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

Document Type

Article

Publication Date

1-1-2023

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.

Identifier

85116552966 (Scopus)

Publication Title

International Journal of Public Administration

External Full Text Location

https://doi.org/10.1080/01900692.2021.1981941

e-ISSN

15324265

ISSN

01900692

First Page

43

Last Page

55

Issue

1

Volume

46

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