Truace
Subscribe
68/100

Moderate evidence

Trace: a gain and a problemPublished recordLifestyleThe Trace

Full story
More
Export record JSONEmailLinkedInX

AI Disclosure in Advertising: Visual Attention and Consumer Responses across Construal Levels

Truvace record
TRV-2026-2678raw data (JSON)
Version
v1Published
First published
Evidence type
Controlled or validated comparison
Sources
1 sourcePeer-reviewed
sha256
598e3c2ab835…check it

Disclosure that advertising content was created using artificial intelligence is becoming common, yet its effects on consumer processing remain poorly understood. Prior research has focused primarily on evaluative outcomes, overlooking how AI disclosure may influence visual attention during ad exposure. Drawing on Construal Level Theory, this study examines how AI disclosure shapes visual attention and consumer responses across different construal levels. Conceptualizing AI disclosure as a transparency cue in di…

60Gain side

How the two sides scoreClose call

57Problem side

The gain

In a laboratory eye-tracking experiment with Instagram-style ads, AI disclosure reduced attention to the model's face and increased attention to the advertised product, particularly among low-construal consumers.

The problem

In a laboratory eye-tracking experiment with Instagram-style ads, AI disclosure lowered model attitude and purchase intention among high-construal consumers, with model attitude mediating the effect.

A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.

What the scores mean

G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.

P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.

What this doesn’t fix

The findings come from a laboratory eye-tracking experiment with Instagram-style advertisements, so effects in real feeds are untested.

Sources

1 on file
  1. Journal of the Association for Information Systemsaisel.aisnet.org
    Peer-reviewed2026-07-05

Cite this record

v1

Truvace Impact Record TRV-2026-2678, v1: “AI Disclosure in Advertising: Visual Attention and Consumer Responses across Construal Levels.” Truvace, 2026-11-12. /record/TRV-2026-2678 (accessed on the day you copy it). sha256 598e3c2ab835293c…

Change history

1 version

Every change made to this record since it was first published, and why. Nothing has changed since it went up.

  1. Publishedv1598e3c2ab835…

    Certified into the record

Check this record hasn’t been altered

Check this record hasn’t been altered
How to check it yourself, without trusting this page

Download any version from /api/record/TRV-2026-2678 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.