Navigating autonomy and control in human-AI delegation: User responses to technology- versus user-invoked task allocation
Open accessAbstract
Users can increasingly delegate to information systems (IS) – that is transferring rights and responsibilities regarding certain tasks – even to the degree that IS can act autonomously (i.e., without the intervention or supervision of users). What is more, IS increasingly offer to assume the rights and responsibilities for a task not only in response to user prompts (i.e., user-invoked delegation) but also without user prompts (i.e., IS-invoked delegation). Yet, little is known about whether, how, and why users agree to delegation when they are asked by the IS in contrast to when they self-initiate the delegation. Drawing on self-affirmation theory, we investigate user acceptance of IS- versus user-invoked delegation in two complementary online experiments in software development. Our core findings reveal that IS-invoked (vs. user-invoked) delegation increases users' perceived self-threat and thus decreases their willingness to accept delegation. This threatening effect is larger the less (vs. more) the user perceives control after the potential delegation. Taken together, we uncover defensive user responses to IS-invoked delegation. Furthermore, we shed light on the underlying and moderating mechanisms representing the reasons and contextual features that explain and mitigate these defensive measures. These findings have significant implications for IS designers seeking to improve user-IS collaboration and outcomes by employing IS-invoked delegation.
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Contextual factors such as task complexity and initiation mode of delegation shape whether consumers accept or resist autonomous AI decision-making.
“Research also suggests that contextual factors, such as task complexity and initiation mode of delegation, shape whether consumers accept or resist autonomous AI decision-making (Adam et al. 2024;Spitzer et al. 2025).”
From Machine marketing: rethinking the customer in the age of generative AI (2026) · cites this paper as Adam et al. 2024
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References 80 · Cited by 62
- Authors
- Adam, M., Diebel, C., Goutier, M., & Benlian, A.
- Affiliations
- University of Göttingen (DE)
- Technische Universität Darmstadt (DE)
- Corresponding author
- Adam, Martin
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2024-05
- Volume
- 180
- Pages
- 114193
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W4391999255
- Cited by
- 62
- References
- 80
- Citations by year
- 2026: 46 · 2025: 15 · 2024: 1
- FWCI
- 29.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Decision Support Systems (publishedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, other-oa)
- Funders
- German Research Foundation (52002093, 461985572, 471168026)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Keywords
- user acceptance, self-affirmation theory, perceived control, user autonomy, task allocation, software development
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