Recognition of Customers’ Impulsivity from Behavioral Patterns in Virtual Reality

Virtual reality (VR) in retailing (V-commerce) has been proven to enhance the consumer experience. Thus, this technology is beneficial study behavioral patterns by offering opportunity infer customers’ personality traits based on their behavior. This aims recognize impulsivity using patterns. For goal, 60 subjects performed three tasks—one exploration task and two planned tasks—in a virtual market. Four noninvasive signals (eye-tracking, navigation, posture, interactions), which are available commercial VR devices, were recorded, set of features extracted categorized into zonal, general, kinematic, temporal, spatial types. They input support vector machine classifier I-8 questionnaire, achieving an accuracy 87%. The results suggest that, while can reveal general impulsivity, other subscales such as perseverance sensation-seeking more related tasks. also show that posture interaction most informative signals. Our findings validate recognition customer sensors incorporated devices. Such information provide personalized shopping experience future shops.