Indicators for complex innovation systems

Abstract

Performance indicators such as national wealth (GDP per capita), R&D intensity (GERD/GDP) and scientific impact (citations/paper) are used to compare innovation systems. These indicators are derived from the ratio of primary measures such as population, GDP, GERD and papers. Frequently they are used to rank members of an innovation system and to inform decision makers. This is illustrated by the European Research Area S&T indicators scoreboard used to compare the performance of member states.A formal study of complex systems has evolved over the past few decades from common observations made by researchers from many fields. Complex systems are dynamic and many of their properties emerge from the interactions among the entities in them. They also have a propensity to exhibit power law or scaling correlations between primary measures used to characterize them.Katz [Katz, J.S., 2000. Scale independent indicators and research assessment. Science and Public Policy 27, 23-36] showed that scientific impact (citations/paper) scales with the size of the group (papers). In this paper it will be shown that two other common measures, R&D intensity and national wealth, scale with the sizes of European countries and Canadian provinces. Some of these scaling correlations are predictable. These findings illustrate that a performance indicator derived from the ratio of two measures may not be properly normalized for size. This paper argues that innovation systems are complex systems. Hence scaling correlations are expected to exist between the primary measures used to characterize them. These scaling correlations can be used to construct scale-independent (scale-adjusted) indicators and models that are truly normalized for size. Scale-independent indicators can more accurately inform decision makers how groups of different sizes contribute to an innovation system. The ranks of member groups of an innovation system by scaleindependent indicators can be subtly and profoundly different than the ranks given by conventional indicators. The differences can result in a shift in perspective about the performance of members of an innovation system that has public policy implications.

Knowledge spillovers, entrepreneurship and economic development

“…There are many definitions of regional innovation systems and in keeping with these one may define a regional innovation system (RIS) as the hard (physical) and soft (institutional) infrastructure that enables a region to detect effects of external as well as endogenous change and to mobilize and structure/restructure itself in ways that effectively promote its sustained growth and development, including by production and use of scientific and technical knowledge to adjust well to changing conditions such as those induced by cyclical factors, new generic technology, natural and man made disasters, and changing industrial structures (e.g., de-industrialization). While this definition is our creation, it is not inconsistent with that by Carlsson et al (2002) or others that appear in the literature (e.g., Katz 2006). From this it is easy to see the significant role that knowledge plays in the innovation process, generally and at the regional level, in that without knowledge production on the one hand and its spillover into the innovation and economic growth/development process economic growth and development would be considerably limited.…”

Untitled

“…In order to eliminate the correlation interference from the ratio (Y/X) of scaling relationship Y-X, Katz constructed an RMI (relative magnitude indicator) index, which is a method of measurement involving using the ratio of the statistical data and the expected value to measure the relative influence. Mathematical expression of RMI is RMI = Y / Y E (Y is the actual data value, Y E is the expected value obtained by power law function) [3][4][5]. These are used to evaluate the real effect of the independent variable X on the dependent variable Y.…”

National innovation system capabilities among leader and follower countries: widening gaps or global convergence?

“…Thus, here the broad definition of NISs was adopted as the framework for the empirical investigation. At the same time, however, when considering NISs according to the broad definition, the empirical measurement of NISs becomes highly problematic, since the complexity of the approach renders quantitative analysis challenging (Katz 2006). Castellacci and Natera (2011) have proposed a division of six distinct dimensions to be considered when threating NISs empirically: 1) innovation and technological capabilities, 2) education system and human capital, 3) infrastructures, 4) economic competitiveness, 5) political-institutional factors, and 6) social capital.…”