## 3 Method
### Data Collection and Analysis  
A mixed-method approach was employed in this study. A survey was conducted to quantitatively evaluate the UX of interactions between drivers and the three types of voice agents using five subjective measures: intimacy, trust, intention to use, perceived safety, and perceived anthropomorphism. These measures are known to directly or indirectly impact the acceptance of AI-enabled technologies, as demonstrated in previous research [29; 30; 45; 47; 48; 49; 50; 51]. These measures serve as key UX evaluation indicators for voice agents, and their selection is grounded in their relevance to understanding user interactions with HLMs, as discussed in Sections 1 and 2. In particular, intimacy and trust are essential for fostering positive relationships between users and technology, while intention to use, perceived safety, and perceived anthropomorphism are critical factors in determining user engagement and acceptance of voice agents in autonomous vehicles. Using collected quantitative data, a one-way ANOVA was conducted to compare mean values across different agent conditions, with statistical significance set at 0.05, using SPSS software (Version 29). Sphericity was assessed using [PERSON]'s test. In cases where the assumption of sphericity was violated, the degrees of freedom were adjusted using either the Greenhouse-Geisser correction (when \(\varepsilon<0.75\)) or the Huynh-Feldt correction (when \(\varepsilon>0.75\)) [52]. Post-hoc analyses were performed with multiple comparisons whenever a significant difference was found in the ANOVA. Semi-structured interviews were conducted for qualitative data collection. Participants' responses regarding the positive and negative aspects of each agent were recorded, and additional questions were posed based on the researcher's discretion. The qualitative data were then structured and analyzed through content analysis. Following the conventional content analysis method described by [PERSON] and [PERSON] [53], two researchers ([PERSON] and [PERSON]) independently identified initial coding schemes from the raw data. Subsequently, all researchers participated in collaborative discussions to derive the final coding scheme, which serves as a framework for organizing qualitative data. Through this analysis process, the outcomes were determined and are presented in Section 4.2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Agent** & **Conversation** \\ \hline \multirow{4}{*}{Agent A and B} & Agent: Hello. This is your assistant. \\  & Driver: I need to go to the gas station. \\  & Agent: The gas station is about 20 min away. \\  & Driver: Alright, let’s go. \\  & Agent: I’ll take you there. (_While driving_) There’s a car accident 30 m ahead. (_Changes lanes without announcing_) \\  & Driver: We just passed the accident scene. Let’s go quickly. \\  & Agent: Understood. (_While driving_) We’ve arrived at the gas station. \\ \hline \multirow{4}{*}{Agent C} & Agent: Hello, I’m your assistant. Nice to see you. \\  & Driver: I need to go to the gas station. \\  & Agent: The gas station is about 20 min away. \\  & Driver: Alright, let’s go. \\  & Agent: I’ll take you there. (_While driving_) There’s a car accident 30 m ahead. I’ll slow down. (_Reduces speed_) \\  & Agent: I’ll change lanes now. \\  & Driver: Okay. (_Changes lanes_) \\  & Driver: We just passed the accident scene. Let’s go quickly. \\  & Agent: I agree. At this pace, we’ll arrive later than expected, so we need to speed up a bit. How about driving faster but within the speed limit? \\  & Driver: Yes, that sounds good. \\  & Agent: Is this speed comfortable for you? \\  & Driver: Yes, it’s fine. (_While driving_) \\  & Agent: We’ve arrived at the gas station. Have a great day. \\ \hline \hline \end{tabular} Note: Since Agent A and Agent B only differ in the level of anthropomorphism in their voices, their dialogues are identical, and the italicized text indicates the vehicle’s actions.  
\end{table}
Table 4: Conversation with agents.