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- Title
Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study.
- Authors
Koivumäki, Timo; Pikkarainen, Minna; Pekkarinen, Saara; Lappi, Minna; Väisänen, Jere; Juntunen, Jouni
- Abstract
<bold>Background: </bold>Constantly increasing health care costs have led countries and health care providers to the point where health care systems must be reinvented. Consequently, electronic health (eHealth) has recently received a great deal of attention in social sciences in the domain of Internet studies. However, only a fraction of these studies focuses on the acceptability of eHealth, making consumers' subjective evaluation an understudied field. This study will address this gap by focusing on the acceptance of MyData-based preventive eHealth services from the consumer point of view. We are adopting the term "MyData", which according to a White Paper of the Finnish Ministry of Transport and Communication refers to "1) a new approach, a paradigm shift in personal data management and processing that seeks to transform the current organization centric system to a human centric system, 2) to personal data as a resource that the individual can access and control."<bold>Objective: </bold>The aim of this study was to investigate what factors influence consumers' intentions to use a MyData-based preventive eHealth service before use.<bold>Methods: </bold>We applied a new adoption model combining Venkatesh's unified theory of acceptance and use of technology 2 (UTAUT2) in a consumer context and three constructs from health behavior theories, namely threat appraisals, self-efficacy, and perceived barriers. To test the research model, we applied structural equation modeling (SEM) with Mplus software, version 7.4. A Web-based survey was administered. We collected 855 responses.<bold>Results: </bold>We first applied traditional SEM for the research model, which was not statistically significant. We then tested for possible heterogeneity in the data by running a mixture analysis. We found that heterogeneity was not the cause for the poor performance of the research model. Thus, we moved on to model-generating SEM and ended up with a statistically significant empirical model (root mean square error of approximation [RMSEA] 0.051, Tucker-Lewis index [TLI] 0.906, comparative fit index [CFI] 0.915, and standardized root mean square residual 0.062). According to our empirical model, the statistically significant drivers for behavioral intention were effort expectancy (beta=.191, P<.001), self-efficacy (beta=.449, P<.001), threat appraisals (beta=.416, P<.001), and perceived barriers (beta=-.212, P=.009).<bold>Conclusions: </bold>Our research highlighted the importance of health-related factors when it comes to eHealth technology adoption in the consumer context. Emphasis should especially be placed on efforts to increase consumers' self-efficacy in eHealth technology use and in supporting healthy behavior.
- Publication
Journal of Medical Internet Research, 2017, Vol 19, Issue 12, p1
- ISSN
1439-4456
- Publication type
journal article
- DOI
10.2196/jmir.7821