ArticleBehavioral sciences (Basel, Switzerland)2026
Job Demands and Resources During Digital Transformation in Public Administration: A Qualitative Study.
Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Digital transformation poses significant challenges to employee well-being, particularly in public administration, where hierarchical structures, increasing digitalization pressures, and high mental health-related absenteeism underscore the need to understand individual and job demands and resources. This study explores these aspects from the perspectives of employees and supervisors in public administration. Between September 2023 and February 2024, semi-structured interviews were conducted with eight employees and eleven supervisors from public administration organizations in Northern Germany and analyzed using deductive-inductive qualitative content analysis based on the Job Demands-Resources model. Identified individual resources included technical affinity, error tolerance, and willingness to learn, while key job resources involved early and transparent communication, attentive leadership, technical support, and counseling services, with most job resources linked to leadership behavior and work organization. Reported job demands comprised insufficient participation, inadequate planning, and lengthy procedures, whereas personal demands included fears and concerns about upcoming changes and negative attitudes toward transformation. The variation in perceived demands and resources highlights the individuality of the employees' experiences. The findings provide initial insights into factors influencing psychological well-being at work during digital transformation, emphasizing the importance of participatory communication, employee involvement, leadership awareness of stressors, and competence development. Future research should employ longitudinal and interventional designs to improve causal understanding and generalizability.
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