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

Florian Popp (), Bernhard Lutz () and Dirk Neumann ()
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Florian Popp: University of Freiburg
Bernhard Lutz: University of Freiburg
Dirk Neumann: University of Freiburg

A chapter in Information Systems and Neuroscience, 2021, pp 1-8 from Springer

Abstract: 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.

Keywords: Textual data; Taxonomy; Information processing; Decision-making; NeuroIS (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-88900-5_1

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