# Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment
## 3 Research Model and Hypotheses Development
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Context/Focus** & **Type of Study** & **Main Findings** \\ \hline Automatic lane-change system [35] & Experimental design with 1823 & Driver acceptance of the system was evaluated using performance index. \\ \hline Parking assistance systems [36] & Survey & Driver acceptance is influenced by the system reliability. \\ \hline Trust in technology, effect on driving skills and behavior & Standard focus group methodology and & Driver acceptance is influenced by trust and reliability of the vehicle technology. \\ and technology preferences among teens [37] & purposive sampling methods & \\ \hline ACC, FCW, LDW, blind-spot monitoring, driver & & Driver acceptance is influenced by perceived safety benefit of the systems. \\ drowsiness detection system, traffic sign recognition & Survey & Driver acceptance is influenced by knowledge regarding system automation level. \\ \hline Forward collision warning and mitigation (FCWM) [38] & Online survey & Driver acceptance is influenced by attitude, perceived usefulness and subjective norms. \\ \hline Forward collision warning and lane departure warning [39] & Questionnaire & Driver acceptance is influenced by attitude, perceived usefulness and subjective norms. \\ \hline Fatigue monitoring system or an adaptive cruise control & Driving simulator and online survey & Driver acceptance can be modeled using TAM and TPB. \\ \hline Adaptive cruise control (ACC) and lane-keeping assistance (LKA) [40] & Survey & Driver owners have different understanding of ACC and LKA systems and tend to over-estimate the system capabilities. \\  & & There is no relationship between trust and frequent usage of the systems. \\ \hline Night-view assist, blind-spot assist, parking sensors, driver & & Driver acceptance is influenced by system usefulness, reassurance and trust as well as system level \\ \hline control and emergency stop system [41] & & Driver acceptance is positively influenced by factors related to driver convenience and trust. \\ \hline Vehicle system related to driving convenience and safety [4] & Online survey & Driver acceptance is positively influenced by factors related to driver convenience and trust. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)from United States and Canada revealed that drivers who are not vehicle owners but have better knowledge of system capabilities have lower trust level. However, the trust level of drivers who are also vehicle owners does not seem to be influenced by the knowledge of system capabilities [40]. In another study, [PERSON] et al. studied the effect of trust in the user acceptance of 5G-connected autonomous vehicles. Authors concluded that trust has mediating effect on PU, PE and SI with BI [43]. Apart from that, ref. [35] mentioned that users are more accepting toward the technology if they are certain of the recommendation provided by the system. This may be reflected using T and PU. In addition, personal characteristics (PCs) of users refer to gender, age, prior knowledge about in-vehicle applications, self-capabilities and involvement in accidents as well as driving distance per week which may influence a user's trust, the expectation of system characteristics and impact of social influence toward the in-vehicle applications. Individuals who are highly educated and earn a good paycheck may be more willing to use the in-vehicle applications [15]. The older a driver is, the more concerned they are regarding the PU, T and SC [41]. Furthermore, ref. [39] concluded that drivers in Jakarta community are positively and significantly influenced by only AT, PU and subjective norms to adopt FCW and LDW. Hence, based on our literature, the research model is designed as in Figure 2 and the research questions of this study are listed in Table 5 with corresponding hypotheses.