Partial least squares structural equation modeling (PLS-SEM) in the AI Era: Innovative methodological guide and framework for business research
Year: 2025
Type: article
Abstract: This is the author-archived version of the article titled“Partial Least Squares Structural Equation Modeling (PLS-SEM) in the AI Era: Innovative Methodological Guide and Framework for Business Research”,published in Magna Scientia Advanced Research and Reviews, Vol. 13, Issue 2, 2025. The official published version is available at: https://doi.org/10.30574/msarr.2025.13.2.0048 URL: https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2025-0048.pdf Metadata: https://arcr-debug.github.io/pls-sem-ai-article/This version has been deposited on Zenodo to increase academic accessibility and ensure indexing by scholarly databases such as Google Scholar. 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.
Author: Chinnaraju, Arunraju
Institution: Westcliff University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6912106554
FWCI:
Cites:
Cited by:
Related to: