• Knowledge representation, reasoning, and inference emerged as a unifying methodological core, integrating cognitive science, AI, and social theory through explicit representations (graphs, plans, beliefs) and formalized inference across multiple domains [9], [11], [12], [13], [14], [15], [19].

• Organizational knowledge management and policy of knowledge transfer emphasize production, dissemination, and practical utilization within firms, research institutes, and industry, highlighting management–technology alignment, information systems, and dissemination strategies [1], [4], [6], [8], [10], [20].

• Tacit knowledge, trust, and social construction influence knowledge creation and diffusion. Teams and networks tacitly share know-how, with narratives and social relations shaping scientific networks and beliefs [3], [9], [16], [17], [19].

• Computational tools for real-world knowledge, including knowledge-based systems and information-system planning, begin to formalize knowledge capture and reasoning, foreshadowing decision-support and AI-style knowledge processing [7], [14], [20].

• Knowledge creation, growth, and the socio-economic structuring of knowledge processes analyze how knowledge emerges, is valued, and diffuses within science and society, connecting knowledge interests with social and economic dynamics [2], [3], [16], [17].