Evaluating Machine Learning Approaches in Structural Equation Modelling to Improve Predictive Accuracy in Marketing Research

Aug 3, 2026

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Background: This study compares traditional Structural Equation Modelling (SEM) with hybrid Bayesian–Machine Learning (ML) approaches in marketing research, addressing limited use of these advanced techniques. Purpose: The research evaluates how integrating Bayesian SEM with machine learning can improve predictive performance, handle complex data structures, and strengthen marketing applications. Design/methodology/approach: A systematic comparative review of 262 scholarly articles was conducted, with 23 studies meeting inclusion criteria for model development and evaluation. Findings/Result: Traditional SEM offers strong theoretical modelling and interpretability but weaker predictive accuracy and robustness; Bayesian SEM improves robustness via prior distributions, ML further boosts predictive performance, and hybrid models achieve the best balance. Conclusion: Hybrid models can substantially enhance predictive accuracy and robustness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Limitations of traditional SEM

Bayesian SEM and ML integration

Abstract

Background and purpose

Design/methodology/approach

Findings and conclusion

Indonesian Journal of Business and Entrepreneurship, Vol. 11 No. 1, January 2025 Available online at Permalink/DOI: [http://dx.doi.org/10.17358/IJBE.11.1.93](http://dx.doi.org/10.17358/IJBE.11.1.93) [http://journal.ipb.ac.id/index.php/ijbe](http://journal.ipb.ac.id/index.php/ijbe) EVALUATING MACHINE LEARNING APPROACHES IN STRUCTURAL EQUATION MODELLING TO IMPROVE PREDICTIVE ACCURACY IN MARKETING RESEARCH Chacha Magasi Marketing Department, College of Business Education, Mwanza, Tanzania P.O.Box 1968, Dar es Salaam, Dar es Salaam, Tanzania Article history: Received 13 August 2024 Revised 14 October 2024 Accepted 21 October 2024 Available online 24 January 2025 This is an open access article under the CC BY license ([https://](https://)[ ](https://)[creativecommons.org/](creativecommons.org/)[ ](creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) ABSTRACT Background: This study aimed to fill a critical research gap by comparing traditional Structural Equation Modelling (SEM) with hybrid Bayesian-Machine Learning (ML) models in marketing research, focusing on the limited exploration of these advanced techniques. Purpose: This study aimed to evaluate the effectiveness of integrating Bayesian SEM with advanced machine learning techniques to enhance predictive model performance, manage complex data structures, and improve marketing applications. Design/methodology/approach: The study employed a systematic comparative research design to assess the predictive accuracy and robustness of traditional SEM in comparison to hybrid Bayesian-(Bayesian-ML) models. A rigorous review of 262 scholarly articles from major databases was conducted, with 23 studies meeting inclusion criteria to inform the model development and evaluation. Findings/Result: The findings show that traditional SEM excels in theoretical modelling and interpretability but lacks predictive accuracy and robustness, which Bayesian SEM improves by using prior distributions. ML techniques further enhance predictive accuracy and robustness, while hybrid models combining Bayesian SEM with ML achieve the highest levels of both. Conclusion: Adopting hybrid models can substantially enhance the predictive accuracy of marketing outcomes and the robustness of model analyses. Originality/value (State of the art): This study contributes to knowledge by advancing methodological approaches through challenging existing data analysis paradigms, methods and approaches and therebefore offering practical guidance for future studies. Keywords: accuracy, bayesian methods, hybrid models, machine learning, predictive, robustness, structural equation modelling (SEM) How to Cite: Magasi C. (2025). Evaluating Machine Learning Approaches in Structural Equation Modelling to Improve Predictive Accuracy in Marketing Research. Indonesian Journal of Business and Entrepreneurship (IJBE), 11(1), 93. [https://doi.org/10.17358/](https://doi.org/10.17358/)[ ](https://doi.org/10.17358/)[ijbe.11.1.93](ijbe.11.1.93) 1 Corresponding author: [Email: magasitza@gmail.com](Email: magasitza@gmail.com) Copyright © 2025 The Author(s), ISSN: 2407-5434; EISSN: 2407-7321 93 Indonesian Journal of Business and Entrepreneurship, Vol. 11 No. 1, January 2025 INTRODUCTION This study assesses the limitations of traditional Structural Equation Modelling (SEM) in managing complicated and non-linear complex data structures, which can lead to overfitting and reduced accuracy. The study evaluates how integrating the Bayesian SEM with machine learning techniques can enhance predictive accuracy and robustness. Studies covered are those focusing marketing research related to consumer behaviour, preferences, customer segmentation, and forecasting. The study starts by defining SEM asthe sophisticated statistical framework that analyses complex and complicated relationships between observed and latent variables using both factor and path analysis. The major essence is to evaluate

FAQs

What problem does the study address in marketing research?

The study addresses limitations of traditional SEM in handling complex, non-linear data structures and achieving predictive accuracy, robustness, and reliable model performance.

How was the research designed to compare the modelling approaches?

A systematic comparative review was conducted, reviewing 262 scholarly articles and selecting 23 studies that met inclusion criteria to inform model development and evaluation.

What do the results indicate about traditional SEM versus hybrid Bayesian-ML models?

Traditional SEM is strong for theoretical modelling and interpretability, but it underperforms in predictive accuracy and robustness; Bayesian SEM and ML improve these aspects, and hybrid models combining both deliver the highest balance.

Aug 3, 2026

Upload by Levi