Description
Inspired by the progress in the field of artificial intelligence and by the assimilation of AI capabilities into a variety of IT applications, including personal assistant apps such as Siri, this study examines how user experiences with such assistant apps differs from their interactions with more traditional IT artifacts. Using text analysis of user reviews of 10 assistant and 15 non-assistant apps, we identify 50 topics that reflect user experiences and evaluations. Most identified topics map well into existing IS constructs, such as ease of use, system quality and affordances, suggesting that current IS theory adequately reflects user experiences with IT, and is relevant to the analysis of AI-enabled applications. The results also highlight differences in how users experience their interactions with assistant and non-assistant apps, pointing towards potential shifts in how we experience and theorize user-IT interactions in the AI-enabled world.
Recommended Citation
Nguyen, Quynh N. and Sidorova, Anna, "AI capabilities and user experiences: a comparative study of user reviews for assistant and non-assistant mobile apps" (2017). AMCIS 2017 Proceedings. 5.
https://aisel.aisnet.org/amcis2017/SemanticsIS/Presentations/5
AI capabilities and user experiences: a comparative study of user reviews for assistant and non-assistant mobile apps
Inspired by the progress in the field of artificial intelligence and by the assimilation of AI capabilities into a variety of IT applications, including personal assistant apps such as Siri, this study examines how user experiences with such assistant apps differs from their interactions with more traditional IT artifacts. Using text analysis of user reviews of 10 assistant and 15 non-assistant apps, we identify 50 topics that reflect user experiences and evaluations. Most identified topics map well into existing IS constructs, such as ease of use, system quality and affordances, suggesting that current IS theory adequately reflects user experiences with IT, and is relevant to the analysis of AI-enabled applications. The results also highlight differences in how users experience their interactions with assistant and non-assistant apps, pointing towards potential shifts in how we experience and theorize user-IT interactions in the AI-enabled world.