Employee turnover: a neural network solution

Authors:
Randall S. Sexton;Shannon McMurtrey;Joanna O. Michalopoulos;Angela M. Smith
Affiliations:
Computer Information Systems, Southwest Missouri State University, 901 South National, Springfield, MO;Graduate School of Computer and Information Sciences, Nova Southeastern University, Fort Lauderdale, FL;Business and Administration, Southwest Missouri State University, 901 South National, Springfield, MO;Business and Administration, Southwest Missouri State University, 901 South National, Springfield, MO
Venue:
Computers and Operations Research
Year:
2005

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Abstract

In today's working environment, a company's human resources are truly the only sustainable competitive advantage. Product innovations can be duplicated, but the synergy of a company's workforce cannot be replicated. It is for this reason that not only attracting talented employees but also retaining them is imperative for success. The study of employee turnover has attempted to explain why employees leave and how to prevent the drain of employee talent. This paper focuses on using a neural network (NN) to predict turnover. If turnover can be found to be predictable the identification of at-risk employees will allow us to focus on their specific needs or concerns in order to retain them in the workforce. Also, by using a Modified Genetic Algorithm to train the NN we can also identify relevant predictors or inputs, which can give us information about how we can improve the work environment as a whole. This research found that a NNSOA trained NN in a 10-fold cross validation experimental design can predict with a high degree of accuracy the turnover rate for a small mid-west manufacturing company.