## 4 Results
### Qualitative Data Analysis Results
One participant specifically noted that \"the machine voice felt more trustworthy than the human voice\" (28 th participant). This suggests that, for some users, a machine-like tone can enhance perceptions of reliability by avoiding the potential inconsistencies or unpredictability associated with human voices. It highlights an important nuance in voice interaction design: while human-like voices may improve familiarity and empathy, they do not necessarily inspire greater trust. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Attribute** & \multicolumn{2}{c}{**Agent A**} & \multicolumn{2}{c}{**Agent B**} & \multicolumn{2}{c}{**Agent C**} & \multirow{2}{*}{**Frequency (n)**} \\  & + & \(-\) & & + & \(-\) & + & \(-\) & \\ \hline Conciseness and Clarity & 6 & 1 & 5 & 0 & 1 & 2 & 15 \\ Naturalness of conversation flow & 5 & 2 & 3 & 4 & 7 & 2 & 23 \\ Familiarity and Intimacy & 2 & 9 & 10 & 10 & 10 & 4 & 45 \\ Understandability and Accuracy & 8 & 4 & 6 & 1 & 0 & 0 & 19 \\ Trust & 11 & 4 & 4 & 7 & 5 & 6 & 37 \\ Empathy and Emotion Expression & 1 & 2 & 2 & 3 & 8 & 7 & 23 \\ Information adequacy & 0 & 11 & 4 & 14 & 14 & 2 & 45 \\ Autonomy & 0 & 0 & 0 & 0 & 9 & 0 & 9 \\ \hline Frequency (n) & 33 & 33 & 34 & 39 & 54 & 23 & 216 \\ \hline \hline \end{tabular} Note: + and \(-\) indicate positive and negative comments, respectively.  
\end{table} 
Table 7: The frequency of interview data for each classification. Further exploration of how different tones of voice impact user trust would be a valuable area for future research. * **Empathy and Emotion Expression:** This code evaluates how effectively the voice agent conveys empathy and expresses emotions during interactions. Agent C stood out the most in this area, receiving both positive and negative feedback. Some participants appreciated the emotional engagement of the agent, noting that it created a more human-like and empathetic experience. One participant commented that they felt the agent was \"continuously caring for the driver\" (first participant). However, for other users, especially those who preferred more functional or task-oriented interactions, the expression of empathy was perceived as unnecessary or even distracting, with one participant stating that the empathetic comments \"felt like a distraction\" (28 th participant). The variation in feedback suggests that while emotional tone can enhance the user experience for some, it may not be universally preferred. As such, a more detailed analysis of how different user groups respond to the emotional tone of voice agents could be an interesting direction for future research. * **Information Adequacy:** This refers to the sufficiency and relevance of the information provided by the agent. Agent C stood out in this regard, offering comprehensive and relevant information that participants found helpful in decision-making. One participant appreciated that Agent C \"explained why it was increasing speed\" (26 th participant), adding a layer of transparency to its actions. On the other hand, Agents A and B were noted for lacking adequate explanations for their decisions, with participants expressing a desire for more context in their responses (11 th participant). * **Autonomy:** This measures the agent's ability to act independently without needing constant user input. Agent C was the only agent noted for its autonomous capabilities, especially in adjusting speed based on road conditions. A participant commented that it was \"good that the agent adjusted the speed autonomously based on the characteristics of the road\" (third participant). This feature was well-received by participants who valued the agent's ability to take proactive actions for a safer and smoother driving experience. In contrast, Agents A and B did not exhibit significant autonomous behavior, and there were no notable comments regarding their performance in this area.