Understanding central bank digital currency adoption: a bibliometric and AI-driven analysis

Purpose This study aims to investigate the key factors influencing central bank digital currency (CBDC) adoption by conducting a meta-analysis of scholarly literature. It introduces a novel keyword-network analysis framework using bibliometric metadata from the Web of Science and Scopus databases, validated through artificial intelligence (AI)-based topic modeling. Design/methodology/approach Grounded in the unified theory of acceptance and use of technology (UTAUT) framework, this study identifies core and extended constructs related to CBDC adoption. It applies VOSviewer for keyword co-occurrence analysis on the bibliometric metadata and proposes a new method to calculate the relative importance of adoption factors based on link strength. This study also reveals dominant themes refined and validated through advanced AI-based topic modeling, signifying research trends on CBDC-adoption literature. Findings This study reveals dominant research themes, identified from topic keywords, demonstrating the breadth and depth of CBDC-adoption research and research trends on CBDC-adoption literature. Network-based weight calculations prioritized key adoption constructs such as performance expectancy, effort expectancy, social influence, usefulness, awareness, financial literacy, acceptance, behavior, intention, attitude, adoption intention and regulation, offering a structured understanding of CBDC-adoption dynamics. Practical implications The findings provide valuable insights for policymakers, regulators and financial institutions by highlighting the critical variables that drive or hinder CBDC adoption. The proposed bibliometric-AI hybrid methodology offers a replicable model for future digital currency and FinTech adoption studies. Originality/value This research pioneers a bibliometric and AI-integrated methodology to classify CBDC-adoption factors systematically. It extends the literature by linking thematic clusters to adoption constructs using quantitative co-occurrence analysis and advanced topic modeling.