An Experiment on AI Adoption

Last registered on June 16, 2025
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Pre-Trial

Trial Information

General Information

Title
An Experiment on AI Adoption
RCT ID
AEARCTR-0016177
Initial registration date
June 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
June 16, 2025, 7:28 AM EDT

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

Locations

Country
Italy
Region

Primary Investigator

Name
Ester Faia
Affiliation
Goethe University
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Ricardo Perez-Truglia
PI Affiliation
UCLA
Contact Investigator
PI Name
Zoe Cullen
PI Affiliation
Harvard Business School
Contact Investigator
PI Name
Elisa Guglielminetti
PI Affiliation
Bank of Italy
PI Name
Concetta Rondinelli
PI Affiliation
Bank of Italy

Additional Trial Information

Status
On going
Start date
2025-03-01
End date
2025-12-31
Keywords
Firms & Productivity
Additional Keywords
strategic complementarities, network externalities, learning and knowledge spillovers
JEL code(s)
D, E
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
In partnership with Banca d'Italia, we conducted an information-provision experiment with a sample of up to 5,000 Italian firms. We added a new module to a long-standing survey of firms to elicit prior beliefs about the share of competitors that have already adopted advanced technologies (defined as predictive AI, generative AI, or robotics). Half of the firms were then randomly assigned to receive accurate information about the actual share of competitors who had adopted these technologies. After the information-provision stage, we elicited posterior beliefs about the share of competitors expected to adopt advanced technologies in the future, as well as the firm’s own intentions to adopt these technologies in the near term. The hypothesis is that beliefs about competitors’ adoption may influence a firm’s own adoption decisions.

We have already collected and analyzed the baseline survey responses. This pre-registration pertains to the next step: merging and analyzing two additional sources of post-treatment data: (i) a follow-up survey to be collected approximately 12 months after the experiment, and (ii) outcome variables from administrative records, such as firm-level employment from employer-employee datasets.
External Link(s)

Registration Citation

Citation
Cullen, Zoe et al. 2025. "An Experiment on AI Adoption." AEA RCT Registry. June 16. https://doi.org/10.1257/rct.16177-1.0
Sponsors & Partners

Partner

Name
Banca D'Italia
Type
government
URL
https://www.bancaditalia.it/
Experimental Details

Interventions

Intervention(s)
The treatment consists of information about the technological adoption of competitors.
Intervention (Hidden)
Intervention Start Date
2025-03-01
Intervention End Date
2025-05-31

Primary Outcomes

Primary Outcomes (end points)
The baseline survey includes two outcomes of interest: (i) the expected future adoption of competitors; (ii) the firm's own ex-ante intention to adopt in the future. A third outcome of interest is the firms ex-post adoption, as measured in the follow-up survey (to be conducted 12 months after the baseline). Lastly, our intention is to merge the survey data to administrative records to measure a broader set of behavioral outcomes. For example, our intention is to use an employer-employee dataset to identify the firm's subsequent employment choices. According to basic economic theory, the adoption of these technologies may be expected to lower employment in certain positions while increase it in others. For example, the adoption of robots may decrease demand for low-skill workers, who are substitutes for the technology, while increasing demand for high-skill workers, who are complementary to it.

Furthermore, we plan to merge the experimental data with input-output VAT data. Those can act as mediator and can be sued to identify the channels through firms form expectations about competitors and the channels through which they decide to adopt themselves (for instance through learning).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The main hypothesis is that beliefs about competitors’ adoption may influence a firm’s own adoption decisions. An additional hypothesis is that a firm’s adoption of advanced technologies may affect firm-level outcomes such as employment.

A copy of the survey instrument is attached to this form. The baseline survey begins by eliciting prior beliefs about the share of competitors that have already adopted advanced technologies (defined as predictive AI, generative AI, or robotics). Half of the firms were then randomly assigned to receive accurate information about the actual share of competitors who had adopted these technologies. After the information-provision stage, we elicited posterior beliefs about the share of competitors expected to adopt advanced technologies in the future. We also elicited the firm’s own intentions to adopt these technologies in the future.

Following the standard design of information-provision experiments, we leverage treatment heterogeneity in prior beliefs. Specifically, individuals who underestimated the share of adopters are expected to revise their beliefs upward; those with accurate priors should exhibit little or no belief updating; and those who overestimated the share are expected to revise their beliefs downward.

Due to the panel nature of the survey, some of the firms will be surveyed again a year later, in which they will be asked again about their own AI adoption. With those responses we will be able to measure whether the information they received in the baseline survey affected their adoption choices 12 months later. Moreover, we may be able to measure the effects on other outcomes measured from administrative records, such as firm-level employment from employer-employee datasets.
Experimental Design Details
Randomization Method
The randomization was conducted by Banca d'Italia
Randomization Unit
Firms
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Up to 5,000 firms
Sample size: planned number of observations
up to 5,000
Sample size (or number of clusters) by treatment arms
Half of the firms receive the information, half of the firms do not
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

Documents

Document Name
Survey Instrument
Document Type
survey_instrument
Document Description
File
Survey Instrument

MD5: ddb41256c103674d6801505429d83ac1

SHA1: da26af2975db34a334de97718353b8fb590392bd

Uploaded At: June 10, 2025

IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials