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Towards Personalization by Information Savviness to Improve User Experience in Customer Service Chatbot Conversations

Tim Polzehl; Yuexin Cao; Vicente Ivan Sanchez Carmona; Xiaoyi Liu; Changjian Hu; Neslihan Iskender; André Beyer; Sebastian Möller
In: HUCAPP 2022 - 6th International Conference on Human Computer Interaction Theory and Applications. International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP-2021), Springer, 2021.


Information savviness describes the ability to find, evaluate and reflect information online. Customers with high information savviness are more likely to look up product information online, read customer reviews before making a purchase decision. By assessing Information Savviness from chatbot interactions in a technical customer service domain, we analyze its impact on user experience (UX), expectations and preferences of the users in order to determine assessible personalization targets that acts dedicatedly on UX. To find out which UX factors can be assessed reliably, we conduct an assessment study through a set of scenario-based tasks using a crowd-sourcing set-up and analyze UX factors. We reveal significant differences in users' UX expectations with respect to a series of UX factors 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 information savviness for the personalization of technical customer support chatbots.