Purpose This study maps the evolution and intellectual structure of LinkedIn-related scholarship and situates it within the broader framework of online professional information behavior (OPIB). Design/methodology/approach A bibliometric analysis of 876 publications indexed in the Web of Science Core Collection (2000–2026) was conducted using VOSviewer and Bibliometrix. Co-authorship, co-citation, and keyword co-occurrence analyses were employed to examine collaboration patterns, knowledge foundations and thematic configurations. Findings LinkedIn research demonstrates sustained growth and increasing international collaboration. Co-citation analysis identifies three major research paradigms: user behavior and technology application, organizational use and career development and strategic management and digital transformation. Keyword clustering further reveals seven thematic areas, ranging from platform technology and data science to professional identity construction, online social capital and organizational diversity. Thematic evolution shows a shift from functional recruitment concerns toward identity-oriented and structurally embedded analyses, and more recently toward algorithmic governance and technologically mediated professional behavior. Research limitations/implications Despite reliance on a single database, this study offers clear theoretical and practical implications for OPIB. Theoretically, the findings clarify how social cognitive theory and self-determination theory shape research on professional identity and signaling, contributing to conceptual consolidation within OPIB. Practically, the results support more transparent recruitment practices by highlighting algorithmic evaluation and potential bias in platform-mediated hiring. Originality/value This study provides a comprehensive bibliometric overview of LinkedIn-related research, offering a quantitative mapping of scholarly discourse on OPIB. The findings serve as a reference point for future interdisciplinary research and theoretical development. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2025-0555
Building similarity graph...
Analyzing shared references across papers
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Xiangfeng He
Zhirong Yang
Online Information Review
Jinan University
Building similarity graph...
Analyzing shared references across papers
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He et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d896566c1944d70ce07bd3 — DOI: https://doi.org/10.1108/oir-07-2025-0555