Authors: Ina S. Markham
Addresses: Department of Computer Information Systems and Management Science, College of Business, James Madison University, Harrisonburg, VA 22807, USA
Abstract: This research compares the results of utilising an ordinary least squares (OLS) approach vs. a classification and regression tree (CART) approach for identifying employees with a high likelihood of being productive. Relevant performance data were collected from 378 employees of a large garment manufacturer. Past research (Markham et al., 2006) has shown that a combined genetic algorithm with an artificial neural network substantially outperformed (R² = 0.30) an equivalent OLS solution (R² = 0.14) when predicting individual level productivity. The current research compares the use of CART to OLS using the same data set. With an R² of 0.43, the CART results were even more powerful in identifying and classifying high performance employees. The implications of this finding for the field of productivity research and employee selection are discussed.
Keywords: employee productivity; CART; classification; regression trees; employee selection; biodata; piece rate; job performance; ordinary least squares; OLS; garment manufacturing; clothing indutry; garment industry; apparel industry.
International Journal of Productivity and Quality Management, 2011 Vol.8 No.3, pp.313 - 332
Available online: 14 Sep 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article