Partial least squares structural equation modeling (PLS-SEM) in the AI Era: Innovative methodological guide and framework for business research
Doctorate in Business Administration Student, Westcliff University, College of Business, California, USA.
Review Article
Magna Scientia Advanced Research and Reviews, 2025, 13(02), 062-108
Article DOI: 10.30574/msarr.2025.13.2.0048
Publication history:
Received on 24 February 2025; revised on 01 April 2025; accepted on 03 April 2025
Abstract:
Partial Least Squares Structural Equation Modeling (PLS-SEM) serves as a comprehensive methodological framework, critically addressing theoretical underpinnings, rigorous analytical approaches, and state-of-the-art modeling techniques vital for contemporary business research. The methodological discussion includes detailed exploration of reflective and formative measurement models, structural model specification, reliability, and validity assessments, alongside advanced analytical methods such as Confirmatory Tetrad Analysis (CTA-PLS) and Importance-Performance Matrix Analysis (IPMA). Advanced algorithms including bootstrapping and blindfolding procedures are elaborated, emphasizing predictive relevance and methodological precision. Partial Least Squares Structural Equation Modeling further offers robust analytical capabilities to evaluate modern AI-driven innovations, facilitating sophisticated assessment of user trust, perceived accuracy, and satisfaction with recommender systems, voice assistants, autonomous vehicles, AI-driven healthcare diagnostics, personalized educational platforms, and fraud detection technologies. Ethical considerations, reporting best practices, computational tools (SmartPLS, SEMinR), and Explainable AI (XAI) integration enhance the comprehensive nature of this framework. Furthermore, integration of cutting-edge analytical approaches such as moderation, mediation, Multi-Group Analysis (MGA), nonlinear modeling, machine learning integration, and quantum computing potential positions PLS-SEM as indispensable for contemporary business and technology research, ultimately promoting actionable scholarly insights and ensuring maximum methodological impact.
Keywords:
Partial Least Squares Structural Equation Modeling; PLS-SEM; Reflective and Formative Models; CTA-PLS; IPMA; AI Product Innovations; Machine Learning; Quantum Computing; Explainable AI (XAI); Methodological Rigor; Predictive Analytics
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0