Assessing the Effect of Drivers’ Gender on Their Intention to Use Fully Automated Vehicles

Abstract

Although fully automated vehicles (SAE level 5) are expected to acquire a major relevance for transportation dynamics by the next few years, the number of studies addressing their perceived benefits from the perspective of human factors remains substantially limited. This study aimed, firstly, to assess the relationships among drivers’ demographic factors, their assessment of five key features of automated vehicles (i.e., increased connectivity, reduced driving demands, fuel and trip-related efficiency, and safety improvements), and their intention to use them, and secondly, to test the predictive role of the feature’ valuations over usage intention, focusing on gender as a key differentiating factor. For this cross-sectional research, the data gathered from a sample of 856 licensed drivers (49.4% females, 50.6% males; M = 40.05 years), responding to an electronic survey, was analyzed. Demographic, driving-related data, and attitudinal factors were comparatively analyzed through robust tests and a bias-corrected Multi-Group Structural Equation Modeling (MGSEM) approach. Findings from this work suggest that drivers’ assessment of these AV features keep a significant set of multivariate relationships to their usage intention in the future. Additionally, and even though there are some few structural similarities, drivers’ intention to use an AV can be differentially explained according to their gender. So far, this research constitutes a first approximation to the intention of using AVs from a MGSEM gender-based approach, being these results of potential interest for researchers and practitioners from different fields, including automotive design, transport planning and road safety.

Environmentally friendly, but behaviorally complex? A systematic review of e-scooter riders’ psychosocial risk features

“…Thus, they explain a greater intention of being adopted among individuals having higher degrees of previous interaction with other technologies. At the same time, they remain more open to new technology-related experiences [ 73 , 74 ], but also more prone to assume new (and sometimes greater) levels of risk [ 75 ]. This is precisely another key outcome to be subsequently discussed in this study.…”

Psychological factors affecting potential users’ intention to use autonomous vehicles

“…More importantly, these college students will be important potential buyers and users of driverless cars. However, currently there are many studies on tourists [17,18] and drivers with driving experience [34,62], and there is a lack of research on college students as samples. At the same time, the samples of many current studies [17,34,105,106] are mainly European and American, while the research on Chinese users in the huge Chinese market is still insufficient.…”

“…However, the work related to studying the users' acceptance of AVs is quite limited, and the exploration into the relevant influencing psychological factors has led to few results [59,60]. Many current studies focus on demographic variables [14,61], and currently there are many studies on tourists [17,18] and drivers with driving experience [34,62], and there is a lack of student groups as samples. However, the only individual research [63] with college students as samples is from the designer's perspective.…”

Analysing the effect of gender on the human–machine interaction in level 3 automated vehicles

“…Abraham et al 37 found significant gender differences in terms of the methods used in learning to use in-vehicle systems and automated vehicles, with men more inclined to learn by themselves using online instructions and manuals, while women preferred to be taught. In addition, Useche et al 38 found gender difference on drivers’ intention to use an automated vehicle, with male users’ intentions is influenced by connectivity, fuel consumption, energy and trip efficiency and safety-related issues, while female’s intention is associated with driving demands, trip efficiency and safety features. Moreover, Muslim et al 39 identified significant effect of gender on people’s reaction time when interacting with automated driving systems and also reported significant interaction effect between gender and the design of the human–machine interface of the automated driving systems.…”