Knowledge Management in Information Science and Knowledge Studies: Trend Analysis and Future Prediction Using Time Series

Document Type : Original Article

Authors

1 Associate Professor, Department of Knowledge and Information Science, University of Qom, Qom, Iran

2 Qom university

Abstract

Objective: This study aims to analyze the intellectual structure, examine quantitative trends, and predict the future trajectories of the Knowledge Management (KM) field within Information Science and Library Science (LIS). Given the lack of a comprehensive understanding of its internal structure, thematic clusters, growth and decline patterns, and emerging research fronts, this study seeks to fill this research gap by employing a scientometric approach and time series analysis to map the scientific landscape of this domain. The primary research questions focus on identifying the current structure, forecasting the growth of publications and citations, analyzing scientific collaboration patterns, and identifying emerging topics.



Methodology: This study employed bibliometric and scientometric methods. Data were extracted from the Web of Science (WoS) database by searching the keyword "knowledge management" within the "Information Science & Library Science" subject category, without time or language restrictions, totaling 4,940 articles. To analyze the intellectual structure, co-authorship and co-citation networks, and identify thematic clusters, tools such as VOSviewer and CiteSpace were utilized. To forecast future trends in the number of publications, citations, and references, ARIMA (Autoregressive Integrated Moving Average) time series analysis models were applied to both domestic data (from 2000 to 2023) and international data.



Findings: The analysis revealed that the field of KM in LIS is divided into four main clusters: "knowledge management technology" (accounting for the highest number of articles and citations), "knowledge sharing," "innovation & knowledge management," and "knowledge creation." Time series forecasts indicate a continuous upward trend in the number of publications at both domestic and international levels. However, a key difference was observed: while the number of citations shows steady growth internationally, domestic research exhibits volatility and a predicted declining trend. Emerging global topics are shifting towards "big data," "social media," "sustainability," and "open innovation," whereas the domestic focus remains on core concepts like "organizational culture" and "academic libraries."



Conclusion: This research demonstrates that Knowledge Management in Information Science is a dynamic and growing field, but the developmental patterns and thematic focus of domestic and international research differ significantly. The gap between the growth trend in publications and their impact (citations) in domestic research, as well as the divergence in research topics, requires special attention. To enhance the quality and impact of domestic research, it is recommended to increase focus on emerging technologies (such as AI and big data), strengthen international scientific collaboration, adopt data-driven analytical methods, and develop indigenous models for implementing knowledge management in organizations.

Keywords

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Applied Scientometric Studies
Volume 2, Issue 3 - Serial Number 7
November 2025
Pages 46-75

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