Artificial intelligence is a typical interdisciplinary field that integrates multiple disciplines and domains, exhibiting strong pervasiveness and convergence. Against the backdrop of the deepening implementation of the "AI+" initiative, adopting an integrative perspective to dissect the underlying logic of AI-enabled empowerment and systematically clarifying its essential mechanisms is of paramount theoretical and practical significance. This effort is crucial for identifying key pathways for the deep integration of AI with various sectors, promoting the practical application of AI, and optimizing AI governance. Based on this, this paper proposed that the essence of AI-enabled empowerment lies in the triple integration of "data integration, knowledge integration, and system integration". Specifically: (1) Data integration enables the integration of cross-modality, cross-spatiotemporal, and cross-domain datasets; (2) Knowledge integration drives five core capabilities: association recognition, causal reasoning, contradiction discovery, convergence approximation mutation emergence; (3) System integration achieves the engineering implementation of foundational technologies, functional technologies, and domain-specific technologies. To address challenges in integration—including inadequate data circulation mechanisms, algorithmic bias and decision-making deviations, and increased system security vulnerabilities—the following recommendations are proposed: (1) data circulation frameworks and standardization protocols should be refined to strengthen full-lifecycle data security safeguards; (2) algorithmic bias governance should be enhanced to improve model transparency and interpretability; and (3) the implementation of intelligent system integration should be pioneered to advance engineering resilience and ethical compliance capabilities. This study will provide valuable insights and references for promoting the healthy development of artificial intelligence and driving the intelligent transformation and upgrading of industries.

  Realizing the value of data elements constitutes both a significant theoretical issue and an urgent practical demand for enterprise development in the digital economy era. This study systematically reviewed the literature on data value published between 1991 and 2024 in the fields of management and law. Drawing on the grounded theory for data coding, it distilled three fundamental questions of data value: which categories of data are of value? What forms of value can be derived from data? How is the value of data realized? The three issues respectively correspond to the value attributes of data, contents of data value, and the mechanisms by which that value is realized. The research indicated that: (1) The value attributes of data manifest as inherent intangibility, technical reusability, increasing returns to scale in economic terms, and limited exclusivity of rights; (2) The value of data encompasses economic, social, and political dimensions. From the perspectives of data stakeholders, namely data operators, data providers, and data governors, their respective value objectives are to enhance productive efficiency, achieve digital equity, and ensure data security; (3) The mechanism for realizing data value primarily encompasses the stages of value creation, value circulation, and value distribution. Data value creation is fundamentally grounded in data standardization, which specifically includes the assetization, productization, and capitalization of data. The circulation of data value faces pressing challenges in pricing, necessitating the orderly advancement of data trading, openness, and sharing. Through initial allocation and subsequent redistribution of data value, improvements in economic efficiency and the realization of social equity can be promoted. Finally, the paper outlined prospective research directions concerning the theoretical frameworks and methodologies underlying the systemic operation and realization mechanisms of data value.

  Under the background of green transformation, Chinese enterprises are accelerating the promotion of sustainable development, and digital technology has become a key approach for transformation. This study adopted the green transformation of Fengdeng Environmental Protection (CONBA) as an example and conducts an exploratory single case study to construct a logical system of enterprise green dynamic capability guided by green cognition and practical experience. The study found that the enterprise has formed a "double cognition-investment behaviour-green dynamic capability" capacity building mechanism in the process of green transformation. First, the double cognition undergoes specific evolutionary processes, with green cognition undergoing a process of "aggregation-disintegration-reconstruction", while digital cognition shows a change from "enlightenment-awakening-expansion". Second, green knowledge and green performance guidance is the key to implementing digitalization investment, and the digitalization investment behaviour has shifted from passive acceptance to active adoption, and has developed into an active expansion of external opportunities. Finally, in the green cognition-guided digitalization investment practice, the enterprise gradually builds a green dynamic capability system that covers green value perception, green value reconstruction, and green value transformation. The theoretical framework of this study will provide important insights into how green transformation enterprises can drive green competitiveness through dual cognition.

   The development of "Specialized, Refined, Distinctive and Innovative" (SRDI) small and medium-sized enterprises (SMEs) and tackling key technological challenges in critical areas are of significant importance for China to build a self-reliant and controllable industrial supply chain. Using the sample of A-share listed companies from 2011 to 2021, this study employed the methods such as the difference-in-differences (DID), propensity score matching-DID (PSM-DID), and instrumental variable (IV) regression to examine the impacts of SRDI certification on corporate technological innovation and its mechanisms. The study found that the certification significantly promotes technological innovation, particularly in SMEs, and this result remains robust after controlling endogeneity. Further analysis revealed that high-intensity government subsidies weaken the policy effect, with this inhibitory effect being more pronounced for medium and large-sized enterprises. In contrast, the development of digital finance alleviates the constraints imposed by the imbalance in traditional finance, thereby enhancing the policy′s impact on technological innovation. The findings of this study will provide theoretical support for the rationality of China′s specialized innovation certification policy system. However, the study suggests that government departments should reconsider the traditional selection mechanism of "self-declaration-expert review-government funding" and promote digital finance as a positive complement to traditional finance to further optimize resource allocation efficiency in the certification process.