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Edited by Peter K. Kresl
Marcela A. Munizaga
After many years of data scarcity in transportation-related sciences, we have now entered the era of big data. Large amounts of data are available from GPS devices, mobile phone traces, payment transactions, social media, and other sources. The opportunities that this new availability presents are enormous. High-quality data is available at very low or negligible cost. These data can be used to develop new tools, to explore and understand travel behavior and to formulate new policies. However, the challenges are also big: the access to the data is not guaranteed, confidentiality has to be considered, the capacity of processing and enriching these databases has to be developed, and only then will they become really useful for decision-making and for the definition of public policies. This chapter presents an overview of the current state of play, and discusses the future perspectives, focusing on the challenges of building new predictive models.
Edited by John Stanley and David A. Hensher
Economics, Community and Methods
Edited by Richard D. Knowles and Fiona Ferbrache
Sander Faber and Marina van Geenhuizen
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.
Lasse Gerrits and Stefan Verweij
We explain and demonstrate how the selected cases have to be prepared for the actual comparison. This involves a serious effort with regard to the interpretation of the case materials. In QCA, this process of interpreting data is guided by calibration, where raw (qualitative) case data are transformed into quantitative values. Calibration is important because it systematizes interpretation and makes it transparent. There are three principle types of calibration in QCA: crisp-set QCA, fuzzy-set QCA, and multi-value QCA. We explain and demonstrate the different types of calibration using real examples. We also discuss good practices that will help the researcher in making sound decisions when calibrating. The calibration results in a calibrated data matrix, which forms the input for the formal comparison in QCA. Having completed this chapter, the researcher will be able to start the comparison.
Lasse Gerrits and Stefan Verweij
We explain why it is important to research specific cases and how exactly cases are to be understood and studied using QCA. Cases allow the researcher to account for the heterogeneity, uniqueness, and contextuality of projects. Whereas the term ‘case’ is often used indiscriminately, in QCA it is a clearly defined and important building block. In QCA, cases are conceptualized as configurations of conditions. This configurational nature highlights the complexity of the case. Cases can be researched in two principal ways: case-driven and theory-driven. The case-driven route is decidedly grounded in empirical material, with the boundaries and aspects of cases being constructed during the empirical research process. In the more theory-driven route, the boundaries and aspects of cases are defined by prior theories. Both routes constitute dialogues between data and theory. The chapter explains the concrete research steps involved in both routes.
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.
Pieter E. Stek
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.