Tracing the Path from Industry 4.0 to Industry 5.0 through Topic Modeling Analysis - Cheng, Zeng; Hong, Cheng Ding; Wai Yie, Leong; Ya, Fei Li (INTI International University, 2025)
This study explores the evolution of research from Industry 4.0 to Industry 5.0 using Latent Dirichlet Allocation (LDA) to uncover key research topics and trends. This paper utilizes the Web of Science (WOS) database to collect literature on Industry 4.0 and 5.0 from 2015 to 2024. Through an LDA-based analysis, five key topics were identified, including IoT and automation, adoption frameworks, digital business transformation, smart manufacturing systems, and AI-driven models. The research highlights the growing importance of human-machine collaboration, blockchain security, and sustainable practices in the transition from Industry 4.0 to Industry 5.0. This study contributes to the understanding of evolving industrial research and offers insights into the future direction of industrial innovation.