Research and innovation are increasingly recognized as drivers of productivity and economic growth, as well as vital resources in addressing societal challenges. Many governments therefore attempt to stimulate research and innovation and improve the innovative performance of their regions, industries and universities. However, in order to stimulate innovation it is essential to obtain insight in the underlying mechanisms by which innovation arises; to have a model of innovation. Several models of innovation are provided by the innovation literature. These models often stress the fact that innovation is a process involving many different types of actors, interactions and feedback. Furthermore, innovation is unevenly distributed among regions, sectors and technologies.
What means do governments and firms have at their disposal to measure and model innovation? The ability to determine the scale of innovation activities, the characteristics of innovating firms and research organizations, and the internal and systemic factors that can influence innovation is a prerequisite for the pursuit and analysis of private and public policies aimed at fostering innovation.
The course on measuring and modelling innovation teaches students how to translate theories into models and use these models to analyze innovation at different levels of aggregation. Modelling and Measuring Innovation also addresses innovation processes at the level of nations, industries, clusters and organizations. Furthermore this course explicitly addresses the operationalization of theory into models and indicators. Several influential models of science and innovation are introduced and different approaches of quantitatively measuring the development of science and innovation are discussed.
This course begins by describing the science-technology-innovation system as one that is continuously and rapidly evolving. The dramatic growth over the last 20 years in the use of science, technology and innovation (STI) models and indicators is the result of a combination of the ease of computerized access to an increasing number of measures of STI and, on the other hand, the interest in a growing number of public policy and private business circles in such models and measurements.
Scientometrics is the science of measuring and analysing science. Modern scientometrics is mostly based on the work of Derek J. de Solla Price and Eugene Garfield. The latter founded the Institute for Scientific Information (ISI) which is still heavily used for scientometric analysis. Methods of research include qualitative, quantitative and computational approaches.
In addition to scientometric data, patents provide a wealth of information to measure innovative developments. Like scientometric data, patent data are not without shortcomings. This lesson focuses on the difficulties involved in formulating an analytically meaningful conceptualization of technology. Furthermore, we look into recent research using patent data as indicators of technological activity. The conceptual and methodological problems of ‘measuring’ technology are discussed, with a classification of the types of information which can be drawn from patent databases of both innovations and the innovative efforts of firms and countries. The findings and the methodological strengths and weaknesses of such studies are reviewed, considering first the evidence at the firm level, second the analysis of the industrial structure and finally the evidence at the country level and the process of globalization.
In recent years, the analysis and modeling of networks, and also networked dynamical systems, have been the subject of considerable interdisciplinary interest in science and innovation studies. Innovation is the result of the interaction among an network of institutions in the public and private sectors whose activities and interactions initiate, import, modify and diffuse new technologies, and the term ‘innovation system’ is used to emphasize this. In this lesson, we turn to social network analysis as a tool to map the network properties of developments in science and innovation
Evolutionary models in science and innovation have focused mostly on the issue of changes in technology and routines. If the change occurs constantly in the economy, then some kind of evolutionary process must be in act, and there has been a proposal that this process is Darwinian in nature. Then, mechanisms that provide selection, generate variation and establish self-replication, must be identified. Variation may be pre-structured by selection, but nevertheless one can expect variation to be changing more rapidly than selective structures. Selection is deterministic (determined by the structure of the selecting system), while variation introduces randomness (exploration). The selective structures in science are provided by the networks of ideas which are retained in journal articles and their relations.
This lesson is concerned with the spatialities of science and innovation; with how the spatial structures in research and innovation emerge from the micro-behaviours of local agents; with how, in the absence of central coordination or direction, the economic landscape exhibits self-organisation; and with how the processes of path creation and path dependence interact to shape geographies of economic development and transformation, and why and how such processes may themselves be place dependent.
Concepts and ideas from evolutionary economics (and evolutionary thinking more broadly) help interpret and explain how the economic landscape changes over historical time, but also help to reveal how situating the economy in space adds to our understanding of the processes that drive economic evolution, that is to say, to demonstrate how geography matters in determining the nature and trajectory of evolution of the economic system.
The most important insight that has dominated the field of innovation studies in recent decades is the fact that innovation is a collective activity. It takes place within the context of a wider system. This wider system is coined ‘the innovation system’ or ‘the innovation ecosystem’. The success of innovations is to a large extent determined by how the innovation system is build up and how it functions.