The knowledge base concept in the past has often been applied in its pure form, i.e. it was assumed that there are dominant knowledge bases in particular sectors and firms, that shape the knowledge- and innovation process and related networks. For analytical sectors such as biotech it has been argued that codified knowledge generated by universities and R&D organisations are key for innovation, whereas synthetic sectors such as machinery were seen to innovate more incrementally by recombining existing knowledge often drawn from suppliers or service firms. Empirical literature partly has confirmed these basic patters, but also has demonstrated that more complex knowledge processes are underlying these overly schematic expectations. In particular, it was argued more recently that combinations of different knowledge bases might enhance the innovation performance of firms. This implies that innovation processes e.g. in analytical sectors might benefit not just from new and basic knowledge generated by research, but also from recombining existing and applied knowledge or by drawing on symbolic knowledge. The same argument for the relevance of combinatorial knowledge bases applies for synthetic and symbolic sectors, but in different forms. The paper investigates if the reliance on combinatorial knowledge leads to a better innovation performance than the use of more narrow knowledge bases. It analyses the topic both conceptually and empirically by drawing on findings for the ICT sector in regions of Austria.
|Publication status||Published - 1 Dec 2014|
|Series||SRE - Discussion Papers|