Trust is a key factor in consumer decisions about website engagement. Consumers will engage with sites they deem trustworthy and turn away from those they mistrust. In this paper we present a framework for understanding trust factors in web based health advice. The framework is derived from a staged model of trust and allows predictions to be made concerning user engagement with different health websites. The framework is then validated via a series of qualitative, longitudinal studies. In each study genuine consumers searched online for information and advice concerning their specific health issue. They engaged in free searching and were directed towards sites previously reviewed using the framework. Thematic analysis of the group discussions provided support for the framework and for the staged model of trust wherein design appeal predicted rejection (mistrust) and credibility of information and personalisation of content predicted selection (trust) of advice sites. The results are discussed in terms of the merits of the framework, its limitations and directions for future work.
In recent years the number of health related websites has increased dramatically and so have concerns regarding the quality of online information. The sheer volume of sites and the variety of information available have left health consumers potentially with greater choice but it is not clear whether these online changes are reflected in user behaviour. This study addresses whether users are becoming more proficient in searching for credible, high quality information and whether they are more demanding of the type of information being sought and less ready to trust online health advice. This paper describes changes in the use of the Internet for health advice over a five-year period. It compares findings from two large-scale online questionnaire studies undertaken in 2000 and 2005. Key changes and similarities in usage and trust practices are noted. The rise in unregulated sites is discussed in terms of patients ''acting as scientists'' using websites to test out theories regarding their health. The increasing importance of design issues is also highlighted and implications for website designers and content providers are presented.
BackgroundHow 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.ObjectiveThe 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.MethodsParticipants 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.ResultsWe 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: χ2
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.ConclusionsAdding variables specific to eHealth enhanced the ability of a model of trust to predict trust and readiness to act on advice.
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