Virtual agents increasingly rely on cloud-based services, which makes them vulnerable to unpredictable network latency and service response times. In this work, we evaluate the use of conversational fillers to mitigate the impact of delay in system response time on users’ perception of a virtual agent. These fillers are uttered by the agent to keep the user engaged until the response is ready. We present the findings of a study run on the Mechanical Turk platform with 360 participants who interacted with a virtual agent. We tested two types of conversational fillers. The first type were generic utterances that simply asked the user to hold. The second adopted contextualized fillers that assume some semantic knowledge of the input and contain some of its elements. To test the generalizability of the different fillers, we ran two task-based experiments. The first task was to get a recipe and the second was to find a restaurant. Contextualized fillers positively affected participants’ rating of the agent’s response time but did not impact the agent’s likeability.

We present a study involving 160 participants investigating the effect of associating professional status and ethnicity with an agent by manipulating its appearance, language, and level of education. We aim to discern perceptions of status and ethnicity with respect to participants' cultural background by inviting participants from two different cultural groups (Middle Eastern and Western) to take part in our study. Results revealed that participants' cultural background had a strong impact on their ratings of the agent and its message. However, neither the agent's portrayed status nor its ethnicity appeared to have an effect on participants' perceptions of the agent. We further found that participants from both cultural backgrounds holding a negative attitude towards robots in general tend to perceive the presented message by the agent more negatively. Middle Eastern participants had a more positive attitude towards robotic agents than Western participants, which might have been the main influence on their perception of the message presented by the agent. In addition, participants who identified the agent as a member of their own cultural group perceived the presented message more positively than those from the other cultural group. We discuss our results with an intention to inform design implications for agents in a cross-cultural context.

As social robots begin to enter our lives as providers of information, assistance, companionship, and motivation, it becomes increasingly important that these robots are capable of interacting effectively with human users across different cultural settings worldwide. A key capability in establishing acceptance and usability is the way in which robots structure their speech to build credibility and express information in a meaningful and persuasive way. Previous work has established that robots can use speech to improve credibility in two ways: expressing practical knowledge and using rhetorical linguistic cues. In this paper, we present two studies that build on prior work to explore the effects of language and cultural context on the credibility of robot speech. In the first study (n=96), we compared the relative effectiveness of knowledge and rhetoric on the credibility of robot speech between Arabic-speaking robots in Lebanon and English-speaking robots in the USA, finding the rhetorical linguistic cues to be more important in Arabic than in English. In the second study (n=32), we compared the effectiveness of credible robot speech between robots speaking either Modern Standard Arabic or the local Arabic dialect, finding the expression of both practical knowledge and rhetorical ability to be most important when using the local dialect. These results reveal nuanced cultural differences in perceptions of robots as credible agents and have important implications for the design of human-robot interactions across Arabic and Western cultures.