Tao Wang, Shangde Gao, Pinchao Liao, Tsenguun Ganbat and Junhua Chen
The purpose of this paper is to construct a two-stage risk management framework for international construction projects based on the meta-network analysis (MNA) approach. A…
Abstract
Purpose
The purpose of this paper is to construct a two-stage risk management framework for international construction projects based on the meta-network analysis (MNA) approach. A plethora of international construction studies seems to assume risks as independent and therefore, risk intervention strategies are usually critiqued as ineffective.
Design/methodology/approach
In the risk assessment stage, a multi-tiered risk network structure was developed with the project objectives, risk events, risk factors and stakeholders, and critical risk factors were selected based on a series of calculations. In the risk intervention stage, targeted risk intervention strategies were proposed for stakeholders based on the results of the first stage. A highway construction project in Eastern Europe was selected as a case study.
Findings
The results showed that 17 risk factors in three categories – external, stakeholder-related and internal – are critical, and the project manager, construction management department, supplier and contract department are the most critical stakeholders that affect the entire project performance. Based on the critical risk factors and project stakeholders, targeted risk intervention strategies were proposed. The risk assessment results of MNA were found to be more reliable and consistent with the project conditions than the risk matrix method; the risk intervention strategies of MNA can effectively address project objectives.
Originality/value
This study modeled risk priorities based on risk associations and put forward a new method for risk management, supplementing the body of knowledge of international construction. The results of this study are of critical importance in management practices.
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Zhangming Ma, Heap-Yih Chong and Pin-Chao Liao
Human error is among the leading causes of construction-based accidents. Previous studies on the factors affecting human error are rather vague from the perspective of complex and…
Abstract
Purpose
Human error is among the leading causes of construction-based accidents. Previous studies on the factors affecting human error are rather vague from the perspective of complex and changeable working environments. The purpose of this paper is to develop a dynamic causal model of human errors to improve safety management in the construction industry. A theoretical model is developed and tested through a case study.
Design/methodology/approach
First, the authors defined the causal relationship between construction and human errors based on the cognitive reliability and error analysis method (CREAM). A dynamic Bayesian network (DBN) was then developed by connecting time-variant causal relationships of human errors. Next, prediction, sensitivity analysis and diagnostic analysis of DBN were applied to demonstrate the function of this model. Finally, a case study of elevator installation was presented to verify the feasibility and applicability of the proposed approach in a construction work environment.
Findings
The results of the proposed model were closer to those of practice than previous static models, and the features of the systematization and dynamics are more efficient in adapting toward increasingly complex and changeable environments.
Originality/value
This research integrated CREAM as the theoretical foundation for a novel time-variant causal model of human errors in construction. Practically, this model highlights the hazards that potentially trigger human error occurrences, facilitating the implementation of proactive safety strategy and safety measures in advance.
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Hazard warning schemes provide efficient hazard recognition and promote project safety. Nevertheless, these schemes perform poorly because the warning information is calibrated…
Abstract
Purpose
Hazard warning schemes provide efficient hazard recognition and promote project safety. Nevertheless, these schemes perform poorly because the warning information is calibrated for individual characters and is not prioritized for the entire system. This study proposes a hazard warning scheme that prioritizes hazard characters from the inspection process based on the inspectors' experience.
Design/methodology/approach
First, hazard descriptions were decomposed into their characters, forming a double-layer network. Second, warning schemes based on cascading effects were proposed. Third, character-based warning schemes were simulated for various experiences.
Findings
The results show that when a specific hazard is detected, the degree centrality is the most effective parameter for prioritization, and hazard characters should be prioritized based on betweenness centrality for experienced inspectors, whereas degree centrality is preferred for novice inspectors.
Originality/value
The warning scheme theoretically supplements the information-processing theory in construction hazard warnings and provides a practical warning scheme with priority for the development of automated hazard navigation systems.
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Qing-Wen Zhang, Heap-Yih Chong, Pin-Chao Liao and Yao-Lin Wan
This study aims to determine the influences of explanatory factors on the efficacy of the implementation of corporate safety policy (CSP) in international projects from the…
Abstract
Purpose
This study aims to determine the influences of explanatory factors on the efficacy of the implementation of corporate safety policy (CSP) in international projects from the perspective of international contractors.
Design/methodology/approach
Four explanatory factors were identified for the implementation of CSP in international projects based on literature review. A questionnaire survey was then conducted among Chinese organizations that have been involved in international projects. In total, 121 valid responses were received from the questionnaire survey and were modeled using logistic regression to examine the impact of each factor on the observed event of interest.
Findings
The factors related to the effectiveness of implementing CSP, including “attitudes toward safety management measures (ASMM),” “operational mechanism for safety regulations (OM),” “safety knowledge management system (SKMS)” and “systematic safety training scheme (STS),” were selected. The results revealed that OM and SKMS were significant predictors (p < 0.05) of the odds of implementation satisfaction of CSP, but ASMM and STS were not. The probability of satisfactory CSP implementation increased as the value of SKMS increased, whereas the probability of unsatisfactory implementation improved as the value of OM increased.
Research limitations/implications
The questionnaire was distributed to respondents in international contractors headquartered in China. Other types of international organizations can be covered in future research. Furthermore, other factors, such as the local construction environment, should be considered in future studies.
Practical implications
The results provide new insights on CSP implementation overseas. Effective implementation of CSP contributes to the improvement of the safety performance of contractors. The practical significance of interpreting the influence factors is that the contractors can implement more efficient and targeted approaches and tools in the execution of their CSP. The impact of OM reminds safety managers of the synchronization of CSP as well as its implementation environment and characteristics. The effect of ASMM encourages contractors to adopt Web-based and digital knowledge management systems to improve the implementation efficiency of CSP.
Originality/value
The novelty of this study lies in the selection of factors and their impacts on CSP implementation in international projects. This study has also extended knowledge on normative safety in international projects based on quantitative modeling.
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Qing-Wen Zhang, Pin-Chao Liao, Mingxuan Liang and Albert P.C. Chan
Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality…
Abstract
Purpose
Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality failures (LFQF) extracts experience from previous quality events and converts them into preventive measures to reduce or eliminate future construction quality issues. This study aims to investigate the influence factors of LFQF in the construction of grid infrastructure.
Design/methodology/approach
The related factors of LFQF, including quality management (QM) practices, quality rectification, and individual learning, were identified by reviewing literature about organizational learning and extracting experience from previous failures. A questionnaire survey was distributed to the grid companies in North, Northeast, Northwest, East, Central, and Southwest China. 381 valid responses collected and analyzed using structural equation modeling (SEM) to test the influence of these factors on LFQF.
Findings
The SEM results support that QM practices positively affect individual learning and LFQF. Quality rectification indirectly impacts LFQF via individual learning, while the results did not support the direct link between quality rectification and LFQF.
Practical implications
The findings strengthen practical insights into extracting experience from poor-quality issues and continuous improvement. The contributory factors of LFQF found in this study benefit the practitioners by taking effective measures to enhance organizational learning capability and improve the long-term construction quality performance in the grid infrastructure industry.
Originality/value
Existing research about the application of LFQF still stays at the explorative and conceptual stage. This study investigates the related factors of LFQF, including QM practices, quality rectification, and individual learning, extending the model development of learning from failures (LFF) in construction QM.