Date Available

7-20-2018

Year of Publication

2018

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Graduate School

Department/School/Program

Public Policy and Administration

First Advisor

Dr. Nicolai Petrovsky

Abstract

There have been various approaches to studying the effectiveness of government performance in public administration. While some have focused on broad organizational factors, others have taken an individual level approach by applying concepts and methods from psychology and behavioral economics. This three-essay dissertation continues this latter approach by examining the role of cognitive mechanisms in explaining citizen attitudes toward government performance as well as collaborative behaviors in the public sector.

The first essay explored the role of detailed versus abstract mental construals in understanding the relationship between expectations of public service performance and attitudes toward a government. Type of thinking, when it fit well with the information about either how or why public services were provided, was predicted to produce more positive attitudes toward government than in the absence of fit. However, these predictions were not confirmed.

The second essay induced either an abstract or a detailed mode thinking in participants. Because abstract thinkers are more likely to focus on the desirability of outcomes, and detailed thinkers are more likely to focus on the feasibility of outcomes, it was predicted that abstract thinking, compared to detailed thinking, would create higher expectations of public services and lower perceived government performance. The findings were inconclusive.

The final essay, combining prospect theory and expectancy-disconfirmation concepts, proposed a new model testing the relationship between citizen attitudes and collaborative behavior. Using a cross-sectional data set of US citizens, the results revealed a predicted non-linear relationship between citizen satisfaction with government performance and co-production.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2018.271

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