بررسی روند پژوهشی تاثیر اشتراک‌ دانش بر قابلیت‌های سازمانی و عملکرد سازمانی با رویکرد کتاب‌سنجی

Purpose: In the era of a knowledge-based economy, knowledge sharing is recognized as a key factor shaping organizational capabilities and enhancing organizational performance. The advancement of emerging technologies, such as artificial intelligence (AI), has highlighted their role in creating sustainable competitive advantages. This study aims to examine the trends in scientific studies on the impact of knowledge sharing on organizational capabilities and organizational performance over the past 25 years (2000 to 2025) using bibliometric methods. The goal was to provide a conceptual map of the evolution of this field and identify research gaps. By analyzing 1719 research articles extracted from the Scopus database, this study offers comprehensive insights into the path of research development, key trends, main sources, and influential authors. This bibliometric analysis not only maps the quantitative growth in publications but also explores the integration of knowledge sharing with modern technologies such as AI, which acts as a catalyst for improving performance and innovation in organizations. Furthermore, it emphasizes how knowledge quality and organizational culture reinforce each other to achieve sustainable competitive advantages. The research question guiding this study is: "What has been the trend of research in the field of knowledge management regarding its impact on organizational capabilities over the last 25 years?" This inquiry is rooted in resource-based view (RBV) theory, which posits that intangible assets, such as knowledge, culture, and innovation, are primary sources of competitive advantage. Intangible assets, such as organizational learning and knowledge, can lead to greater profitability than physical or financial resources. For instance, organizational culture, as a core competency, plays a vital role in maintaining competitive advantage by fostering an environment in which knowledge is shared and innovation is strengthened.
Design/methodology/approach: This quantitative study employs a bibliometric approach, focusing on metadata from research articles, keywords, and citations. The population consisted of all relevant articles in the Scopus scientific database, one of the most reputable international sources containing over 80 million articles and 16 million authors. The sample includes 1719 articles, selected through targeted keyword searches within the time frame of 2000 to 2025. The data collection tool comprised output files from the database, including metadata such as publication year, authors, abstracts, keywords, citations, and DOIs. The validity of the research was ensured by selecting the reputable Scopus database, and its reliability was confirmed through the repeatability of the search and the analysis methods.
Two primary keyword searches were conducted to gather data. The first search used: "Knowledge management" OR sharing AND organization AND capability, yielding 508 articles within the specified period. The second search used: "Knowledge sharing" AND organization AND performance, resulting in 1281 articles. After integrating these results and removing duplicates, a final CSV file with 1719 records was created. Each record represents an article and includes details such as authors, full author names, author IDs, article title, publication year, journal title, citation count, affiliations, link, abstract, keywords, references, article language, title summary, document type, publication stage, ISSN code, and DOI identifier.
Data analysis involved descriptive statistical analyses (e.g., publication trends, top countries, and authors) using Excel software and its Power Query add-on. Network analyses, such as keyword co-occurrence, co-authorship, and source citation, were performed using VOSviewer software. This combination allowed us to examine the temporal changes in research topics. For the co-citation analysis of authors, a minimum of 100 citations was set to identify influential researchers. Similarly, for journals, co-citation networks were drawn with a minimum of 300 citations to focus on high-impact journals. Keyword trends were analyzed using overlay visualizations in VOSviewer, where color spectra indicate the recency of terms (darker colors for older terms and brighter colors for more recent ones). An analysis of keyword frequency over time was conducted for the top 30 keywords, revealing shifts in the research focus. This methodological rigor ensured a systematic and reproducible examination of the literature, aligning with established bibliometric practices.
Findings: The findings indicate that The number of articles from 2000 to 2025 has shown an upward trend, reaching over 200 articles per year in recent years (post-2020). Descriptive statistical analysis in Excel revealed that the United States and China account for more than 40% of the articles, making them the top countries. Authors such as Nonaka, with over 50 citations, are among the most influential in this field. In the VOSviewer analysis, the keyword co-occurrence network, comprising over 500 main keywords, identified clusters such as "artificial intelligence" (with a linkage strength of 150), "organizational culture" (linkage strength of 120), and "innovation" (linkage strength of 180), which constituted more than 60% of occurrences in the studied period. The trend analysis of topic changes also confirmed a 35% increase in the focus on integrating knowledge sharing with emerging technologies, such as artificial intelligence, in the last decade.
Publication trends show a significant increase starting from 2007, peaking in 2024 with 172 articles, although a slight decline to 89 in 2025. The top active authors include Salleh Kalsom (8 articles, 125 citations) and Kianto Aino (7 articles, 937 citations). The co-authorship network highlights clusters where Nonaka I. is central and has the highest total link strength. Journal co-citation analysis positions the Journal of Knowledge Management as the most cited journal, with strong connections to other management and innovation journals. The top 20 most-cited articles include works such as "Knowledge sharing and firm innovation capability: An empirical study" by Lin (2007, 1161 citations) and "Knowledge networks: Explaining effective knowledge sharing in multiunit companies" by Hansen (2002, 1132 citations).
The subject area distribution shows dominance in Business, Management and Accounting (824 articles) and Computer Science (597 articles). Keyword trends reveal consistent growth in terms like "knowledge sharing" and "innovation," but recent years show emergence of "knowledge hiding," "leadership," and "social network analysis." The overlay visualization confirms that AI-related terms are recent additions, clustered with business performance, tacit knowledge, and sustainability. The AI cluster is strongly linked to supply chain management, indicating a focus on this area, whereas other business domains show research gaps. Overall, the integration of AI with knowledge sharing is emerging as a driver of ambidexterity, enhancing both exploitative and explorative capabilities, with knowledge quality mediating these effects.
Conclusion: This study concludes that integrating knowledge sharing with artificial intelligence, as a key driver, improves organizational performance by strengthening innovation and ambidextrous capabilities, with knowledge quality and organizational culture playing reinforcing roles. Explicit knowledge sharing impacts exploitative ambidexterity, whereas tacit knowledge sharing enhances explorative innovation. Organizational culture fosters trust and collaboration, leading to better decision-making and competitive advantage. Recent trends highlight knowledge hiding as a barrier and social networks as enablers of KCP. This evolution suggests a shift towards digital knowledge economies, where AI automates processes and combines with human interaction for the creation of new knowledge. Policymakers and managers should prioritize AI integration in knowledge management to maintain competitiveness.
Originality/value: This study is the first to provide a comprehensive bibliometric analysis of the 25-year trend in this field, with a focus on AI integration. It offers novel evidence of crossing traditional methodological boundaries (combining quantitative and network analysis) and interdisciplinary borders (knowledge management and information technology), proposing new ideas for organizational policymaking in the digital economy. Unlike prior studies, this study identifies gaps in AI applications beyond supply chains and emphasizes emerging concepts such as knowledge hiding, adding value for researchers and practitioners in advancing sustainable, innovative organizations.