Standard-setting and knowledge dynamics in innovation clusters - Julian P. Christ; André P. Slowak (Universität Hohenheim, 2009)
Extensive research has been conducted on how firms and regions take advantage of spatially concentrated assets, and also why history matters to regional specialisation patterns. In brief, it seems that innovation clusters as a distinctive regional entity in international business and the geography of innovation are of increasing importance in STI policy, innovation systems and competitiveness studies. Recently, more and more research has contributed to an evolutionary perspective on collaboration in clusters. Nonetheless, the field of cluster or regional innovation systems remains a multidisciplinary field where the state of the art is determined by the individual perspective (key concepts could, for example, be industrial districts, innovative clusters with reference to OECD, regional knowledge production, milieus & sticky knowledge, regional lock-ins & path dependencies, learning regions or sectoral innovation systems). According to our analysis, the research gap lies in both quantitative, comparative surveys and in-depth concepts of knowledge dynamics and cluster evolution. Therefore this paper emphasises the unchallenged in-depth characteristics of knowledge utilisation within a cluster's collaborative innovation activities. More precisely, it deals with knowledge dynamics in terms of matching different agents' knowledge stocks via knowledge flows, common technology specification (standard-setting), and knowledge spillovers. The means of open innovation and system boundaries for spatially concentrated agents in terms of knowledge opportunities and the capabilities of each agent await clarification. Therefore, our study conceptualises the interplay between firm- and cluster-level activities and externalities for knowledge accumulation but also for the specification of technology. It remains particularly unclear how, why and by whom knowledge is aligned and ascribed to a specific sectoral innovation system. Empirically, this study contributes with several descriptive calculations of indices, e.g. knowledge stocks, GINI coefficients, Herfindahl indices, and Revealed Patent Advantage (RPA), which clearly underline a high spatial concentration of both mechanical engineering and biotechnology within a European NUTS2 sample for the last two decades. Conceptually, our paper matches the geography of innovation literature, innovation system theory, and new ideas related to the economics of standards. Therefore, it sheds light on the interplay between knowledge flows and externalities of cluster-specific populations and the agents' use of such knowledge, which is concentrated in space. We find that knowledge creation and standard-setting are cross-fertilising each other: although the spatial concentration of assets and high-skilled labour provides new opportunities to the firm, each firm's knowledge stocks need to be contextualised. The context in terms of 'use case' and 'knowledge biography' makes technologies (as represented in knowledge stocks) available for collaboration, but also clarifies relevance and ownership, in particular intellectual property concerns. Owing to this approach we propose a conceptualisation which contains both areas with inter- and intra-cluster focus. This proposal additionally concludes that spatial and technological proximity benefits standard-setting in high-tech and low-tech industries in very different ways. More precisely, the versatile tension between knowledge stocks, their evolution, and technical specification & implementation requires the conceptualisation and analysis of a non-linear process of standard-setting. Particularly, the use case of technologies is essential. Related to this approach, clusters strongly support the establishment of technology use cases in embryonic high-tech industries. Low-tech industries in contrast rather depend on approved knowledge stocks, whose dynamics provide better and fast accessible knowledge inputs within low-tech clusters.
Keywords: D89; L22; M20; O32; 330; innovation clusters; standard-setting; knowledge externalities and flows; knowledge alignment; mechanical engineering; biotechnology