During the third wave of industrial development, Japan’s unique tacit knowledge-driven model achieved a remarkable success by nationwide collaborative efforts. The lineage of Nobel laureates at Kyoto University and the success of the Very Large Scale Integration (VLSI) project underscored the pivotal role of apprenticeship-based laboratories and the spirit of craftsmanship in achieving break through innovation. Tacit knowledge, through socialization transmission and externalization mechanisms, became the foundational logic of Japan’s “technologyical nation-building”. However, the highly context-dependent nature of tacit knowledge also created a “negative legacy” : disciplinary barriers hindered knowledge combination innovation, and international collaboration was constrained, resulting in Japan’s limited output of explicit knowledge in emerging fields. In the era of artificial intelligence, this tension has become more pronounced. Although generative AI can accelerate the externalization of tacit knowledge, it cannot replace the core role of humans in complex decision-making, interdisciplinary innovation, and collective cognition. The rise of dark knowledge further challenges the traditional tacit-to-explicit knowledge transformation model and highlights the urgent need to develop a collaborative paradigm between human tacit knowledge and AI explicit capability. At the recently concluded “Pujiang Innovation Forum—Shanghai Forum on Science of Science·2025 International Science, Technology and Innovation Think Tank Forum”, multiple experts unanimously pointed out that innovation lies not only in the accumulation of explicit knowledge, but more crucially, in the deep sedimentation of tacit knowledge and the effective transformation of implicit knowledge into explicit knowledge. This study reminds key insights for intelligence professionals at a time when knowledge sovereignty is undergoing profound reconstruction. They must recognize the strategic value of tacit knowledge and its irreplaceable role in forecasting trends, integrating cognition, and stimulating innovation. At the same time, they should actively harness AI tools
to enhance knowledge externalization while preserving human subjectivity and value judgment in the processes of integration and collaboration, so as to avoid the alienation of innovation under the dominall of technological nationality. Only in this way can we truly grasp the deeper driving force and direction of scientific and technological innovation.

In view of the differences between the existing empirical studies on the influencing factors of executive environmental scanning behavior, a comprehensive analysis of the relevant empirical studies in this field is of great significance for the development of organizational decision-making and future research on environmental scanning. This study employs meta-analytic methods, utilizing a random-effects model to synthesize 68 relevant empirical studies from both domestic and international sources. The analysis focuses on 28 influencing factors of executive environmental scanning behavior and the corresponding 223 independent effect values. Through heterogeneity tests, publication bias assessments, and subgroup analyses, the study explores the reasons for inconsistencies among the included studies, thereby evaluating the impact of moderating variables. The results indicate that
among the influencing factors of executive’ environmental scanning behavior across environmental, organizational, and individual dimensions, 18 factors have a positive impact. Key factors include managerial orientation, perceived strategic volatility, and perceived information quality. These factors are critical in influencing environmental scanning behavior. Additionally, the level of national economic development plays a moderating role in affecting organizational environmental scanning behavior.

With the development of green economy, China has formulated a number of policies on the promotion and application of new energy vehicles. The market share of new energy vehicles is increasing, and it is very important to identify their competitors and competitive products referred to as “competitiors”. The purpose of this study is to construct a competitive product identification model for new energy vehicles by using multi-source reviews with complementary advantages. The co-occurrence analysis method and the sentiment analysis method are used to construct a new energy vehicle competitive product identification model consisting of four modules: data collection and processing, preliminary screening of competitive products, identification of competitive products and analysis of competitive products. The Xiaomi SU7 new energy vehicle is taken as an example for empirical analysis. Using this model to analyze the competitive products of Xiaomi SU7, on the one hand, the advantages and disadvantages of Xiaomi SU7 are identified, and on the other hand, the core competitive products of Xiaomi SU7 are identified, and the effectiveness of the model is verified.

Since the China-US friction, trademark litigation has become a significant factor restricting the overseas development of Chinese enterprises. During the period from 2019 to 2023, the number of trademark litigation cases involving China has been on the rise, with Chinese enterprises often being defendants and the rate of default judgments remaining high. This predicament stems from both internal and external factors: externally, there are challenges such as targeted policies from the US government, judicial environment bias, and differences in legal systems; internally, there are shortcomings like lagging strategic awareness, incomplete management systems, and weak litigation response capabilities. Addressing these issues requires a systematic solution, including optimizing trademark layout and enhancing litigation response capabilities at the enterprise level, and at the national level, building a multi-level rights protection service system for cross-border cases.

This study takes the typical intelligence research products of well-known foreign think tanks as research cases, and analyzes several aspects such as the information sources, release time, and utilization characteristics of the open source information they adopt. It summarizes the methods and processes of developing and utilizing open source information, and based on current needs and the characteristics of open source information, it proposes a process for the development and utilization of foreign open source information in China’s intelligence work, with the aim of providing certain references for the research of open source intelligence in China.

In the evolving judicial landscape where the logic of “information as evidence” is increasingly emphasized, library and information institutions are gradually transforming from traditional information intermediaries into litigation support actors in intellectual property (IP) disputes. Using three representative cases involving the Shanghai Library, this paper examines how such institutions contribute to evidentiary construction in trademark infringement through generic term validation, in design patent infringement through image-to-text retrieval, and in utility patent infringement through specialized data acquisition. It systematically analyzes the practices by which information is effectively transformed into admissible evidence and distills four operational principles: Timeliness of Retrieval Boundaries, Priority of Source Credibility in Literature Acceptance, Precise Semantic Translation, and Optimization of Collaborative Interfacing. The study offers a practical paradigm for expanding the judicial support functions of library and information institutions, and provides a strategic reference for professionals seeking to maintain technical relevance and institutional value amid the growing prevalence of AI in the digital era.