The federal government is often criticized for performance that fails to meet the public's expectations. Its traditional pay system receives much of the blame for rewarding seniority instead of performance. While everyone agrees that performance matters, they don't always agree on the best way to improve it. My research investigates human resource management strategies designed to motivate better performance and productivity. Specifically, I examine the credibility and feasibility of implementing pay for performance throughout the federal government and identify ways that managers can promote greater productivity through human capital investment. I conduct an extensive review of work motivation theories and synthesize findings from previous academic and government studies in order to develop models that are tailored to the federal workplace. I test these models using federal survey data from the Merit Principles Surveys of 2000 and 2005. A variety of attitudes, perceptions, expectations, and work environment factors are expected to influence job performance. Findings reveal that pay for performance belief and success are greatly affected by performance management, fair treatment in all personnel matters, supervisory fairness in decision-making, and organizational culture. Further results indicate that managers can markedly improve productivity by ensuring employees are highly engaged in their work, delivering effective performance management, providing a supportive organizational culture, and giving employees adequate resources and training. With federal agencies constantly striving to improve performance and productivity, these findings have practical implications for government as they suggest ways that public managers can achieve better performance and greater productivity through increased work motivation.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/39544 |
| Date | 29 March 2011 |
| Creators | Frank, Sue Ann |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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