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Where NeuroIS Helps to Understand Human Processing of Text: A Taxonomy for Research Questions Based on Textual Data

In: Information Systems and Neuroscience

Author

Listed:
  • Florian Popp

    (University of Freiburg)

  • Bernhard Lutz

    (University of Freiburg)

  • Dirk Neumann

    (University of Freiburg)

Abstract

Several research questions from information systems (IS) are based on textual data, such as product reviews and fake news. In this paper, we investigate in which areas NeuroIS is best suited to better understand human processing of text and subsequent human behavior or decision making. To evaluate this question, we propose a taxonomy to distinguish these research questions depending on how users’ corresponding response is formed. We first review all publications about textual data in the IS basket journals from 2010–2020. Then, we distinguish text-based research questions along two dimensions, namely, if a user’s response is influenced by subjectivity and if additional information is required to make an objective assessment. We find that NeuroIS research on textual data is still in its infancy. Existing NeuroIS studies focus on texts, where users’ responses are subject to a higher need for additional data, which is not part of the text.

Suggested Citation

  • Florian Popp & Bernhard Lutz & Dirk Neumann, 2021. "Where NeuroIS Helps to Understand Human Processing of Text: A Taxonomy for Research Questions Based on Textual Data," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 1-8, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-88900-5_1
    DOI: 10.1007/978-3-030-88900-5_1
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