Appreciating the Performance of Neuroscience Mining in NeuroIS research: A Case Study on Consumer's Product Perceptions in the Two UI Modes—Dark UI vs. Light UI

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dc.contributor.author Costello, Francis
dc.contributor.author Kim, Min Gyeong
dc.contributor.author Lee, Kun Chang
dc.date.accessioned 2022-12-27T18:56:20Z
dc.date.available 2022-12-27T18:56:20Z
dc.date.issued 2023-01-03
dc.description.abstract The goal of the current study was to provide information on the potential of neuroscience mining (NSM) for comprehending NeuroIS paradigms. NSM is an interdisciplinary field that combines neuroscience and business mining, which is the application of big data analytics, computational social science, and other fields to business problems. Therefore, NSM makes it possible to apply predictive models to NeuroIS datasets, such as machine learning and deep learning, to find intricate patterns that are hidden by conventional regression-based analysis. We predicted 28 individual EEG power spectra separated brainwave data using a Random Forest (RF) model. Next, we used NSM to precisely predict how consumers would perceive a product online, depending on whether a light or dark user interface (UI) mode was being used. The model was then used to extract more precise results that could not be obtained using more conventional linear-based analytical models using sensitivity analysis. The benefits of using NSM in NeuroIS research are as follows: (1) it can relieve the burden of the three-horned dilemma described by Runkel and McGrath; (2) it can enable more temporal data to be directly analyzed on the target variables; and (3) sensitivity analysis can be performed on a condition/individual basis, strengthening the rigor of findings by reducing sample bias that can be lost in grand averaging of data when analyzed with methods like GLM.
dc.format.extent 10
dc.identifier.doi 10.24251/HICSS.2023.124
dc.identifier.isbn 978-0-9981331-6-4
dc.identifier.uri https://hdl.handle.net/10125/102754
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Digital Mobile Services for Everyday Life
dc.subject consumers' attractiveness/usefulness perception
dc.subject eeg
dc.subject machine learning
dc.subject neuroscience mining
dc.subject user interface mode
dc.title Appreciating the Performance of Neuroscience Mining in NeuroIS research: A Case Study on Consumer's Product Perceptions in the Two UI Modes—Dark UI vs. Light UI
dc.type.dcmi text
prism.startingpage 1010
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