Unsupervised Machine Learning for Profiling Quiet and Passive Quitting

The study aimed to identify employee profiles reflecting combinations of quiet quitting, passive quitting, and work engagement. Using a person-centred approach and unsupervised learning, survey data from 1,040 employees were analysed. Clustering relied on composite indices derived from abbreviated quiet and passive quitting scales and the Utrecht Work Engagement Scale-9 (UWES-9). Multiple algorithms (k-means, hierarchical clustering, spectral clustering, Gaussian mixture models) were compared, and the optimal solution was selected using separation metrics (Silhouette coefficient, Davies–Bouldin index, Calinski–Harabasz index), information criteria (Bayesian Information Criterion [BIC], Akaike Information Criterion [AIC]), and bootstrap stability (Adjusted Rand Index [ARI]). Four distinct employee profiles emerged, differing in boundaries, exhaustion, and energy. Findings suggest quiet quitting and passive quitting are related but distinct withdrawal mechanisms. The study advances profile-based research on employee withdrawal and highlights implications for targeted human resources (HR) interventions.