# Recognition of Customers' Impulsivity from Behavioral Patterns in Virtual Reality
## 4 Discussion  
### Results Discussion  
This study was conducted to investigate the possibility that impulsivity (based on the I-8 model) could be predicted using the behavior of consumers while they shopped. The results showed that POS & INT features were the best signal for predicting impulsivity with a kappa equal to 0.64 in the exploration task. That was the best result amongst all the investigated signals. The results also showed that impulsivity and body posture are strongly correlated. This is in line with previous studies that showed that body posture could affect impulsive buying [74]. Moreover, the exploration task was very informative for revealing impulsivity. That is logical, since impulsive purchases happen when the customer decides to buy a product suddenly--not when he has a target product or a purchase plan. On the other hand, general category and kinematic features were more selected for impulsivity recognition. This means that impulsivity changes the overall shopping behavior considering the whole shopping time. For the trait of urgency, the planned task of buying sneakers was more informative. In that task, the shoppers were told to buy only one pair of sneakers. Those are purchased only occasionally, and they have relatively high prices. However, in the second task, the participants could choose more than one daily cheap product. It seems that purchasing sneakers put more emotional pressure on the shoppers, and this, in turn, elicited the trait of urgency, which is the tendency to act rashly in response to heightened effect. This impact could be recognized in all the signals recorded from the participants. This means that if the task is chosen carefully, urgency can be clearly recognized in ET, NAV, and POS & INT signals, with POS & INT more discriminative than the other two signals. Premeditation was the hardest trait to predict. According to our findings, to recognize this trait, the combination of all the tasks and signals was required. The results show that premeditation had a low impact on the behavior of the person. However, this trait, which is related to having a plan before doing a task, was evoked more by the exploration task. It can be inferred that when there is a plan to make a purchase, all the customers act similarly, but when there is no prior purchase plan, individuals with a high level of premeditation face a new situation and act differently. Furthermore, this trait is correlated with zonal and kinematic features. It seems when shoppers are in the zone of interest and must take a decision, they act differently if their personality requires them to have a plan. Otherwise, this trait does not change the behavior in the rest of the task period significantly. On the second level of difficulty of prediction, perseverance required all the signals and the tasks to be combined. However, the POS & INT signal was more informative than the others. Besides, the second and then the first planned task evoked perseverance more than the exploration task. Additionally, the combination of these two tasks could improve kappa by a considerable amount. This trait is related to following a plan to the end. Hence, planned tasks elicit this trait more, since an exploration task has no plan to accomplish. Moreover, zonal features contributed more toward recognizing this trait. This is because the person accomplished the plan in the zones of interest. In this study, the easiest trait to predict was sensation-seeking, since it could be recognized in all the tasks and through two out of three signals, i.e., ET and POS & INT signals. Regarding the tasks, this result can be justified by the fact that there are risks and novel senses to be explored. On the other hand, this trait affects behavior more when the person is in the zones of interest, based on our findings. By observing the study as a whole, HBT signals, including eye-tracking, body posture, navigation, and interaction can reveal impulsivity and very likely other personality traits.