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

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  • Mark John Somers

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.

Suggested Citation

  • Mark John Somers, 2023. "A Test of a Configurational Model of Agency Performance in the United States Federal Government Using Machine Learning Methodology," International Journal of Public Administration, Taylor & Francis Journals, vol. 46(1), pages 43-55, January.
  • Handle: RePEc:taf:lpadxx:v:46:y:2023:i:1:p:43-55
    DOI: 10.1080/01900692.2021.1981941
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