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.

Migrant construction workers make up a significant portion of the workforce in many countries and play a crucial role in alleviating the skilled labor shortage. Although vocational education and training (VET) is essential for equipping these workers with the skills needed to enhance workforce quality and bridge the skills gap, their intentions to attend VET (IAVET) remain relatively low. Drawing on the theory of planned behavior (TPB), this study investigates the antecedents of IAVET among migrant construction workers and explores the moderating role of work pressure. A questionnaire survey was conducted among 547 construction workers in China, followed by exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. The results show that social support has a significant positive correlation with IAVET. Moreover, the three planned behavioral factors mediate the relationship between social support and IAVET, with the mediating effects varying depending on the level of work pressure experienced by workers. Notably, subjective norms (SN) emerge as the strongest mediator, while work pressure (WP) significantly moderates both the direct and indirect pathways, highlighting their critical roles in shaping VET participation intentions. These findings provide valuable insights into the mechanisms through which social support influences migrant construction workers’ IAVET, offering practical implications for improving workforce skills and addressing the skilled labor shortage in the construction sector and similar industries worldwide. Overall, the study strengthens the theoretical explanatory power of the extended TPB framework and offers actionable guidance for policymakers and construction enterprises to enhance migrant workers’ engagement in VET.