Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 776

The DOI matches its Crossref record.

The Stopping Power of Advertising: Measures and Effects of Visual Complexity

Pieters, Rik, Wedel, Michel, Batra, Rajeev·2010·Journal of Marketing

Abstract

Advertising needs to capture consumers’ attention in likable ways, and the visual complexity of advertising plays a central role in this regard. Yet ideas about visual complexity effects conflict, and objective measures of complexity are rare. The authors distinguish two types of visual complexity, differentiate them from the difficulty of comprehending advertising, and propose objective measures for each. Advertisements are visually complex when they contain dense perceptual features (“feature complexity”) and/or when they have an elaborate creative design (“design complexity”). An analysis of 249 advertisements that were tested with eye-tracking shows that, as the authors hypothesize, feature complexity hurts attention to the brand and attitude toward the ad, whereas design complexity helps attention to both the pictorial and the advertisement as a whole, its comprehensibility, and attitude toward the ad. This is important because design complexity is under direct control of the advertiser. The proposed measures can be readily adopted to assess the visual complexity of advertising, and the findings can be used to improve the stopping power of advertisements.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1509/jmkg.74.5.048

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 776

Metadata

Authors
Pieters, R., Wedel, M., & Batra, R.
Venue
Journal of Marketing
Published
2010-09
Volume
74
Issue
5
Pages
48-60
ISSN
  • 0022-2429 (ISSN-L)
  • 1547-7185
OpenAlex
W2180553342
Cited by
776
References
0
FWCI
41.61
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The Stopping Power of Advertising: Measures and Effects of Visual Complexity. https://app.cochat.ai/papers/10.1509/jmkg.74.5.048
The paper
Pieters, R., Wedel, M., & Batra, R. (2010). The Stopping Power of Advertising: Measures and Effects of Visual Complexity. Journal of Marketing. https://doi.org/10.1509/jmkg.74.5.048

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.