PROJECT RISK RANKING BASED ON PRINCIPAL COMPONENT ANALYSIS - AN EMPIRICAL STUDY IN MALAYSIA-SINGAPORE CONTEXT

被引:0
|
作者
Pang, Der-Jiun [1 ]
Shavarebi, Kamran [2 ]
Ng, Sokchoo [1 ]
机构
[1] Int Univ Malaya Wales IUMW, Fac Arts & Sci, Blok & Blok C,Kampus Kota, Kuala Lumpur 50480, Malaysia
[2] INTI Int Univ, Fac Engn & Quant Surveying, Persiaran Perdana BBN, Putra Nilai 71800, Nilai, Malaysia
来源
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2022年 / 18卷 / 06期
关键词
Project risk; Ranking; Assessment; Analysis; Principal component analysis; MANAGEMENT; GOVERNANCE; PERCEPTION; SUCCESS; MODEL;
D O I
10.24507/ijicic.18.06.1857
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Information Technology (IT) remains a robust and sustainable industry, re-sulting in high demand for IT project practitioners. Nevertheless, the high failure rate of IT projects has resulted in significant losses for many companies. This crucial issue needs immediate attention. One of the focusing points should be adopting a practical and proactive project risk management approach. This study aims to determine whether Prin-cipal Component Analysis (PCA) can be used in project risk management. The survey was conducted on targeted project managers in the Malaysia-Singapore region. Under-lying trends and patterns were analyzed based on an intrinsic risk ranking study. PCA was performed to isolate highly associated key risks from less associated lower-ranked risks. As a result, PCA effectively removed weakly correlated risk factors while identify-ing significant components and retaining the data information. The results showed that combining PCA with established risk management approaches provides a credible risk assessment based on criticality.
引用
收藏
页码:1857 / 1870
页数:14
相关论文
共 50 条
  • [41] Research on Credit Risk of Corporate Bond Based on Principal Component Analysis and Cluster Analysis
    Liu, Jingwei
    Luo, Tianyong
    PROCEEDINGS OF THE FIFTH SYMPOSIUM OF RISK ANALYSIS AND RISK MANAGEMENT IN WESTERN CHINA (WRARM 2017), 2017, 152 : 191 - 196
  • [42] Sluice Risk Consequence's Evaluation Based on Principal Component Analysis Method
    Zheng, Haoyao
    Zhuang, Deli
    Xu, Luoshi
    Long, Zhifei
    Yand, Dewei
    PROGRESS IN STRUCTURE, PTS 1-4, 2012, 166-169 : 2740 - +
  • [43] A gray model based on principal component analysis in the evaluation of the port risk degree
    Xu, Shi-bo
    Chen, Yan-cai
    Zhou, Zhao-xin
    2012 7TH INTERNATIONAL CONFERENCE ON SYSTEM OF SYSTEMS ENGINEERING (SOSE), 2012, : 519 - 521
  • [44] A comparative study to examine principal component analysis and kernel principal component analysis-based weighting layer for convolutional neural networks
    Mehrabinezhad, Amir
    Teshnehlab, Mohammad
    Sharifi, Arash
    COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION, 2024, 12 (01):
  • [45] Constructing and empirical research on producer services development evaluation system based on principal component analysis
    Xu, Benhua
    Journal of Applied Sciences, 2013, 13 (17) : 3453 - 3458
  • [47] Neural Networks for Biomedical Signals Classification Based on Empirical Mode Decomposition and Principal Component Analysis
    Abdou, Abdoul Dalibou
    Ngom, Ndeye Fatou
    Sidibe, Samba
    Niang, Oumar
    Thioune, Abdoulaye
    Ndiaye, Cheikh H. T. C.
    INNOVATION AND INTERDISCIPLINARY SOLUTIONS FOR UNDERSERVED AREAS, 2018, 204 : 267 - 278
  • [48] Crustal structure study based on principal component analysis of receiver functions
    Zhang, Jianyong
    Chen, Ling
    Wang, Xu
    SCIENCE CHINA-EARTH SCIENCES, 2019, 62 (07) : 1110 - 1124
  • [49] Crustal structure study based on principal component analysis of receiver functions
    Jianyong ZHANG
    Ling CHEN
    Xu WANG
    ScienceChina(EarthSciences), 2019, 62 (07) : 1110 - 1124
  • [50] STUDY ON FEATURE EXTRACTION OF PIG FACE BASED ON PRINCIPAL COMPONENT ANALYSIS
    Yan, Hongwen
    Hu, Zhiwei
    Cui, Qingliang
    INMATEH-AGRICULTURAL ENGINEERING, 2022, 68 (03): : 333 - 340