An empirical study on using decision support systems to solve very large choice decision problems

Wang, H.

doi:10.1504/ijmed.2004.004640

Cited by 11 publications

(2 citation statements)

References 25 publications

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“…DSS tools span the entire spectrum from simple tools aiding in ''what-if?'' analyses to complex knowledge-based tools that can be used to automate systems [43,44]. Decision support systems can help managers make better decisions.…”

Section: Hierarchical Analysis On Employee Turnover Risk Factorsmentioning

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: Hierarchical Analysis On Employee Turnover Risk Factorsmentioning

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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“…It is usually used in short‐ or medium‐term stock predicting. The techniques and models researched in our past studies have been utilized in this research such as enterprise systems (Chen et al, 2022; Duan & Xu, 2012; Li et al, 2018; Sigov et al, 2022; Wang, 2017; Xu, 2011, 2015), decision support systems (Wang, 2004; Wang & Chu, 2004) and artificial intelligence techniques (Chen et al, 2021; Jiang et al, 2009; Norinder & Norinder, 2022; Shen et al, 2016; Wang, 2005; Wang et al, 2003; Zhu et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Predicting stock index movement using twin support vector machine as an integral part of enterprise system

Zou

Wang

et al. 2022

Syst Res Behav Sci

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In order to improve the predicting performance of stock index movement, this study proposes a new predicting model called Twin Support Vector Machines (TWSVM), which will be used to predict the trend of Shanghai Securities Composite Index (SSCI) and Standard and Poor's 500 Index (S&P500 Index), respectively. Thirteen indicators constructed by stock index historical data are selected as input features of the predicting model. The predicting target is the stock index daily movement, up or down. The decision tree (DT), Naive‐Bayes (NB), random forests (RF), probabilistic neural network (PNN) and support vector machine (SVM) are set as contrast experiments. The experiment results indicate that the TWSVM predicting model has a better predicting performance on both stock price and index daily movement.

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