Value alignment in the public sector: from roles to managers to AI and citizens

Last registered on June 03, 2026

Pre-Trial

Trial Information

General Information

Title
Value alignment in the public sector: from roles to managers to AI and citizens
RCT ID
AEARCTR-0018794
Initial registration date
May 31, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
June 03, 2026, 9:54 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Affiliation

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

Status
On going
Start date
2026-05-21
End date
2026-06-22
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines how organizational management styles and artificial intelligence (AI) framing influence the decision-making behavior of street-level bureaucrats in citizen-facing contexts. Using an online survey experiment, participants will be randomly assigned to scenarios that manipulate management style (e.g., bureaucratic, innovative, and participatory) and AI system value framing. Participants will then respond to hypothetical situations and be asked to answer post-vignette survey questions, allowing researchers to assess how these factors shape their behavioral orientations and reliance on AI-assisted recommendations in a decision-making process.
This research project involves three research questions: 1) how does organizational management style interacted with role perceptions shape the behavioral orientation of street-level bureaucrats in citizen-facing situations? 2) how does the interaction between management style and AI value framing shape which behavioral logic street-level bureaucrats default to in citizen-facing situations? 3) does value congruence between management style and AI framing increase bureaucrats' willingness to rely on the AI system? Data collected will consist of survey responses to experimental vignettes and follow-up questions measuring attitudes, behavioral intentions, and perceived appropriateness of AI use.
External Link(s)

Registration Citation

Citation
Choi, Iseul, Suyeon Jo and Sabina Schnell. 2026. "Value alignment in the public sector: from roles to managers to AI and citizens." AEA RCT Registry. June 03. https://doi.org/10.1257/rct.18794-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-05-21
Intervention End Date
2026-06-22

Primary Outcomes

Primary Outcomes (end points)
Bureaucrats' behavior; Bureaucrats' perceptions to AI; Bureaucrats' willingness to use AI
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In the online survey experiment, we use a 3 x 4 factorial design where participants will be randomly assigned to scenarios that manipulate management style (3 conditions) and AI system value framing (4 conditions). Participants will then respond to hypothetical situations and be asked to answer post-vignette survey questions, allowing researchers to assess how these factors shape their behavioral orientations and reliance on AI-assisted recommendations in a decision-making process.
Experimental Design Details
Not available
Randomization Method
Use randomizer options in Qualtrics
Randomization Unit
There are 12 groups where each participant is randomly assigned to.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1800 individuals
Sample size: planned number of observations
1800 individuals
Sample size (or number of clusters) by treatment arms
1800 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB North Carolina State University
IRB Approval Date
2026-05-13
IRB Approval Number
29221