Cluster Governance And Cluster Learning: Relationship And Co-evolution

Since the late 1990s, the phenomenon of cluster learning attracts a lot of attentions in the field of industrial cluster research. Some literature show that the learning effects as knowledge diffusion and knowledge creation are the main reason for incremental-innovation dynamics and competitive advantages of industrial clusters. However, with regard to industrial clusters in China, the main problems currently are: 1) intensive competition induced by market saturation and industry maturity destroyed the original relationships in clusters. Meanwhile, ineffective coordination mechanisms also restrict the capability of cluster firms to build new network relations. Therefore, the overall learning effect and innovation efficiency of industrial cluster are increasingly reduced. Recently, some literature indicate that cluster governance activities, centered on institution construction, can significantly improve these situations through coordination and reconstruction network relations of an industrial cluster, thereby re-energize its learning potential. However, the research of cluster governance is still in its starting phase. The concept boundary is vague, and the endogenous dimensions are not clearly defined yet.Based on this, this dissertation examines the fundamental question of "what is the causality between industrial cluster governance and learning". Firstly, this dissertation collects qualitative data of five industrial clusters in Zhejiang Province to proceed exploratory case study, and concludes concept prototype of cluster governance based on field study, and proposes a series of initial propositions about the causalities between cluster governance, innovative network relationship and cluster learning. Secondly, I complement the field study model with theoretical deduction and refine the initial propositions into fifteen empirical hypotheses, based on which I build up the conceptual model of influential mechanisms of cluster governance on cluster learning. Thirdly, I conduct statistical test based on 37 cluster samples extracted from 367 firm questionnaires. The result shows that most of the hypotheses are supported. Fourthly and finally, after the cross-sectional study, the thesis further examines the dynamic co-evolution of cluster governance and cluster learning, following two steps: 1) builds the conceptual model of co-evolution mechanism and path between cluster governance and learning through normative research, and 2) specifies and deepens the conceptual model by a longitudinal case study of Shaoxing textile industrial cluster, and examines those specific phenomena in the observation.Drawing on the above analysis and appraisal, the main conclusions can be presented as follow:1) Cluster Governance (CG) is defined as the whole endogenous coordination mechanisms on the overall level of industrial cluster, which can constrain or encourage the economic interactions of cluster participants. And the four endogenous mechanisms are local regulation, economic hierarchy, community norms and association autonomy.2) Cluster Learning (CL) is defined on overall level of cluster as the collective learning activities with the core features of interactions, including two dimensions of explorative learning and exploitative learning.3) From a relative static perspective, all mechanisms of CG have positive effects on the explorative dimension and exploitative dimension of CL in varying degrees. And Cluster Innovation Network Relation (CINR) acts as the intermediary variable.4) From a dynamic perspective, CG and CL has the relations of reciprocal causality and reciprocal selection. Therefore in the long-term, the significant characteristics of co-evolution can be observed.Compared to the existing achievements in related research fields, this dissertation has three contributions as follow:1) Focusing on institution level, this dissertation makes convergence to the theory of industrial cluster governance and refines the concept. Existing researches on cluster governance are scattered and premature. This study clearly defines the concept of Cluster Governance, and refines the construct with field study and empirical research in order to make it suitable to quantitative research. This contribution is exploratory and of great significance in promoting the systematicness of cluster governance research.2) This dissertation reveals the specific mechanisms of the causality between cluster governance and learning based on the integration of institutional perspective and knowledge-based view. This research builds a causality analysis model of "cluster governance-innovative network relationship-cluster learning", using exploratory case study, normative analysis and statistical methods, and specifies the relationship in dimensions at the micro level. This contribution not only helps the integration of the theory of governance within industrial cluster and learning theory, but also provides a solid basis for cluster governance practices.3) This dissertation analyses the endogenous mechanisms and possible paths of the co-evolution of cluster governance and learning from the dynamic perspective. This research normatively applies the co-evolution theory and field study methodology to the co-evolution research of cluster governance and learning, and thus the enriched and deepened results are of high validity and theoretical explanatory power after the longitudinal case study. Besides, the detailed data of qualitative research and integration of research process and research context makes the results more originate and inspired.