmenswerten wird schon seit Jahrzenten durchgeführt. Es gibt verschiedene Modelle, wie zum Beispiel den Ansatz von Hofstede oder das „Universal Value Structure Modell“ von Schwartz, welche die Unternehmenswerte darstellen. Aktuelle Studien empfehlen Unternehmenswerte unter der Nutzung von Natural Language Processing und definierten Wortlisten bei größeren Textdaten zu untersuchen. Ziel der vorliegenden Analyse war zu identifizieren, ob die deutschen Übersetzungen der bisher englischsprachigen Ansätze im Kontext von Online-Stellenanzeigen funktional anwendbar sind. Daher wurden ca. 151.000 online-Stellenanzeigen von ca. 29.000 Unternehmen mittels drei Wortlisten analysiert. Der Umfang der verwendeten Listen lag zwischen 126 und 944 deutschen Wörtern. Die Ergebnisse zeigen, dass a) alle angewendeten Wortlisten grundsätzlich funktional sind, b) dass einer der Listen jedoch herausragende Ergebnisse aufweist, welcher jedoch c) um fehlende Begriffe der beiden anderen Ansätze erweitert werden kann. Somit können Unternehmen ein eigenes Werteprofil Ihrer externen Online-Kommunikation ermitteln und dieses mit den internen Wertvorstellungen bzw. dem intern erhobenen realen Werteprofil abgleichen.
Der Fachkräftemangel im Gesundheitssektor beschleunigt die Digitalisierung, wodurch die Anforderungen an das Personal steigen. Die zunehmenden technischen Möglichkeiten verändern die notwendigen Kompetenzen an die Ärzte in Richtung Digitalisierung. Daher sollten im Personal-Recruitment digitale Kompetenzen bereits in den Stellenausschreibungen berücksichtigt werden, um die zukünftigen Herausforderungen bewältigen zu können. Dieser Beitrag untersucht, inwieweit a) digitale Kompetenzen bereits in den am Markt befindlichen Stellenprofilen von Ärzten Eingang gefunden haben und b) ob diese durch einen datengetriebenen methodischen Ansatz sinnvoll extrahiert werden können. Dabei werden 1707 Stellenanzeige mit der Latent Semantic Analysis (LSA) ausgewertet. Die unterschiedlichen methodischen Ansätze innerhalb der LSA zeigen, dass kaum ein Fokus auf digitale Kompetenzen im Gesundheitssektor bei Stellenausschreibungen besteht.
On the background of the supplementary applicant-job fit theory, this study tests the application of a weighted closed vocabulary approach as introduced by Ostendorf on a large corpus of online job advertisements (OJA). Therefore, a large dataset of online job advertisements (OJAs) was scraped from a German job portal (N = 151k), and a sample (n = 3,239) was selected based upon Porter’s Value Chain and corresponding job functions. The dataset was pre-processed with techniques of natural language processing, and the predefined words extracted from the OJAs were weighted with the average subject matter expert rating, as offered in the vocabulary of Ostendorf. The results reveal that, first, 0 to 12 words describing personality are used across OJAs, second, that HR, sales, and finance positions show values higher than sample mean (3.22), third, that HR positions show loadings across all dimensions of personality above the sample means (extroversion: 2.32, agreeableness: 3.98, conscientiousness: 4.28, emotional stability: 3.54, openness: 2.0) followed by sales. The job profiles being significantly parallel on equal levels are HR clerk and referee, as well as IT, R&D, and procurement. While conscientiousness is reported to be an important predictor of performance, our study showed only low to average importance in OJAs.
