Designing Trustworthy Genai: Citation Mechanisms For Calibrating Employee Trust In Organizational Contexts

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

Large language models hold transformative potential for knowledge-intensive work, yet their tendency to generate inaccurate or misattribute sources undermines employee trust and limits responsible adoption. This study addresses the challenge by developing and evaluating design principles for citation mechanisms in Generative Artificial Intelligence (GenAI) systems, with a focus on professional organizational contexts. Drawing on a Design Science Research (DSR) approach, we integrate insights from a structured literature review with 21 semi-structured interviews at a tier-one German bank piloting an enterprise GenAI assistant. The study identifies nine recurring issues and twelve user requirements, which are synthesized into sixteen prescriptive design principles clustered across four thematic areas: (1) content accuracy and verifiability, (2) source credibility and institutional endorsement, (3) transparency and explainability, and (4) personalization and cognitive alignment. Expert evaluation confirms their sociotechnical utility while highlighting context-dependent applicability. This work contributes theoretically by extending IS trust research to the sociotechnical design of GenAI citation.

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