![]() ![]() The results showed that the goodness of fit index of the model is 0.638. PLS-SEM was employed to analyze data collected from a questionnaire survey of 314 participants comprising the clients, contractors, and consultants working in oil and gas construction projects. That allows an estimation of complex cause–effect relationships in path models with latent variables. The partial least squares path modeling or partial least squares structural equation modeling (PLS-PM, PLS-SEM) is a method for structural equation modeling Statistical analysis, relative important index method, and probability impact matrix analysis were carried out to classify and rank the risk factors. The data were collected through a structured questionnaire. Hence, this study focused on identifying, classifying, and modeling the risk factors that have negative effects on the success of construction projects in Yemen. However, these projects usually face chronic risks that lead to time overrun, cost overrun, and poor quality, affecting the projects’ success. Oil and gas construction projects are of great importance to support and facilitate the process of operation and production. The purpose of the risk analysis, such as monitoring economic and financial outcomes, verifying quality variation, or tracking time delays, may also serve as a criterion for determining the most effective risk management approaches to be used in the situation. Brainstorming Change Analysis (ChA) Checklist Interviews Delphi Method Expert Judgement Cause and Consequence Analysis Fuzzy Logic The Event Causal Factor Charting (ECFCh) The Expect Monetary Value (EMV) Failure Mode-Effects Analysis (FMEA) Hazard-Operability (HAZOP) The Fault Tree Analysis (FTA) Hazard Review (HR) Pareto Analysis (PA) Risk Breakdown Matrix (RBM) Monte Carlo Method The Risk Breakdown Structure (RBS) Probability and Impact Matrix Relative Importance Index (RII) Risk Index The Strengths-Weaknesses-Opportunities-Threats Furthermore, each step of the risk assessment requires a distinct amount of information and depth, necessitating the use of appropriate approaches. There is a significant contribution expected from this research, especially for companies operating in the oil and gas sector and other organizations that plan to invest in this field, in addition to expected benefits for the Yemeni Government and researchers because of lack of research in this area. This paper identifies the matrix for risk factors affecting the success of construction projects in the oil and gas industry in Yemen. Classification and ranking of these factors by using the risk matrix provide the basis for risk response planning to enhance the chances of project success. Practically, this study highlights the top risk factors in oil and gas construction projects, which might cause an adverse effect on project success in Yemen. The research was limited to the oil and gas construction projects in Yemen. Moreover, the factors under feasibility study and design and resources and material are the most categories effect on project success. The PIM analysis for risk factors found that five factors are located in the dark red zone, as top risks factors have a very high impact and very high probability of occurring 40 factors are located in the light red zone six factors are located in the yellow zone and no factors are located in the green zone (light and dark), which is considered an indication of the importance of risk factors under study and their impact on the success of construction projects in the oil and gas sector. These zones are light green, dark green, yellow, light red and dark red. ![]() Five zones were used in the matrix according to the degree of risk factor’s severity on the success of the project. The risk factors were tabulated in a questionnaire form, which was sent to a total of 400 participants asking their contribution in identifying the risk matrix for the risk factors in terms of impact and probability of occurrence during the project life cycle. ![]() ![]() In total, 51 risk factors that might affect construction projects in the oil and gas sector are defined through a detailed literature review and expert judgment. This study aims to identify and assess the significant risks in Yemen oil and gas construction projects based on their risk rating (impact and probability) by using probability–impact matrix (PIM). ![]()
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