CRITICAL ASSESSMENT OF CHANGE ORDER MANAGEMENT PERFORMANCE IN CONSTRUCTION PROJECT MANAGEMENT

Abstract

Change order management is a major challenge in the construction business due to the associated disputes, claims, productivity losses, delays, and cost implications. In order to assure the success of building projects, proper change order management (CM) is necessary. Cost overruns and schedule slippages resulting from change orders have been identified and studied by academics and construction professionals for decades. In contemporary construction management, however, other performance parameters influence the performance of CM throughout construction operations. This study contributes to the existing body of knowledge by identifying a comprehensive and multidimensional set of performance factors affecting CM and by developing an adaptive neurofuzzy inference system (ANFIS) to quantitatively model these factors and evaluate CM implementation performance in the construction industry. By conducting a comprehensive literature search and consulting with experts, 49 CM performance criteria were found and categorized into seven categories. The relative relevance of each criterion and group was then determined by collecting 334 answers from construction professionals through an online survey. The acquired data were evaluated using partial least-squares structural equation modeling (PLS-SEM). The ANFIS model was developed using a fuzzy clustering strategy that included the clustering of input and output data sets, the fuzziness degree of clusters, and the optimization of five Gaussian membership functions. Thereafter, the ANFIS model was verified using qualitative structural and behavioral testing (k-fold cross validation). The results of this study may be used as construction management guidelines for controlling and assessing the overall CM performance index of construction projects.