%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 19
%N 12
%P e429
%T Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study
%A Koivumäki,Timo
%A Pekkarinen,Saara
%A Lappi,Minna
%A Väisänen,Jere
%A Juntunen,Jouni
%A Pikkarainen,Minna
%+ Martti Ahtisaari Institute, Oulu Business School, University of Oulu, PO Box 4600, Oulu, 90014, Finland, 358 40 5073631, timo.koivumaki@oulu.fi
%K health behavior
%K consumer behavior
%K eHealth
%K surveys and questionnaires
%K personal health record
%K patient-accessible health record
%K adoption
%K UTAUT
%K PHR
%D 2017
%7 22.12.2017
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: 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." 
 Objective: The aim of this study was to investigate what factors influence consumers’ intentions to use a MyData-based preventive eHealth service before use. Methods: 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. Results: 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). Conclusions: 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. 
%M 29273574
%R 10.2196/jmir.7821
%U http://www.jmir.org/2017/12/e429/
%U https://doi.org/10.2196/jmir.7821
%U http://www.ncbi.nlm.nih.gov/pubmed/29273574