Measuring the Structure of a Technology System for Directing Technological Transition

Abstract

Technological advancements have generated a “techno‐sphere” within which all humans live. However, the capacity to direct technology development lags far behind technology development itself. This study deciphers the structural characteristics of a technology system using three pairs of features: systemicity and complexity (scalar), centrality and diversity (structural), and adaptability and inertia (structural); and at micro‐, meso‐, and macrolevels. By applying this approach in Chinese agricultural and water technology systems in the Yellow River Region and the Yangtze River Region from the beginning of agriculture in ≈8000 BC to the end of preindustrial agriculture in 1911, it is found that there exist trade‐off relationships between the centrality and diversity of a technology system, there exist alternative dominations of adaptivity and inertia in development of a technology system, and there exist time‐lag phenomena of change in a technology system between mesolevel and macrolevel. It is also identified that a larger‐scale, more diverse and adaptive technology system is observed in the Yellow River Region whereas the technology system in the Yangtze River Region is more rapidly expanding in scale and mainly dominated by inertia. These discoveries will assist increasing the capacity of managing and directing technological transition in future.

Structural gaps of water resources knowledge in global river basins

“…After duplicates, special characters, and meaningless stop words were removed, these keywords were stemmed and ranked based on their term frequency-inverse document frequency (TF-IDF). TF-IDF was calculated to give higher weights to keywords with a high appearance frequency in its corresponding section and a low overall appearance frequency in the entire text collection to avoid a bias towards general terms and to grasp the newly appeared keywords (Xiong et al, 2014).…”

“…We define the knowledge development in a river basin as a complex system involving scientific disciplines and management issues, each of which have their respective evolutionary dynamics (Von Bertalanffy, 1968;Wu et al, 2020). Network analysis, which can simplify the real systems while preserving the essential information of their interactive structures that lead to the emergence of complex phenomena (Zeng et al, 2017), was used to investigate the development of the water resources knowledge structure.…”