Cover Image

Assessing stakeholder’s credit risk using data mining in construction project

Nowadays, the rapid growth of national and global economic demands an efficient,innovative and cost effective for building and infrastructure project. Partnering in construction projects are complex in nature due to human and non-human factors variable.For instance, credit capacity is a common attri...

Full description

Saved in:
Bibliographic Details
Main Authors: Zakaria, NurHafizah, Mohd Shaharanee, Izwan Nizal, Jamil, Jastini, Mohd Nawi, Mohd Nasrun
Format: Article
Language:English
Published: AENSI Journals 2015
Subjects:
QA76 Computer software
TH Building construction
Online Access:http://repo.uum.edu.my/13881/1/Assess.pdf
http://repo.uum.edu.my/13881/
http://www.aensiweb.com/AEB/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uum.repo.13881
record_format eprints
spelling my.uum.repo.138812016-04-17T04:24:05Z http://repo.uum.edu.my/13881/ Assessing stakeholder’s credit risk using data mining in construction project Zakaria, NurHafizah Mohd Shaharanee, Izwan Nizal Jamil, Jastini Mohd Nawi, Mohd Nasrun QA76 Computer software TH Building construction Nowadays, the rapid growth of national and global economic demands an efficient,innovative and cost effective for building and infrastructure project. Partnering in construction projects are complex in nature due to human and non-human factors variable.For instance, credit capacity is a common attribute from client’s perspectives when selecting partners in their construction project. However, the assessment of the credit risk capacity of partners (such as project manager, quantity surveyor, consultant, and contractor) is neglected particularly involving design build projects in Malaysia.Due to unforeseen risk associated to credit capacity, project delay and cost overrun occur frequently in Malaysian construction industry.Thus, this research aims to develop a framework for accessing credit risk using data mining for design build project. This study will employ case study approach in order to gather information, develop data mining model and validation with real case projects involving public clients.The framework will enable public client to select appropriate partners for their construction project with minimal risk. It is anticipated that this study will yield an efficient artifact to improve the existing government procurement system such as ePerolehan and e-Perunding. AENSI Journals 2015-05 Article PeerReviewed application/pdf en cc4_by http://repo.uum.edu.my/13881/1/Assess.pdf Zakaria, NurHafizah and Mohd Shaharanee, Izwan Nizal and Jamil, Jastini and Mohd Nawi, Mohd Nasrun (2015) Assessing stakeholder’s credit risk using data mining in construction project. Advances in Environmental Biology, 9 (5). pp. 65-70. ISSN 1995 - 0756 http://www.aensiweb.com/AEB/
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
TH Building construction
spellingShingle QA76 Computer software
TH Building construction
Zakaria, NurHafizah
Mohd Shaharanee, Izwan Nizal
Jamil, Jastini
Mohd Nawi, Mohd Nasrun
Assessing stakeholder’s credit risk using data mining in construction project
description Nowadays, the rapid growth of national and global economic demands an efficient,innovative and cost effective for building and infrastructure project. Partnering in construction projects are complex in nature due to human and non-human factors variable.For instance, credit capacity is a common attribute from client’s perspectives when selecting partners in their construction project. However, the assessment of the credit risk capacity of partners (such as project manager, quantity surveyor, consultant, and contractor) is neglected particularly involving design build projects in Malaysia.Due to unforeseen risk associated to credit capacity, project delay and cost overrun occur frequently in Malaysian construction industry.Thus, this research aims to develop a framework for accessing credit risk using data mining for design build project. This study will employ case study approach in order to gather information, develop data mining model and validation with real case projects involving public clients.The framework will enable public client to select appropriate partners for their construction project with minimal risk. It is anticipated that this study will yield an efficient artifact to improve the existing government procurement system such as ePerolehan and e-Perunding.
format Article
author Zakaria, NurHafizah
Mohd Shaharanee, Izwan Nizal
Jamil, Jastini
Mohd Nawi, Mohd Nasrun
author_facet Zakaria, NurHafizah
Mohd Shaharanee, Izwan Nizal
Jamil, Jastini
Mohd Nawi, Mohd Nasrun
author_sort Zakaria, NurHafizah
title Assessing stakeholder’s credit risk using data mining in construction project
title_short Assessing stakeholder’s credit risk using data mining in construction project
title_full Assessing stakeholder’s credit risk using data mining in construction project
title_fullStr Assessing stakeholder’s credit risk using data mining in construction project
title_full_unstemmed Assessing stakeholder’s credit risk using data mining in construction project
title_sort assessing stakeholder’s credit risk using data mining in construction project
publisher AENSI Journals
publishDate 2015
url http://repo.uum.edu.my/13881/1/Assess.pdf
http://repo.uum.edu.my/13881/
http://www.aensiweb.com/AEB/
_version_ 1644281307862138880
score 13.211869

Similar Items