Employee turnover: a neural network solution

Sexton, Randall S.; McMurtrey, Shannon; Michalopoulos, Joanna O.; Smith, Angela M.

doi:10.1016/j.cor.2004.06.022

Cited by 75 publications

(36 citation statements)

References 17 publications

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“…In fact, the problem of employee turnover is becoming an important cause influencing the stability and development of enterprises [35] and should not be ignored or underestimated. If the employee turnover risk cannot be managed effectively, the organization will be left in a very weak position.…”

Section: Introductionmentioning

confidence: 99%

Constructing a decision support system for management of employee turnover risk

Wang

Zhang

et al. 2011

Inf Technol Manag

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In the time of knowledge economy, competency among enterprises is focused on the competency of human resources. Employee turnover risk is becoming an important facet influencing the stability and development of enterprises. After analyzing employee turnover risk factors that could threaten enterprise production and operations, we proposed a multi-level model of the evaluation system for employee turnover risk based on the analysis of factors influencing employee turnover risk. A decision support system is proposed for employee turnover risk management using message processing mechanisms and software combination technology and system integration. Data, information and knowledge needed in the system come from enterprises' internal application systems and external systems. The structure and components of the system which is based on Web Services are presented. We also proposed an analytic system based on knowledge management. At last, strategies to effectively manage employee turnover risk are proposed.

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Section: Introductionmentioning

confidence: 99%

Constructing a decision support system for management of employee turnover risk

Wang

Zhang

et al. 2011

Inf Technol Manag

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“…Since in this initial experiment there was a slight trend for the error to decrease as the number of hidden neurons increased, in a follow-up experiment additional ANNs with four to seven hidden neurons were trained. Several authors have studied the choice of an adequate number of neurons in the hidden layer [6,9,18], since a number of them larger than necessary will result in the loss of the ANN's prediction capability. This phenomenon is called overtraining.…”

Section: Obtaining the Anns To Be Used In The Proposed Methodsmentioning

confidence: 99%

Time series: Empirical characterization and artificial neural network-based selection of forecasting techniques

Marroquín

Cervantes

Flores

et al. 2009

IDA

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In this work a method to facilitate the elaboration of a forecast for people with little statistical training is proposed. The method uses a rather simple yet sufficiently accurate time series characterization that allowed training a series of artificial neural networks (ANNs) to predict the forecasting performance of several statistical techniques. A case study is presented to demonstrate the application of the method. All techniques used, including the ANN, were conveniently coded in MS Excel so the computational requirements are modest. Furthermore, the results can be tabulated for quick consultation.

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“…In the business world, applications of this type are becoming more and more frequent: in finance (Parag & Pendharkar, 2005;Rada, 2008;Tian-Shyug et al, 2006), marketing (Kaefer, Heilman, & Ramenofsky, 2005), and human resource management (Sexton & McMurtrey, 2005). There has been a renewed interest in this type of technique in the last few years due to the difficulties inherent in such new problems as dealing with data mining, document classification, financial forecasts, web-mining, etc.…”

Section: Introductionmentioning

confidence: 98%