# Recognition of Customers' Impulsivity from Behavioral Patterns in Virtual Reality
###### Abstract
As tangible instances, Alibaba, one of the most renowned retailers, as well as Coca-Cola, McDonald's, and IKEA are currently employing VR technology [28; 29]. VEM properties, such as the sense of presence and transferability to reality [30; 31; 32], make it a suitable tool for studying human behavior. It is known that behavioral signals can reveal subconscious processes and psychological traits [4; 33; 34]. Thus, VEM can be used to track such signals to study and predict the psychological traits of shoppers as they interact with the virtual environment. Not only can such prediction be applied in the real environment, but it can also help retailers to provide personalized shopping experiences [18] as well as adaptive environments and advertisements [35] in VR-based retailing. An important personality trait for investigation in VEM is impulsivity, since previous studies indicate that 39% of purchases are the result of impulsive buying [36]. Other studies show that about 50-70% of purchase decisions in supermarkets involve impulsive buying [37]. Impulsive buying is a complicated process in which shoppers decide to purchase immediately without considering the consequences [38]. Impulsive buying is conceived as a problem from the consumers' point of view, but from the marketers' perspective, it is a tactic to enhance the purchase rate [39; 40]. In the late 1960s, [PERSON] and [PERSON] [41] conducted one of the earliest studies of the relationship between impulsive buying and personality traits [42]. Although, the concept of impulsive buying was not new, in the early 1950s, some attempts were made to grasp the relation between impulsive buying and different product categories [43]. Impulse buying has been researched from two perspectives: the state of the mind created by the shopping environment [44] and the personality traits of consumers [45]. In this regard, several environmental cues have been studied to determine their effect on the consumer's state of mind and whether these cues urge consumers to buy impulsively [46]. Other studies have investigated the influence of consumers' impulsiveness on the intention to purchase [47; 48]. To our knowledge, there has yet to be a study that investigates the relative effects of impulsivity on consumer behavior. Therefore, the goal of this paper is twofold: to examine the influence of impulsivity on consumer behavior using VR, and then, to analyze how behavioral patterns can predict impulsivity. Impulsivity is an important psychological construct that appears in most major scales of personality [49]. In recent decades, several scales have been proposed to assess impulsivity [50; 51; 52; 53]. Among them is the impulsivity behavior scale (I-8) [54], which is based on the conceptualization of this trait in [49]. It has been used in several studies [55; 56; 57]. The scale uses a questionnaire that indicates the impulsivity level of the subject in four dimensions: urgency, (lack of) premeditation, (lack of) perseverance, and sensation-seeking [57]. The trait of urgency is defined as the tendency to act rashly when distressed [58]. Premeditation means that the person tends to think and have a plan before acting [59]. Lack of perseverance is described as the inclination to quit tasks that are difficult or boring, and sensation-seeking is the need to look for novel, thrilling, or risky stimulation [60]. In behavioral studies, statistical inference and machine learning are the two most frequently used tools for analysis. However, researchers have become more inclined toward exploiting machine learning [61], which can learn from instances to predict the relationship between inputs and outputs [62]. This property can be applied to data gathered from several types of physiological or behavioral sensors such as eye-tracking, electroencephalography (EEG), heart rate variability (HRV), and functional magnetic resonance imaging (fMRI) [63].