Turning to the set of basic explanatory variables, their estimated coefficients in the model with country dummies still provide basic support to the characteristics of the taxonomy, although some of them differ slightly from the previous model. The most notable difference refers to the variables SCIENCE and USERS, which both turn out to be not significant in the estimations. A possible explanation of this finding is that the interactions between innovative firms, the science system and the users do not only vary across sectors, but are also characterized by a strong cross-country variability that is related to the characteristics and specificities of nationalsystems of innovation (Nelson, 1993; Malerba and Orsenigo, 1995, p.49). When we control for these relevant country-specific factors, therefore, the estimates of the cross-sectoral dimension become less statistically significant. This finding will be further investigated in the following sections.
In contrast to Japan, the Anglo-Saxon 'occupational community' model can better accommodate a science-driven, entrepreneurial approach to innovation and perform well in sectors in which radical learning is important. A major underlying structural weakness of this model, however, is the marked segmentation between professional and production workers, and the bias of the competence building system in favour of the interests of high-technology firms (Angles 2000). Denmark, on the other hand, has developed a specialisation pattern in low- and medium-technology sectors with a focus on an incremental innovation strategy. The Danish case also suggests that an innovation-driven redeployment of competencies can be organised more collectively by public agency action and an emphasis on workforce vocational training and lifelong learning. The so-called 'new economy' configuration as observed in Silicon Valley based upon de-regulated labour markets and excellence in scientific personnel is not necessarily the benchmark for fostering innovation and economic growth. It is also important to emphasize that learning is an activity going on in all parts of the economy, including so-called low-tech and traditional sectors. As a matter of fact, learning taking place in traditional and low-tech sectors may be more important for economic development than learning taking place in a small number of insulated high-tech firms. The learning potential (technological opportunities) may differ between sectors and technologies but in most broadly defined sectors there will be niches where the potential for learning is high. This is important in a period where knowledge policy tends to be equated with science policy and with support to science-based firms.
The empirical results indicate that innovative capability and absorptive capacity variables are indeed linked by a set of long-term structural relationships over the period 1980-2008. Specifically, the dynamics of nationalsystems of innovation appears to be driven by the coevolution of two sets of factors: the three innovative capability variables (technological output, scientific output, innovative input), on the one hand, and three of the absorptive capacity factors (income per capita, infrastructures and international trade), on the other. Further, human capital (measured by tertiary education), the factor typically emphasized by most previous technology-gap and imitation-based growth models, does not turn out to have a direct effect on the dynamics of innovation activities and results, but rather an indirect effect by sustaining the growth of GDP per capita (which in turn feeds back and sustains the innovation dynamics over time).
Missing data represent an important limitation for cross-country analyses of nationalsystems, growth and development. This paper presents a new cross-country panel dataset with no missing value. We make use of a new method of multiple imputation that has recently been developed by Honaker and King (2010) to deal specifically with time-series cross-section data at the country-level. We apply this method to construct a large dataset containing a great number of indicators measuring six key country-specific dimensions: innovation and technological capabilities, education system and human capital, infrastructures, economic competitiveness, political-institutional factors, and social capital. The CANA panel dataset thus obtained provides a rich and complete set of 41 indicators for 134 countries in the period 1980-2008 (for a total of 3886 country-year observations). The empirical analysis shows the reliability of the dataset and its usefulness for cross-country analyses of nationalsystems, growth and development. The new dataset is publicly available.
The scholars use the term innovation when they are referring to new products, services, process, materials or organizations forms that arrives to the market, or create new markets, regardless of their successfulness’; because the newness become attractive more widely, usually, after a process of broader use. In an industrial context, most innovations are based on some kind of problem solving, when firms facing a particular problem turns to a supplier, or some other related actor, to get help in specifying the problem and defining procedures for its solution.
By now we have two building blocks of our framework, TR and institutional set-up. Basically they are not something new in the area of innovation theories (See Rosenberg, 1976, 1982; Freeman and and Perez, 1988). What is new is the notion of technological regimes which enables us to be more specific in conceptualising the link between technology and institutional set up as structuring forces of SI. Pre- market and market selection mechanisms are the two following elements which give dynamics to our framework.
long-term relationship. Hence CME’s are assumed to invest and emphasize heavily for the development of the employee skill level. Also the development of the employee skill level is expected to lead towards a competitive advantage for businesses e.g., the combination of firm-specific labor skills and industry-specific labor skills is not likely to be framed by competitors due to the unique employee skills (Hall and Soskice, 2001). Hence CME generally disregards the competitive advantage of an effective and efficient market mechanism for managing and regulating products, since this will increase competition, and thereby will disrupt the development of the employee skill level (Hall and Soskice, 2001). Hall and Soskice (2001) note that the two types of economic systems identified (i.e., CME and LME) are ideal types at the poles of a spectrum. In other words, there is a wide variety within the characteristics of CME’s and LME’s, hence only the characteristics are provided which could explain certain mechanisms within the corporate governance angle. An illustration of the small differences around the spectrum is for example, Germany who is assumed to prefer an industry- based coordination, while Japan is expected to favor a group-based coordination. However according to Hall and Soskice (2001) both economies can be characterized as a coordinated market economy. The social preference for vocational training of employees and educational systems is also assumed to affect the political economy variety (Hall and Soskice, 2001). Put bluntly, the vocational training and educational system for employees provides an indication for the investment in additional skills of human capital which relates to specific firm skills or industry skills. Although both natures of economy (i.e., LME and CME) are determined to invest in vocational training and educational system, the expectations for the realization of the development differ. As merely mentioned, LME’s tend to invest in assets whose value can be realized in the short-term. Moreover the assets invested in LME are preferred to realize her value if diverted to other purposes. Hence it is expected that businesses in LME formally evaluate the vocational training and educational system immediately or some months (i.e., short-term) after the training. The latter assumption is a preliminary indication of calculative HRM practices as identified by Gooderham et al. (1999). Gooderham et al (1999) distinguished two “systems” of HRM practices:
In 2007 the University of Twente started a research program called competences for innovation. The purpose of this research program was to support the development of competitive power for the local industry market. In line with the competences for innovation research, this research focus on the involvement of Human Resource Management (HRM) to support innovation. Innovation is a word which appears more and more in daily live. Advertisements screaming about new innovative products, new innovative computer technologies and innovative health insurance plans are all examples of the word innovative which appears in daily live. Although innovation is thus a common used word people have different understanding. Some people confuse innovation with the word invention which is only a small part of innovation. Originally the term innovation comes from the Latin word “innovare’ which means ‘to make something new’ (Tidd et.al. 2005). To make something new is actually the key sentence of innovation. Therefore, in this study, innovation is defined as: an idea, practice or material artifact perceived to be new by the relevant unit of adoption (Dewar & Dutton, 1986). Innovation can be subdivided in two definitions. An incremental innovation and a radical innovation. An incremental innovation represents a small or minor improvement to an already existing product, service, process or technology. On the contrary a radical innovation represents a fundamental change in a product, service, process or technology. For example in 1970 the first VCR was sold. At that time it was a radical innovation, it was the first time people were able to record television programs, in order to watch this program at a time of their choice. In order to record the consumer had to set the start and end time of the VCR. Later on manufactures developed codes, that are composed of starting time and ending time. When a consumer entered the code the VCR knew when it had to start and to end recording. This code can be seen as an incremental innovation, because it is a small improvement to the already existing product (Narayanan, 2001). Innovations have a time path (Dewar & Dutton, 1986). In 1970 every person would agree that a VCR was an innovation. Nowadays people do not see the VCR as an innovation anymore. In other words novel products change over time into mature products (Dewar & Dutton, 1986).
In spite of the apparent similarity with biological processes, one should not mistakenly equate evolutionary economics with evolutionary biology. Freeman [1994b] highlights two fundamental differences. First, selection is at least partly conscious in the innovation process as decision-makers can choose between various ‘mutations’ (that is, new products, processes and organisational forms). Moreover, their expectations, hopes, plans and values also shape the ‘evolution’ of these ‘mutations’. Ethical and social considerations, therefore, play an increasingly important role in the innovation process, notably in the development and utilisation of nuclear energy and biotechnology, as opposed to the process of biological evolution. Second, selection is taking place at a number of levels in the course of competition: among products, firms (organisations), sectors, regions, countries and socio-economic systems. There are some autonomous rules and laws of the selection process at these different levels. Strong interrelations and interdependencies, however, can also be observed. Technological innovations are shaping both their natural and socio-economic environment, while the success of innovations strongly depends on their environment, including the quantity, quality and distribution of accumulated capital in the form of production equipment, roads, railways, communications networks, bridges, etc., as well as policies, attitudes and norms, that is, institutions in short.
As mentioned above, however, the bulk of China's R&D is presently being carried out by enterprises, many of which are large SOEs. China's large SOEs not only did not die out, but have managed so far to resist and even to thrive after over a quarter-century of market-oriented structural changes. SOEs reforms were carried out in the framework of a complex, ever-changing and opaque institutional environment, characterized by a weak and ambiguous -albeit increasing- degree of protection of property rights in general and of intellectual property rights (IPR) in particular. Shedding light on this apparent (for orthodox economics) paradox, most studies on innovation among Chinese productive enterprises found that substantial progress was going on, and that SOEs were capturing the bulk of S&T resources, but exhibiting a less-than-satisfactory capability of translating them into true production improvements. The innovative capability of SOEs, however, appears to have been further enhanced in the mid-2000s, thanks at least partly to the economies of scale and scope made possible by the "grasping the big, enlivening the small" policy. The combined profit of the 150 or so companies controlled by China's central government reached Rmb1,000 bn (USD140bn), more than 200% higher than five years earlier. By end- 2007, the list of the world’s 10 most valuable companies contained four groups controlled by the Chinese state. The behavior of Chinese SOEs is also becoming more modern and effective in a number of areas, including their ability to attract top executive talents (Dodson (2008).
Historically, concentration of budget funding in a limited number of large corporations is viewed as an inhibiting factor for R&D. The fact that in the last 75 years individual researchers and small business in the United States accounted for over 50 % of major technical innovation speaks in favor of this trend in innovation policy. Comparing opportunities of small business and large corporations in such sectors as microelectronics, biotechnology and successful commercialization of a large number of promising ideas and in- ventions, the U.S. government began to take active steps to strengthen their capacity. In the early 1980s, the Manufacturing Extension Partnership was initiated with the key objective to provide consultative and technical assis- tance to small businesses in achieving compliance with international quality standards. The Small Business Innovation Development Act was adopted in 1982 to expand the financial support for active research and development firms. This statute required that federal agencies should help small businesses obtain government contracts for research and development, including all benefits and privileges. Under this law, 11 federal agencies which finance science were required to allocate 0.2 % of their scientific budget for the Small Business Innovation Research Program (SBIR). In 1989 the compulsory allo- cation was increased to 1.25 % and from 1992 it increased annually and its rate was 2.5 % as of 1 October 1996. As a result, a modest USD50 million program in 1983 grew up to USD1 billion in the 1997 budget; 55,000 projects were funded during 1983–1999 from the SBIR for the total amount of USD9.7 billion. 34
In the context of the "system", it will be very important to understand the main components of the system, namely "organization" and "institution". Likewise with the context of the nationalinnovation system, organizations and institutional innovation are very important to note. Edquist (2009: 187) argues that these two components are often interpreted as overlapping with each other. Edquist argues that in the nationalinnovation system, organizations are "players or actors" (player or actor), while institutions are the composition of general habits, norms, routines, existing practices, rules and conditions - legal provisions governing relationships and interactions between players or actors. Institution can be interpreted as "rules of the game" (rule of the game). The relationship between organizations and institutions is important for the operationalisation of the innovation system. Organizations are influenced and formed very strongly by institutions. Organizations adhere to institutional environments. On the other hand, institutions are embedded and developed in the organization. Edquist (2009: 187) also reveals that there are various relationships between organizations and institutions that have different patterns, including: (a) organizations create institutions that affect other organizations; (b) institutions become the basis for the creation of organizations; and (c) institutions related to other institutions. Or in other words, different institutions can support and strengthen each other, or can also conflict with each other. The form or pattern of connectedness can be done in various different ways with different extensions as well.
context of Japan as follow: a network of entities- public and private- whose activities and interactions result in development, introduction, modification and broadcasting of new technologies . Lundvall (1992) announces that nationalinnovation system is a set of elements and relations that are active in the domain of economic producing, broadcasting and implementing a new knowledge within the national borders . Nelson asserts that nationalinnovation system is a set of entities whose interactions lead to innovative performance of national firms . Nationalinnovation system, as a scientific research resultant from perfectionisteconomy, is the outcome of interaction between scientific theoreticians and policymaking senior experts such as Freeman, Deci, Lundvall, Nelson and ,Landual, Nelson and Edcois within two European and American domain . Choosing the right method and implementing the required steps to develop the innovation system need high intelligence and strong will.Conducting study and research on developed systems in other countries can help us to make good use of their experience. However, the formulation and design of such systems in any given country depend on the circumstances, needs, problems, and environmental features of that country. As such, these systems cannot be copied or imitated by other countries. Countries are different based on the production, deployment and use of the information. Different factors such as technological and industrial context, the level of participation among institutions, investment innovation models, the type of approach towards risk, labor market regulation, the role of private and public sector as well as small and large companies affect the adoption of an innovation system . Structure, function, and completeness of various components of the nationalinnovation system have big impacts on the promotion of nationalinnovation capabilities. Nationalinnovation system can be seen as a management system that includes a main body, structural elements and the external environment : The main body consists of economic entities (industries), universities, scientific and technological parks and incubators, research centers, governmental agencies, and financial institutions.Structural elements consist of competitive market, research, innovative infrastructures, participation, collaboration, and
Norway’s dependence on natural resources has always been controversial within domestic politics. During the post- 1945 period, a strong and politically powerful lobby of ‘mod- ernizers’ gained political power and argued that a moderni- zation of the industrial structure of the country in the direction of ‘high-tech’ industry, particularly ICT, was a must. The ‘modernizers’ were strongly influenced by the achieve- ments of US and British scientists, military research facilities and ‘high-tech’ firms during and after the Second World War and wanted to create a similar dynamic in Norway by sup- porting military R&D, public research labs (particularly within ICT) and selected ‘high-tech’ firms. The national industrial research council (NTNF) and national defense research es- tablishment (FFI), both established in 1946, were central in- stitutional actors in the ‘modernizing’ network, along with other public and semi-public laboratories (Wicken, 2009b; Gulbrandsen and Nerdrum, 2009a).
capability building for efficient innovation management, or consultancy on IPR issues, support to the identification of suitable business partners. The experts of the public innovation intermediaries, e.g. Enterprise Ireland; Tekes, Finland; Design Council, UK; Austrian Research Promotion Agency, Japan Science and Technology Agency, evaluated the commercial potential of the scientific results and helped grantees to elaborate IPR and commercialization strategies. Experts and grantees jointly decided about the adequate commercialization channel (licensing, or start-up formation, contract research). Once this latter decision had been taken, innovation agencies offered channel-specific services: if start-up formation was the decided commercialization mode, academic entrepreneurs were offered consultancy services with respect to the design of the business plan. The agencies assisted beneficiaries also by building and mediating linkages to third party funding providers.
Educators and innovation policy-makers have looked to the U.S. for its innovation, its lack of regulation and for the role that the federal government plays in education. The U.S. higher education system is characterised by diversity. The total of 4,000 higher education institutions in America include State-funded community colleges and universities as well as privately-funded universities. It is worth noting that of the top 20 universities in America, 18 are private. Additionally, not every university teaches engineering and only 235 colleges are regarded as research universities. The diversity within the U.S. higher education sector notwithstanding there is a willingness to jointly undertake common activities. Four of the leading universities are, for example, involved in a joint distance learning programme because they have found that it is too expensive to do it on their own. Irish universities, on the other hand, have shown little evidence of drawing together and seeking common causes. One suggestion is that the four Dublin- based universities and the Dublin Institute of Technology should get together to help with the establishment of a national science park.
Innovation is often slowed down by the significant bureaucratic hurdles required for obtaining and maintaining product registration license approvals worldwide. Therefore, strengthening the take-up of the regulatory sciences, as well as the application of multidimensional approaches, by which approval prerequisites and timelines taking into account individual products characteristics, components and health benefits should be given enough priority to build a regulatory system addressing the dilemma of flexible product introduction without compromising rigor.
NationalInnovation System (NIS) is the web of institutions taking part in process and the facilitation of innovations in a country. Freeman (Freeman 1987) first put forward the concept of NIS and proposed that the a network of institutions both in the private and public sector make up the system of innovation in country by initiating, importing, modifying, and diffusing new technologies into the economy. An extended explication of NIS holds that the innovation system consists of the relationships and elements within the boundaries of a state which cooperate with each other for the production, diffusion, and use of new and economically lucrative knowledge (Lundvall 2007). Nationalinnovation system is a set of institutions which determines the innovativeness of a country’s enterprises (Nelson 1991),. Patel & Pavitt regard innovation system as a set of national institutions, their competencies and structure of incentives which guide technological learning in a country (Patel and Pavitt 1994). Metcalfe, however, furthers the concept of NIS and includes the policy formulation process of government into the system of innovation (Metcalfe 1995). According to Metcalfe, NIS is the set of distinguished institutions which separately and collectively develop and diffuse novel technologies. This set of institutions also provides the framework in which governments devise and implement policies in order to stimulate the process of innovation. It is a structure of institutions interconnected with each other to generate, accumulate, and transmit the knowledge, skills, and artefacts which delineate innovative technologies. The set of institutions in NationalInnovation System contribute to the development of new technologies. The institutions also play their part in technology diffusion throughout the economy. These actors of the system also provide the mechanism in which government not only makes the policies but also devises the framework to implement those policies. In this way, the system of innovation takes a form of interconnectedness to create, store, and transfer knowledge, skills, and objects which outlines novel technologies. Both the actors of the system and the contextual elements, are important dimensions of innovations system for the creation and use of knowledge for economic growth (Metcalfe 1995).
Our results for the Individualism/Collectivism dimension support prior research examining the relationship of the dimension with various measures of innovation. Rossberger & Krause [8, 9] found a negative relationship between the GLOBE project In-Group Collectivism dimension and innovation but no relationship between Institutional Collectivism and innovation. In-Group Collectivism and Institutional Collectivism are proposed by House et al.  to represent two different aspects of Hofstede’s Individualism/Collectivism variable. In- Group Collectivism is the degree to which individuals express pride, loyalty, and cohesiveness in their organizations or families. Institutional Collectivism is the degree to which organizational and societal institutional practices encourage and reward collective distributions of resources and collective action. Hofstede’s Collectivism represents a preference for a tightly-knit society in which individuals can expect their relatives or members of their in-group to look after them in exchange for unquestioning loyalty. There is a great deal of debate in the literature as to whether the two GLOBE dimensions are the equivalent of the Hofstede dimension [28, 29]. Nonetheless, our findings for the Hofstede Individualism/Collectivism dimension and innovation are supportive of the In-Group Collectivism and innovation finding by Rossberger & Krause [8, 9]. Societies that are more individualistic are more apt to be innovative than societies that favor cohesive family-oriented collectivism.
Hungary has all the major elements of a potentially successful nationalinnovation system (NIS): a fully fledged education system; internationally recognised research units both at universities and the institutes of the Academy of Sciences; an increasing number of business R&D units, several of them operated by multinational firms and thus integrated into international networks; a number of government bodies engaged in STI policy-making and a considerable number of policy schemes in place; various types of professional associations and chambers; a functioning capital market, complete with venture capital funds; a legal infrastructure up to international standards; norms and values compatible with the requirements of a market economy based on private property; creative people; etc. Yet, performance is far from satisfactory. In brief, two major reasons can be identified. First, although these ‘nodes’ of the NIS are set up, a number of them do not work satisfactorily, or still fledgling. Second, as innovation studies stress, the major factor determining the overall innovation performance is not the performance of the individual organisations, but the intensity and quality of linkages and co- operation among them. (Fagerberg et al. (eds), 2005; Lundvall et al., 2002; Niosi, 2002)