J. Med. Internet Res. - Perceived threat and corroboration: key factors that improve a predictive model of trust in internet-based health information and advice.

Tópicos

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Resumo

CKGROUND: How do people decide which sites to use when seeking health advice online? We can assume, from related work in e-commerce, that general design factors known to affect trust in the site are important, but in this paper we also address the impact of factors specific to the health domain.OBJECTIVE: The current study aimed to (1) assess the factorial structure of a general measure of Web trust, (2) model how the resultant factors predicted trust in, and readiness to act on, the advice found on health-related websites, and (3) test whether adding variables from social cognition models to capture elements of the response to threatening, online health-risk information enhanced the prediction of these outcomes.METHODS: Participants were asked to recall a site they had used to search for health-related information and to think of that site when answering an online questionnaire. The questionnaire consisted of a general Web trust questionnaire plus items assessing appraisals of the site, including threat appraisals, information checking, and corroboration. It was promoted on the hungersite.com website. The URL was distributed via Yahoo and local print media. We assessed the factorial structure of the measures using principal components analysis and modeled how well they predicted the outcome measures using structural equation modeling (SEM) with EQS software.RESULTS: We report an analysis of the responses of participants who searched for health advice for themselves (N = 561). Analysis of the general Web trust questionnaire revealed 4 factors: information quality, personalization, impartiality, and credible design. In the final SEM model, information quality and impartiality were direct predictors of trust. However, variables specific to eHealth (perceived threat, coping, and corroboration) added substantially to the ability of the model to predict variance in trust and readiness to act on advice on the site. The final model achieved a satisfactory fit: (2) (5) = 10.8 (P = .21), comparative fit index = .99, root mean square error of approximation = .052. The model accounted for 66% of the variance in trust and 49% of the variance in readiness to act on the advice.CONCLUSIONS: Adding variables specific to eHealth enhanced the ability of a model of trust to predict trust and readiness to act on advice.

Resumo Limpo

ckground peopl decid site use seek health advic onlin can assum relat work ecommerc general design factor known affect trust site import paper also address impact factor specif health domainobject current studi aim assess factori structur general measur web trust model result factor predict trust readi act advic found healthrel websit test whether ad variabl social cognit model captur element respons threaten onlin healthrisk inform enhanc predict outcomesmethod particip ask recal site use search healthrel inform think site answer onlin questionnair questionnair consist general web trust questionnair plus item assess apprais site includ threat apprais inform check corrobor promot hungersitecom websit url distribut via yahoo local print media assess factori structur measur use princip compon analysi model well predict outcom measur use structur equat model sem eq softwareresult report analysi respons particip search health advic n analysi general web trust questionnair reveal factor inform qualiti person imparti credibl design final sem model inform qualiti imparti direct predictor trust howev variabl specif ehealth perceiv threat cope corrobor ad substanti abil model predict varianc trust readi act advic site final model achiev satisfactori fit p compar fit index root mean squar error approxim model account varianc trust varianc readi act adviceconclus ad variabl specif ehealth enhanc abil model trust predict trust readi act advic

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