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

In: Information Systems and Neuroscience

Author

Listed:
  • David Baumgartner

    (University of Applied Sciences Upper Austria)

  • Thomas Fischer

    (University of Applied Sciences Upper Austria)

  • René Riedl

    (University of Applied Sciences Upper Austria
    Johannes Kepler University)

  • Stephan Dreiseitl

    (University of Applied Sciences Upper Austria)

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.

Suggested Citation

  • David Baumgartner & Thomas Fischer & René Riedl & Stephan Dreiseitl, 2019. "Analysis of Heart Rate Variability (HRV) Feature Robustness for Measuring Technostress," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph (ed.), Information Systems and Neuroscience, pages 221-228, Springer.
  • Handle: RePEc:spr:lnichp:978-3-030-01087-4_27
    DOI: 10.1007/978-3-030-01087-4_27
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    Cited by:

    1. Marc Schlömmer & Teresa Spieß & Stephan Schlögl, 2021. "Leaderboard Positions and Stress—Experimental Investigations into an Element of Gamification," Sustainability, MDPI, vol. 13(12), pages 1-20, June.

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