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 “technological 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 domination 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.