Organizational Pathways for Artificial Intelligence Adoption in Smart Buildings and Construction 4.0
Theoretical Foundations for AI Implementation
The research develops an integrated theoretical framework drawing on institutional theory, the resource-based view, and dynamic capabilities. Institutional theory explains how external pressures influence organizational behavior, while the resource-based view and dynamic capabilities highlight the internal assets and adaptive capacities required for AI adoption. Together, these perspectives provide a comprehensive lens for understanding AI implementation in complex construction organizations.
Methodological Approach and Empirical Validation
To validate the proposed framework, the study employs a quantitative research design using a survey of large construction firms in China. Partial Least Squares Structural Equation Modeling (PLS-SEM) is applied to analyze the relationships between institutional pressures, organizational factors, and AI implementation outcomes. This methodological approach enables robust testing of complex causal relationships within organizational contexts.
Key AI Implementation Domains in SBC4.0
The findings identify three primary domains of AI implementation in Smart Buildings and Construction 4.0: smart building operation and health management, construction and material optimization, and offsite manufacturing and automation. Each domain represents a distinct application area with unique technological and organizational requirements, highlighting the multifaceted nature of AI adoption in the construction sector.
Institutional Pressures and Organizational Configurations
The study reveals that each AI implementation domain is shaped by different configurations of normative and mimetic pressures, coercive forces, organizational management practices, technological feasibility, and available resources and capabilities. These configurations explain why firms prioritize certain AI applications over others and how external and internal factors jointly influence strategic decision-making.
Policy Implications and Future Research Directions
The proposed framework offers a practical roadmap for policymakers in both industry and government seeking to navigate the complexities of AI adoption in SBC4.0. By clarifying the organizational conditions that support effective AI implementation, the study contributes to improved operational efficiency and organizational performance. Additionally, it establishes a foundation for future cross-national research examining how AI adoption varies across different construction markets and regulatory environments.
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