Patterns of technological learning within the knowledge systems of industrial clusters in emerging economies: Evidence from China |
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| Authors: | Bin Guo Jing-Jing Guo |
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| Institution: | 1. Management Science and Engineering at the School of Business, East China University of Science and Technology, 130 Meilong Road Box 114, Shanghai 200237, China;2. Marketing and Management & Organization, Director of the Center for Global Innovation and Neely Chair of American Enterprise at the Marshall School of Business, University of Southern California, P.O. Box 90089-0443, Los Angeles, CA, USA;1. Department of Strategy & International Business, Kent Business School, University of Kent, UK;2. Newcastle University London, UK;3. Department of Strategy & International Business, Birmingham Business School, The University of Birmingham, UK;1. University of Dubrovnik, Department of Economics and Business Economics, Dubrovnik, Croatia;2. The Institute of Economics Zagreb, The Department for Innovation, Business Economics and Business Sectors, Zagreb, Croatia;1. School of Economics and Management, Tsinghua University, Beijing, 100084, China;2. School of Guanghua Management, Peking University, Beijing, 100084, China;3. Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China;4. Central South University, Changsha, 410083, China |
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| 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. |
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