Abstract
Technostress has become an important topic in the scientific literature, particularly in Information Systems (IS) research. Heart rate variability (HRV) has been proposed as a measure of (techno)stress and is widely used in scientific investigations. The objective of the pilot study reported in this paper is to showcase how the preprocessing/cleaning of captured data can influence the results and their interpretation, when compared to self-report data. The evidence reported in this paper supports the notion that NeuroIS scholars have to deliberately make methodological decisions such as those related to preprocessing of physiological data. It is therefore crucial that methodological details are presented in NeuroIS papers in order to create a better understanding of the study results and their implications.
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Acknowledgements
This research was funded by the Upper Austrian Government as part of the Ph.D. program “Digital Business International”, a joint initiative between the University of Applied Sciences Upper Austria and the University of Linz, and as part of the project “Digitaler Stress in Unternehmen” (Basisfinanzierungsprojekt) at the University of Applied Sciences Upper Austria.
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Baumgartner, D., Fischer, T., Riedl, R., Dreiseitl, S. (2019). Analysis of Heart Rate Variability (HRV) Feature Robustness for Measuring Technostress. In: Davis, F., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A. (eds) Information Systems and Neuroscience. Lecture Notes in Information Systems and Organisation, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-01087-4_27
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