{"title":"基于平衡计分卡和人工神经网络的IT部门绩效评估模型","authors":"Yurong Zeng, Lin Wang, Yonggang Wang","doi":"10.1109/IITA.2007.53","DOIUrl":null,"url":null,"abstract":"In this ever-changing world, the function of information technology (IT) department is becoming increasingly important. The objective of this study is to construct an approach based on balanced scorecard (BSC) and artificial neural network (ANN) for evaluating an IT department in the manufacturing industry. A novel hybrid particle swarm optimization (HPSO)-based learning method is utilized in the ANN. The reliability of the ANN model is tested by a practical example. The results show that the ANN model learned by HPSO algorithm has relative high accuracy and is better than the back propagation algorithm. The empirical findings suggest that the ANNs model can be a persuasive analytical tool for the performance evaluation of the IT department.","PeriodicalId":191218,"journal":{"name":"Workshop on Intelligent Information Technology Application (IITA 2007)","volume":"33 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Novel Model for Evaluating Performance of IT Department based on Balanced Scorecard and Artificial Neural Network Approach\",\"authors\":\"Yurong Zeng, Lin Wang, Yonggang Wang\",\"doi\":\"10.1109/IITA.2007.53\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this ever-changing world, the function of information technology (IT) department is becoming increasingly important. The objective of this study is to construct an approach based on balanced scorecard (BSC) and artificial neural network (ANN) for evaluating an IT department in the manufacturing industry. A novel hybrid particle swarm optimization (HPSO)-based learning method is utilized in the ANN. The reliability of the ANN model is tested by a practical example. The results show that the ANN model learned by HPSO algorithm has relative high accuracy and is better than the back propagation algorithm. The empirical findings suggest that the ANNs model can be a persuasive analytical tool for the performance evaluation of the IT department.\",\"PeriodicalId\":191218,\"journal\":{\"name\":\"Workshop on Intelligent Information Technology Application (IITA 2007)\",\"volume\":\"33 4\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on Intelligent Information Technology Application (IITA 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IITA.2007.53\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Intelligent Information Technology Application (IITA 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IITA.2007.53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Novel Model for Evaluating Performance of IT Department based on Balanced Scorecard and Artificial Neural Network Approach
In this ever-changing world, the function of information technology (IT) department is becoming increasingly important. The objective of this study is to construct an approach based on balanced scorecard (BSC) and artificial neural network (ANN) for evaluating an IT department in the manufacturing industry. A novel hybrid particle swarm optimization (HPSO)-based learning method is utilized in the ANN. The reliability of the ANN model is tested by a practical example. The results show that the ANN model learned by HPSO algorithm has relative high accuracy and is better than the back propagation algorithm. The empirical findings suggest that the ANNs model can be a persuasive analytical tool for the performance evaluation of the IT department.