Exploring the Impacts of Robot Voice, Appearance, and Ambient Sound on User Experience in Museums

Few studies have examined robot voice, appearance, and ambient sound within a single framework to understand the user experience. To address this gap, this study explores how these three factors influence the user experience, specifically focusing on the appearance-voice matching effect, user likability, and user satisfaction when robots serve as museum docents, thereby offering design-oriented strategic insights for robot deployment in museums. Audio recordings of museum docents were collected and clustered into four categories. Accordingly, four distinct voice models were synthesized, and subjective user perceptions of these models were evaluated. Subsequently, three different agents, including an iPad, a self-designed robot prototype and a Yanshee robot, were employed as experimental platforms. These agents were each equipped with different voice models to conduct the experiments. The experimental data were analyzed by repeated measures ANOVA and mixed-model analysis. The results indicate that robot appearance significantly affects the appearance-voice matching effect, while its voice moderates this relationship in a museum scenario. Robot voice primarily drives user likability and satisfaction. In contrast, robot appearance shapes the two dimensions differently. Appropriate ambient sound significantly enhances the perceived appearance-voice congruence, and significantly affects naturalness and liveliness within user likability, as well as the fun of the experience and pleasantness of use within user satisfaction. The congruence can positively predict user likability and satisfaction, but this predictive effect has boundaries. These findings can provide references from the perspective of design strategies for human–robot interaction in museums.