ADOPTING READINESS LEVEL OF DIGITAL TRANSFORMATION IN THE BUILDING CONSTRUCTION INDUSTRY

Abstract

Construction projects have a significant impact on the global economy. Nevertheless, the construction industry continues to underperform in the adoption of digital technologies, which have the potential to reduce inefficiencies. Moreover, low productivity and inadequate research and development (R&D) plague the construction industry. Additionally, there is a scarcity of construction professionals who possess the necessary skills to implement digital technologies. As new technologies emerge, the construction industry has begun to recognize the importance of digital transformation in the construction phase. Site monitoring, wearable devices, sensors, and hazard identification have witnessed a significant amount of digital transformation. The culture of the construction industry is evolving as the sector's leading actors increasingly recognize the extensive opportunities and associated advantages of utilizing digital tools and adopt these technologies to enhance the performance and results of the entire project lifecycle. This study employs the Delphi technique to identify 20 factors that contribute to the digital transformation of the construction industry, categorizing them into three groups: technology, policy, and infrastructure. This thesis proposes the Digital Transformation Readiness Level Index in Building Construction (DTRLIIBC) to examine digital transformation in the construction industry, with a particular emphasis on the construction phase. It does so by identifying the technologies, Decision makers, and policy incentives required to ensure best practices and the infrastructure necessary for the seamless implementation of digital technologies. The author distributed a survey to executive managers, department managers, project managers, senior engineers, and administrators in the construction industry. This thesis employed the Delphi method to confirm the accuracy and dependability of crucial elements, drawing on the perspectives and experiences of these industry professionals. This thesis also conducted interviews with 13 experts, each with over 20 years of experience. The findings highlight potential factors shaping digital transformation and factors that contribute to the DTRLIIBC measurement model's effectiveness. This study employs structural equation modeling (SEM) to pinpoint the causal relationships between variables and reduce measurement errors, and applies regression analysis to boost efficiency. This thesis included a comprehensive evaluation of the robustness and stability of the model, aiming to enhance its overall performance in the construction industry. This evaluation included tests for multivariate normality conformance, validity and reliability assessments, and accuracy evaluations. The utilization of SEM offers a distinct decision-making alternative and a prospective vision for construction industry firms to enhance productivity and efficiency in construction projects.