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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalInternational Journal of Public Administration
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Public Administration

Keywords

  • agency performance
  • configurational models
  • machine learning
  • US Government

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