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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