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

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.