@Article{信息:doi 10.2196 / / jmir。1837年,作者=“Crutzen, Rik和Cyr, Dianne和de Vries, Nanne K”,标题=“为电子健康带来忠诚度:使用三种互联网传递干预的理论验证”,期刊=“J医学互联网研究”,年=“2011”,月=“9”,日=“24”,卷=“13”,数=“3”,页=“e73”,关键词=“电子忠诚度;依从性;磨损;用户感知;理论;互联网;背景:互联网提供的干预措施可以有效地改变健康风险行为,但一旦目标群体访问网站,他们实际使用这些干预措施的频率通常非常低(高流失率,低依从性)。因此,当人们到达干预网站后,关注与干预使用相关的因素是相关的和必要的。我们关注的是导致电子忠诚度的用户感知(即再次访问某一干预措施并将其推荐给他人的意图)。 A background theory for e-loyalty, however, is still lacking for Internet-delivered interventions. Objective: The objective of our study was to propose and validate a conceptual model regarding user perceptions and e-loyalty within the field of eHealth. Methods: We presented at random 3 primary prevention interventions aimed at the general public and, subsequently, participants completed validated measures regarding user perceptions and e-loyalty. Time on each intervention website was assessed by means of server registrations. Results: Of the 592 people who were invited to participate, 397 initiated the study (response rate: 67{\%}) and 351 (48{\%} female, mean age 43 years, varying in educational level) finished the study (retention rate: 88{\%}). Internal consistency of all measures was high (Cronbach alpha > .87). The findings demonstrate that the user perceptions regarding effectiveness (betarange .21--.41) and enjoyment (betarange .14--.24) both had a positive effect on e-loyalty, which was mediated by active trust (betarange .27--.60). User perceptions and e-loyalty had low correlations with time on the website (rrange .04--.18). Conclusions: The consistent pattern of findings speaks in favor of their robustness and contributes to theory validation regarding e-loyalty. The importance of a theory-driven solution to a practice-based problem (ie, low actual use) needs to be stressed in view of the importance of the Internet in terms of intervention development. Longitudinal studies are needed to investigate whether people will actually revisit intervention websites and whether this leads to changes in health risk behaviors. ", issn="1438-8871", doi="10.2196/jmir.1837", url="//www.mybigtv.com/2011/3/e73/", url="https://doi.org/10.2196/jmir.1837", url="http://www.ncbi.nlm.nih.gov/pubmed/21946128" }
Baidu
map