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The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Attention web designers: You have 50 milliseconds to make a good first impression!

Lindgaard, Gitte, Fernandes, Gary, Dudek, Cathy, Brown, J.·2006·Behaviour & Information Technology

Abstract

Three studies were conducted to ascertain how quickly people form an opinion about web page visual appeal. In the first study, participants twice rated the visual appeal of web homepages presented for 500 ms each. The second study replicated the first, but participants also rated each web page on seven specific design dimensions. Visual appeal was found to be closely related to most of these. Study 3 again replicated the 500 ms condition as well as adding a 50 ms condition using the same stimuli to determine whether the first impression may be interpreted as a 'mere exposure effect' (Zajonc 1980). Throughout, visual appeal ratings were highly correlated from one phase to the next as were the correlations between the 50 ms and 500 ms conditions. Thus, visual appeal can be assessed within 50 ms, suggesting that web designers have about 50 ms to make a good first impression.

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Metadata

Authors
Lindgaard, G., Fernandes, G., Dudek, C., & Brown, J.
Venue
Behaviour & Information Technology
Published
2006-03
Volume
25
Issue
2
Pages
115-126
ISSN
  • 0144-929X (ISSN-L)
  • 1362-3001
OpenAlex
W2126103897
Cited by
1,025
References
9
FWCI
50.55
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Attention web designers: You have 50 milliseconds to make a good first impression! https://app.cochat.ai/papers/10.1080/01449290500330448
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Lindgaard, G., Fernandes, G., Dudek, C., & Brown, J. (2006). Attention web designers: You have 50 milliseconds to make a good first impression! Behaviour & Information Technology. https://doi.org/10.1080/01449290500330448

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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.