Detecting latent topics and trends in IoT and e-commerce using BERTopic modeling

The rapid development of the Internet of Things (IoT) is reshaping e-commerce, driving business model innovation and enhancing operational efficiency. However, existing research primarily focuses on specific application scenarios of IoT, while lacking a systematic exploration of its overall development trends, core research topics, and challenges. To address this gap, this study employed BERTopic topic modeling to systematically analyze key research themes and evolutionary trends of IoT in the e-commerce domain, based on 169 highly relevant papers from the Web of Science database (2010–2024). The findings revealed four core themes: (1) the transformation of e-commerce business models driven by IoT technologies, (2) the role of blockchain in data security and trust mechanisms, (3) the synergy between smart logistics and e-commerce, and (4) privacy protection and personal data management in the IoT ecosystem. Additionally, this study identified a shift in IoT applications from an initial focus on supply chain optimization to an increasing emphasis on data-driven decision-making, intelligent business models, and data privacy protection. By conducting an in-depth analysis of the dynamic evolution of these themes, this research not only fills the knowledge gap regarding the current state and trends of IoT research in e-commerce, but also provides the academic community with an innovative method applicable to large-scale text data analysis. Furthermore, for businesses and policymakers, strengthening cross-sectoral technological integration, improving privacy protection mechanisms, and enhancing policy support are suggested to promote the sustainable development of IoT and e-commerce. This research enriches the academic discourse on the synergy between IoT and e-commerce in the context of digital transformation, and provides strategic guidance for practitioners.