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.
Core concepts; National Innovation System
The innovation systems approach has become popular as an approach that captures the complex nature of innovation. However, this complexity also makes it difficult to construct indicators to classify innovation systems and measure their performance. Consequently, analyses of innovation systems are often mainly descriptive. Nowadays, a wide range Science, Technology and Innovation indicators are available.
Science and technology (S&T) indicators are widely used in policy documents as well as in science and technology studies. Godin (2003) traces their origins and shows that it was the Organization for Economic Co-operation and Development (OECD) that first imagined and developed science and technology indicators. In the 1960s, the debate on technological gaps between the United States and Europe gave the OECD the opportunity to develop the first world-wide indicators on science and technology. The National Science Foundation (NSF) in the USA followed in the 1970s and improved the methodology of indicators on science and technology with its publication entitled Science Indicators. Science and technology indicators remain contested however, because centered on inputs rather than outputs, and because preoccupied mainly with the economic dimension of science and technology.
Currently, there is a growing interest from governments in improving the understanding of how science, technology and innovation create value in the form of increased productivity and profits, and contribute to the valuation of enterprises, and ultimately stimulate the growth and competitiveness of economies. For example;
The Frascati Manual is a document setting forth the methodology for collecting statistics about research and development. The Manual was prepared and published by the OECD. In 2002 the 6th edition of the Frascati Manual was published.
ERAWATCH provides information on European, national and regional research policies, actors, and programmes in the EU and beyond.
The European Innovation Scoreboard (EIS) was introduced as part of the Lisbon strategy. It measures, on a yearly basis, the innovation performance of Member States, drawing on statistics from a variety of sources (a.o. the Community Innovation Survey).
The industrial R&D Investment Scoreboard, an annual study of the European Commission, analyzes the performances of the 2000 industrial companies (1000 based within the European Union, 1000 outside) with the most important annual R&D investments.
The OECD provides a wide variety of databases of internationally comparable statistics in the areas of science, technology and industry. These statistics and indicators underpin policy-related analytical work, particularly with respect to links between technology, competitiveness and globalisation.
Additional literature; big data
The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey’s Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.
Some further reading on these issues is provided by Chris Anderson. Anderson claims the end of models and theory in WIRED: The Data Deluge Makes the Scientific Method Obsolete. NASSIM N. TALEB strongly disagrees in his article in WIRED; “But beyond that, big data means anyone can find fake statistical relationships, since the spurious rises to the surface. This is because in large data sets, large deviations are vastly more attributable to variance (or noise) than to information (or signal). It’s a property of sampling: In real life there is no cherry-picking, but on the researcher’s computer, there is. Large deviations are likely to be bogus”.
Gault provides some words of caution on statistical indicators. How do they develop, change behaviours and intersect with broader social change? And, how does this apply to science, technology and innovation indicators?
The science-technology-innovation system is 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.
A recent example of the rising importance of quantitative indicators is provided by highly visible university attention that attract a lot of media attention and play an increasing role in the governance of universities. Important examples include the Shanghai ranking, The Times Higher Education World University Rankings and the CWTS Leiden Ranking.
Gault, Fred. Social impacts of the development of science, technology and innovation indicators. UNU-MERIT, Maastricht Economic and Social Research and Training Centre on Innovation and Technology, 2011.
Godin, B. (2003). The emergence of S&T indicators: why did governments supplement statistics with indicators? Research Policy, 32(4), 679–691.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
OECD. (2002). Frascati Manual 2002: Proposed Standard Practice for Surveys on Research and Experimental Development. Paris.
Taleb, Nassim N. “Beware the big errors of ‘big data’.” Wired Opinion, August(2013).