Context sensitivity of regional complex knowledge: From an analytical framework to empirical studies

Abstract

Regional complex knowledge evolution has become a popular topic in the economic geography literature. Scholars measure regional complex knowledge to explain regional economic complexity or the agglomeration of innovative activities. According to the literature, such knowledge is tacit in nature, and it is mainly static and ingrained in the workers, companies, and institutions of specific locations. While studies have provided valuable insights into the agglomerative spatial patterns of complex knowledge production, making significant advancements in how it is measured and evaluated, they have not addressed the sensitivity of the context of complex regional knowledge in economic geography. To address such a gap, this dissertation aims to advance the understanding of complex knowledge by examining knowledge base combinations. I do so by exploring and comparing knowledge evolutionary processes in two industries in Shanghai: high-end medical devices and electric vehicles. This dissertation makes four main contributions. First, it advances the understanding of complex knowledge from a CKB perspective, providing a complementary approach to measuring complex knowledge in economic geography. Second, it introduces a contextsensitive theory of complex knowledge evolution by combining the concepts of CKBs and ISR. Third, it draws on a recent empirical study of the Shanghai medical device and automobile industries to illustrate the theory and shed light on complex knowledge trajectories and the relations among multiple sectors at the regional level. Fourth, it examines upstream–downstream interactions in the Shanghai medical device and electric vehicle industrial chains, refining complex knowledge research at different spatial scales and transitional contexts