Empowering researchers: an innovative approach to systematic literature reviews
(A2025-125524)

AUTHORS

Yilong Liang, Bordeaux University; Juliette Passebois-Ducros, IAE University School of Management, Univ. Bordeaux; Jean-Philippe Galan, Univ. Bordeaux, IRGO, EA 4190, F-33000 Bordeaux, France

ABSTRACT

This study aims to assist researchers in selecting appropriate tools for analyzing a unique type of qualitative data, academic texts. Leveraging the PRISMA framework and taking brand love as an example, this study identifies, screens, and analyzes 911 articles, integrating three advanced modeling methods: supervised learning (STM and Clustering analysis) and unsupervised learning (Alceste). Compared to traditional systematic literature reviews, model-based analyses improve transparency, reproducibility, and comprehensiveness, producing results that are more objective and precise. STM is suitable for in-depth exploration of topic structures, Alceste visualizes topic relationships and evolution, and clustering analysis maps research trends and visualizes interconnections. The combination of these three methods enhances the precision and comprehensiveness of systematic literature reviews. This study provides researchers with step-by-step guidance to efficiently utilize the PRISMA process and diverse modeling methods in marketing research, ultimately improving the quality and effectiveness of systematic literature review.