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Abstract
Conversational agents (CAs) have recently become ubiquitous. Smart speakers, mobile phone voice assistants, and in-car voice assistants have entered our lives. Studies have examined some factors influencing the user experience (UX) of CAs. However, there is little research on CAs’ reply design, especially when the information the users need is uncertain, which is regarded as an uncertain information scenario. The current research mainly focuses on measuring the UX with CAs’ replies in uncertain information scenarios. We designed two reply strategies of CAs, namely, a further inquiry strategy and a list-style reply strategy, to improve UX in this kind of uncertain information scenario. Two studies were designed and conducted based on the E-prime platform to verify the effect of the two reply strategies on UX. In Study 1, we verified the influence of inquiry strategy with different address terms for users on UX. In Study 2, we verified the influence of the list-style reply strategy (explicit and implicit) on UX. The gender differences in the evaluation of the two reply strategies were also examined. The results showed that, in the uncertain information scenarios, the reply strategy of further inquiries received a higher UX evaluation than direct replies in Study 1. Moreover, male participants preferred the “master” address to the “nin” address. However, male participants had no significant preference for further inquiries. For male participants, there was no significant difference in the UX evaluations for the “master” address and “nin” address. List-style replies with ranking information received the highest UX ratings, followed by list-style replies without ranking information and direct replies in Study 2, indicating that users preferred the explicit reply design of CAs. In addition, we found that male participants tended to have a higher rating of CA replies for all reply methods than female participants in our studies, suggesting that women have higher expectations for the reply design of CAs. In general, these results may contribute to the design of CA replies and highlight the importance of personalizing CA language.
Disclosure statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Qianli Ma
Qianli Ma is a master candidate at the School of Economics and Management, Beihang University, China. He has experience designing research for user-centered approaches to conversational agents. His research interests are: Human-Computer Interaction, Human-Centered Design, User Experience, Warning design in automated vehicles.
Yaping Zhang
Yaping Zhang is a PhD candidate at the School of Economics and Management, Beihang University, China. Her research interests include human-AI interaction and user experience, human factors and road safety. She is currently focusing on voice interaction issues in user experience and driver behavior issues in automated driving.
Wenti Xu
Wenti Xu is an associate professor at the School of Economics and Management, Beihang University, China. Her research interests include the structural changes in the retail industry, the effect of the Internet retailing to the overall retail structure, and customer service and customer satisfaction related to business success.
Ronggang Zhou
Ronggang Zhou is a professor at the School of Economics and Management, Beihang University, China. He received his PhD degree at the Institute of Psychology, Chinese Academy of Sciences, in 2005. His research interests include human factors, behavioural decision-making, human-AI interaction, road safety and health.