Thinking differently: Assessing nonlinearities in the relationship between work attitudes and job performance using a Bayesian neural network

Abstract

The relationship between work attitudes and individual job performance was investigated using artificial neural networks (ANNs). ANNs use pattern recognition algorithms that are well suited to capturing nonlinear relationships among variables thereby providing a new perspective on research on this topic area. Results from the neural network analysis provided strong evidence of nonlinearity suggesting that nonlinear models are needed to understand the work attitude‐job performance relationship. In so doing, the neural network model had greater predictive accuracy than did traditional OLS regression. Implications of this finding for theory development and future research were discussed.