Application of two neural network paradigms to the study of voluntary employee turnover

Two neural network paradigms-multilayer perceptron and learning vector quan tization-were used to study voluntary employee turnover with a sample of 57 7 hospital employees. The objectives of the study were twofold. The Ist was to asses whether neural computing techniques offered greater predictive ac curacy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies o ffered new insights into turnover processes. When compared with logistic re gression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to th e correct classification of leavers. In addition, these neural network para digms captured nonlinear relationships that are relevant for theory develop ment. Results are discussed in terms of their implications for future resea rch.