Improving the prediction of employee productivity: a comparison of ordinary least squares versus genetic algorithms coupled with artificial neural networks
Source: International Journal of Productivity and Quality Management, Volume 1, Number 4, 28 February 2006 , pp. 379-396(18)
Publisher: Inderscience Publishers
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.
Document Type: Research Article
Affiliations: 1: Department of Management, Pamplin College of Business, Virginia Tech, Blacksburg, VA 24061, USA. 2: Department of Computer Information Systems and Management Science, James Madison University, Harrisonburg, VA 22807, USA. 3: Department of Information Systems and Operations Management, University of North Carolina at Wilmington, Wilmington, NC 28403, USA
Publication date: February 28, 2006