Everything Is Relative: How Citizens Form and Use Expectations in Evaluating Services. (10th November 2020)

• Record Type: Journal Article
• Authors: Favero, Nathan; Kim, Minjung
• Is Part Of: Journal of public administration research and theory. Volume 31:Number 3(2021)
• Page Start: 561
• Page End: 577
• Publication Date: 2020-11-10
• Subjects: Public administration -- Periodicals; Policy sciences -- Periodicals; 351.05
• DOI: 10.1093/jopart/muaa048
• Languages: English
• ISSNs: 1053-1858

Abstract

Abstract: In recent years, studies of citizen satisfaction have increasingly relied on the expectancy–disconfirmation model, which highlights the role that expectations play in driving citizen evaluations of government services. But most empirical studies within public administration of the relationship between expectations and satisfaction indicate that expectations have little-to-no net effect on satisfaction. We argue that these results may be largely driven by the weaknesses of existing measurement approaches and inattention in many studies to the distinction between two types of expectations: those about what should happen (normative expectations) versus those about what will happen (predictive expectations). Distinguishing between these two types of expectations is important because they are likely to have different—and perhaps even opposite—effects on satisfaction. We recruited 972 US residents via Amazon's Mechanical Turk to complete a survey vignette experiment and found that normative expectations are strongly (and negatively) related to satisfaction levels, whereas predictive expectations are barely related to satisfaction at all. We also find that comparative performance information generally has a much stronger effect on predictive expectations than on normative expectations. These findings suggest that theories of satisfaction should more consistently distinguish between different types of expectations. Our results also leave us somewhat optimistic about the ability of ordinary residents to follow a reasonable process when assigning normative meaning to performance information.