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Project risk ranking based on principal component analysis – an empirical study in Malaysia-Singapore context

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 p...

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Main Authors: Pang, Der-Jiun, Shavarebi, Kamran, Ng, Sokchoo
Format: Article
Published: ICIC International 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/43908/
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spelling my.um.eprints.439082023-11-24T03:35:44Z http://eprints.um.edu.my/43908/ Project risk ranking based on principal component analysis – an empirical study in Malaysia-Singapore context Pang, Der-Jiun Shavarebi, Kamran Ng, Sokchoo QA75 Electronic computers. Computer science 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 Principal Component Analysis (PCA) can be used in project risk management. The survey was conducted on targeted project managers in the Malaysia-Singapore region. Underlying 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 identifying 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. © 2022, ICIC International. All rights reserved. ICIC International 2022-12 Article PeerReviewed Pang, Der-Jiun and Shavarebi, Kamran and Ng, Sokchoo (2022) Project risk ranking based on principal component analysis – an empirical study in Malaysia-Singapore context. International Journal of Innovative Computing, Information and Control, 18 (6). pp. 1857-1870. ISSN 1349-4198, DOI https://doi.org/10.24507/ijicic.18.06.1857 <https://doi.org/10.24507/ijicic.18.06.1857>. 10.24507/ijicic.18.06.1857
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Pang, Der-Jiun
Shavarebi, Kamran
Ng, Sokchoo
Project risk ranking based on principal component analysis – an empirical study in Malaysia-Singapore context
description 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 Principal Component Analysis (PCA) can be used in project risk management. The survey was conducted on targeted project managers in the Malaysia-Singapore region. Underlying 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 identifying 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. © 2022, ICIC International. All rights reserved.
format Article
author Pang, Der-Jiun
Shavarebi, Kamran
Ng, Sokchoo
author_facet Pang, Der-Jiun
Shavarebi, Kamran
Ng, Sokchoo
author_sort Pang, Der-Jiun
title Project risk ranking based on principal component analysis – an empirical study in Malaysia-Singapore context
title_short Project risk ranking based on principal component analysis – an empirical study in Malaysia-Singapore context
title_full Project risk ranking based on principal component analysis – an empirical study in Malaysia-Singapore context
title_fullStr Project risk ranking based on principal component analysis – an empirical study in Malaysia-Singapore context
title_full_unstemmed Project risk ranking based on principal component analysis – an empirical study in Malaysia-Singapore context
title_sort project risk ranking based on principal component analysis – an empirical study in malaysia-singapore context
publisher ICIC International
publishDate 2022
url http://eprints.um.edu.my/43908/
_version_ 1783876768913948672
score 13.211869

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