My research focuses on work addiction and its associations with mental health outcomes, burnout, and overall well-being. I aim to identify vulnerable populations and examine socio-demographic factors, psychopathological correlates, prevalence rates, and well-being indicators such as life satisfaction, job satisfaction, and work-life balance. Leveraging my expertise in psychology, finance, and technology, the project will take an open-science perspective and employ advanced quantitative methods, including network analysis with machine learning techniques, confirmatory factor analysis and structural equation modelling to unravel the intricate dynamics underlying work addiction’s impact on individual and collective well-being.