An empirical and comparative analysis of E-government performance measurement models: Model selection via explanation, prediction, and parsimony

Abstract

Driven by the growing importance of the digital provision of government services (e-government), recent research has sought to develop and test conceptual models of citizen satisfaction and trust with these services. Yet, there remains little agreement on how to optimally model these relationships with regards to the somewhat divergent goals of explanation and prediction of citizen trust. In this paper, we test two prominent modeling paradigms of the e-government satisfaction-trust relationship: the “service quality” model and the “expectancy-disconfirmation” model. We compare several variations of these models for their in-sample explanatory abilities, out-of-sample predictive abilities, and parsimony. To test the models, we examine a pooled, cross-agency sample of survey data measuring citizens' experiences with and perceptions of three important and widely accessed U.S. federal e-government services—the webpages of the Social Security Administration, the Internal Revenue Service, and the U.S. Census Bureau. Our findings suggest that while the expectancy-disconfirmation paradigm performs well in explanation, a parsimonious model with an “overall quality-satisfaction-trust” link is best suited for predicting trust. In addition, the service quality paradigm offers the best compromise between predictive accuracy and explanatory power. These findings offer new insights for academic researchers, government agencies, and practitioners, especially those deciding upon an empirical model to adopt to measure e-government satisfaction and its impact upon citizen trust.