Patterns of technological learning within the knowledge systems of industrial clusters in emerging economies: Evidence from China

Abstract Through an interview-based exploratory study and a follow-up survey-based quantitative analysis, this paper investigates the technological learning pattern in terms of structure and mechanisms of interaction within the knowledge system of two industrial clusters in China. Unlike the recent studies that suggest that industrial cluster comprises disconnected leader-centered communities, we argue that the different leader-centered communities within the knowledge systems of industrial clusters are not disconnected from each other. Instead, those communities are inter-connected through the so-called ‘knowledge spanning mechanisms’. Regarding the interaction dimension of technological learning pattern, this paper argues that in analyzing learning behavior in the knowledge networks of industrial clusters, it is necessary to synthesize the learning opportunity perspective and the absorptive capacity perspective to better understand and explain the similarities and dissimilarities in technological learning behavior among different cluster types, across cognitive subgroups, and between product innovation and process innovation. Our study reveals that in the context of emerging countries, the following four factors are decisive for technological learning opportunities inside the knowledge networks of industrial clusters: the underlying complexity of technology in clusters, the inter-connectedness between product and process, path dependency in knowledge searching, and the incremental nature of a cluster’s technological development.