Identifying effective coping strategies against mobbing for Generations Y and Z using a Pythagorean fuzzy decision support mechanism.

Mobbing is a major workplace problem that harms employees' mental and physical well-being, reduces organizational productivity, and undermines social stability. In many regions and organizational contexts, understanding how different generations cope with mobbing has become increasingly important; however, the existing literature provides limited and fragmented evidence. This study offers a comprehensive and context-based analysis by identifying the most effective coping strategies for Generations Y and Z and examining the qualifications that influence their implementation. Data were obtained from five experts specializing in mobbing and organizational behavior. Experts' importance weights were calculated using a machine learning-based method that incorporates demographic characteristics, criteria weights were determined using the Entropy technique, and strategies were ranked through the CRADIS approach. To address uncertainty in expert evaluations, Pythagorean fuzzy numbers were integrated into the decision-making process. The proposed model contributes to the literature by (1) incorporating demographic-based expert weighting through machine learning, an approach rarely applied in previous studies; (2) developing separate analytical models for Generations Y and Z, thereby clarifying generational differences within a defined regional and organizational context; and (3) applying Pythagorean fuzzy numbers to enhance methodological robustness. Results indicate that for Generation Y, psychological resilience is the most critical qualification, and social activities such as meditation or sports constitute the most effective strategies. For Generation Z, academic education emerges as the key qualification, and leaving the job is identified as the most suitable strategy. The findings confirm distinct generational patterns in coping with mobbing and demonstrate the practical value of the proposed analytical model.