Physiological, Psychological, and Behavioral Measures in the Study of IS Phenomena: A Theoretical Analysis of Triangulation Strategies

Kevin Hill () and Stefan Tams ()
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Kevin Hill: HEC Montréal
Stefan Tams: HEC Montréal

A chapter in Information Systems and Neuroscience, 2018, pp 101-107 from Springer

Abstract: Abstract Recent NeuroIS research has suggested that physiological measures could contribute to an improved explanation and prediction of IS phenomena. However, few studies have examined a combination of different kinds of measures, raising the question of how the propagated improvement in explaining and predicting IS phenomena can be achieved. Therefore, research is needed that sheds light on the interrelationship amongst physiological measures (i.e., NeuroIS), psychological measures (i.e., perceptual, self-report), and behavioral measures (i.e., directly observed behaviors). Drawing on the methodological triangulation approach, this research essay endorses the use of multiple measures in the study of IS phenomena, and it discusses two strategies that can be useful in this endeavor: convergent validation and holistic representation. The former aims to explain and predict variance in IS dependent variables with greater certainty, while the latter intends to increase the amount of variance explained. The essay concludes that—although both strategies have merit—holistic representation is where NeuroIS could play an especially important role.

Keywords: NeuroIS; Self-report; Perceptual; Behavioral; Physiological data (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-319-67431-5_12

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DOI: 10.1007/978-3-319-67431-5_12

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