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

David Baumgartner; Thomas Fischer; René Riedl; Stephan Dreiseitl

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

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

David Baumgartner, Thomas Fischer, René Riedl, Stephan Dreiseitl

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

17 Citations (Scopus)

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.

Original language	English
Title of host publication	Lecture Notes in Information Systems and Organisation
Publisher	Springer
Pages	221-228
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-030-01087-4_27
Publication status	Published - 2019
Event	NeuroIS Retreat 2018 - Vienna, Austria Duration: 19 Jun 2018 → 21 Jun 2018 http://www.neurois.org

Publication series

Name	Lecture Notes in Information Systems and Organisation
Volume	29
ISSN (Print)	2195-4968
ISSN (Electronic)	2195-4976

Conference

Conference	NeuroIS Retreat 2018
Country/Territory	Austria
City	Vienna
Period	19.06.2018 → 21.06.2018
Internet address	http://www.neurois.org

Keywords

Data preprocessing
Heart rate variability (HRV)
NeuroIS research methodology
Signal feature
Stress
Technostress

Access to Document

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

Cite this

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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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Baumgartner, D, Fischer, T , Riedl, R & Dreiseitl, S 2019, Analysis of Heart Rate Variability (HRV) Feature Robustness for Measuring Technostress. in Lecture Notes in Information Systems and Organisation. Lecture Notes in Information Systems and Organisation, vol. 29, Springer, pp. 221-228, NeuroIS Retreat 2018, Vienna, Austria, 19.06.2018. https://doi.org/10.1007/978-3-030-01087-4_27

Analysis of Heart Rate Variability (HRV) Feature Robustness for Measuring Technostress. / Baumgartner, David; Fischer, Thomas ; Riedl, René et al.
Lecture Notes in Information Systems and Organisation. Springer, 2019. p. 221-228 (Lecture Notes in Information Systems and Organisation; Vol. 29).

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

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T1 - Analysis of Heart Rate Variability (HRV) Feature Robustness for Measuring Technostress

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AU - Fischer, Thomas

AU - Riedl, René

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KW - Heart rate variability (HRV)

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KW - Signal feature

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

Abstract

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Conference

Keywords

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