BWL XII: Research article accepted in the Journal of Information Systems Research
We are very pleased to announce the acceptance of an article entitled Eye-Tracking-Based Classification of Information Search Behavior using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments in a very high-ranking journal for information systems. In this article, we use approaches of machine learning to learn about the buyer at an early stage purely by means of eye movements.
We use both eye-tracking data, which we have recorded while shopping in real supermarkets, and eye-tracking data in virtual supermarkets.
The item is still in print and will soon be available online.
Titel: Eye-Tracking-Based Classification of Information Search Behavior using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments
Autoren: Jella Pfeiffer, Thies Pfeiffer, Martin Meißner und Elisa Weiß
Abstract: Classifying information search behavior helps tailor recommender systems to individual customers' shopping motives. But how can we identify these motives without requiring users to exert too much effort? Our research goal is to demonstrate that eye tracking can be used at the point of sale to do so. We focus on two frequently investigated shopping motives -- goal-directed and exploratory search. To train and test a prediction model, we conducted two eye-tracking experiments in front of supermarket shelves. The first experiment was carried out in immersive virtual reality, the second in physical reality, in other words as a field study in a real supermarket. We conducted a virtual reality study, because recently launched virtual shopping environments suggest that there is great interest in using this technology as a retail channel.
Our empirical results show that support vector machines allow the correct classification of search motives with 80% accuracy in virtual reality and 85% accuracy in physical reality. Our findings also imply that eye movements allow shopping motives to be identified relatively early in the search process: our models achieve 70% prediction accuracy after only 15 seconds in virtual reality and 75% in physical reality. Applying an ensemble method increases the prediction accuracy substantially to about 90%. Consequently, the approach that we propose could be used for the satisfiable classification of consumers in practice. Furthermore, both environments' best predictor variables overlap substantially. This finding provides evidence that, in virtual reality, information search behavior might be similar to the one used in physical reality. Finally, we also discuss managerial implications for retailers and companies that are planning to use our technology to personalize a consumer assistance system.