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

Steven E. Markham, Ina S. Markham and Barry A. Wray

International Journal of Productivity and Quality Management, 2006, vol. 1, issue 4, 379-396

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.

Keywords: employee productivity; genetic algorithms; GA; artificial neural networks; ANN; employee selection; biodata; piece-rate; job performance; ordinary least squares; demographics; garment industry; apparel industry; clothing industry; high-performance employees. (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpqma:v:1:y:2006:i:4:p:379-396

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