In recent years, the effort‐reward‐imbalance (ERI) model has become widely used as a framework for examining job characteristics and employee's health. The present study tested the predictive validity of the ERI model's components ‐ ERI, over‐commitment (OC) and their interaction ‐ on the basis of self‐reported health outcomes. In a cross‐sectional study, data were obtained from 1,587 employees working in the aircraft manufacturing industry in southern Germany. Results suggested that all components of the ERI model (effort‐reward‐ratio, effort, reward and over‐commitment) are associated with health‐related quality of life, vital exhaustion, depression and quality of sleep. The separate variables effort and reward explained more of the observed variance than the effort‐reward ratio. No interaction between ERI and OC in predicting measurements of self‐reported health could be found. The findings suggest (1) that the ERI ratio does not provide more information than the separate use of the variables effort and reward, and (2) that there are main effects of ERI and OC but no interaction effect on employees' health. Implications for theory and applied research are discussed.
Effort-reward imbalance, lack of support by supervisors or coworkers, negative affectivity, exhaustion, and impaired health perception were significantly associated with absence spells and the time lost index. Job demands and job control as well as overcommitment were unrelated to absenteeism indices. Multivariate models suggest mediation through impaired health-related quality of life.
Usability and the use of automated static analysis tools in the software development process have been an evolving subject of research in the last decades. Several studies shed light on issues like high false positive rates and low comprehensibility, which hinder tool adoption for even software engineers. Yet, the tools' perceived usefulness and ease of use play a much larger role when it comes to untrained software developers, as is usually the case in scientific software development. In this paper, we outline a multi-stage interview study to learn more about how scientists come to accept and use static analysis tools.
Guidelines and Tools Knowledge and Experience Exchange Trainings and Consulting Knowledge Exchange Workshops Regular knowledge exchange workshops are held to actively involve DLR scientists and to foster exchange. Concept
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