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    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
    (Mdpi, 2025) Elsherbeny, Hesham Ahmed; Gunduz, Murat; Ugur, Latif Onur
    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.