Knowledge Development Trajectories of the Radio Frequency Identification Domain: An Academic Study Based on Citation and Main Paths Analysis

Abstract

The study collected papers on radio frequency identification (RFID) applications from an academic database to explore the topic’s development trajectory and predict future development trends. Overall, 3820 papers were collected, and citation networks were established on the basis of the literature references. Main path analysis was performed on the networks to determine the development trajectory of RFID applications. After clustering into groups, the results are twenty clusters, and six clusters with citation counts of more than 200 were obtained. Cluster and word cloud analyses were conducted, and the main research themes were identified: RFID applications in supply chain management, antenna design, collision prevention protocols, privacy and safety, tag sensors, and localization systems. Text mining was performed on the titles and abstracts of the papers to identify frequent keywords and topics of interest to researchers. Finally, statistical analysis of papers published in the previous 4 years revealed RFID applications in construction, aquaculture, and experimentation are less frequently discussed themes. This study provides planning directions for industry, and the findings serve as a reference for business domain. The integrated analysis successfully determined the trajectory of RFID-based technological development and applications as well as forecast the direction of future research.

Methods and Applications of Data Mining in Business Domains

“…In addition to these papers collectively contributing to the field of data-driven applications and predictive modelling across a variety of domains, they offer specific approaches, such as innovative approaches to optimizing product layouts in supermarkets using sequential pattern mining and optimization algorithms [22]. Furthermore, they introduce a novel supervised learning algorithm for financial risk assessment [23] and explore the development trajectory of radio frequency identification (RFID) applications through academic citation and text mining analysis [24]. Another paper presents a deep learning framework for predicting important trading points in the stock market [25], focusing on high-margin opportunities.…”