Customer Decision-Making Processes Revisited: Insights from an Eye Tracking and ECG Study Using a Hidden Markov Model

Tobias Weiß (), Lukas Merkl () and Jella Pfeiffer ()
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Tobias Weiß: Justus Liebig University Gießen
Lukas Merkl: Justus Liebig University Gießen
Jella Pfeiffer: Justus Liebig University Gießen

A chapter in Information Systems and Neuroscience, 2024, pp 221-230 from Springer

Abstract: Abstract Good timing is key for many activities in business and society. In the context of adaptive user assistance, it can work as door opener to further engage with the user. This paper presents a virtual commerce study which combines eye tracking, electrocardiography, and virtual reality with the goal to detect decision phases in two different purchase scenarios. We therefore collect objective sensor data in combination with subjective decision phase annotations. Shifts between decision phases are determined subjectively by the participants via retrospective video analysis. For decision phase recognition, we demonstrate how to use the neurophysiological sensor data to train a Hidden Markov Model with multivariate mixed Gaussian emission distributions and how to use it for inference. A main benefit of our approach is its lightweight character regarding both training and inference.

Keywords: Customer behavior; Decision making; Eye tracking; Electrocardiography; Hidden Markov model; Gaussian mixture model; Machine learning; Virtual commerce; Virtual reality (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-58396-4_19

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DOI: 10.1007/978-3-031-58396-4_19

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