The Effect of Beliefs on Generative-AI Adoption by Entrepreneurs

Last registered on August 18, 2025
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Pre-Trial

Trial Information

General Information

Title
The Effect of Beliefs on Generative-AI Adoption by Entrepreneurs
RCT ID
AEARCTR-0016544
Initial registration date
August 12, 2025

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
August 18, 2025, 6:30 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
Juan Pedro Ronconi
Affiliation
Universidad de los Andes, Chile
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Other Primary Investigator(s)

PI Name
Marcos Balmaceda
PI Affiliation
KU Leuven
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Additional Trial Information

Status
In development
Start date
2025-08-11
End date
2026-04-01
Keywords
Behavior, Firms & Productivity
Additional Keywords
Generative-AI
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Generative AI could help vulnerable entrepreneurs overcome information disadvantages, yet adoption remains minimal. Among Chilean microentrepreneurs—most without higher education or formal business training—AI tools remain largely unused despite their potential to democratize access to business expertise. We test whether beliefs about AI benefits explain this adoption gap through a randomized controlled trial. We randomize entrepreneurs into receiving information on the potential of GenAI as a business consultant or as a content creator, with a control group receiving neutral technical information. Through incentivized willingness-to-pay elicitation, we measure how information affects beliefs about GenAI's business value. We evaluate the impact of exogenously changing beliefs on GenAI take-up and on firm performance. Results inform whether low-cost information interventions can help reduce inequality in entrepreneurial success.
External Link(s)

Registration Citation

Citation
Balmaceda, Marcos and Juan Pedro Ronconi. 2025. "The Effect of Beliefs on Generative-AI Adoption by Entrepreneurs." AEA RCT Registry. August 18. https://doi.org/10.1257/rct.16544-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We implement a randomized information experiment with two treatment arms and one control group to test how different framings of AI capabilities affect entrepreneurial beliefs and adoption intentions.

The intervention consists of brief (2-minute) information treatments delivered through an online survey. Each treatment provides information about generative AI applications for business, while the control receives neutral technical information without actionable business applications.
Intervention Start Date
2025-08-15
Intervention End Date
2025-09-12

Primary Outcomes

Primary Outcomes (end points)
We consider different measures of GenAI adoption depending on the survey round.

At baseline (immediately post-treatment): Interest in adopting GenAI measured through (i) behavioral outcome: signing up to receive information about the AI online course offered by our partner; and (ii) self-reported likelihood of using or learning more about ChatGPT for their business in the next two weeks on a 1-7 Likert scale.

At midline (2 months) and endline (6 months): (i) behavioral outcome: indicator for signing up for the AI online course offered by our partner; (ii) self-reported use of GenAI, including extensive margin (any use) and intensive margin (frequency of use); and (iii) types of business applications implemented.
Primary Outcomes (explanation)
Our primary outcomes focus on technology adoption through multiple channels. Interest in receiving course information captures immediate engagement with AI learning opportunities without conflating interest with actual enrollment. Course sign-up at follow-up provides an objective behavioral measure of demand for formal AI training, representing the direct behavioral response to updated beliefs. We focus on sign-up rather than course completion as it captures the immediate decision to invest in AI learning without being confounded by post-randomization factors such as course quality, time availability, or business shocks that affect completion rates. Self-reported AI use complements course enrollment by capturing those who adopt AI through informal channels without taking formal training.

Secondary Outcomes

Secondary Outcomes (end points)
The first stage in our empirical model corresponds to the impact of the treatments on entrepreneurs' beliefs about the benefits of using GenAI. We will measure this through an index that combines questions on how entrepreneurs expect their income and profits to change if they adopted GenAI. We will also elicit willingness to pay (WTP) for a tutoring session on how to use GenAI to improve business operations. This measure is also related to the belief about the benefits of adopting GenAI and has the advantage of being incentivized.

Moreover, besides GenAI take-up, as a secondary outcome we will also examine the impact of our treatments on revenue and profits at midline and endline.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We partner with ChileConverge, a platform that offers online training for entrepreneurs in Chile. Entrepreneurs in their database are invited to complete a survey, with a raffle to incentivize participation. We stratify the sample into thirds based on baseline characteristics available in our partner’s database (e.g., gender, education level, and business sector) to improve balance across treatment arms. Random assignment occurs within strata, with equal probability allocation (1/3 to T1, 1/3 to T2, 1/3 to Control). Treatments are administered immediately after baseline measures, within the same survey session. Follow-up surveys are conducted 2 and 6 months after baseline.
Experimental Design Details
Not available
Randomization Method
Stratified randomization in Stata.
Randomization Unit
Randomization is done at the individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
n/a
Sample size: planned number of observations
Minimum of 900 observations. We will continue collecting observations until one of the following conditions is satisfied: (i) We collect 10,000 observations; or (ii) Four weeks of data collection have passed. Therefore, final N will be between 900 and 10,000.
Sample size (or number of clusters) by treatment arms
One third assigned to T1, one third to T2, one third to control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We use data from a survey implemented by our partner institution in May 2025 that asked 204 entrepreneurs in Chile whether they used GenAI or not, among other things. 29% of respondents report using it frequently (although the type of use was not specified in the question). We set power at 0.8 and significance at 0.05. This implies that with a sample size of 900 observations (300 in each group), we can detect a reduced form effect of 10.5 percentage points in each treatment arm (36% over the baseline). With a sample size of 10,000 observation (3,333 in each group), we can detect an effect of 3.14 percentage points in each treatment arm (11% over the baseline).
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
KU Leuven's Social and Societal Ethics Committee
IRB Approval Date
2025-08-04
IRB Approval Number
G-2025-9674-R2(MIN)
Analysis Plan