REVIEW
Guo Yu, Yu Zhiting, Lü Yan, Han Xuewen
[Purpose/Significance] Digital intelligence governance(DIG) is a novel research field formed by integrating digital technologies and intelligent governance concepts, and it has progressively emerged as a crucial agenda in academia. In the realm of DIG, Information Resource Management(IRM) undertakes pivotal functions such as data integration, knowledge organization, and governance decision support. From the perspective of IRM, this study aims to clarify the conceptual evolution of DIG in China, reveal the structural composition and inherent logic of its research themes, and explore future developmental trajectories. By doing so, it seeks to provide a theoretical foundation and practical implications for the paradigm transition of China's governance model from “experience-driven” to “data-intelligence-driven.” [Method/Process] Taking domestic literature related to DIG retrieved from the China National Knowledge Infrastructure(CNKI) as the research object, this study employed the Latent Dirichlet Allocation(LDA) topic modeling method to conduct topic identification and semantic clustering on literature abstracts. By comprehensively evaluating the dual indicators of perplexity and coherence, the optimal number of topics was determined. Subsequently, by integrating manual coding and content analysis methods, a systematic categorization, conceptual differentiation, and paradigm review of the primary research themes were conducted. Furthermore, by introducing the techno-economic paradigm theory and holistic governance theory, and focusing on the reorganization process of the “datafication—intelligentization—collaboration—securitization” value chain within the governance system, an analytical framework for DIG research was constructed from the IRM perspective. [Result/Conclusion] The research identified 8 distinct themes within China's DIG research. Through semantic clustering and content analysis, these themes were condensed into 4 core categories: “digital intelligence construction,” “digital intelligence transformation,” “digital intelligence development,” and “digital intelligence risk governance.” The findings reveal the phased logic and evolutionary characteristics of DIG research, delineating a progressive structural framework from foundational infrastructure to systemic transformation, value expansion, and security assurance. Specifically, digital intelligence construction constitutes the foundational layer emphasizing institutional leadership and resource integration; digital intelligence transformation acts as the upgrading layer focusing on industrial revitalization and organizational mechanism reform; digital intelligence development serves as the value-expansion layer oriented toward strategic scenarios like rural revitalization and new quality productive forces; and digital intelligence risk governance operates as the cross-cutting trust-assurance layer addressing technical risks and emergency responses. These results systematically deepen the understanding of China's DIG knowledge system and elucidate the operational logic of governance from an IRM perspective. [Innovation/Value] The innovation and value of this study lie in its unique IRM perspective, which transcends traditional disciplinary boundaries by highlighting the intelligent allocation of governance resources and knowledge empowerment. It explicitly delineates the conceptual evolution spectrum from “data governance” and “digital governance” to “digital intelligence governance,” clarifying the essential transition from an “information-driven” to an “intelligence-driven” paradigm. Moreover, the constructed theoretical framework integrates the IRM value chain with governance evolution, providing a structured map that elucidates how data resources, algorithmic capabilities, and knowledge organization collectively drive the dynamic cycle of “data-driven—intelligent decision-making—collaborative execution,” thereby offering a significant theoretical reference for future research and practice. [Insufficient/Improvement] The primary limitation of this study is its exclusive focus on domestic(Chinese) literature and practices concerning digital intelligence governance, which may constrain the global applicability of the findings. For future improvement, research should expand its international vision and engage in cross-domain collaboration. It is imperative to systematically investigate the frontier issues, policy frameworks, and governance models of DIG across different nations. Furthermore, exploring global collaborative mechanisms for DIG involving multiple stakeholders and constructing a theoretical system with Chinese characteristics and international influence will provide deeper academic support and a practical reference for IRM and related disciplines.
