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Some information systems research has considered that individual personality traits influence whether users feel stressed by information and communication technologies. Personality research suggests, however, that personality traits do not act individually, but interact interdependently to constitute a personality profile that guides individual perceptions and behavior. The study relies on the differential exposure-reactivity model to investigate which personality profiles of the Big Five personality traits predispose users to perceive techno-stressors. Using a questionnaire, data was collected from 221 users working in different organizations. That data was analyzed using fuzzy set Qualitative Comparative Analysis. Based on the results, six different personality profiles that predispose to perceive high techno-stressors are identified. By investigating personality traits in terms of profiles, it is shown that a high and a low level of a personality trait can influence the perception of techno-stressors. The results will allow users and practitioners to identify individuals who are at risk of perceiving techno-stressors based on their personality profile. The post-survey analysis offers starting points for the prevention of perceived techno-stressors and the related negative consequences for specific personality profiles.

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Technostress is a major challenge for employees using information technology. Technostress research has revealed the causes, i.e. techno-stressors, and resulting adverse consequences for employees and companies. However, there is a lack of practical insights guiding companies on how to reduce technostress. To offer such practical insights, we follow a mixed-methods approach. The qualitative study bases on eleven expert interviews and reveals seven measures that reduce technostress. We then elaborate on these interview results with a quantitative study of 110 employees. The quantitative results reveal the degree to which the seven measures are useful to reduce specific techno-stressors. Our results show that although there are measures used in practice, none reduces all different techno-stressors. We complement existent theoretical technostress research by offering practice-oriented recommendations on how to reduce technostress. Based on the illustration of which measures are useful for which techno-stressors, practitioners can choose the measures that best fits their needs.