Structuring the prediction model of project performance for international construction projects: A comparative analysis

Du Y. Kim, Seung H. Han, Hyoungkwan Kim, Heedae Park

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)

Abstract

Early understanding of project conditions is crucial so as to proactively respond to the variable situations of a project. Particularly, international construction projects are affected by more complex and dynamic factors than domestic projects; frequently being exposed to serious external uncertainties such as political, economical, social, and cultural risks, as well as internal risks from within the project itself. This study develops a structural equation model (SEM) to predict the project success of uncertain international construction projects. Through a comparative analysis of SEM with a multiple regression analysis and artificial neural network, SEM shows a more accurate prediction of performance because of its intrinsic ability to consider various risk variables in a systematic and realistic way. In addition, the use of SEM allows for visually depicting the paths of how those complicated variables are interrelated so as to promote the clear understanding of the complex system and its underpinned causes that critically affect the project success.

Original languageEnglish
Pages (from-to)1961-1971
Number of pages11
JournalExpert Systems with Applications
Volume36
Issue number2 PART 1
DOIs
Publication statusPublished - 2009 Mar

Bibliographical note

Funding Information:
This research was supported by a grant (06 CIT A03) from Construction Infra Technology Program funded by Ministry of Construction & Transportation of Korean government, and also supported by the Korean Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2006-D00219). The authors wish to express their sincere appreciation to Professor So Young Sohn, Yonsei University, for her contribution to this research.

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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