| description abstract | Managing change order is a critical area of concern in the construction industry, owing to the resulting disputes, claims, productivity losses, delays, and cost repercussions. There have been various studies proposing both quantitative and qualitative approaches to tackle the effect of change orders on project performance and success. Previous studies have almost solely focused on specific organizational policies, procedures, causes, and recommendations with the goal of capturing the essence of change-order management (CHM) within an area, organization, or kind of contract. However, their contributions are context-specific, limited to simple statistical method and make no reference to an inclusive concept of CHM performance factors. Furthermore, the existing literature urges a need for a realistic strategy to managing change orders, as well as limiting their negative consequences. Therefore, to address the research gap in the literature, this study proposes an operational and systematic CHM framework based on multidimensional performance factors to enhance the overall project success (OPS) factors, utilizing a partial least squares structural equation modeling (PLS-SEM) approach to develop the model. For evaluating CHM’s performance in terms of OPS, a conceptual model was developed. A questionnaire survey was used to conduct an empirical analysis to test the conceptual model. The information was gathered from 334 construction specialists that work in the field. The result of the analysis found that the explanatory power (R2) value of the CHM model is 0.665, which revealed that CHM performance factors in projects is responsible for 66.5% of project performance, which has a significant impact and demonstrating a strong influence. Furthermore, the management of communication and relationships, dispute resolution, and financial issues have a significant impact on CHM based on outer loading values of (β) 0.433, 0.432, and 0.422, respectively. Documentation, procurement, design, and quality management come next in terms of influence on CHM effectiveness, with outer loading values of (β) 0.402, 0.367, 0.345, and 0.289, respectively. This study adds to the body of knowledge by providing new insights and assisting in the identification and better understanding of CHM performance factors in construction projects and their impact on OPS, as well as the use of an advanced statistical method, PLS-SEM. The outcomes of this study can be utilized as a starting point for implementing CHM in construction projects. The practical applications of the proposed quantitative approach model can be utilized by the client, the consultant, and the contractor to successfully plan, manage, evaluate, monitor, and control the CHM performance. The findings of this study will aid policymakers and decision makers around the world in focusing on the industry’s most critical difficulties and challenges. | |
