Expectations, Disconfirmation, and Citizen Satisfaction with the US Federal Government: Testing and Expanding the Model

• 成果类型: Article
• 署名作者: Morgeson, Forrest V., III
• 刊物名称: JOURNAL OF PUBLIC ADMINISTRATION RESEARCH AND THEORY
• ISSN/ISSBN: 1053-1858
• DOI: 10.1093/jopart/mus012
• 发表日期: 2013
• 页码: 289-305
• 关键词: customer satisfaction; expectancy disconfirmation; performance; CONSEQUENCES; antecedents; index; trust

摘要

Recent research on citizen satisfaction with government services has examined the expectancy-disconfirmation model (EDM), a model suggesting that satisfaction judgments are formed through a cognitive process relating prior expectations to perceived performance and the confirmation or disconfirmation of expectations relative to performance. The results of these studies have been promising and largely supportive of the application of this model to the domain of government services, helping to clarify the processes by which citizens form satisfaction judgments about these services. Thus far, however, the model has only been tested in its most basic form and only applying survey data of citizens experiencing urban, local, or state government services. In this article, we expand on the extant research in two ways. First, we test the EDM in relation to US federal government services and compare our results to the findings of earlier studies focused on local government services. Second, we expand the model by including some antecedents we hypothesize will influence citizens' expectations of their experiences at the federal level of government, including the respondent's political ideology, party identification, and overall trust in the federal government. Our results suggest that the EDM functions well in regard to federal government services, confirming and building upon the findings of earlier studies of this model vis-a-vis local government services. Furthermore, analysis of the expanded EDM finds a significant relationship between party ID, ideology, trust and expectations, suggesting a new direction for future research using this model.