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

Document Type

Article

Publication Date

4-1-1999

Abstract

Two neural network paradigms - multilayer perceptron and learning vector quantization - were used to study voluntary employee turnover with a sample of 577 hospital employees. The objectives of the study were twofold. The 1st was to assess whether neural computing techniques offered greater predictive accuracy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies offered new insights into turnover processes. When compared with logistic regression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to the correct classification of leavers. In addition, these neural network paradigms captured nonlinear relationships that are relevant for theory development. Results are discussed in terms of their implications for future research.

Identifier

0033106480 (Scopus)

Publication Title

Journal of Applied Psychology

External Full Text Location

https://doi.org/10.1037/0021-9010.84.2.177

ISSN

00219010

PubMed ID

10361841

First Page

177

Last Page

185

Issue

2

Volume

84

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