Analysing the significant influences of cost performance in Malaysian rail projects: exploratory factor analysis / Mohamad Zahierruden Ismail, Zuhaili Mohamad Ramly and Mohd Saidin Misnan
Delivering successful rail projects has always been extremely difficult. The fact is widely discussed in developed nations as their outstanding rail networks were known to have significant cost overruns. A comprehensive rail network is progressing in developing nations, and cost overruns have alread...
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Format: | Article |
Language: | English |
Published: |
Universiti Teknologi MARA, Perak
2024
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Subjects: | |
Online Access: | https://ir.uitm.edu.my/id/eprint/98941/1/98941.pdf https://ir.uitm.edu.my/id/eprint/98941/ https://journal.uitm.edu.my/ojs/index.php/MySE/issue/view/62 |
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Summary: | Delivering successful rail projects has always been extremely difficult. The fact is widely discussed in developed nations as their outstanding rail networks were known to have significant cost overruns. A comprehensive rail network is progressing in developing nations, and cost overruns have already occurred in nations like China and India. The problem is critical in Malaysia, where poor cost performance has been highlighted for decades. This study aims to establish the significant influences on cost performance of rail projects, by assessing the critical influences that may affect project cost management. Primary data was gathered from 100 cost managers in the rail construction industry. Five (5) main component elements were discovered with a total of fifty (50) related items based on prior studies. Project estimation, project planning, project management, technical experience, and project complexity can be categorized as the primary component aspects. The influences and their respective items are then utilized to create the questionnaire for gathering data. Collected data were then analyzed using Exploratory Factor Analysis (EFA), which focused on the five (5) components and fifty (50) influences that affected rail construction cost performance. The overall variance percentage was 62.514%. Each component has a minimum of four variables, thus complying with the requisite that every component needs a minimum of three variables. |
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