Discovering technological opportunities by identifying dynamic structure-coupling patterns and lead-lag distance between science and technology

Technological opportunities are bred in intricate and interactive connections between science and technology (S&T). To identify these potential opportunities, lexicalor topic-based similarity approaches have been extensively applied to quantify S&T linkages; however, these lack consideration of different interaction patterns and lead-lag relationships between S&T. To this end, this study proposes a novel approach to detect technological opportunities within specific S&T topics by incorporating their structure-coupling patterns and temporal lead-lag distance. By transforming S&T knowledge systems into knowledge networks, a network coupling approach is employed to elaborate dynamic interaction patterns of S&T, and a time-lagged cross-correlation analysis is conducted to calculate their lead-lag distance under different time shifts. An evidence analysis from the energy conservation field demonstrates the feasibility and reliability of the proposed methodology in identifying technological opportunities implicit in S&T shared (exists in both S&T) and private topics (exists only in science or technology) from a topical dimension.