Impact of Overcoming Digitalization Implementation Barriers on Building Projects Development Success: A Hybrid Structural Equation Modeling and Deep Neural Network Approaches

Digital methods in buildings have matured over the past two decades to become a standard practice informed by set principles. Implementing digitalization can aid in improving the primary goals in terms of quality, time, and cost. However, the building industry is slow to embrace technology due to the industry’s reliance on informal methods. Thus, this study aims to establish a framework for successfully incorporating digital technologies into building construction projects by analyzing the link between overcoming digitalization implementation barriers and overall project success (OPS).To leverage the power of data science, a questionnaire was disseminated to building industry experts to assess the significance of digitalization adoption barriers. Subsequently, these barriers were classified using data science techniques such as exploratory factor analysis (EFA). To further explore the relationship between these barriers and the OPS, advanced data science methods including deep artificial neural networks (ANN) and partial least squares-structural equation modeling (PLS-SEM) were utilized. The use of deep ANN and PLS-SEM allowed for a more comprehensive analysis of the correlations between digitalization implementation barriers and OPS. These data science techniques enhanced the accuracy of the results and facilitated more accurate forecasting, providing valuable insights into the impact of digitalization barriers on OPS.The findings indicated a moderate correlation, with mitigating obstacles associated with digitalization execution accounting for 55.5% of the overall project performance. The results can assist decision-makers as they adopt digitalization to cut costs and increase productivity in the building construction sector.