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. The concept of the innovation system stresses that the flow of technology and information among people, enterprises and institutions is key to an innovative process. It stresses the interaction between actors who are needed in order to turn an idea into a successful process, product or service in the marketplace. Many innovation systems are characterized by some flaws that greatly hamper the development and diffusion of innovations. These flaws are often labelled as system failures or system problems. Intelligent and evidence based innovation policy therefore evaluates how innovation systems are functioning, tries to create insight in the system problems and develops policies accordingly. Innovation systems have been categorized into geographical innovation systems (national or regional), sectoral innovation systems (specific socio-economic developments) and technological innovation systems. These three approaches to analyse innovation systems represent three analytical dimensions of the interaction among an ecology of actors (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. Given these specifications, one can create a model of the three dimensions and their interaction terms as follows: Figure 1: Three interacting dynamics of Innovation Systems (adapted from Leydesdorff 2010) The term National System of Innovation originated at the same time in the work of Christopher Freeman and Bengt-Åke Lundval in the late 1980s. The national innovation system approach emphasises the continued importance of national institutions and arrangements; the quality of basic research, workforce skills, systems of corporate governance, the degree of competitive rivalry and local inducement mechanisms, such as abundant raw materials, the price of labor and energy, and persistent patterns of private investment of public procurement all play a determining role in the innovative capacity of a country.
Sectors differ along several dimensions related to technology, production, innovation and demand. And that they differ in the type and degree of change. The empirical evidence (Malerba-Orsenigo, 1996) suggests also the existence of differences across sectors and of similarities across countries in the patterns of innovative activities for a specific sector.
Technological Innovation System is a concept developed within the context of the Innovation System approach focusing on explaining the nature and rate of technological change. A Technological Innovation System can be defined as the set of actors and rules that influence the speed and direction of technological change in a specific technological area. A key article is provided by Hekkert, M.P., R.A.A. Suurs, S.O. Negro, R.E.H.M. Smits and S. Kuhlmann, 2007, Functions of Innovation Systems: A new approach for analyzing technological change, Technological Forecasting and Social Change, Volume 74, Issue 4, May 2007, Pages 413-432
The purpose of analysing a Technological Innovation Systemis to analyse and evaluate the development of a particular technological field in terms of the structures and processes that support or hamper it. The basic steps that are taken are the following: First, we analyze the structure of the innovation system. These are the actors and rules that make up the system. Second, we analyze how the system is functioning. We will use seven system functions that stem from theory and are empirically validated as indicators. We analyze each function, but also the interaction between the functions. Finally, after we have established at what state of development a technological innovation system is, we can analyze the system problems that block the well functioning of the innovation system. All innovation systems can be characterized by the same basic building blocks or components. These are actors, institutions, networks and technology. Examples of actors are organizations responsible for education, R&D, industrial activities, and consumers. Examples of institutions are supportive legislation and technology standards. Examples of networks are the linkages between organisations in research projects and advocacy coalitions. Technology is part of the innovation system as it enables and constrains the activities of actors in the innovation system. We will present a categorization of all components that are important in a Technological Innovation System and we will develop indicators to measure the size of these components. In this manual these will be applied to the case of the offshore wind innovation system as an example. Even though different innovation systems may have similar components, they may function in a completely different way. Therefore, measuring how innovation systems are functioning is considered as the big breakthrough in innovation systems research. In a number of scientific articles lists of evaluation criteria are presented to evaluate how innovation systems are functioning. These assessment criteria are labelled in the literature ‘functions of innovation systems’. In Hekkert et al. (2007) the following functions of innovation systems are put central:
- entrepreneurial activities,
- knowledge development,
- knowledge exchange,
- guidance of the search,
- formation of markets,
- mobilization of resources,
- counteracting resistance to change.
The important difference with the structure of the innovation system is that these system functions are much more evaluative in character. Focusing on functions allows us to address the performance of an innovation system. In other words: the structure presents insight in who is active in the system, the system functions present insight in what they are doing and whether this is sufficient to develop successful innovations. In addition to quantitative indicators, the functioning of an innovation system needs to be assessed by experts or key stakeholders that are active in the innovation system. However, in this lesson we will limit ourselves to quantitative analysis and desk research. The reason to evaluate the innovation system by means of expert opinions is that it is impossible at the moment to solely evaluate an innovation system based on quantitative criteria. The reason for this is that technologies and regions are different from each other and that it is impossible to define an optimal configuration of the innovation system. Consequently, benchmarking innovation systems is difficult; what works in one country may not work in another country. Furthermore, the development of an innovation system often depends strongly on the competition in other parts of the world and very often has very technology specific dynamics. For some technologies much more R&D funding is necessary than for others.
A recent example of the application of TIS can be found in the JRC Scientific & Policy Report: “A Systemic Assessment of the European Offshore Wind Innovation. Insights from the Netherlands, Denmark, Germany and the United Kingdom”.
The revealed comparative advantage is an index used in innovation studies and economics for calculating the relative advantage or disadvantage of country in a certain sector or technology. It is based on the Ricardian comparative advantage concept.
It most commonly refers to an index introduced by Béla Balassa (1965):
RCA = (Oij / Oit) / (Onj / Ont)
O Output (e.g. number of patents, exports or publications)
i Country index
n Set of countries
j Technology/sector index
t Set of technologies/sectors
That is, the RCA is equal to the proportion of the country’s output that are of the class under consideration (Oij / Oit) divided by the proportion of world output (sectors/technologies) that are of that class (Onj / Ont).
A comparative advantage is “revealed” if RCA>1. If RCA is less than unity, the country is said to have a comparative disadvantage in the technology or industry.
Example: Many countries are, for example, producing cars. To establish whether a country, say Japan, holds a particularly strong position in the car industry, Balassa argued that one should compare the share of car exports in Japan’s total exports with the share of car exports in a group of reference country’s total exports (e.g. the global share). The Balassa index is therefore essentially a normalized export share. More specifically, if BIAj is country A’s Balassa index for industry j, this is defined as to:
If BIAj >1, country A is said to have a revealed comparative advantage in industry j, since this industry is more important for country A’s exports than for the exports of the reference countries.
Decomposition analysis: Shift-Share.
The national innovation system approach emphasises the continued importance of national specificities of a country, while the sectoral innovation approach suggests the existence of differences across sectors and of similarities across countries in the patterns of innovative activities. Likewise, the TIS approach highlights the differences among technologies.
First, we focus on sectors and countries. In order to better understand the role of country specificities and sector specificities, we can consider quantitatively the relative contributions of the sectoral contribution of innovative developments visàvis the national contributions.
Decomposition analysis is a widely used analytical technique for retrospectively decomposing changes in a set of countries. The analysis identifies the comparative advantage in particular sector of patent production. Decomposition analysis recognizes that some national patenting activities are likely to be growing at a faster rate compared to the total set of patenting activities under study and other countries will be growing more slowly. Countries are expected to specialize in certain sectors and produce output in that sector, thereby increasing its output in that sector.
As such, the decomposition analysis reveals how well countries are performing by systematically examining the total, national, and sectoral components of change in technological knowledge production. A decomposition analysis will provide a dynamic account of total patenting growth of a country that is attributable to growth of the total patent production, a mix of faster or slower than average growing sectoral activities, and the competitive nature of the national activities in the context of their national innovation systems.
As stated above, the decomposition analysis divides the change in national output into three components:
1. Total share (TS) – The share of national growth in patents that can be attributed to growth of the total global patent production. Specifically, if the total patent production is growing, one would expect a positive growth influence on the national activities. Thus, this factor describes the change that would be expected simply by virtue of the fact that each country is part of a changing global landscape.
2. Sector mix (SM or TM) – The share of national patent output growth that can be attributed to the country’s portfolio of secoral (or technological) output. This second factor is the change in national technological knowledge production that would be attributable to the growth or decline of the total technological knowledge production in a set of sectors. This component isolates the fact that globally, some sectors (technologies) have grown faster or slower than others. It represents the contribution that a specific line of research has made to the change in the number of patents in a country.
3. National share (NS) – This share of national output growth of a country describes the extent to which factors unique to the research location have caused growth or decline in output. Even during periods of general growth, some countries and still some sectors grow faster than others do. This can be attributed to some national comparative advantage such as institutions, infrastructures or nationally available (human) resources.
4. Shift-share (SS) and the national share component in particular, can point to research activities that enjoy national comparative advantage.
The equations for each component are
iNATIONALt-1 represents the number of patents in a sector or field (i) at the beginning of the analysis period (t-1)
iNATIONALt represents the number of patents in a sector or field (i) at the end of the analysis period (t)
GLOBALt-1 represents the total number of patents, globally, at the beginning of the analysis period (t-1)
GLOBALt represents the total number of patents, globally, at the end of the analysis period (t)
iGLOBALt-1 represents the number of patents, globally, in a sector or field (i) at the beginning of the analysis period (t-1)
iGLOBALt represents the number of patents, globally, in a sector or field (i) at the end of the analysis period (t)
Generally, countries experience changes within their patenting output that are more concentrated in certain sectors (technologies) than the global patent output as a whole. This difference can be attributed to the specialisation of a country. Countries with one or more rapidly growing sectors of research might display a high rate of output gain as a result of the expansion. Likewise, an country with several declining sectors of research might experience (relative) output loss. In examining the national contributions, we will divide those changes into various structural effects lends insight into national trends by means of the decomposition analysis.
In this assignment, we use dynamic Shift-Share analysis to account for changing patterns at national and global level simultaneously (Knudsen, 2000). Calculating the Total Share, Sector Mix, national Share and the Shift Share on an annual basis and then averaging the results over the study period provides a more accurate allocation of changes among the shift-share effects (Barff & Knight, 1988).
Within our sample, we can identify those countries with large positive or negative absolute changes by means of Shift-Share analysis. The analysis allows us to specify the specialisation patterns. For example, if a sector or technology is declining globally, certain countries can still show a positive national share effect if it were declining at a slower rate than the global sectoral or technological patent production. Most important from a comparative advantage point of view are those developments with both a positive sector (technological) mix effect and a positive national share.
Final Assignment (total 400 points)
The final assignment consists of three seperate presentation; A, B and C.
Part A: In-depth scientometric analysis
In this assignment, you will provide an in-depth analysis of the Innovation Studies section at Utrecht University. The best presentation will receive 25 bonus points and a bottle of wine. I will make this presentation available for the IS section. The presentation should be clear, concise and self-explanatory.
The Staff of the Copernicus Institute of Sustainable Development, Section Innovation Studies (ISUU) consists of the following people; dr. Floortje Alkemade, dr. Jelle Behagel, dr. Jan Faber, dr. Jacco Farla, dr. Gaston Heimeriks, prof. dr. Marko Hekkert, dr. Andrea Herrmann, prof. dr. ir. van Lente, prof. dr. Ellen Moors, dr. Simona Negro, dr. Louis Neven, dr. Eva Niesten, dr. ir. Alexander Peine, dr. Frank van Rijnsoever.
Using ISI Web of Science publication data of these authors, please provide the following analyses, with a clear explanation and interpretation. Make sure to check that only publications of these staff members are included! (Note that the personal staff pages often provide a list of publications that you can use for checking your publication data).
Update: not all publications that are listed by the staffmembers are included in the ISI Web of Science (some articles are not yet included, others are published in journals that are not included at all in the WoS). That is not a problem. However, make sure NOT to include publications that are not by ISUU scholars.
- What are the most popular journals in which ISUU scholars publish?
- What are the most frequently cited references?
- What are the most frequently cited journals?
- What are the most frequently used keywords?
- What are the most frequently used abstract words?
- Please provide a visualisation of the co-author network?
- Please provide a geographical visualization of the co-author network? Where are the most important collaborators located?
- Please provide a network visualisation of the most important abstract words in the publications of ISUU?
- Please provide a network visualisation of the authors based on the similarities in their abstract words?
- Please download the records of recent publications in one (or more) important Innovation Journal(s) (see previous questions). Combining these records with the publication records of ISUU can you identify adjacent reseach possibilities? (e.g. Topics that are related to existing topics at ISUU?). What promising topics would you suggest for members of the ISUU?
Part B: Patentometric analysis of countries, sectors and technologies.
- In this assignment we use patent data (provided through blackboard). Please discuss briefly the use of patents as indicator of technological activity. Specifically, what are the advantages and limitations of patent data for measuring and modeling national specificities, sectoral specificities and technological specificities?
- Can you provide some descriptive statistics that are derived from this data? What (changing) patterns are visible with respect to the patent distributions over countries, sectors and technologies? How do these results relate to the statistics that the OECD provides (http://www.oecd-ilibrary.org/economics/oecd-factbook_18147364)?
- Please summarise and discuss the following articles:
- Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: a new approach for analysing technological change. . Technological Forecasting and Social Change, 74(4), 413–432.
- Lundvall, B.-Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy, 31(2), 213–231.
- Malerba, F. (2002). Sectoral systems of innovation and production. Research policy, 31, 247–264.
- Please calculate the revealed comparative advantage for all countries with respects to their sectoral and technological activities. What remarkable changes over the period 1986-2005 can you identify?
- Discuss the (selected) results in some detail using the insights from the literature. Justify your selection. You may also consider to aggregate country data in larger regions (e.g. EU, BRIC, ASIA, etc..) What patterns with respect to distribution over countries, sectors and technologies can you identify? Is this in line with the theoretical claims?
- Please calculate the Shift-share (SS) components. What innovative activities enjoy national comparative advantages? Please discuss.
- Please provide a network visualisation of the countries based on similarities in their secoral activities? Please discuss.
- Please provide a network visualisation of the countries based on similarities in their technological activities? Please discuss.
- Please provide a network visualisation of the technologies based on similarities in their occurrences in countries? Please discuss.
- Which country has changed position most in the period under study? (E.g difference in distance/similarity to other countries). Perhaps you can provide additional network statistics to illustrate this development? Please discuss.
- Optional; you may be able to produce a movie of (one of the previous) changing networks over time? Please discuss.
Part C: Your comments and Suggestions.
Here you can write any remarks regarding the course. Your feedback is not obligatory, but highly appreciated!