An Integrated Structural Equation Model of eHealth Behavioral Intention

Gayle L. Prybutok, Anh Ta, Xiaotong Liu, V. Prybutok
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引用次数: 1

Abstract

eHealth offers promising tools and services to manage and improve the quality of health as well as the potential to provide accessible health information all over the world. The relatively low adoption rates among eHealth users motivates us to develop an integrated model to explain the learning process and provide essential antecedents of eHealth behavioral intention. The integrated model is empirically tested by using different structural equation modeling (SEM) methods, including partial least squares SEM (PLS-SEM), PLSc, and covariance-based SEM (CB-SEM). The model successfully explains the learning process and provides essential antecedents of eHealth behavioral intention. The findings support the interplay of social, cognitive, and personal factors that impact 18-30-year-old users' learning process related to eHealth behavioral intention. The results empirically show that these three types of SEM techniques provide consistent results with respect to path coefficients and coefficients of determination. The findings indicate that CB-SEM and PLS-SEM provide adverse consequences of interaction-term path coefficients.
电子健康行为意愿的集成结构方程模型
电子保健为管理和提高卫生质量提供了有前途的工具和服务,并有可能在全世界提供可获得的卫生信息。电子健康用户中相对较低的采用率促使我们开发一个综合模型来解释学习过程,并提供电子健康行为意愿的基本前提。采用不同的结构方程建模(SEM)方法,包括偏最小二乘SEM (PLS-SEM)、PLSc和基于协方差的SEM (CB-SEM),对集成模型进行了实证检验。该模型成功地解释了学习过程,并提供了电子健康行为意愿的基本前提。研究结果支持社会、认知和个人因素的相互作用,这些因素会影响18-30岁用户与电子健康行为意愿相关的学习过程。实验结果表明,这三种类型的扫描电镜技术在通径系数和确定系数方面提供了一致的结果。结果表明,CB-SEM和PLS-SEM提供了相互作用项路径系数的不利影响。
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