About the Course
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. For these insights, accurate data, methodologies and models of innovation are required.
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.
As a starting point, we assume three analytical dimension of innovation (see figure 1). Geographically positioned units of analysis (e.g., firms, institutions), economic exchange relations, and (technological) novelty production cannot be reduced to one another. However, these independent dimensions can be expected to interact to varying extents.
In this course, we focus on the dynamics of knowledge in relation to economic and geographical developments as indicated by patent and publication data. The course on Innometrics (measuring and modelling innovation) teaches students how to translate theories into models and use these models to analyze innovation at different levels of aggregation using patent and publication data. In addition to original material, this course builds on the online course developed by Prof. Loet Leydesdorff and on tools developed by Rathenau Institute. A longer list of tools and resources to measure and model innovation is provided at the resources page.
What You Will Learn
After this course you will be better informed about important conceptual and methodological issues concerning measuring and modeling innovation. You will be able to describe and discuss different models of innovation; to translate these theoretical models into suitable indicators and measures of innovation (using patent and publication data); know about the most important sources of data (such as the EC, OECD, Eurostat, Scopus and patent data sources), their strengths and their limitations; use these indicators and measurements to analyse the innovative performance of nations, sectors, industries, clusters, researchers and firms.
It has long been recognized that an improved standard of living results from advances in knowledge and technology, not from the accumulation of capital. It has also become clear that what separates successful firms and countries from less-successful ones is not just a gap in resources or output, but a gap in knowledge. Thus, to understand how countries, regions, sectors and firms grow and develop, it is essential to know about their knowledge base and the resulting innovative activities.
The aim of this lesson is to introduce the most important sources of data that have been used for measuring and modeling innovation. 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 (’big data’), on the other hand, the interest in a growing number of public policy and private business circles in such models and measurements. In this introduction, key concepts such as model, indicator, hypothesis, data and statistics will be discussed.
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. This class focuses on the strengths and weaknesses of scientometrics data. Furthermore, several software tools will be introduced to organise scientometrics data in a relational database. In this course we will use SAINT (developed by Rathenau Institute) and ISI (developed by Loet Leydesdorff). Also, CorText Manager provides an online platform interfacing users with a range of analysis tools developed by CorText corpus. This application enables to upload data sets from disparate sources, and initiate treatments (scripts) to perform remote analyses/maps of primary data. A private space allows users to launch and test on their own chains of treatments before possibly making results public.
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.
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.