Against the backdrop of the knowledge economy and digital intelligence technology, manufacturing industry clusters have gradually become crucial in promoting regional economic development and strengthening national competitiveness.Over the past two decades, China has been developing a vast and highly skilled workforce that is adapted to the most tech-intensive industries.In this process, some companies have cultivated their amazing manufacturing capabilities by contracting foreign products, and thus the manufacturing clusters have formed the knowledge ecology base in the formation process.However, the "theoretical darkbox" of how knowledge ecology affects the transformation of manufacturing clusters as well as the combination effect of influencing factors.How to promote its efficient transformation has attracted the attention of both scholars and practitioners.
According to the knowledge ecology theory, the knowledge ecosystem contains the interaction between knowledge subjects within a cluster and between subjects and the environment, maintaining the order of interspecific relationships and niche evolution at different levels.Therefore, the six elements of knowledge subject capability, knowledge chain integration, knowledge network construction, knowledge evolution and collaboration, knowledge transformation guarantee, and academic research assistance are regarded as the antecedent variables for the transformation and upgrading of the manufacturing industry.The transformation and upgrading performance is used as the outcome variable, and a path framework for the transformation and upgrading of the manufacturing industry cluster is constructed with knowledge innovation as the core and knowledge flow as the link .The study tracks 20 official websites of automobile companies using Python to obtain two-year data from January 1, 2019 to December 31, 2020.On the basis of the data, the multiple concurrent linkage effects of three types and six antecedents in the transformation and upgrading of manufacturing clusters are analyzed from the perspective of knowledge ecology by the necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis (fsQCA) methods.
The results show that all knowledge ecological elements are not necessary for high transformation performance, and only the condition of "knowledge subject capability" plays a more general role in high transformation performance.The transformation and upgrading paths with high performance are in the types of "government-industry-academia-research synergy", "high knowledge cohesion" and "high knowledge cohesion".When the ability of the knowledge subject exists as the core condition, there is a progressive relationship among the three paths, and there is a potential substitution relationship between the knowledge ecological structure and the knowledge environment.For manufacturing clusters, the construction of a sound knowledge ecological structure and high knowledge innovation behavior are key to achieving high transformation and upgrading performance.
In terms of the theoretical contributions of this study, it firstly constructs an integrative model to analyze the linkage and collaboration mechanism built by knowledge ecological structure, innovation behavior and knowledge environment, as well as the interaction relationship among various knowledge elements, so as to reveal the complex causal mechanism of knowledge ecological factors affecting the transformation performance of manufacturing clusters.Secondly, focusing on the performance and development of knowledge innovation behavior in different ecological structures, it clarifies its internal logic and helps to transfer the empirical method to the higher dimension of the cluster level, so as to improve the overall competitiveness.Thirdly, it sorts out the mechanism of knowledge innovation in manufacturing clusters and expands the theoretical research on the transformation and upgrading of manufacturing clusters from the perspective of knowledge ecology.
As to the practical contributions of this study, it first provides practical paths and methods for the transformation of manufacturing clusters against the background of digital economy and intelligent manufacturing.Second, it makes up for the lack of theories and methods in the upgrading of industrial chain, and provides practical methods for the high-quality development of clusters.Third, it provides management suggestions, practical paths, and a policy basis for the government to formulate talent introduction and industrial policies.