Article: Improving the prediction of employee productivity: a comparison of ordinary least squares versus genetic algorithms coupled with artificial neural networks Journal: International Journal of Productivity and Quality Management (IJPQM) 2006 Vol.1 No.4 pp.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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Steven E. Markham, Ina S. Markham, Barry A. Wray

Addresses: Department of Management, Pamplin College of Business, Virginia Tech, Blacksburg, VA 24061, USA. ' Department of Computer Information Systems and Management Science, James Madison University, Harrisonburg, VA 22807, USA. ' Department of Information Systems and Operations Management, University of North Carolina at Wilmington, Wilmington, NC 28403, USA

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.

DOI: 10.1504/IJPQM.2006.009093

International Journal of Productivity and Quality Management, 2006 Vol.1 No.4, pp.379 - 396

Published online: 28 Feb 2006 *

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

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