Search

ObjectivesHow the work environment contributes to employees’ overall subjective well-being remains inadequately explored. Building upon the seminal Job Demands-Resources model, this study aims to test a complex model that combines leadership, job demands, and job resources, as factors contributing either indirectly (via job satisfaction) or directly to employees’ subjective well-being (SWB).MethodsThe cross-sectional data (N = 1,859) of the Belgian National happiness study (2020) were used. Leadership (satisfaction with leadership; perceived supervisor support), job demands (role conflict; job insecurity; work-private conflict; perceived working conditions), job resources (autonomy; relatedness; competence; skill utilization; personal growth), job satisfaction and subjective well-being (life evaluation; positive affect; negative affect) were assessed via self-report questionnaires. The proposed model investigates the direct impact of job demands and resources on SWB, as well as the indirect impact with job satisfaction as mediating factor, and was tested using the Structural Equation Modeling technique.ResultsFindings supported the proposed model. Both job demands and job resources have a direct relationship with SWB. Job resources are positively related to overall SWB, whereas job demands negatively affected SWB. Moreover, job resources are more strongly related to SWB compared to job demands. The demands and resources also indirectly contribute to employee’s SWB via job satisfaction as job satisfaction appeared to mediate these relationships.ConclusionThe current study shows that both job demands and resources directly and indirectly contribute to employees’ SWB. Creating a supportive and healthy work environment is thus of paramount importance in order to foster employees’ SWB. In particular, investing in improving job resources may be a fruitful approach to promote employees’ overall subjective well-being.

One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational optimization problem. This paper proposes a framework developed to help policy-makers plan the best combination of non-pharmaceutical interventions and to change them over time. We developed a hybrid machine-learning epidemiological model to forecast the infection trends, aggregated the socio-economic costs from the literature and expert knowledge, and used a multi-objective optimization algorithm to find and evaluate various intervention plans. The framework is modular and easily adjustable to a real-world situation, it is trained and tested on data collected from almost all countries in the world, and its proposed intervention plans generally outperform those used in real life in terms of both the number of infections and intervention costs.