DIGITAL TECHNOLOGIES AND CIRCULAR ECONOMY: BIBLIOMETRIC EVIDENCE FROM INDUSTRY 4.0 - Anna, A.N.; Mohamad, Zam Zuriyati; Kumaran, Vikniswari Viji (ADVANCED INTERNATIONAL JOURNAL OF BUSINESS, ENTREPRENEURSHIP AND SME'S (AIJBES), 2025)
The accelerating convergence of Industry 4.0 (I4.0) digital technologies and circular-economy imperatives presents both a research opportunity and a coordination challenge. Although scholars increasingly examine how Artificial Intelligence (AI), Internet of Things (IoT), and other digital technologies act as enablers for resource-efficient business models and closed-loop systems, the literature remains scattered across disciplines, geographies, and methods. Here, this study seeks to fill that gap through a systematic bibliometric and network analysis of 689 Scopus-indexed records retrieved via an advanced Scopus query, cleaned and harmonized in OpenRefine. The data are profiled using Scopus Analyzer for descriptive statistics and visualized with VOSviewer to generate co-occurrence and co-authorship maps. Numerical findings reveal a concentrated production profile, with China leading with 251 documents, 6,406 citations, and a Total Link Strength (TLS) of 117, followed by the United Kingdom (UK) and the United States (US). Network analysis produced seven robust thematic clusters in the keyword co-occurrence map and nine stable country collaboration clusters, identifying core themes (technology enablers, circular business models, environmental performance) alongside policy and regional empirical strands. Collaboration topology is dominated by China, with the UK acting as a high-connectivity collaborator and several emergent regional hubs such as Malaysia and India. Collectively, these results synthesize the field’s intellectual structure, reveal under connected yet promising topic bridges related to digital finance and circular business models, and indicate priority areas for causal empirical research and cross-cluster collaboration. We conclude by recommending a targeted research area integrating causal identification, multi-level datasets, and reproducible materials to accelerate theory consolidation and evidence-based policy in the digitally enabled transition to Circular Economies (CEs).