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 - 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 -