From Human-Human to Human-AI Delegation: A Leadership Theory Driven Investigation of Delegation
Journal
Annual Meeting of the Academy of Management (AOM)
Type
conference paper
Date Issued
2025-07-25
Author(s)
Research Team
IWI6
Abstract
The emergence of generative AI (GenAI) has transformed work by enabling humans to delegate tasks like writing and coding to GenAI agents such as ChatGPT. While existing studies highlight AI capability awareness and perceived competence as drivers of delegation, they overlook parallels between human-AI and human-human delegation. Our ongoing research proposes that human-AI delegation can be understood through a leadership lens, with leadership experience and traits as key predictors. Hence, we investigate whether individuals with leadership experience demonstrate higher delegation levels than those without such experience. In an initial online experiment (n=48), participants were grouped by leadership experience and AI transparency to decide whether to delegate or personally perform image classification tasks. Preliminary findings indicate that under a low-transparency condition, leadership experience results in higher delegation rates. However, leadership alone does not significantly predict delegation. Transparency in GenAI consistently leads to higher delegation, while greater domain knowledge corresponds to lower delegation rates. Our ongoing research seeks to deepen understanding of delegation behavior and its predictors in the age of GenAI.
Language
English
Keywords
Generative AI
Delegation
Leadership
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher place
Copenhagen, Denmark
Volume
Poster
Pages
11
Event Title
Annual Meeting of the Academy of Management (AOM)
Event Location
Copenhagen, Denmark
Event Date
25.07.2025
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
JML_1016.pdf
Size
358.17 KB
Format
Adobe PDF
Checksum (MD5)
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