The Use of the Bibliometric Method for Analyzing of Knowledge Management and Big Data Research Publications - Waisurasingha, Chutima; Waisurasingha, Chattichai (Journal of Information Science Research and Practice, 2024)
Purpose: To study the current state of knowledge management and big data research, and to analyze future research content, directions, and trends. Methodology: This research employed bibliometric analysis of documents related to knowledge management and big data, published between 2007 and 2023 in the Scopus database. The macro level analysis reflected the general state of research at the national, organizational, publication source, and researcher levels. Meanwhile, the micro-level analysis examined the content and directions of research related to knowledge management and big data. Findings: The macro-level analysis reveals that research in this field is distributed across all world regions, with China and the United States of America producing the highest publication outputs. Academic institutions produce most of this research output. The micro-level analysis indicates that the future direction of research in knowledge management and big data involves interdisciplinary integration, leading to the development of new tools. Examples include research on artificial intelligence (AI), machine learning, deep learning, and data analytics aimed at extracting knowledge from big data repositories for organizational benefit. Significant research also focuses on technologies supporting semantic analysis, such as the semantic web and ontologies, which help create connections and represent relationships between terms. Furthermore, there is prominent research on the Internet of Things (IoT) for collecting, accessing, and processing big data within cloud computing platforms. Applications of the Study: The results indicate that bibliometric analysis can be used to analyze and synthesize research both broadly and deeply. Broadly, it demonstrates the advancement of research by identifying the countries and institutions involved and those with expertise in specific research areas. In contrast, the in-depth analysis reveals the core content between the two analyzed disciplines and their connections to other related fields. Additionally, bibliometric analysis is particularly suitable for studying the relationship between knowledge management and big data. The analysis results show a significant correlation, with big data serving as a vast repository of organizational knowledge, while knowledge management is a tool for extracting and utilizing this knowledge. However, due to the diversity and depth of big data, extracting helpful knowledge requires integrating knowledge management processes and tools with various disciplines, such as artificial intelligence, machine learning, deep learning, and ontology. The approach to extracting organizational knowledge depends on the data type and the context in which the knowledge from big data is applied. This perspective highlights the unique outcomes of bibliometric analysis, suggesting that this method could also be applied to analyze research or knowledge in other fields.