Analyzing Strategies for Promoting the Adoption of Construction Robots: A DEMATEL–ISM–FBN Approach

Abstract

Construction robots (CRs) are regarded as a promising solution to improve productivity, safety, and labor efficiency in the construction industry, yet their adoption remains limited. Although several studies have attempted to identify promotion strategies for CR adoption, few have explored the dynamic interdependencies among them. Using China as a case study, this research develops a hybrid framework integrating DEMATEL, Interpretive Structural Modeling (ISM), and a Fuzzy Bayesian Network (FBN) to examine the causal mechanisms, structural hierarchies, and dynamic sensitivities of strategies for promoting CR adoption. The results identify two strategies as the most influential, which exert broad effects on other strategies. ISM results indicate that three strategies, which are all financial related, serve as underlying causes that fundamentally support the overall CR adoption pathway. Furthermore, both DEMATEL and FBN analyses highlight that establishing standardized systems for CRs covering functionality, performance, and safety and improving CR compatibility with other intelligent construction technologies are the most critical strategies, as they achieved the highest integrated scores across sensitivity and importance dimensions, indicating their pivotal role in driving system-wide improvements. The findings provide valuable insights for policymakers and industry practitioners to better understand and implement multidimensional strategies to improve robot adoption in the construction industry.