# Effects of Voice and Lighting Color on the Social Perception of Home Healthcare Robots
## 3 Results  
### Manipulation Check  
Before the experiment, participants (N = 12) were asked to evaluate voices and lighting colors. One-way analysis of variance (ANOVA) was used in the manipulation checks regarding the three voice types and three colors (Table 4). The participants perceived the female voice (M = 4.92, SD = 0.29) to be significantly more feminine than the other two voices [F(8, 107) = 172.89; \(p<0.00\)], they perceived the male voice (M = 5.00, SD = 0.00) to be significantly more masculine than the other two voices, and they perceived the child voice (M = 4.83, SD = 0.39) to be more childish than the other two voices. The colors were also successfully controlled for [F(8, 107) = 38.89; \(p<0.00\)]. The warm lighting (M = 4.67, SD = 0.49) was perceived to be significantly warmer than the cool and white lighting, whereas the cool lighting (M = 4.83, SD = 0.39) was perceived to be significantly cooler than the warm and white lighting. The white lighting (M = 3.83, SD = 1.03) was perceived to be white than the other lighting colors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **female** & **male** & **child** & **F** & \(p\) \\ \cline{2-6}
**Voice Type** & 4.92 & 5.00 & 4.83 & 172.89 ** & 0.00 \\  & 0.29 & 0.00 & 0.39 & 172.89 ** & 0.00 \\ \hline  & **warm** & **cool** & **white** & **F** & \(p\) \\ \cline{2-6}
**Lighting Color** & 4.67 & 4.83 & 3.83 & 38.89 ** & 0.00 \\  & 0.49 & 0.39 & 1.03 & 38.89 ** & 0.00 \\ \hline \hline \end{tabular} Note: *** \(p<0.001\)**.  
\end{table} 
Table 4: Results of the manipulation check. Effects of Robot Voice Type and Lighting Color on the Participants' Social Perception and Overall Acceptance of the Healthcare Robot  
Two-factor multivariate ANOVA (MANOVA) was conducted to analyze the effects of robot voice type and lighting color on the participants' feelings of warmth, competence, and discomfort during HRI and on their overall acceptance of the robot (Table 5). The reliabilities of the three subscales of the RoSAS were also analyzed. The [PERSON]'s \(\alpha\) values for the warmth, competence, and discomfort subscales were 0.800, 0.819, and 0.874, respectively. The results of main effects analysis indicated that robot voice type had different effects on warmth [F(2, 558) = 20.43, \(p<0.001\)] and competence [F(2, 558) = 31.68, \(p<0.001\)] scores. In particular, the child voice was considered the warmest (M = 3.30, SD = 0.71), followed by the adult female voice (M = 3.05, SD = 0.72) and adult male voice (M = 2.85, SD = 0.62). The adult male voice (M = 3.53, SD = 0.62) was considered the most competent, followed by the adult female voice (M = 3.40, SD = 0.63) and child voice (M = 3.31, SD = 0.70). Moreover, the adult male voice (M = 2.26, SD = 0.95) and adult female voice (M = 2.13, SD = 0.84) caused considerably stronger feelings of discomfort than did the child voice (M = 1.97, SD = 0.72). The lighting color of the robot was discovered to have main effects on the participants' feelings of warmth [F(2, 558) = 3.68, \(p<0.05\)], competence [F(2, 558) = 11.42, \(p<0.001\)], and discomfort [F(2, 558) = 23.51, \(p<0.001\)], as well as on their overall acceptance of the robot [F(2, 558) = 23.51, \(p<0.05\)]. Warm lighting (M = 3.15, SD = 0.66) and white lighting (M = 3.09, SD = 0.77) were rated to be considerably warmer than cool lighting (M = 2.96, SD = 0.68). Moreover, white lighting (M = 3.42, SD = 0.68) was perceived to be associated with higher competence than cool lighting (M = 3.39, SD = 0.73) and warm lighting (M = 3.13, SD = 0.66). Cool lighting (M = 2.38, SD = 0.94) caused the most discomfort, followed by warm lighting (M = 2.17, SD = 0.81) and white lighting (M = 1.81, SD = 0.67). Furthermore, white lighting was associated with significantly higher user acceptance of the robot compared to warm lighting and cool lighting. In the discomfort evaluation, the interaction between robot voice type and lighting color had crucial effects on various parameters [F(4, 558) = 3.22, \(p<0.05\); Figure 4].