Title
Employee turnover: a neural network solution
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.
Department(s)
Information Technology and Cybersecurity
Document Type
Article
DOI
https://doi.org/10.1016/j.cor.2004.06.022
Keywords
artificial intelligence, genetic algorithm, neural networks, parsimonious, employee turnover
Publication Date
2005
Recommended Citation
Sexton, Randall S., Shannon McMurtrey, Joanna O. Michalopoulos, and Angela M. Smith. "Employee turnover: a neural network solution." Computers & Operations Research 32, no. 10 (2005): 2635-2651.
Journal Title
Computers and Operations Research
