MétaCan
Menu
Retour à la cohorte
Enregistrement W2105751491 · doi:10.18438/b8hw58

Consumer Health Information Websites with High Visual Design Ratings Likely to Be also Highly Rated for Perceived Credibility

2010· article· en· W2105751491 sur OpenAlexvenueno aff
Kate Kelly

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2010
Typearticle
Langueen
DomaineComputer Science
ThématiqueInnovative Human-Technology Interaction
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCredibilityPsychologySource credibilityThe InternetPerceptionApplied psychologyAdvertisingSocial psychologyComputer scienceWorld Wide WebBusinessPolitical science

Résumé

récupéré en direct d'OpenAlex

A Review of: 
 Robins, D., Holmes, J., & Stansbury, M. (2010). Consumer health information on the web: The relationship of visual design and perceptions of credibility. Journal of the American Society for Information Science and Technology, 61(1), 13-19.
 
 Objective – To answer two research questions: 1) What is the relationship between the visual design of a consumer health information web site and perceptions of the credibility of information found on it? 2) Is there a relationship between brand recognition, visual design preference, and credibility judgments?
 
 Design – Qualitative (correlation of rating of response to stimulus) and quantitative (credibility coding of participant comments)
 
 Setting – Not stated; assumed to be academic institutions in the United States.
 
 Subjects – Thirty-four participants over the age of 35 (34 for statistical power and age over 35 on the hypothesis that this age group is most likely to seek health information on the Internet). 
 
 Methods – Screen shots of 31 consumer health information sites chosen from the results of a Google search using the term “consumer health information” were converted to slide format and shown to participants. The 31 sites included 12 of the top ranked consumer health information sites derived from three sources: the Consumer and Patient Health Information Section (CAPHIS) of the Medical Library Association (MLA), the MLA itself, and Consumer Reports. Participants were read and shown a script explaining the process prior to being asked to view and rate the 31 sites. Participants were first shown a blank slide with a crosshair to focus attention. Then a stimulus slide was shown for 2.8 seconds. A blank black screen was shown while they determined their rating. Participants were first asked to rate the visual design and aesthetics of the 31 web sites using a rating scale of -4 to -1 for negative judgments and +1 to +4 for positive judgments. Then they were asked to remember why they had made positive or negative ratings and why some web sites were preferred to others. The process was repeated with the slides re-ordered, and participants were asked to rate the credibility of the sites using the same rating scales. Upon completion, participants were asked to recall their reasons for positive or negative credibility ratings. All ratings were converted to positive numbers and a scale of 1-8 was used to present results. A variety of statistical analyses were carried out on the data, including correlation, means ratings, and rankings. In addition, all solicited comments on credibility were coded using Fogg’s four types of credibility (surface, earned, presumed, and reputed) in order to try to understand why participants rated the credibility of sites as they did.
 
 Main Results – For the first question, concerning the relationship between visual design preferences and perceived credibility, the results are complicated. A statistically significant correlation was reported between visual design preference and perceived credibility in 8 of the 31 sites (26%). In these instances where visual design is rated highly, so is credibility. When visual design ratings were ranked highest to lowest, credibility ratings followed the same pattern. Similarly, when credibility ratings were ranked highest to lowest, visual, design ratings followed. A t-test confirmed that sites perceived to have higher credibility were also perceived to have better visual design. Furthermore, when design and credibility ratings were compared to site traffic rankings, as measured by Alexa (http://www.alexa.com), the trend was for both visual design and credibility ratings to decline as the site traffic ranking declined. This finding was also confirmed by a t-test. While there is not an exact relationship, the tendency is for sites with higher visual design ratings to also receive higher ratings for perceived credibility. 
 
 On the second question, concerning the relationship between brand recognition and visual design and perceived credibility judgments, the results suggest a possible influence of brand name. This relationship is not clear, and as visual designs were always presented and rated first, there is possibly a co-founder. The analysis of participant comments found that participants performed credibility judgments in a very short time using a variety of criteria, including visual design, source of the site, reputation of the site, and prior use. There were negative reactions to the use of advertisements, drug and insurance company sponsorship, and dot com sites, as well as some suspicion that non-US consumer health information sites were less trustworthy.
 
 Conclusions – Visual design judgments bore a statistically significant similarity to credibility ratings. Sites with recognizable brands were highly rated for both credibility and visual design, but this relationship was not statistically significant. The relationship is complicated and more research is needed on what visual design cues are important to credibility judgments.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
Catégories consensuellesCommunication savante
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,856
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,304
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,294
Écart entre enseignants0,272 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2010
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueEvidence Based Library and Information PracticeMême sujetInnovative Human-Technology InteractionTravaux en français237 207