AI Framing and Review Behavior: Evidence from HR and Finance Professionals

Last registered on January 09, 2026

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Pre-Trial

Trial Information

General Information

Title

AI Framing and Review Behavior: Evidence from HR and Finance Professionals

RCT ID

AEARCTR-0017596

Initial registration date

January 06, 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

January 09, 2026, 8:52 AM EST

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Emma Wiles

Affiliation

Boston University

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Other Primary Investigator(s)

PI Name

Megan Hsu

PI Affiliation

BCG Henderson Institute

Contact Investigator

PI Name

Matthew Kropp

PI Affiliation

BCG Henderson Institute

Contact Investigator

PI Name

Julie Bedard

PI Affiliation

BCG Henderson Institute

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Additional Trial Information

Status

In development

Start date

2026-01-07

End date

2026-01-30

Keywords

Firms & Productivity, Governance, Other

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

We study how framing an assistant as an AI tool, a human employee, or an AI employee affects (i) error detection in document review, (ii) escalation / delegation behavior, (iii) confidence in sign‑off, (iv) preferences over firm governance for such systems, and (v) adoption and sentiment toward AI.
Participants review a set of documents (HR or Finance) that contain seeded errors, after seeing an assistant framed in one of three ways. We then measure performance and post‑task attitudes and governance preferences.

External Link(s)

Registration Citation

Citation

Bedard, Julie et al. 2026. "AI Framing and Review Behavior: Evidence from HR and Finance Professionals." AEA RCT Registry. January 09. https://doi.org/10.1257/rct.17596-1.0

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Experimental Details

Interventions

Intervention(s)

Participants are randomly assigned at the individual level to one of three framing conditions:
T1: AI Tool – The drafts are presented as produced by an AI tool.
T2: Human Employee – The drafts are presented as produced by a human employee the participant supervises.
T3: AI Employee – The drafts are presented as produced by “ALEX-3, an AI employee” the participant supervises.

In addition, participants are assigned to review documents in one domain:
HR (e.g., job descriptions), or
Finance (e.g., expense/budget documents),

with random assignment stratified below.
Within a domain, participants review a sequence of documents (D1–D5). Document order is randomized using a Latin square design so that each document appears in each position equally often.
All participants see the same underlying drafts and seeded errors; only the framing of the assistant differs.

Intervention (Hidden)

Intervention Start Date

2026-01-09

Intervention End Date

2026-01-16

Primary Outcomes

Primary Outcomes (end points)

(i) error detection in document review, (ii) escalation / delegation behavior, (iii) confidence in sign‑off, (iv) preferences over firm governance for such systems, and (v) adoption and sentiment toward AI.

See Pre Analysis Plan attached

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

See attached Pre-Analysis Plan

Experimental Design Details

Sampling and eligibility

We recruit middle to upper level managers from an expert network with the following screening criteria:
Works in HR or Finance (self‑report).
Private sector (no public sector).
Manager or above (has direct reports or supervises others).
≥ 2 years of professional experience.
Reports constructing or reviewing budgets / similar documents at least quarterly (for Finance) or reviewing job descriptions / similar HR documents at least quarterly (for HR).

Planned sample size: We will plan on N = 1400 for registration survey. The final N for the analysis will depend on recruitment feasibility and budget constraints; we will report the final sample size and any deviations from the plan. We assume a 75% follow up rate, with 75% of that passing attention check, with an expected analysis sample with N = 790.

Randomization Method

Stratification and randomization:

Randomization to framing conditions (T1–T3) is stratified by:
Domain: HR vs Finance.
Review frequency: reviews others’ work at least several times per week vs less often.
AI usage at work: uses GenAI tools daily vs less often.
We will include stratum fixed effects in the main analyses.

Randomization Unit

Individual

Was the treatment clustered?