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Using Artificial Neural Networks to Model Nonlinearity: The Case of the Job Satisfaction--Job Performance Relationship

^* To whom correspondence should be addressed. E-mail: somers{at}adm.njit.edu.

	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 perception 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.

First published on January 10, 2008, doi:10.1177/1094428107309326

Organizational Research Methods 2009;12:403.

A more recent version of this article appeared on July 1, 2009


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