AI · Abstract-supported quantitative empirical study

What drives artificial intelligence adoption and use among firms? Empirical evidence from Brazilian companies

Digital Policy, Regulation and Governance · 2026

Rafael Katz, Joon Jung

Summary

Company size positively influenced AI adoption and use; digitized environments increased adoption likelihood through network effects; competition intensity had a nonlinear effect that initially stimulated adoption. For a founder, use this as a question to test in your own context, not a universal playbook.

For your marketing

Possible founder application

Treat this as evidence for the specific customer behavior and adoption question studied. Compare the setting and sample to your company before changing a growth decision; use a local experiment where the paper does not match your context.

Reported result

What the study found

Research question

What the authors investigated

Which firm and environmental factors predict AI adoption, diffusion within firms and intensity of use?

Design

Microdata from Brazilian firms analyzed with standard, ordered and multivariate probit models.

Sample and setting

Brazilian enterprises; the accessible abstract does not report the microdata sample size.

Scope and transfer

Limitations to keep in view

The publisher abstract does not report sample size or detailed limitations; country-specific microdata and observational modeling warrant editorial review.

Research sources

Publisher DOI record and accessible article/abstract directly support the bibliographic record and the summary fields; editorial review remains appropriate for duplicate checking against the unseen 300-study library. Correction notices were not separately verified in this collection; consult the current publisher record before applying the findings.

Study strength

2.5/5 Exploratory evidence; interpret cautiously

How this was assessed

The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.

Design · 1/2

Synthesis or analytical design addresses the question, but conclusions depend on included literature or model assumptions.

Sample · 0.4/1

The accessible source does not report a usable sample size.

Comparison · 0.5/1

The comparison structure is limited or depends on the paper's model/context.

Can we check it? · 0.5/1

Only publisher abstract/metadata was retrieved; full methods and robustness could not be checked.

Double-blind? Blinding was not established from the captured source; it may not apply to this study design.