Analysis of Heart Rate Variability (HRV) Feature Robustness for Measuring Technostress

David Baumgartner (), Thomas Fischer (), René Riedl () and Stephan Dreiseitl ()
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David Baumgartner: University of Applied Sciences Upper Austria
Thomas Fischer: University of Applied Sciences Upper Austria
René Riedl: University of Applied Sciences Upper Austria
Stephan Dreiseitl: University of Applied Sciences Upper Austria

A chapter in Information Systems and Neuroscience, 2019, pp 221-228 from Springer

Abstract: 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.

Keywords: Data preprocessing; Heart rate variability (HRV); NeuroIS research methodology; Technostress; Signal feature; Stress (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-01087-4_27

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DOI: 10.1007/978-3-030-01087-4_27

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