TY - JOUR A1 - Herold, Marcel A1 - Roedenbeck, Marc T1 - AI-Driven Research in the Recruitment and Selection Process: Application of an AI Taxonomy With a Systematic Literature Review JF - SAGE Open N2 - 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. KW - AI KW - HRM KW - computational literature review KW - literature review KW - recruitment and selection KW - artificial intelligence KW - human resources management (HRM) Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-20736 VL - 15 IS - 3 PB - Sage ER - TY - CHAP A1 - Herold, Marcel A1 - Roedenbeck, Marc ED - Stolzenburg, Frieder ED - Reinboth, Christian ED - Lohr, Thomas ED - Vogel, Kathleen T1 - Vergleichende Analyse von Unternehmenswerten in Online Stellenanzeigen mittels NLP / LIWC T2 - NWK 2023 - Tagungsband zur 23. Nachwuchswissenschaftler*innenkonferenz N2 - 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. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18515 UR - https://www.hs-harz.de/dokumente/extern/Forschung/NWK2023/Beitraege/Vergleichende_Analyse_von_Unternehmenswerten_in_Online_Stellenanzeigen_mittels_NLP_-_LIWC.pdf UR - https://www.hs-harz.de/nwk2023/tagungsband-nwk-2023 SN - 2627-5708 SP - 52 EP - 62 PB - Hochschule Harz CY - Wernigerode ER - TY - JOUR A1 - Roedenbeck, Marc A1 - Poljsak-Rosinski, Petra T1 - Artificial neural network in soft HR performance management: new insights from a large organizational dataset JF - Evidence-based HRM N2 - Purpose This study investigates whether the artificial neural network approach, when used on a large organizational soft HR performance dataset, results in a better (R2/RMSE) model compared to the linear regression. With the use of predictive modelling, a more informed base for managerial decision making within soft HR performance management is offered. Design/methodology/approach The study builds on a dataset (n > 43 k) stemming from an annual employee MNC survey. It covers several soft HR performance drivers and outcomes (such as engagement, satisfaction and others) that either have evidence of a dual-role nature or non-linear relationships. This study applies the framework for artificial neural network analysis in organization research (Scarborough and Somers, 2006). Findings The analysis reveals a substantial artificial neural network model performance (R2 > 0.75) with an excellent fit statistic (nRMSE <0.10) and all drivers have the same relative importance (RMI [0.102; 0.125]). This predictive analysis revealed that the organization has to increase six of the drivers, keep two on the same level and decrease one. Originality/value Up to date, this study uses the largest dataset in soft HR performance management. Additionally, the predictive results reveal that specific target values lay below the current levels to achieve optimal performance. KW - soft HRM KW - performance KW - drivers KW - artificial neural network KW - non-linearity KW - prediction Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18377 SN - 2049-3991 VL - 11 IS - 3 SP - 519 EP - 537 PB - Emerald ER - TY - JOUR A1 - Roedenbeck, Marc A1 - Poljsak-Rosinski, Petra A1 - Herold, Marcel T1 - Investigating the mediating effect of quiet quitting on turnover intention across generations X, Y and Z JF - Journal of Human Resource Management – HR Advances and Developments N2 - 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. KW - GenX KW - GenY KW - GenZ KW - mediation analysis KW - quiet quitting KW - turnover intention Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-21039 VL - 28 IS - 2 SP - 38 EP - 54 PB - Comenius University Bratislava ER - TY - JOUR A1 - Roedenbeck, Marc A1 - Herold, Marcel T1 - Personality Profiles in Online Job Advertisements: Applying a Weighted Closed Vocabulary Approach to Identify Generic Profiles and Clusters JF - SAGE Open N2 - 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. KW - LIWC KW - cluster analysis KW - natural language processing KW - online job advertisement KW - personality KW - profile analysis Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-21282 VL - 16 IS - 1 PB - Sage ER -