Paper Number
ECIS2026-1804
Paper Type
CRP
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.
Recommended Citation
Tas, Eylem and Bittner, Eva, "Designing Trustworthy Genai: Citation Mechanisms For Calibrating Employee Trust In Organizational Contexts" (2026). ECIS 2026 Proceedings. 7.
https://aisel.aisnet.org/ecis2026/resp_AI/resp_AI/7
Designing Trustworthy Genai: Citation Mechanisms For Calibrating Employee Trust In Organizational Contexts
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.