Enhancing the Knowledge of Construction Business Failure: A Social Network Analysis Approach

被引:73
作者
Assaad, Rayan [1 ]
El-adaway, Islam H. [2 ,3 ,4 ,5 ]
机构
[1] Missouri Univ Sci & Technol, Dept Civil Architectural & Environm Engn, Rolla, MO 65409 USA
[2] Missouri Univ Sci & Technol, Construct Engn & Management, Rolla, MO 65409 USA
[3] Missouri Univ Sci & Technol, Civil Engn, Rolla, MO 65409 USA
[4] Missouri Univ Sci & Technol, Missouri Consortium Construct Innovat, Dept Civil Architectural & Environm Engn, Rolla, MO 65409 USA
[5] Missouri Univ Sci & Technol, Dept Engn Management & Syst Engn, Rolla, MO 65409 USA
关键词
DEFAULT PREDICTION MODEL; CONTRACTOR DEFAULT; FINANCIAL PERFORMANCE; CORPORATE FAILURE; DEA-DA; FIRMS; COMPANIES; INSOLVENCY; CENTRALITY; PROJECTS;
D O I
10.1061/(ASCE)CO.1943-7862.0001831
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The failure of construction companies is quite crucial because it results in unfinished projects and responsibilities that in turn result in many losses to governments, economies, owners, creditors, and surety companies. Previous research on the prediction of construction business failure utilized insolvency causes that were either arbitrary or based on the availability of data. Many scholars have shown the absence of, and need for, a holistic framework for the identification of the causes of construction business failure. As such, this paper reviews previous literature incorporating construction business failure applications, with the objective of identifying existing knowledge, current gaps, and needed future research directions on the different failure factors in a comprehensive approach. To this end, the authors (1) performed a meta-analysis of previous research work for a 30-year period spanning from 1988 to 2018; (2) identified and defined failure factors that impact the business operations of construction firms; and (3) utilized social network analysis to quantitatively identify the overlooked and missing construction business failure factors. Research results indicate that there are 20 factors that could collectively contribute to business failures of construction firms. It is also shown that there is a dire need for future research to better explore the impacts of some understudied critical factors, including the effect of inadequate company organizational structure and human capital on construction business failure. Another important finding is the absence of models that include a holistic incorporation of all 20 construction business failure factors. The findings herein are a significant contribution to the body of knowledge on construction business failure because they integrate the outcomes of previous works and use them to provide robust foundations for knowledge advancement. The presented guidelines are believed to close areas where an abundance of research work occurs and to unveil areas where additional research is necessary.
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页数:17
相关论文
共 121 条
[91]  
SFAA (Surety and Fidelity Association of America), 2018, WHY DO CONTR FAIL
[92]  
Shane S., 2018, SMALL BUSINESS FAILU
[93]   Evaluating the financial health of construction contractors [J].
Singh, D. ;
Tiong, R. L. K. .
PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-MUNICIPAL ENGINEER, 2006, 159 (03) :161-166
[94]  
SIO (Surety Information Office), 2018, WHY DO CONTR FAIL
[95]   Benefits and Limitations of the Social Network Analysis when explaining instances of ineffective communication in two Chemical, Biological, Radiological, Nuclear, and Explosives simulations [J].
Stojmenovic, Milica ;
Lindgaard, Gitte .
2013 IEEE THIRD INTERNATIONAL CONFERENCE ON CLOUD AND GREEN COMPUTING (CGC 2013), 2013, :327-334
[96]  
Strischek D., 2008, The RMA Journal, V90, P72
[97]   DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry [J].
Sueyoshi, Toshiyuki ;
Goto, Mika .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2009, 199 (02) :576-594
[98]   Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment [J].
Sueyoshi, Toshiyuki ;
Goto, Mika .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2009, 199 (02) :561-575
[99]  
Sun J., 2013, Recent Patents on Computer Science, V6, P47, DOI DOI 10.2174/2213275911306010007
[100]   PREDICTION OF DEFAULT PROBABILITY FOR CONSTRUCTION FIRMS USING THE LOGIT MODEL [J].
Tserng, H. Ping ;
Chen, Po-Cheng ;
Huang, Wen-Haw ;
Lei, Man Cheng ;
Tran, Quang Hung .
JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 2014, 20 (02) :247-255