R&D, Spillovers, Innovation Systems and the Genesis of Regional Growth in Europe. Bruges European Economic Research (BEER) Papers 5/October 2006

Research on the impact of innovation on regional economic performance in Europe has fundamentally followed three approaches: a) the analysis of the link between investment in R&D, patents, and economic growth; b) the study of the existence and efficiency of regional innovation systems; and c) the examination of geographical diffusion of regional knowledge spillovers. These complementary approaches have, however, rarely been combined. Important operational and methodological barriers have thwarted any potential cross-fertilization. In this paper, we try to fill this gap in the literature by combining in one model R&D, spillovers, and innovation systems approaches. A multiple regression analysis is conducted for all regions of the EU-25, including measures of R&D investment, proxies for regional innovation systems, and knowledge and socio-economic spillovers. This approach allows us to discriminate between the influence of internal factors and external knowledge and institutional flows on regional economic growth. The empirical results highlight how the interaction between local and external research with local and external socioeconomic and institutional conditions determines the potential of every region in order to maximise its innovation capacity. They also indicate the importance of proximity for the transmission of economically productive knowledge, as spillovers show strong distance decay effects. In the EU-25 context, only the innovative efforts pursued within a 180 minute travel radius have a positive and significant impact on regional growth performance.

Overcoming the “European Paradox”: The role of Regional Innovation Systems (RIS) in translating R&D investments into economic and employment growth

The emergence of the so-called “European Paradox” shows that R&D investment is not maximally effective and that increasing the scale of public R&D expenditures is not sufficient to generate employment and sustained economic growth. Increasing Governmental R&D Investment is far from being a “panacea” for stagnant growth. It is worth noting that Government R&D Investment does not have a statistically significant impact on employment, indicating the need to assess the trade-offs of policies that could lead to significant increases in government expenditure. Surprisingly, Governmental R&D Employment does not contribute to “mass-market” employment, despite its quite important role in reducing Youth-Unemployment. Despite the negative side-effects of Governmental R&D Employment on both GVA and GDP, University R&D Employment appears to have a quite important role in reducing Unemployment, especially Youth-Unemployment, while it also does not have a downside in terms of economic growth. Technological Capacity enhancement is the most effective instrument for reducing Unemployment and is a policy without any downside regarding sustainable economical development. In terms of wider policy implications, the results reinforce the idea that European Commission Research and Innovation policies must be restructured, shifting from a transnational framework to a more localised, measurable and operational approach.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

UK innovation systems for new and renewable energy technologies: drivers, barriers and systems failures

A better understanding of the systemic processes by which innovation occurs is useful, both conceptually and to inform policymaking in support of innovation in more sustainable technologies. This paper analyses current innovation systems in the UK for a range of new and renewable energy technologies, and generates policy recommendations for improving the effectiveness of these innovation systems. Although incentives are in place in the UK to encourage innovation in these technologies, system failures—or ‘gaps’—are identified in moving technologies along the innovation chain, preventing their successful commercialisation. Sustained investment will be needed for these technologies to achieve their potential. It is argued that a stable and consistent policy framework is required to help create the conditions for this. In particular, such a framework should be aimed at improving risk/reward ratios for demonstration and pre-commercial stage technologies. This would enhance positive expectations, stimulate learning effects leading to cost reductions, and increase the likelihood of successful commercialisation.

Argentina: how to study and act upon local innovation systems

Includes bibliography

From national innovation systems to national innovation capacity:internationalisation and technology transfer perspectives

The last decade or so has witnessed the emergence of the national innovation system (NIS) phenomenon. Since then, many scholars have investigated NIS and its implementation in different countries. However, there are very few investigations into the relationship between the NIS of a country and its national innovation capacity. This paper aims to make a contribution in this area by examining the link that currently exists between these two topics. Whilst examining this relationship, we also explore internationalisation and technology transfer, being cognate areas that have been investigated during the same period. This follows our assertion that the link between NIS and national innovation capacity is the mechanism of internationalisation and technology transfer. The NIS approach was introduced in the late 1980s (see Freeman, 1987; Dosi et al., 1988) and further elaborated later (see Lundvall, 1992; Nelson, 1993; Edquist, 1997). In essence, a country?s NIS is a historically grown subsystem of the entire national economy consisting of organisations and institutions which play a major role in the innovative activity in the country. In the NIS approach, interactions within organisations as well as the interplay between organisations and institutions are of central importance. The NIS approach has been used to reveal the structure of the innovation processes and the main actors involved in them in industrialised and emerging countries. Although the national focus remains strong, it has been accompanied by studies seeking to analyse the notion of systems of innovation at an international level and at a sub-national scale (Archibugi et al., 1999). Dosi in the edition of Archibugi et al. (1999) argues that the general background of the discussion of national systems is the observation of non-random distributions across countries of: corporate capabilities; organisational forms; strategies; and ultimately revealed performances, in terms of production efficiency and inputs productivities, rates of innovation, rates of adoption/diffusion of innovation themselves, dynamics of market shares on the world markets, growth of income and employment. They also mention that there are several approaches to NIS. Nelson (1993) focuses upon the specificities of national institutions and policies supporting directly or indirectly innovation, diffusion and skills accumulation. Patel and Pavitt (1991) have stressed the links between the national patterns of technological accumulation and the competencies and innovative strategies of a few major national companies. Amable et al (1997) and Soskice (1993) and Zysman (1994) focus on the specifics of national institutions including, for example, the forms of organization, financial and labour markets, training institutions, forms of state intervention in the economy etc. However, the most common reference is by Lundvall (1992) who argues that the focus on the national level is associated with the fact that national economies vary according to their production system and their institutional framework and these differences are in turn strengthened by different historical experiences, language and culture. On the other hand, the national innovation capability consists of abilities to create and carry new technological possibilities through to economic practice. The term covers a wide range of activities from capability to invent to capability to innovate and to capability to improve existing technology beyond the original design parameters (Kim, 1997). The term innovation is often associated by many with technological change at international frontiers. However, technological capability is not the same as innovation capability. Technological capability refers to assimilation, use, adaptation, and change to existing technologies. It also enables the creation of new technologies and development of new products and processes in response to changing economic environments. It denotes operational command over knowledge (Kim, 1997). It is manifested not merely by the knowledge possessed, but, more important, by the uses to which that knowledge can be put and by the proficiency with which it is applied in the activities of investment and production and in the creation of new knowledge (Westphal et al., 1985). Therefore, the analytical framework that is used in this paper is based on the way a country derives from its NIS a national innovation capacity. There are two perspectives that are identified on this way. These are internationalisation and technology transfer. Even though NIS is not directly related to national innovation capacity, to achieve national innovation capacity from NIS, the country should have the ability for technology transfer. Technology transfer is a link between these two phenomena. On the other hand, internationalisation can be either the input or the output of the relationship between NIS and national innovation capability. If a company is investing in a country because of its national innovation capacity, this can be regarded as an input to the relationship between NIS and national innovation capacity. If this company is investigating the national innovation capacity of a country then, for its internationalisation, the national innovation capacity should be important, which in turn means this company is active in innovation and innovation is also an important success factor. The interrelationship between the investment of the company and the NIS of the country (assuming that the country is competent and competitive in technology transfer) will generate and improve that country?s national innovation capacity. This is the output of internationalisation from the relationship between NIS and national innovation capacity. When companies are evaluating whether to internationalise, they investigate certain factors in the countries in which they are considering to invest. The ability to transfer technology is dependent on ability to adopt a new technology and also on the learning derived from this technology. If countries wish to attract innovation related investment they need to show their ability to have a NIS and also the capability to transfer technology. Without the technology transfer capability, the NIS is not functioning. Therefore, companies that internationalise will investigate the factors common to NIS, technology transfer, and their business needs. Through this paper we will demonstrate this link though its mechanisms. Our research will be through extensive literature review and identifying relevant aspects of previous research carried out by the authors. It will investigate certain factors of different countries that are successful in attracting innovation related foreign direct investment. Through these, we will point out the factors that are important for the link and mechanisms of NIS and national innovation capability.

Innovation, networking and globalisation:the role of regional innovation systems in the global innovation process

In the global economy, innovation is one of the most important competitive assets for companies willing to compete in international markets. As competition moves from standardised products to customised ones, depending on each specific market needs, economies of scale are not anymore the only winning strategy. Innovation requires firms to establish processes to acquire and absorb new knowledge, leading to the recent theory of Open Innovation. Knowledge sharing and acquisition happens when firms are embedded in networks with other firms, university, institutions and many other economic actors. Several typologies of innovation and firm networks have been identified, with various geographical spans. One of the first being modelled was the Industrial Cluster (or in Italian Distretto Industriale) which was for long considered the benchmark for innovation and economic development. Other kind of networks have been modelled since the late 1970s; Regional Innovation Systems represent one of the latest and more diffuse model of innovation networks, specifically introduced to combine local networks and the global economy. This model was qualitatively exploited since its introduction, but, together with National Innovation Systems, is among the most inspiring for policy makers and is often cited by them, not always properly. The aim of this research is to setup an econometric model describing Regional Innovation Systems, becoming one the first attempts to test and enhance this theory with a quantitative approach. A dataset of 104 secondary and primary data from European regions was built in order to run a multiple linear regression, testing if Regional Innovation Systems are really correlated to regional innovation and regional innovation in cooperation with foreign partners. Furthermore, an exploratory multiple linear regression was performed to verify which variables, among those describing a Regional Innovation Systems, are the most significant for innovating, alone or with foreign partners. Furthermore, the effectiveness of present innovation policies has been tested based on the findings of the econometric model. The developed model confirmed the role of Regional Innovation Systems for creating innovation even in cooperation with international partners: this represents one of the firsts quantitative confirmation of a theory previously based on qualitative models only. Furthermore the results of this model confirmed a minor influence of National Innovation Systems: comparing the analysis of existing innovation policies, both at regional and national level, to our findings, emerged the need for potential a pivotal change in the direction currently followed by policy makers. Last, while confirming the role of the presence a learning environment in a region and the catalyst role of regional administration, this research offers a potential new perspective for the whole private sector in creating a Regional Innovation System.