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

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dc.contributor.authorElsherbeny, Hesham Ahmed
dc.contributor.authorGunduz, Murat
dc.contributor.authorUgur, Latif Onur
dc.date.accessioned2025-10-11T20:47:41Z
dc.date.available2025-10-11T20:47:41Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThe 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.en_US
dc.identifier.doi10.3390/su17041467
dc.identifier.issn2071-1050
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85219211787en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/su17041467
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21530
dc.identifier.volume17en_US
dc.identifier.wosWOS:001431818600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectrisk managementen_US
dc.subjectsustainable constructionen_US
dc.subjectkey performance indicatorsen_US
dc.subjectcritical project success factorsen_US
dc.subjectcontract administrationen_US
dc.subjectcontract managementen_US
dc.subjectoperational frameworken_US
dc.subjectperformance assessmenten_US
dc.titleA 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) Approachen_US
dc.typeArticleen_US

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