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Paper Type
ERF
Abstract
This research project is focused on identifying and understanding the factors that contribute to user frustration during interactions with conversational systems, utilizing the computer user frustration model. It utilizes focus groups as a qualitative method to gather insights that could improve the design and user interaction of CSs. The anticipated contribution of the study is to provide significant theoretical and practical insights, aimed at enhancing our understanding of the underlying causes of user frustration and aiding in the development of more engaging and user-centered conversational systems.
Paper Number
1255
Recommended Citation
Essaied, Khadija; Mellouli, Sehl; and Khoury, Richard, "Predicting user frustration during interaction with conversational systems" (2024). AMCIS 2024 Proceedings. 13.
https://aisel.aisnet.org/amcis2024/ai_aa/ai_aa/13
Predicting user frustration during interaction with conversational systems
This research project is focused on identifying and understanding the factors that contribute to user frustration during interactions with conversational systems, utilizing the computer user frustration model. It utilizes focus groups as a qualitative method to gather insights that could improve the design and user interaction of CSs. The anticipated contribution of the study is to provide significant theoretical and practical insights, aimed at enhancing our understanding of the underlying causes of user frustration and aiding in the development of more engaging and user-centered conversational systems.
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