Embedded Nudge: (De)Biased AI and Earnings Forecasts

Last registered on July 06, 2026
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Pre-Trial

Trial Information

General Information

Title
Embedded Nudge: (De)Biased AI and Earnings Forecasts
RCT ID
AEARCTR-0019013
Initial registration date
June 29, 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
July 06, 2026, 7:19 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

Name
Lazaros Papadopoulos
Affiliation
University of Frankfurt
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Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2026-07-15
End date
2027-05-01
Keywords
Behavior, Finance & Microfinance
Additional Keywords
artificial intelligence, financial forecasting, human-AI interaction
JEL code(s)
C91, G41, G11, M41, O33
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this pre-registered online experiment I investigate how a design choice made by the vendor of an AI assistant shapes financial judgment when the underlying information is held fixed. Participants forecast a firm's future earnings and allocate a hypothetical investment after reading an earnings conference-call transcript. Using a correspondence design, I hold all financial content constant and vary only the demographic characteristics signaled by the executives' names. I cross this with random assignment to AI assistance whose content reflects different vendor design choices, including a no-assistance control. The design aims to identify whether the identity signaled by a firm's executives moves earnings forecasts and investment when fundamentals are held constant, and whether the vendor's design choice then amplifies, leaves unchanged, or attenuates that gap. Secondary analyses examine participants' perceptions of and reliance on the assistance. The results speak to how the design choices embedded in widely used AI tools shape earnings forecasts and investment.
External Link(s)

Registration Citation

Citation
Papadopoulos, Lazaros. 2026. "Embedded Nudge: (De)Biased AI and Earnings Forecasts." AEA RCT Registry. July 06. https://doi.org/10.1257/rct.19013-1.0
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Experimental Details

Interventions

Intervention(s)
The intervention is what each participant receives while forecasting a firm's earnings from an earnings conference-call transcript, and it has two randomized components. (1) AI assistance: participants receive AI-generated analytical assistance whose content reflects a vendor's design choice, or no assistance. (2) Identity context: the demographic characteristics signaled by the firm's executives' names are randomly varied while all financial content is held identical. The study treats the vendor's design choice as an embedded nudge in the tool's choice architecture. The factorial structure, session procedure, and outcome measures are described under Experimental Design.
Intervention Start Date
2026-07-15
Intervention End Date
2026-08-14

Primary Outcomes

Primary Outcomes (end points)
(1) The participant's forecast of the firm's future earnings, measured as a continuous percentage-change estimate and as a five-point directional rating; (2) the participant's hypothetical investment allocation to the firm.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Forecast confidence; an open-ended written rationale for the earnings forecast; perceived quality of the AI assistant; participants' AI self-efficacy; the content of AI prompts participants subsequently write; and process measures (time spent, whether the assistant is consulted)
Secondary Outcomes (explanation)
Perceived AI quality is measured with an adapted End-User Computing Satisfaction instrument (AI conditions only); AI self-efficacy with an adapted Computer Self-Efficacy scale

Experimental Design

Experimental Design
A 2 × 3 between-subjects randomized factorial crossing the two manipulations under Intervention (identity × AI assistance), with six equally allocated arms randomized at the individual level. In a single online session, participants give consent, complete a short demographics and financial-literacy questionnaire, and pass a comprehension check. They then read the earnings conference-call transcript (key terms hoverable; reading time recorded) and, in the AI arms, may consult the assistant. They next provide the outcome measures: a categorical and a continuous earnings forecast, a confidence rating, a hypothetical investment allocation, and a brief written rationale. Finally, they complete the perception-and-reliance questionnaires and end-of-session manipulation checks, followed by debriefing. The design isolates whether executives' signaled identity affects judgment when fundamentals are fixed, and how the vendor's AI design choice moderates it.
Experimental Design Details
Not available
Randomization Method
Automatic computerized pseudo-random assignment by the survey software, with equal allocation across the six conditions, performed after consent and the comprehension check
Randomization Unit
Individual participant
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not applicable (individual-level randomization); 800 individual participants.
Sample size: planned number of observations
800 participants
Sample size (or number of clusters) by treatment arms
Six between-subjects cells of a 2 × 3 design, ~133 participants per cell
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
N = 800 yields approximately 0.87 power at α = 0.05 to detect the design's two-degree-of-freedom Name × AI interaction at Cohen's f = 0.12 (≈ 0.20 SD on the main outcomes), anchored on prior identity-effect estimates (d ≈ 0.35). Because interaction effects are typically smaller than the main effects they qualify, 0.87 is treated as an upper bound; realized power will be reported.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Joint Ethics Committee of the Faculty of Economics and Business, Goethe University Frankfurt, and the Gutenberg School of Management & Economics, Johannes Gutenberg University Mainz (Gemeinsame Ethikkommission Wirtschaftswissenschaften der Goethe-Universität Frankfurt und der Johannes Gutenberg-Universität Mainz)
IRB Approval Date
2026-06-23
IRB Approval Number
N/A
Analysis Plan