Designing Adaptive Work-Life Balance Models for Senior Employees Using Deep Learning Techniques

Promoting healthy lives and well-being has been set as a key priority within the European social agenda and is reflected in the third Sustainable Development Goal (SDG). The urgency to address this issue has been accelerated by serious demographic changes and the relentless process of ageing affecting the population across all European countries. Several studies have showed that a poor work-life balance (WLB) - defined as the inability to effectively reconcile professional and personal responsibilities - is linked to adverse health outcomes and diminished overall well-being. This evidence underscores the importance to examine how specific working conditions affect the individual’s capacity to fulfil both work and non-work roles. The main goal of the present work is to design a useful tool for policy marking that allows to identify how to optimize the allocation of financial resources for improving the work-life balance of senior workers. Into this direction, we first carry on a unique and original survey administered to Italian senior workers and aimed at investigating the well-being, work-life balance, and retirement transition patterns, as well as identifying critical areas for improving quality of work and life; on the other hand, starting from the results of the survey, we implement an innovative hybrid methodology - based on econometric regression and artificial neural networks - for the definition of personalized policy portfolios based on work-life balance dissatisfaction classes.