디지털 커머스 연구의 지식 구조와 주제 변화에 관한 시계열 분석

This study collected metadata (titles, abstracts, and keywords) from 1,271 domestic e-commerce-related journal articles published between 2009 and 2025 and analyzed topic evolution over time using a three-year rolling window with two-year intervals. By applying the state-of-the-art BERTopic algorithm, the study identified key topics for each period and quantitatively examined structural relationships and centrality among topics through co-occurrence network analysis. The results show that from 2009 to 2015, research primarily focused on consumer behavior topics such as shopping, quality, satisfaction, and trust, while from 2016 to 2020, technology convergence themes such as mobile, SNS, and big data became prominent. Since 2021, emerging data- and platform-driven topics—including AI, generative models, metaverse, ESG, live commerce, and OTT services—have rapidly gained attention, whereas traditional subjects like mobile service and social commerce have gradually declined. These findings indicate a paradigm shift in e-commerce research toward technology, data, and platform-centered convergence, accompanied by accelerated knowledge diffusion. By integrating context-based topic modeling with network analysis, this study empirically elucidates the structural evolution of research in the e-commerce domain and provides foundational insights for future academic development and industrial strategy formulation.