The Transformation of China Construction Supply Chain Management by using Intelligent Automation (IA): An Empirical Framework Based on Resource Dependence Theory

Abstract

China’s construction sector is experiencing an accelerated digital transformation propelled by Intelligent Automation (IA) technologies, including Artificial Intelligence (AI), Robotic Process Automation (RPA), and Collaboration and Integration (CI). Conventional supply chains are increasingly inadequate for addressing growing demands for complexity management, sustainability, and real-time responsiveness. This study examines the transformative role of IA in developing smart supply chains by promoting transparency, flexibility, and comprehensive traceability. A mono-method approach was adopted, beginning with a Systematic Literature Review (SLR) to conceptualise the key variables and establish a framework grounded in Resource Dependence Theory (RDT). Subsequently, quantitative data were gathered via a structured questionnaire administered to 218 construction professionals in China. Employing Exploratory Factor Analysis (EFA) and Multiple Linear Regression (MLR), the analysis identified three IA dimensions—AI capability, RPA implementation, and CI extent—that exert a significant impact on supply chain performance. Results indicate that RPA and digital collaboration serve as the primary drivers of enhanced transparency and adaptability, whereas AI’s effect is still emerging. These findings provide empirical support for IA as a mechanism to improve strategic flexibility and mitigate reliance on legacy systems. The study presents a validated IA-SCM performance framework, offering actionable insights for industry practitioners and policymakers seeking to establish resilient and intelligent supply networks.