From insights to impact: tailored, age-responsive strategies for mitigating job insecurity

This study addresses the pressing issue of an aging workforce, concentrating on perceived job insecurity (JI), i.e., the workers' subjective perception of threat to their employment status. Using data from the European Working Condition Survey (EWCS) carried on in 2015, the work combines traditional econometric models and innovative deep learning algorithms to explore a diverse range of predictors of JI. Notable drivers are pinpointed across various categories, revealing age-specific influences. The research also introduces new factors and deploys artificial neural networks (ANNs) to create a survey questionnaire tailored to identify distinct worker clusters sharing similar JI expectations. Analyzing three age groups (under 35, 35-50, and over 50), the study unveils nuanced insights into individual, job-related, and organizational JI determinants. Findings underscore the role of external conditions, organizational policies, and non-standard work options in shaping JI perceptions. Harnessing ANNs, the study pioneers a personalized approach to JI policies, presenting a portfolio of age-specific interventions. The deep learning model aids in feature selection and taxonomy creation, enabling policymakers to devise targeted strategies based on the diverse needs of workers. Overall, the findings enhance our understanding of JI across different career stages, offering practical implications for policymakers and managers aiming to align workforce needs with job security expectations, ultimately fostering the health and engagement of an aging workforce.