Paper Number
ICIS2025-1218
Paper Type
Complete
Abstract
This study investigates how framing specific artificial intelligence(AI)-characteristics affects user trust and reliance in AI-based information systems for consumer decision-making. Drawing on framing theory and trust literature, we conducted a randomized online experiment (N = 592) in the context of dynamic electricity tariffs. We manipulated positive versus negative framings of AI autonomy and learning ability. Results reveal that framing influences trust and reliance independently: positively framed autonomy reduces reliance, while positively framed learning ability increases trust. Trust fully mediates the relationship between learning framing and reliance but not for autonomy. These findings suggest that autonomy and learning evoke different cognitive and emotional user responses. Moreover, our results show that framing shapes how AI-characteristics are perceived, even when factual information remains constant. We contribute to research on human-AI interaction by highlighting the role of attributional framing and trust in shaping users' behavioral responses towards AI-based information systems.
Recommended Citation
Strunk, Jobin Alexander; Nissen, Anika; and Smolnik, Stefan, "The Effect of Framing AI Characteristics on Trust and Reliance" (2025). ICIS 2025 Proceedings. 7.
https://aisel.aisnet.org/icis2025/user_behav/user_behav/7
The Effect of Framing AI Characteristics on Trust and Reliance
This study investigates how framing specific artificial intelligence(AI)-characteristics affects user trust and reliance in AI-based information systems for consumer decision-making. Drawing on framing theory and trust literature, we conducted a randomized online experiment (N = 592) in the context of dynamic electricity tariffs. We manipulated positive versus negative framings of AI autonomy and learning ability. Results reveal that framing influences trust and reliance independently: positively framed autonomy reduces reliance, while positively framed learning ability increases trust. Trust fully mediates the relationship between learning framing and reliance but not for autonomy. These findings suggest that autonomy and learning evoke different cognitive and emotional user responses. Moreover, our results show that framing shapes how AI-characteristics are perceived, even when factual information remains constant. We contribute to research on human-AI interaction by highlighting the role of attributional framing and trust in shaping users' behavioral responses towards AI-based information systems.
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