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Abstract
This study aims to explore these issues by proposing multi-dimensional constructs for assessing generative AI output quality and examining the relationships between these constructs and dependent variables such as user satisfaction and use intention. To achieve these goals, we start conduct our investigation with a comprehensive literature review, identifying thirty-four constructs related to information quality from ninety-seven academic journals, conference papers, web documents, and industry guidelines. Subsequently, we will conduct a focus group study with information quality experts to pinpoint generative AI-specific constructs. Finally, by employing a quantitative cognitive mapping method, we will identify the nomological networks between these constructs and explore their associations with user satisfaction and usage intentions, thereby contributing to a better understanding of information quality in generative AI.
Paper Number
tpp1169
Recommended Citation
Lee, Gabe; Farmer, Allison; Megahed, Fadel; and Xu, Ruiyun, "Investigating the Causal Relationships Among Information Quality Constructs in Generative AI" (2024). AMCIS 2024 TREOs. 80.
https://aisel.aisnet.org/treos_amcis2024/80
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