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Publikation

On the Impact of Self-efficacy on Assessment of User Experience in Customer Service Chatbot Conversations

Yuexin Cao; Vicente Ivan Sanchez Carmona; Xiaoyi Liu; Changjian Hu; Neslihan Iskender; André Beyer; Sebastian Möller; Tim Polzehl
In: IWSDS 2021. International Workshop on Spoken Dialogue Systems Technology (IWSDS-2021), Springer, 2021.

Zusammenfassung

In this paper, we analyze influencing factors for the assessment of user experience (UX) from a chatbot operating in the domain of technical customer support. To find out which UX factors can be assessed reliably in a crowdsourcing setup, we conduct a crowd-based UX assessment study through a set of scenario-based tasks and analyze the UX assessments in the light of influencing user characteristics, i.e., self-reported self-efficacy of individual users. By segmenting users according to self-efficacy, we find significant differences in UX assessment and expectations of users with respect to a series of UX constituents like acceptability, task efficiency, system error, ease of use, naturalness, personality and promoter score. Our results strongly suggest a potential application for essential personalization and user adaptation strategies utilizing self-efficacy for the personalization of technical customer support chatbots. Therefore, we recommend considering its influence when designing chatbot adaptation strategies for maximized customer experience.