Advancing the understanding of the dark side of digitalization: researching personal factors that influence the experience of technostress on the individual

Abstract

Background: Since information technologies have been spreading in working environments, they have provided opportunities for the bettering of productivity, transparency and communication speeds. Techno-optimistic literature has explored the opportunities that this digitalization can provide. But when discussing digitalization, the human side of human-technology interaction that is intensified by digitalization should not be forgotten. This is where there is a knowledge gap that this thesis aims to fill. This study looks into these possible risks and side-effects of digitalization, of which technostress is an important part. Digital literacy is cemented as a way to cope with these side-effects. Further variables taken from literature are involved, such as age and education. Research questions: There are four research questions that help further the understanding of possible risks and side effects of digitalization. The first research question asks if there is a direct relationship between digitalization and technostress. After this the possible moderating roles of age, education and digital literacy are tested. Methods: A quantitative analysis of secondary data was executed. The dataset used is the ‘Risks that matter survey’ from the OECD. The data is analyzed using IBM SPSS 28 and the PROCESS macro. Results: It was found that individuals experience slightly but significantly less technostress in more digitalized working environments than in less digitalized working environments. Age and education moderated this relationship negatively. Digital literacy did not have a significantly moderating role. Conclusions: Although a negative relationship between digitalization and technostress was found, individuals would experience even less technostress if they have a low age or high education when encountering highly digitalized working environments. This thesis is limited by the cross-sectional nature of the dataset, and further research will substantiate the findings.