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

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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 languageEnglish (US)
Pages (from-to)403-417
Number of pages15
JournalOrganizational Research Methods
Volume12
Issue number3
DOIs
StatePublished - Jul 2009

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Strategy and Management
  • Management of Technology and Innovation

Keywords

  • Job performance
  • Job satisfaction
  • Multiple regression
  • Neural networks

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