Paper Type
ERF
Abstract
Information overload threatens analytical clarity and decision quality in professional work. As generative Artificial Intelligence (AI) becomes embedded in knowledge-intensive tasks, how individuals interact with these systems matters as much as whether they use them. Drawing on Conservation of Resources (COR) theory and Cognitive Load Theory (CLT), we propose Deep Structure Usage (DSU) as a proactive coping strategy, defined as user-driven, task-oriented engagement with the generative mechanisms of AI systems, aimed at organizing and integrating complex information. We examine whether DSU reduces perceived information overload and whether this effect is stronger among more experienced professionals. To test this model, we propose a randomized online experiment in which legal professionals perform an information-intensive task. This research introduces DSU as a distinct usage construct in generative AI contexts and provides practical guidance for responsible AI integration in high-stakes professional environments.
Paper Number
1718
Recommended Citation
Aussu, Philippe and Assar, Saïd, "Does Deep Structure Usage Impact Information Overload?" (2026). AMCIS 2026 Proceedings. 18.
https://aisel.aisnet.org/amcis2026/sigadit/sigadit/18
Does Deep Structure Usage Impact Information Overload?
Information overload threatens analytical clarity and decision quality in professional work. As generative Artificial Intelligence (AI) becomes embedded in knowledge-intensive tasks, how individuals interact with these systems matters as much as whether they use them. Drawing on Conservation of Resources (COR) theory and Cognitive Load Theory (CLT), we propose Deep Structure Usage (DSU) as a proactive coping strategy, defined as user-driven, task-oriented engagement with the generative mechanisms of AI systems, aimed at organizing and integrating complex information. We examine whether DSU reduces perceived information overload and whether this effect is stronger among more experienced professionals. To test this model, we propose a randomized online experiment in which legal professionals perform an information-intensive task. This research introduces DSU as a distinct usage construct in generative AI contexts and provides practical guidance for responsible AI integration in high-stakes professional environments.
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