Using artificial neural networks to model nonlinearity: The case of the job satisfaction-job performance relationship

Document Type

Article

Publication Date

7-1-2009

Abstract

Neural networks are advanced pattern recognition algorithms capable of extracting complex, nonlinear relationships among variables. This study examines those capabilities by modeling nonlinearities in the job satisfaction-job performance relationship with multilayer perceptron and radial basis function neural networks. A framework for studying nonlinear relationships with neural networks is offered. It is implemented using the job satisfaction-job performance relationship with results indicative of pervasive patterns of nonlinearity. © 2009 SAGE Publications.

Identifier

67649525592 (Scopus)

Publication Title

Organizational Research Methods

External Full Text Location

https://doi.org/10.1177/1094428107309326

e-ISSN

15527425

ISSN

10944281

First Page

403

Last Page

417

Issue

3

Volume

12

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