A Test of a Configurational Model of Agency Performance in the United States Federal Government Using Machine Learning Methodology

Mark John Somers

International Journal of Public Administration, 2023, vol. 46, issue 1, 43-55

Abstract: This paper takes PA research on organizational performance in a new direction by testing a configurational model using self-organizing maps, a machine learning methodology. The model was built and tested using six performance dimensions from 2017 Federal Employee Viewpoint Survey (FEVS). Four distinct performance profiles or groups were identified: very low performers, average performers, transitional performers, and high performers. Implications for theory development and practice of configurational models of public organizational performance were discussed.

Date: 2023
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DOI: 10.1080/01900692.2021.1981941

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