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This chapter investigates adoption of medical technology in the form of eHealth solutions in hospitals. A model of organizational eHealth adoption is developed and empirically explored using a survey among hospitals in cities in the Netherlands and structural equation modelling (SEM). Technology adoption is seen as a process in different stages, revealing a high level of interest (about 60 per cent of hospitals) but very limited actual adoption (ranging from 6 per cent to 23 per cent). Furthermore, adoption levels tend to be higher in larger cities, and this is confirmed by significant direct influence of urban size on eHealth adoption. Other important factors tend to be organizational readiness and top management of hospitals, but these are not affected by urban size. The results leave the question open as to what makes hospitals in large cities more often adopt new technology if this is not mediated by hospital size and other organizational characteristics.

This chapter investigates innovation in urban passenger transport and clarifies how cities play a leading role. By focusing on liveability, intelligent systems management and new mobility, single innovations are discussed and the results summarized in a matrix. The most important ‘initiators’ are city governments, citizen groups, public transport authorities and universities, with the enterprise world somewhat lagging until recently. On the physical side, larger cities create more inventions and high density plays a role in feasibility of public transport. Universities are important, as is a historical city centre. On the social side, a well-educated population wishing to continue living in the city enhances innovation, but in some developing countries the electorate which does not own cars appears to be important. Also helpful are city governments acting on openness and trust and active political leaders. Furthermore, the early adopting cities often faced a crisis in mobility or failure of projects.

This chapter presents a bibliometric study identifying clusters (cities) that are ‘champions’ in acceleration of invention in solar photovoltaics (PV), using patent analysis. The number of inventions has increased rapidly in the past decades, particularly since 2003. In this process, leading clusters change, in part, over time. Some have held their position since 2000 – Tokyo, Osaka, Seoul and Taipei in East Asia, and San Jose in the US – whereas most high-performing clusters in the US have somewhat lost their position, for example Los Angeles. Over time, there is an increased spread of inventive performance in PV technology across the world. To improve understanding of these patterns, a regression model has been estimated. Using data from 110 clusters, it appears that agglomeration factors and relational factors are equally influential, and they also tend to reinforce each other. Leadership tends to follow from a delicate balance between the size of the cluster and size/diversity of its networks.