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Record W2126103897 · doi:10.1080/01449290500330448

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

2006· article· en· W2126103897 on OpenAlexaff
Gitte Lindgaard, Gary Fernandes, Cathy Dudek, Judith M. Brown

Bibliographic record

VenueBehaviour and Information Technology · 2006
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsCarleton University
Fundersnot available
KeywordsAppealImpressionPsychologyImpression formationPrecedentWeb pageWeb surveyWorld Wide WebSocial psychologyComputer sciencePerceptionSocial perception

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.267
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1,030
Published2006
Admission routes1
Has abstractyes

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