A bibliometric and altmetric analysis of global research on AI-empowering information resource management

Bibliographic Data

ID21500722
AuthorsXu Wang (0000-0002-7634-6716, Yanshan University, corresponding author), Dongyang Shi (0009-0008-2042-9345, Yanshan University), Kiran Shehzadi (0009-0005-8020-5800, Yanshan University)
Year2025
Publication date2025-07-09
Open AccessYes
TypeARTICLE
VenueInformation Development (JOURNAL)
Journal identifiersISSN: 0266-6669 • E-ISSN: 1741-6469
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/02666669251357947
OpenAlexW4412116130
LanguageEN
References cited35

In the era of intelligent paradigms, the rapid advancement of artificial intelligence (AI) technology has injected new research vitality and exploration directions into the field of information resource management (IRM). However, current research shows a general lack of systematic exploration and analysis of the deep integration between AI technology and IRM. Drawing upon this foundation, this study comprehensively utilizes bibliometric, altmetric, and content analysis methodologies. By leveraging China National Knowledge Infrastructure (CNKI), Web of Science, and Altmetric.com as data repositories, it amasses a collection of journal articles published from 2014 to 2024 to serve as the research sample. Employing the CiteSpace visualization software, it generates keyword clustering knowledge mappings and time-zone knowledge mappings for both international and Chinese AI-empowering IRM research. This study reveals that international and Chinese academic communities demonstrate both shared technological paradigms and distinctive research perspectives in AI-empowering IRM research. By analyzing emerging high-frequency keywords and altmetrics indicators, it captures evolving academic hotspots and social focus, enabling systematic projections about future research trajectories. Finally, by constructing a theoretical framework for AI-empowering IRM research, this study puts forward five prospects for the future development of AI-empowering IRM field research. Through comprehensive exploration and systematic analysis, this study aims to provide valuable references and recommendations for subsequent research

Bibliometrics · Data science · Knowledge management · World Wide Web · AI in Service Interactions · Big Data and Business Intelligence · Computer Science · Ethics and Social Impacts of AI

Tools

Open DOI