A Hybrid Model for Enhancing Risk Management and Operational Performance of AEC (Architectural, Engineering, and Construction) Consultants: An Integrated Partial Least Squares–Artificial Neural Network (PLS–ANN) Approach

The operational effectiveness of Architectural, Engineering, and Construction (AEC) consultants, whose services have a substantial impact on project execution and results, depends on effective risk management. Using 336 survey responses from professionals in the construction industry, such as consultants, contractors, and employers working on a range of infrastructure and building projects, this study validates a hybrid Partial Least Squares Structural Equation Modeling–Artificial Neural Network (–ANN) approach. In order to ensure both causal analysis and predictive insights for AEC consultant performance assessment, this study combines PLS–SEM and ANN to develop an integrated performance evaluation framework. While ANN ordered their relative relevance in a non-linear predictive model, the PLS–SEM analysis found that the two most important predictors of consultant performance were communication and relationship management (G03) and document and record management (G06). The hybrid approach is a more efficient and data-driven tool for evaluating AEC consultants than traditional regression models since it accurately captures both causal links and predictive performance. These results contribute to a robust and sustainable framework for performance evaluation in the AEC sector by offering practical insights into risk reduction and operational improvement.