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Publication in MIS Quarterly: Study on trust-building of domain experts in AI-based counterparts

A stylized infographic on a white background illustrates the concept of artificial intelligence. In the exact center of the image are the two large, black letters "AI". A triangular arrangement of three round medallions is placed around this center. The top, central medallion shows a futuristic, dark robot or android, whose head and torso are covered with a luminous blue, neon-like circuit pattern and whose eyes glow bright blue. The two lower medallions show stylized people: on the left, a man with dark hair in a suit with a blue tie, and on the right, a woman with long, dark hair and a red top. All three circles are interconnected by thick, dynamically flowing bands that look like organic cables or data streams in black, orange, red, and blue, symbolizing the connection and interaction between AI, the man, and the woman. © AI generated with Google Gemini
Illustration of the triadic relationship between humans and AI
Prof. Manuel Wiesche published a study in the Management Information Systems Quarterly (MIS Quarterly) Journal together with three colleagues.
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André Hanelt, Manuel Wiesche, Alexander Benlian, Oliver Hinz (2025) “Opening the Network of Trust: How Domain Experts in Triadic Relationships Build Trust in AI-Based Counterparts”, https://doi.org/10.25300/MISQ/2025/18041

The study examines the trust-building process of domain experts toward AI-based counterparts in triadic relationships involving clients. Drawing on longitudinal data from EuroBank's robo-advisor EVA implementation, we investigate how financial advisors navigate trust tensions of opacity vs. performance and replacement vs. complementarity. Through relational interpretation (framing AI opportunities and threats within their relational networks) and relational adaptation (modifying these networks in response) domain experts progress through three distinct trust-building stages. Initially, they avoid vulnerabilities by emphasizing AI imperfections and preventing client interactions. Subsequently, they safeguard vulnerabilities through human support mechanisms and controlled AI integration. Finally, they accept vulnerabilities by self-experimenting with the robo-advisor and hybridizing client relationships. This triadic perspective reveals how social context influences precede and shape trust development, moving beyond technology-centric approaches to encompass relational dimensions of trusting beliefs and behaviors in AI adoption.

The Management Information Systems Quarterly (MIS Quarterly) Journal is considered one of the most influential academic journals in the Information Systems field, setting standards for rigorous research methodology and theoretical contributions.

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