Consumer acceptance of future My Data based preventive eHealth services

The aim of this study is to understand consumers’ acceptance of future My Data based preventive eHealth services so that service designers can develop and market services that are user-driven and attractive to consumers. One of the most discussed benefits of My Data is to combine it with health services to empower consumers to actively participate in preventive health behavior and self-management that could increase the general health of citizens and lead to turn down the costs of public health care. However to reach their fullest potential and nationwide adoption, it is crucial to understand also the consumer perspective to these new health care solutions. Thus to address this research problem, factors affecting consumers’ acceptance of new technology and factors affecting consumers’ intention to engage in preventive health behavior will be investigated. In addition, since My Data based preventive eHealth services include new technologies that are still unfamiliar to the wider population and aim for significant changes in life-styles of consumers, barriers to the acceptance of these services will be investigated. This research was conducted using quantitative methods. First, a literature review on previous research in preventive eHealth services, technology acceptance and health behavior was conducted. Based on the literature review, 13 hypothesizes along with sub-hypothesizes were created that again formed the framework of the research. Hypothesizes and research framework were tested by conducting a quantitative survey. Data for this study was gathered with a web based survey where the link was sent to the email addresses of the staff and student of the University of Oulu. 855 responses were analyzed with SPSS statistics program using confirmatory factor analysis and regression analysis. Based on the survey data analysis, seven direct factors (Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Habit, Vulnerability and Self-Efficacy — technology use) that affect consumers’ Behavioral Intention to use future My Data based preventive eHealth services were identified. In addition, two factors that affect Behavioral Intention through other factors (Severity and Self Efficacy — healthy behavior) were identified. Significant Barriers to the acceptance of future My Data based preventive eHealth services were Resistance to change and personal impediments. Thus the research complements the Unified theory of acceptance and use of technology 2 (UTAUT 2) with the health protective behavior factors Self-Efficacy, Threat Appraisals and Barriers and adapts the model into future My Data based preventive eHealth acceptance context

Consumer adoption of future MyData-based preventive eHealth services:an acceptance model and survey study

Abstract 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