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The Impact of Portal Satisfaction on Portal Use and Health-Seeking Behavior: Structural Equation Analysis

BACKGROUND: Our study addresses a gap in the modern information systems (IS) use literature by investigating factors that explain patient portal satisfaction (SWP) and perceptions about health-seeking behavior (HSB). A novel feature of our study is the incorporation of actual portal use data rather...

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Detalles Bibliográficos
Autores principales: Silver, Reginald A, Subramaniam, Chandrasekar, Stylianou, Antonis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148551/
https://www.ncbi.nlm.nih.gov/pubmed/32217505
http://dx.doi.org/10.2196/16260
Descripción
Sumario:BACKGROUND: Our study addresses a gap in the modern information systems (IS) use literature by investigating factors that explain patient portal satisfaction (SWP) and perceptions about health-seeking behavior (HSB). A novel feature of our study is the incorporation of actual portal use data rather than the perceptions of use intention, which prevails in the modern IS literature. OBJECTIVE: This study aimed to empirically validate factors that influence SWP as an influencing agent on portal use and HSB. Our population segment was comprised of college students with active patient portal accounts. METHODS: Using web-based survey data from a population of portal users (n=1142) in a university health center, we proposed a theoretical model that adapts constructs from the Technology Acceptance Model by Davis, the revised Technology Adoption Model by Venkatesh, the Unified Theory of the Acceptance and Use of Technology 2, and the Health Belief Model by Rosenstock et al. We validated our model using structural equation modeling techniques. RESULTS: Our model explained nearly 65% of the variance in SWP (R(2)=0.6499), nearly 33% of the variance in portal use (R(2)=0.3250), and 29% of the variance in HSB (R(2)=0.2900). Statistically significant antecedents of SWP included social influence (beta=.160, t(499)=6.145), habit (beta=.114, t(499)=4.89), facilitating conditions (beta=.062, t(499)=2.401), effort expectancy (beta=.311, t(499)=11.149), and performance expectancy (beta=.359, t(499)=11.588). SWP influenced HSB (beta=.505, t(499)=19.705) and portal use (beta=.050, t(499)=2.031). We did not find a statistically significant association between portal use and HSB (beta=.015, t(499)=0.513). Perceived severity significantly influenced HSB (beta=.129, t(499)=4.675) but not portal use (beta=.012, t(499)=.488). CONCLUSIONS: Understanding the importance of SWP and the role it plays in influencing HSB may point to future technology design considerations for information technology developers and health care providers. We extend current Expectancy Confirmation Theory research by finding a positive association between SWP and portal use.