Determining Research Trends in the Field of Cross-Border E-Commerce (CBEC) Utilizing Text Mining Techniques

Abstract

Purpose - This paper seeks to analyze numerous abstracts associated with Cross Border E-commerce (CBEC) through the application of text mining techniques. The goal is to pinpoint key research trends and forecast upcoming developments. By delving into the existing literature and spotting emerging trends, this study aims to offer invaluable insights into the present state of research in the domain, and underscore areas warranting further exploration. Design/Methodology/Approach - In this study, text mining techniques were employed to analyze research trends in CBEC. Text mining can distill the core content of a document by analyzing keywords. The analysis encompassed 768 CBEC research articles published in journals indexed by SCI, SCIE, SSCI, and SCOPUS. Findings - After conducting research on CBEC using text mining techniques, this paper identified the primary topics of ‘Convergence Research’, ‘International Logistics’, and ‘China CBEC’. The study suggests that there is an expected transition from a straightforward e-commerce distribution model to a structure that converges with other industries. Research Implications - Given the complexities of international commerce, it is crucial to offer product information that aligns with the customs and culture of the target country. This study suggests that CBEC will likely evolve by integrating IT technologies, such as big data and AI, with international logistics. As a result, it is anticipated to emerge as a converging industry.