## 4 Results
### Qualitative Data Analysis Results  
A total of 216 comments were collected and categorized into eight distinct codes (Table 7): Conciseness and Clarity, Naturalness of Conversation Flow, Familiarity and Intimacy, Understandability and Accuracy, Trust, Empathy and Emotion Expression, Information Adequacy, and Autonomy. Below is a summary of each code and the key findings. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Measures** & \multicolumn{2}{c}{**Agent A**} & \multicolumn{2}{c}{**Agent B**} & \multicolumn{2}{c}{**Agent C**} \\  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Intimacy & 2.467 & 1.049 & 3.111 & 1.557 & 4.522 & 1.391 \\ Trust & 4.267 & 1.064 & 4.238 & 0.941 & 4.467 & 1.104 \\ Intention to use & 4.558 & 1.090 & 3.883 & 1.386 & 4.425 & 1.433 \\ Perceived safety & 4.640 & 1.212 & 4.400 & 1.164 & 4.813 & 1.049 \\ Perceived anthropomorphism & 2.767 & 1.371 & 3.411 & 1.518 & 5.011 & 1.338 \\ \hline \end{tabular}
\end{table} 
Table 6: Descriptive statistics of the measures of each agent. Figure 3: Multiple comparisons between agents for each measure. Note: Values are based on a Likert 7-point scale, and error bars indicate the 95% confidence interval; * indicates a significant difference between agents. * **Conciseness and Clarity**: This refers to the ability of the voice agent to deliver information in a clear and concise manner. Agents A and B received positive feedback for being brief and to the point, while Agent C had more mixed reviews. For example, one participant praised Agent A for its \"clear and concise answers\" (7 th participant), while another noted that Agent C was \"kind but not concise\" (18 th participant). * **Naturalness of Conversation Flow**: This evaluates the smoothness and fluidity of the conversation. Agent A and C were generally well-received, while Agent B's formal tone led to mixed feedback. A participant noted that Agent B's voice felt \"unnatural\" despite being human (20 th participant), while Agent C was described as more natural and conversational (30 th participant). * **Familiarity and Intimacy:** This code assesses the degree to which users feel a sense of connection or emotional closeness with the voice agent. Agent C was most successful in creating a friendly and intimate atmosphere, with multiple participants praising its warm and personable tone. One participant noted that the agent's tone was \"kind and friendly\" (18 th participant), contributing to a more comfortable and engaging interaction. In contrast, while Agent B used a human voice, it was often described as impersonal and rigid, which detracted from the sense of familiarity. For example, one participant mentioned that Agent B felt \"distant and disconnected,\" despite its humanized voice (fifth participant). This suggests that simply using a human voice does not automatically enhance the perception of intimacy if the delivery feels mechanical or formal. * **Understanding and Accuracy:** This measures how well the agent understands and accurately executes user commands. Agents A and B generally performed well in this category, with participants highlighting their ability to immediately comprehend and act on instructions. For instance, one participant remarked regarding Agent B, \"there was no inconvenience because the agent immediately understood and accepted my command\" (11 th participant). In contrast, Agent C received minimal feedback in this area, suggesting it performed adequately without standing out in terms of understandability and accuracy. * **Trust:** Trust reflects users' confidence in the reliability and consistency of the agent's performance. Interestingly, Agent A, which used a machine voice, was perceived as more trustworthy than Agent B, which used a human voice. Some participants mentioned that the machine voice seemed more reliable because it focused solely on providing accurate and consistent information, without attempting to simulate emotional tones.