# Buy and/or Pay Disparity: Evidence from Fully Autonomous Vehicles
## 2 Backgrounds and Literature Review
### Current Research Gaps and Our Contributions  
This study aims to better understand the factors affecting the WTB and PV of FAV choices. This study has several contributions. First and most importantly, to the best of our knowledge, our model is the first to account for the differences between WTB and PV in the context of FAVs. Having a higher PV does not necessarily indicate that someone will purchase an FAV. For example, as mentioned in Section 1, one might evaluate the autonomous vehicle higher than others because s/he is aware of the benefits of FAV technology but would not purchase an FAV because s/he is afraid of potential accidents. Our work and results allow us to understand and distinguish the characteristics of individuals who belong to these groups (those who evaluate FAVs higher but do not purchase, and vice versa), and those who do not belong to this group are crucial to policymakers who are willing to spread the use of FAVs and bring them into the mainstream. Second, we account for technological advantages, fears toward new technology, environmental awareness, and sociodemographic factors (i.e., gender, age, income, and commuting time) together in the model. Each factor encompasses crucial issues (i.e., cyber-security, regulations on accidents, responsibilities on accidents, safety, and malfunction) closely related to the WTB and PV demands and carefully selected in the literature. One advantage of this approach is that we can separately analyze each factor's impact while fixing the other factor: for example, one might be afraid of FAV technology, but at the same time, s/he can support the conservation of the natural environment. Another example would be a person who fears FAV due to possible accidents or malfunction issues, but at the same time, s/he is fond of the advantages that FAV would give. In this case, there is a need to analyze these factors as independent factors separately. For example, our study allows us to look at the impact of fear toward accidents while fixing interest on the natural environment, therefore focusing on the changes of one while leaving the other as it is, and vice versa. Such approaches would allow us to evaluate each factor's 'independent' impact. Additionally, our model allows us to look at the impacts of each survey question on the factors. We discuss the factors more in Section 3.2. Third, we categorize environmental concerns into two categories and investigate their correlations to the WTB and PV of FAVs. There are only a handful of studies on the impact of environmental concerns on FAV adoption and PV [53]. In a broader view, there are previous works on consumers' environmental concerns and their purchasing behaviors, and most of them agree that higher environmental concerns would lead to higher consumer preferencesfor pro-environmental products [54; 55; 56]. Hence, one issue from these works is that they regard environmental awareness as a broad concept: public awareness of the importance of environmental protection. However, purchasing behavior might vary according to the type of environmental awareness. For example, the environmental benefits of adopting FAVs are mostly related to pollution and are not directly correlated to the conservation of the natural environment (i.e., biodiversity preservation). In that sense, those who prioritize these issues more than reducing air pollutants might not show higher PVs than those who regard resolving air pollution as important. Therefore, policy guidelines that do not consider differences between different environmental awareness types might result in misleading policy implications. To resolve this research gap, we categorize environmental awareness into 'Pollution', which refers to the people who emphasize recycling, alleviating air/water pollution, and 'Nature', which refers to the people who prioritize natural environments. Categorizing would also allow for the identification of which types of environmental awareness would be positively/negatively correlated with WTB or PV, respectively.