Abstract

Perceived risk is a foundational construct in information systems (IS) adoption research and is consistently associated with lower technology adoption intention (Featherman & Pavlou, 2003). However, prior IS research primarily conceptualizes risk in contexts where negative consequences are borne personally by the adopter, such as financial loss, privacy exposure, or individual performance failure. This assumption may not hold in regulated professional environments where AI-informed decisions carry institutional accountability consequences. This proposed study argues that financial services professionals face a different consequence structure when adopting AI-enabled systems. In domains such as credit scoring, fraud detection, anti-money laundering screening, and compliance monitoring, professionals remain accountable for AI-informed decisions through regulatory oversight, auditability expectations, and potential professional sanctions (Gomber et al., 2018). Drawing on perceived risk theory and felt accountability theory, the study proposes that accountability structures amplify sensitivity to negative outcomes, thereby strengthening the negative relationship between perceived risk and AI adoption intention (Tetlock, 1985). Human-AI collaboration research further suggests that professionals may exercise heightened caution when delegating judgment to algorithmic systems in consequential decision settings (Fügener et al., 2022). The study will survey financial services professionals who use AI-supported decision systems and will test the proposed model using PLS-SEM. The model examines perceived risk as a predictor of AI adoption intention and considers regulatory exposure and professional role orientation as boundary conditions. This research contributes to IS literature by positioning professional accountability as a boundary condition of perceived risk theory and by extending AI adoption research into regulated professional contexts where institutional consequences shape human-AI interaction decisions.

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