# Assessing the Effect of Drivers' Gender on Their Intention to Use Fully Automated Vehicles
## 4 Discussion  
The core aims of this study were, first, to assess the relationships among drivers' demographic factors, the valuation given to five key features of automated vehicles, and their intention to use AVs, and secondly, to test the predictive role of the valuation given to these five features on drivers' intention, focusing on gender as a key differentiating factor. Therefore, the findings in each one of these regards will be discussed below, in consideration of both the study hypotheses and the existing literature on the matter. In regard to the first aim, it was initially hypothesized that drivers' valuations of five different features greatly known about fully-automated vehicles might be significantly  
Figure 2: Multi-group structural model showing standardized path coefficients for self-reported intention to use AVs: male drivers (**left**) and female drivers (**right**). \(\mathrm{N/S}\) Non-significant path (discontinuous arrows); \({}^{\star}\)\(p\)\(<\) 0.050; \({}^{\star\star}\)\(p\)\(<\) 0.010; \({}^{\star\star\star}\)\(p\)\(<\) 0.001. Additional information on other (miscellaneous) statistical parameters is available in Table 4.  
related to their self-reported intention to switch to AVs. Based on the data provided by this relatively extensive sample of 856 drivers, this study found that, indeed, there are bivariate relationships between drivers' intentions to shift to an automated vehicle and their valuation of five of its most relevant features, within those presented prospectively by the literature [3; 5; 9]. In this regard, it is worth emphasizing that, in a period of approximately 10 years (for when it is expected to be a large enough market in European countries), some other relevant features and/or technological developments could substantially affect how automated vehicles are perceived, and how transport dynamics could be transformed as a consequence of their potential impacts in different spheres [7; 40; 47]. Further, the descriptive results of this study suggest some interesting points worthwhile to be briefly discussed: firstly, that AV feature-related valuations may largely differ on the basis of gender (to be discussed below, when addressing the second study aim), and secondly, the existence of statistically significant differences in the self-rated interaction with ICTs by gender, in which case women present significantly higher scores than their male counterparts. As for this finding, some recent studies have found that, regardless of gender gaps in terms of accessibility in many countries, but especially in emerging economies [48], females might tend to keep a greater mean daily interaction with some \"connected devices\" in terms of frequency, intensity, and even as for subsequent problems derived from it [49; 50], even though findings remain somewhat inconsistent across different literature sources. The second aim of this study (i.e., to test the predictive role of these five key features in a gender-based perspective) allowed to a priori hypothesize that that AV-feature valuations would have a significant (but still differential) effect over drivers' intentions to use them. Overall, the results found endorsed the hypothesis that there exist certain multi-group structural [51] differences in the explanation of the intention to use an automated vehicle based on its differential features, at least between male and female drivers. Although this multivariate technique had not been ever applied to the study of users' preferences, attitudes and/or intentions over automated vehicles (AVs), previous researches have already analyzed the role of gender as a key potential determinant and (in other cases) mediator between, demographic/psychosocial issues and perceptual and behavioral outcomes of several groups of road users (for a summary, please see: [52; 53; 54]). Furthermore, gender differences constitute a \"traditional\" but underused issue in road safety and transport planning studies.