Improving the prediction of employee productivity: a comparison of ordinary least squares versus genetic algorithms coupled with artificial neural networks

Abstract

This research compares the results of utilising an Ordinary Least Squares (OLS) approach versus a combined Genetic Algorithm (GA) with an Artificial Neural Network (ANN) for the task of selecting high-productivity employees. Demographic and piece-rate performance data were collected from 378 employees of a large garment manufacturer. While the OLS model showed only 3 of 11 predictors to be significant, a combined GA procedure coupled with an ANN model found seven determinants to be important in identifying the most productive employees. The ANN model's R² of 0.30 was significantly better at predicting hourly productivity than the OLS model (R² = 0.14). The accuracy of the classification results showed that the two techniques were very different; the ANN results were significantly more accurate for identifying and classifying high-performance employees. The implications of this for the field of productivity and employee selection are discussed.