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

Mark John Somers; Jose C. Casal

doi:10.1177/1094428107309326

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

Mark John Somers
, Jose C. Casal

MT School of Management

Research output: Contribution to journal › Article › peer-review

88 Scopus citations

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.

Original language	English (US)
Pages (from-to)	403-417
Number of pages	15
Journal	Organizational Research Methods
Volume	12
Issue number	3
DOIs	https://doi.org/10.1177/1094428107309326
State	Published - Jul 2009

All Science Journal Classification (ASJC) codes

General Decision Sciences
Strategy and Management
Management of Technology and Innovation

Keywords

Job performance
Job satisfaction
Multiple regression
Neural networks

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10.1177/1094428107309326

Cite this

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

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.",

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AB - 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.

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KW - Multiple regression

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

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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