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

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Virtual reality (VR) is a useful tool to study consumer behavior while they are immersed in a realistic scenario. Among several other factors, personality traits have been shown to have a substantial influence on purchasing behavior. The primary objective of this study was to classify consumers based on the Big Five personality domains using their behavior while performing different tasks in a virtual shop. The personality recognition was ascertained using behavioral measures received from VR hardware, including eye-tracking, navigation, posture and interaction. Responses from 60 participants were collected while performing free and directed search tasks in a virtual hypermarket. A set of behavioral features was processed, and the personality domains were recognized using a statistical supervised machine learning classifier algorithm via a support vector machine. The results suggest that the open-mindedness personality type can be classified using eye gaze patterns, while extraversion is related to posture and interactions. However, a combination of signals must be exhibited to detect conscientiousness and negative emotionality. The combination of all measures and tasks provides better classification accuracy for all personality domains. The study indicates that a consumer’s personality can be recognized using the behavioral sensors included in commercial VR devices during a purchase in a virtual retail store.

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The use of virtual reality (VR) technology in the context of retail is a significant trend in current consumer research, as it offers market researchers a unique opportunity to measure purchase behavior more realistically. Yet, effective methods for assessing the virtual shopping experience based on consumer’s demographic characteristics are still lacking. In this study, we examine the validity of behavioral biometrics for recognizing the gender and age of customers in an immersive VR environment. We used behavior measures collected from eye-tracking, body posture (head and hand), and spatial navigation sources. Participants (n = 57) performed three tasks involving two different purchase situations. Specifically, one task focused on free browsing through the virtual store, and two other tasks focused on product search. A set of behavioral features categorized as kinematic, temporal, and spatial domains was processed based on two strategies. First, the relevance of such features in recognizing age and gender with and without including the spatial segmentation of the virtual space was statistically analyzed. Second, a set of implicit behavioral features was processed and demographic characteristics were recognized using a statistical supervised machine learning classifier algorithm via a support vector machine. The results confirmed that both approaches were significantly insightful for determining the gender and age of buyers. Also, the accuracy achieved when applying the machine learning classifier (> 70%) indicated that the combination of all metrics and tasks was the best classification strategy. The contributions of this work include characterizing consumers in v-commerce spaces according to the shopper’s profile.

BackgroundNon-invasive ventilation (NIV) is a well-established approach in the treatment of acute exacerbation of chronic obstructive pulmonary disease (COPD) with type 2 respiratory failure. Average volume-assured pressure support (AVAPS) mode integrates the characteristics of both volume and pressure-controlled modes of NIV. In bilevel positive airway pressure (BiPAP) mode, volume is the dependent variable, whereas in AVAPS mode, pressure is the dependent variable. In this study, we aimed to compare the role of AVAPS mode with BiPAP spontaneous/timed (S/T) mode for the management of patients with acute exacerbation of COPD with type 2 respiratory failure. MethodologyA hospital-based comparative and analytical study was carried out on 100 patients with acute exacerbation of COPD with type 2 respiratory failure admitted to respiratory disease hospital, Sardar Patel Medical College, Bikaner (Rajasthan, India). Patients were randomly divided into two groups of 50 patients each. Group A patients were treated with AVAPS mode and group B patients with BiPAP (S/T) mode. Arterial blood gases, average duration of hospital stay, and need for invasive mechanical ventilation were compared between the two groups. ResultsThere was a statistically significant difference in favor of group A in terms of improvement in pH and pCO 2 as compared to group B at 6 h (pH, p=0.027; pCO 2 , p=0.012) and 24 h (pH, p=0.032; pCO 2 , p=0.013).The duration of hospital stay was found to be lower in group A (p=0.003). However, no significant difference was found in terms of need for invasive mechanical ventilation between both groups (p=0.338). ConclusionApplication of AVAPS mode results in more rapid and steady improvement in patients of COPD as compared to BiPAP (S/T) mode. Thus, management through non-invasive ventilation AVAPS mode should be considered in patients with acute exacerbation of COPD with type 2 respiratory failure.