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

Abstract

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.

Topics & Concepts