Abstract
The use of agentic, machine-generated information in organisations is rapidly growing, yet little is known about how reliance on this information affects our proficiency at making good choices. To address this gap, we propose a theory of heuristic augmentation for digital representations. Our aim is to advance a general theoretical framework of how human experience, summarised in a frame-of-reference, can support heuristic decision making. To develop and validate the framework, we employed a case study to compare two cases of how experts interpret information about decisions presented by a classic decision support system and a generative artificial intelligence chat bot. The findings validate the proposed theoretical framework in both cases with implications for human responses to automated decision making. This study contributes to an understanding of the construction of human responses to situations when out-of-the-loop. For practitioners the study shows how to improve the decisions they share with machines.
Recommended Citation
Bremhorst, Mark; Jordan, Ree; Intezari, Ali; and Yates, Miriam, "Heuristics and Out-of-the-Loop Decision Making: A
Comparative Study of Experts" (2025). ACIS 2025 Proceedings. 118.
https://aisel.aisnet.org/acis2025/118