A Preliminary Study on the Structural Model of National Innovation Capability

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The Journal of International Management Studies, Volume 7, Number 1, April, 2012 128

A Preliminary Study on the Structural Model of National Innovation Capability

Shu-Hao Chang, Associate Researcher, Science & Technology Policy Research and Information Center, National Applied Research Laboratories, Taiwan

Pin-Hua Lin, Assistant Researcher, Science & Technology Policy Research and Information Center, National Applied Research Laboratories, Taiwan; Graduate Institution of Industrial Economics, National

Central University, Taiwan

ABSTRACT

National innovation system plays a crucial role in economic development, yet previous studies are mainly focused on its theoretical framework reviews. From the system’s point of view, it lacks any relevant study on the structural components of the national innovation capability. This study adopts a systematic approach to verify that the national innovation capability is consist of innovation resources, innovation demands, innovation diffusion and innovation outputs. In addition, it’s also important to start from the perspective of the policymakers for the analysis of the national innovation system, given that the establishment of national innovation capability is in fact based on government governance, market factors and diffusion mechanism. Thus, this study takes on an empirical method to validate the structural components of the national innovation capability and thereby to provide useful policy recommendations. ¹ Keywords: National innovation system, innovation resources, innovation demands, innovation outputs

INTRODUCTION

Global economic development in the 21^st century has been increasingly dependent on the production, diffusion and dissemination of knowledge, and innovation based on knowledge and information has thus become the important means to maintain competitive advantage (Malerba, 2005). Due to the complex interactive relationship between the individual elements of a knowledge innovation system, British scholar Freeman proposed the concept of the national innovation system for the first time in 1987. Theories regarding the national innovative system has hereafter been widely developed and become the conceptual tools and methods for analyzing national innovation capabilities, establishing innovation policies, developing innovation environments as well as evaluating innovation performances.

National innovation capability can be broadly defined as the potential ability of a country to sustain its innovation activities through the operation of its national innovation system. Criterion applied for measuring national innovation capabilities varies according to individual national conditions and analytical levels. Many scholars believe that the national innovation capability can be measured through many aspects such as human resources, knowledge creation, knowledge dissemination & application as well as innovative finance (Commission of the European Communities, 2003). Fagerberg & Srholec (2008) choose to measure the capability through technical and social capabilities, yet Radosevic (2004) applies the factors of attraction capability, dissemination and connection, demands and R&D supplement instead for measuring national innovation capabilities. Kayak (2008) believes that the national innovation system is supposed to cover six sub-systems, including science and technology policy, innovation strategy, technical human support services, technical support services, mobilization of financial resources and international cooperation.

Based on the literatures reviewed above, we can see that the national innovative system is a complex notion which used to be discussed through different research designs and approaches, yet without any broad and commonly accepted consensus being formed so far. Measurement of national innovation capability lies not just on the fundamental analytical issue of science & technology performance, on the contrary, preconditions such as sufficient market scales or more flexible financial mechanisms are sometimes beneficial to the overall promotion of national innovation capabilities as well, hence an integrated approach is required for its analysis and assessment.

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The Journal of International Management Studies, Volume 7 Number 1, April, 2012 129 Although there is a considerable growth recently on the number of literatures regarding national innovation capability (Balzat & Hanusch, 2004; Hekkert & Negro, 2009; Sharif, 2006; Watanabe et al., 2010), the construction of national innovation system remains incomplete and lacks a comprehensive and systematic consensus, with a line of issues still required to be dealt with and solved. Watanabe, Akaike and Shin (2010) proposes the idea of combining the system’s viewpoint with other various mechanisms and categorizing the process of innovation into three sub-systems:

inputs of innovation resources, process of innovation production and outputs of innovation market. From the statements above, we can understand that a national innovation system is in fact a composite sub-system structure formed and composed by many different elements, thus this study aims to explore its overall structural model through a structural approach and its point of view.

Secondly, there exists an absence in the previous studies of a co-existent and clear structural model between the theoretical research and the empirical research of national innovation system. For example, though past literatures have proposed the mechanisms and structures of national innovation system (Chang & Shih, 2004; Hekkert, Suurs, Negro, Kuhlmann & Smits, 2007; Hekkert & Negro, 2009; Kayal, 2008; Liu and White, 2001) for conducting a conceptual and theoretical exploration, it lacks still an integrated empirical structure for interpreting the formation as well as the components of a national innovation system. Therefore, this study aims to remedy these research gaps. In addition, though there have been a few empirical researches which examine the potential innovation capabilities from the perspective of national level (Furman, Porter, & Stern, 2002; Faber & Hesen, 2004; Fagerberg & Srholec, 2008), most analytical modes adopted in these researches are belonging to determination models which applies linear models to determine which factors would directly affect the innovation outputs. Such an empirical approach still cannot fully explain the factor components and their integration with a national innovation process, in other words, current empirical researches are merely capable of partially explaining the relationship between the inputs and the outputs of national innovation system without a sufficient empirical contribution to its internal components.

In view of this, the focus of this study lies on the components of national innovation capability and its relationship with innovation outputs. Related policy implications and recommendations, based on the analytical results of this study, will be provided as a reference basis for the government in promoting economic competitiveness.

LITERATURE REVIEW

Features and Elements of National Innovation Capability

National innovation system can be defined as a collective influential to innovation, including the network by public sector bodies and private sector organizations. The activities of these institutions and their interactions are able to create inspiration, guidance, modification and dissemination. A certain degree of interaction and connection exists among the various sub-systems, thus national innovation capabilities require to be observed from a systematic point of view (Zeng, Xie, & Tam, 2010). In other words, the national innovation system should be an open-ended dynamic feedback system, rather than a single closed system. Therefore, national innovation capability is a complex dynamic system capable of being observed from different dimensions. Based on this approach, this study has developed innovation resources, innovation demands and innovation diffusion for monitoring the relevance between innovation outputs and national innovation capabilities and exploring their interactive relationship.

European Innovation Scoreboard and Australian Innovation Scorecard are commonly used for empirical purpose.

European Innovation Scoreboard includes innovation drivers, knowledge creation, innovation and entrepreneurship, innovation application and intellectual property; Australian Innovation Scorecard includes Knowledge creation, human resources, finance, knowledge diffusion, international industry-university cooperation and market outputs. However, these categorizations lack any systematic concept and without any further interpretation of the relations between different dimensions or systems. Therefore, this study adopts a empirical approach, starting from systematic viewpoints of input, process and output to verify the relations between different sub-systems (innovative resources, innovation demands, innovation diffusion and innovation outputs) and their structures and components.

It is knowable from the statements above that national innovation capabilities can be observed from different dimensions. Despite the fact that the ever adoption of the viewpoint of national innovation capability for exploring the

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issue of human resources (Intarakumnerd, Chairatana, & Tangchitpiboon, 2002), structural relations of innovation system (Chang & Shih, 2004) and its determinants (at Furman et al., 2002) in the past researches, exploration of the components, structures and internal relations of national innovation capabilities remains on the initial stage of development. Thus, this study attempts to sort out the relevant dimensions and indicators of national innovation capability and provide the government with a useful guideline for evaluating the national development and as a reference for the planning of resources allocation and economic development.

Innovation Resources

Numerous past researches believe that innovation resources are key elements which constitute national innovation capabilities. Watanabe et al. (2010) propose that the inputs of innovation resources in the innovation process are one of the mechanisms producing innovation impetus. The path model of knowledge creation proposed by Pakes and Griliches (1980) shows that the growth of knowledge is mainly originated from the R&D investments and scientists/engineers.

Romer (1990) believes that innovation is produced from the previous knowledge stock and human resources and its indicators for measurement include the number and proportion of internet users. The establishment of national innovation capability may come from governance and policy stimulus (Ibata-Arens, 2008), including innovation strategy, science and technology policy, R&D and higher educational organizations, governmental R&D funding and scientists/engineers, etc. (Kayal, 2008). Therefore, Kim and Dahlman (1992) suggest that governments have to invest more innovation-related research energy and services to attract investment and innovation. Fagerberg and Srholec (2008) argue that more attention should be paid to the impacts of government mechanisms on national innovation systems. In fact, the provision of promotion, policy stimulus and innovation supplies from governments usually affect the directions and momentums of national innovation. Based on the references above, this study defines the meaning of innovation resources as the supply promotion of national innovation capability, which is mainly derived from policy stimulus, including human capitals, R&D investments and other innovation resources.

Innovation Demands

The previous demand-pull model assumes that technological innovation comes from the pulling of market demands (Brem & Voigt, 2009). Enterprises were subject to market demands for new technologies or production demands for new materials and methods before commencing technological innovation. Ibata-Arens (2008) compares the national innovation system of the United States with that of Japan and concludes that market is an important pull mechanism for national innovation system. Lee and Park (2006) consider that the study of the national innovation system should emphasize the roles of market and consumers in the demand-pull mechanism. Radosevic (2004) uses relevant national welfare indicators, including finance, competitiveness and overall economic stability to measure the demand aspect of national innovation capacity as they belong to the market-pull aspect of national innovation capacity.

Market capacity means the market capacity which is beneficial to innovation, including market size and market competitiveness (Radosevic, 2004) as it is able to represent the capacities of market financial mobilization and innovation of a country. Kayal (2008) summarizes the previous studies and proposes that technology demands is the driving factor of innovation, including domestic market competition, R&D culture, and international market changes, entrepreneurship, venture capitals, and professional credit of products and services. In summary, this study defines innovation demands as the demand-pull mechanism of national innovation system, which is mainly derived from market scale, market competitiveness and market financial mechanism.

Diffusion Mechanism

The research work “Open Innovation and Globalisation: Theory, Evidence and Implication”, published by EU VISIOHN ERA-NET in 2008, adopts a variety of indicators to measure open innovation (Herstad et al., 2008), including magnitude of co-operation with suppliers, universities, customers and competitors, search range of information, protection of patents and magnitude of external innovation. This survey is the first study on open innovation empirical framework, which shows that a great emphasis has been put on open innovation paradigm by the EU member states as well as the importance of innovation diffusion capability mechanism on open innovation. In

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The Journal of International Management Studies, Volume 7 Number 1, April, 2012 131 addition, A survey conducted by Finland in 2007 shows that open innovation is mainly derived from the establishment of knowledge network platform (Santonen et al., 2007), and the openness of the system will be beneficial to technology transfer and technology transfer and diffusion (Fagerberg & Srholec, 2008), thereby increasing the knowledge flow to stimulate innovation. Therefore, Information Communication Technology (ICT) and knowledge flow play an important role in an open national innovation system. Past researches indicate that a comprehensive network structure shall promote the integration of R&D system and the growth of innovation momentum (Freeman & Soete, 1997; Perez, 2002), and Kayal (2008) also stresses the importance of ICT infrastructure in the connection mechanism as it is one of the essential elements in continual improvement of national innovation capacity. Zeng, Xie, & Tam (2010) state that innovation-supporting subsystems include innovation infrastructure and innovation media, innovation infrastructure refers to the equipments of information and communication and the soundness of which shall contribute to continual innovation. Based on the references above, this study defines innovation diffusion as the diffusion mechanism of an open national innovation system, which, particularly with regard to information & communication technology dissemination and ICT infrastructure, determines the qualities of diffusion and operation of innovation process.

RESEARCH METHODS AND RESULTS

Criteria of Measurement

This study, through a multi-country approach and based on the theories and the review of related literatures, divide the structural models of the national innovation system into four dimensions: innovation resources, innovation demands, innovation diffusion, and innovation outputs. The data are mainly from the World Development Indicators (WDI) and Global Development Finance (GDP) published by the World Bank, International Institute for Management Development (IMD) of Switzerland, World Economic Forum (WEF), World Intellectual Property Organization (WIPO), and United Nations Educational, Scientific and Cultural Organization (UNESCO), which are served as the basis for reference. Listed below are the operational definitions and measurement of the variables.

Innovation resources: This study defines innovation resources as the supply-push mechanism of the national innovation system, which is mainly derived from policy stimuli, including human resources, R&D investments and other innovation resources. A total of six indicators as the measurement items of human resource are listed as follows:

public expenditure on education as percentage of GDP, enrollment rate of tertiary education, proportion of the total population with tertiary education between 25-34 years old; measurement items of R&D investments including the total R&D expenditure as percentage of GDP, R&D expenditure from the business sector as percentage of GDP and R&D expenditure per capita.

Innovation demands: This study defines innovation demands as the demand-pull mechanism of the national innovation system, which is mainly derived from market size, market competitiveness, and market financial mechanisms. A total of 4 indicators as the measurement items of market size are listed as follows: GDP (PPP) per capita, growth rate of GDP per capita, number of population, and consumer price index. 2 indicators as the measurement items of market competitiveness measurement are foreign direct investment as percentage of GDP and foreign trade as percentage of GDP; 2 indicators as the measurement items of market financial mechanism are domestic credit provided by banking sector as percentage of GDP and market capital of listed companies as percentage of GDP.

Innovation diffusion: This study defines the innovation diffusion as the spreading mechanism of the national innovation system, which would determine the spreading and operational quality during the innovation process. It includes the use of information technology and ICT infrastructures for measurement. A total of 3 indicators are listed here: Number of PC users per thousand people, number of Internet users per thousand people, and the total investment in telecommunication as percentage of GDP.

Innovation outputs: This study defines innovation outputs as the actual outputs and results of the national innovation system. A total of four indicators are classified in this section: The measurement issue includes manufacturing ratio accounted for by the total export value of high-tech products, number of patents granted to residents, royalty and license fees, and number of scientific and technical journal articles.

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Analysis Results

Due to the restriction of data assessment, and indicators from the database designed for different years and different countries. Therefore, the main data analyzed here are ranged from 1991 to 2010, and the samples of 46 countries are verified through their valid nation numbers and years. This study applies the factor analysis and the principle component analysis for construct validity. Varimax has been applied for the extraction of the eigenvalue (greater than 1), and the factor loading value of question items between individual factors is greater than 0.5 (Hair, Anderson, Tatham & Black, 1998). Furthermore, the KMO values of innovation resources, innovation demands, innovation diffusion, and innovation outputs are 0.776, 0.661, 0.540, 0.661 respectively, and the Bartlett’s spherical test values are 271.477, 166.258, 90.627 and 167.281 respectively. All KMO values mentioned above are greater than 0.5 and the ^²values of Bartlett’s spherical test have also reached a significant level, which means those are suitable for factor analysis. After conducting the factor analysis, indicators of the total investment in telecommunication as percentage of GDP as well as the total export of high-tech products as percentage of GDP are both removed due to their factor loading values not reaching 0.5. The results of factor analysis are shown in Table 1. In addition, concerning the reliability, the Cronbach’s α values of individual factors are shown in Table 1. The factor 2 and the factor 3 of innovation demands are excluded in the following analysis due to their Cronbach’s α values not reaching 0.7. Factors of other dimensions are all beyond the standard of 0.7 as suggested by Nunnally (1978), thus the reliability of this scale can be accepted.

Table 1: Table of Factor Structural Analysis

Dimension Item Factor loading Cumulative explained variance α value

Innovation Resources

R&D expenditure as percentage of GDP 0.947

67.509% 0.900

Investment from the business sector as percentage of GDP 0.922

R&D expenditure per capita 0.878

Government expenditure on education as percentage of GDP 0.741 Proportion of the total population with tertiary education

between 25-34 years old 0.721

Enrollment ratio of tertiary education 0.681

Innovation demand

Factor I

Bank-provided domestic credit as percentage of GDP 0.902

31.560% 0.814

Price Index 0.854

GDP per capita 0.611

stock market capital as percentage of GDP 0.601

Factor II foreign trade as percentage of GDP 0.888

57.080% 0.663

foreign direct investment as percentage of GDP 0.789

Factor II Number of population 0.908

77.513% 0.670

Growth rate of GDP per capita 0.766

Innovation Diffusion Number of PC users per thousand people 0.980

95.952% 0.958

Number of Internet users per thousand people 0.980

Innovation output

Patent rights and license revenues 0.858

85.092% 0.910

Number of S&T journal articles 0.851

Number of approved patents 0.607

This study measures the dimensions of innovative resources, innovation demands, innovation diffusion and innovation outputs through the Pearson correlation coefficient for further understanding the relevance between them.

The results are shown in table 2:

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The Journal of International Management Studies, Volume 7 Number 1, April, 2012 133 Table 2: Table of Correlation Coefficient

variable 1 2 3

Innovation resources

Innovation demands 0.587**

Innovation diffusion 0.850** 0.722**

Innovation outputs 0.395** 0.392** 0.299*

Note:*p<0.05; **p<0.01

In addition, in order to further analyze the relevance of innovation resources, innovation demands, innovation diffusion and innovation outputs, innovation resources, innovation demands and innovation diffusion as the prediction variable and innovation outputs as the criterion variable are applied to conduct the canonical correlation analysis. R&D expenditure of innovation resources as percentage of GDP, R&D investment from the business sector as percentage of GDP, R&D expenditure per capita, government expenditure on education as percentage of GDP, proportion of the total population with tertiary education between 25-34 years old and enrollment ratio of tertiary education are applied as the prediction variable for the relation between innovation resources and innovation outputs; patent rights and license revenues of innovation outputs, number of S&T journal articles and number of granted patents are applied as the criterion variable for conducting the canonical correlation analysis. The results show that one of the canonical variable is valid (Wilks L.=0.352, p<0.01), as shown in table 3. The canonical correlation structure is shown in Figure 1.

Table 3 shows, the R&D, the expenditure accounts of GDP, enterprises invested in R&D expenditure accounting for the percentage of GDP and patents granted showing the same direction changes, while the canonical correlation coefficient to 0.711, and resources for innovation and innovation output between the existence of a certain degree of relevance and interactive relationship.

Table 3: Canonical correlation analysis of innovation resources and innovation outputs X variables

(Prediction variables)

Canonical variables Y variables (Criterion variables)

Canonical variables

χ1 η1

R&D expenditure as percentage of GDP

R&D investments from business sector as percentage of GDP R&D expenditure per capita

Government expenditure on education as percentage of GDP Proportion of the total population with tertiary education between

25-34 years old

Enrollment ratio of tertiary education

0.468*

0.485*

0.247 -0.334 0.344 0.134

Patent rights and licenses revenues Number of S&T journal articles Number of approved patents

0.173 0.288 0.749*

Extracted variance (%) 12.713 Extracted variance (%) 11.369

Redundancy index (%) 6.425 Redundancy index (%) 22.494

ρ2

ρ 0.505

0.711 Note: * as an important load

Figure 1: The canonical correlation structure between innovation resources and innovation outputs

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Innovation demand and innovation output, the domestic line of credit to the innovation needs of the bank a percentage of GDP, consumer price index, per capita GDP (PPP), stock market capitalization accounted for the percentage of GDP forecast variables; innovation output income of patents and licenses technology the number of journal articles, patents granted for the criterion variable canonical correlation. The results of a canonical variable set up (Wilks L. = 0.436, p <0.01), as shown in Table 4, the canonical correlation structure shown in Figure 2.

Table 4: Canonical correlation analysis of the innovation demands and innovation outputs X variables

(Prediction variables)

Canonical variables Y variables (Prediction variables)

Canonical variables

χ1 η1

bank-provided domestic credit as percentage of GDP Consumer price index

GDP per capita

Stock market capital as percentage of GDP

0.903*

0.333 0.250 0.098

Patent rights and licenses revenues Number of S&T journal articles Number of approved patents

0.682*

0.726*

0.977*

Extracted variance (%) 24.960 Extracted variance (%) 33.375

Redundancy index (%) 12.831 Redundancy index (%) 64.922

ρ2 ρ

0.514 0.717 Note: * as an important load

Figure 2: The canonical correlation structure between innovation demands and innovation outputs

As Table 4 shows, changes with the same direction are displayed in bank-provided domestic credit as percentage of GDP, number of approved patents technology, number of journal articles and number of approved patents while the typical correlation coefficient is 0.717, which show that a certain extent of correlations and interacting relations exist between innovation demands and innovation outputs.

Concerning the relationship between Innovation diffusion and innovation outputs, number of pc users per thousand people as the prediction variables; patent rights and license revenues, number of S&T journal articles and number of approved patents from innovation outputs as criterion variables for the canonical correlation analysis, the results show that the typical variables are established.

The analysis above shows that a significant relevance exists between innovation resources, innovation demands and innovation outputs. In this study we try to use visual graphics to represent the relation between innovation resources, innovation demands and innovation outputs. Results of the analysis are shown in Figure 3. We can see from the figure 3 that there exists a growth trend on the scale of innovation outputs with the increase of innovation resources and innovation demands.

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The Journal of International Management Studies, Volume 7 Number 1, April, 2012 135 Figure 3: Visual chart of innovation resources, innovation demands and innovation outputs

CONCLUSION

Discussions and implications of management

This study applies a systematic integrated framework to analyze the structural components of the internal system.

Innovation outputs, in addition to its creation from innovation resources, it also includes the innovation demands of market factors for proposing an empirical integrated framework. Findings of the whole study, after having deduced its research framework and research hypotheses and with reference to the academic literatures, the main discoveries are as follows:

First, the structure of national innovation capacity, with the empirical result of this study, can be divided into innovation resources, innovation demands, innovation diffusion and innovation outputs. Concerning the relationships between innovation resources and innovation outputs, this study finds that the government can advance the innovation capacity and outputs through increasing the R&D resources (Ibata-Arens, 2008; Romer, 1990). If the government can promote the supply of innovation resources through its policies such as increasing its R&D budgets, it will be able to promote innovation-related outputs, which means that a country’s innovation capacity depends on the investments and efforts of its government, and government mechanism plays also an important role on regulating it.

Concerning the relation between innovation demands and innovation outputs, the traditional perspective emphasizes too much on R&D during the innovation process. However, the innovation outputs are usually driven by the market demands, which is in accordance with the viewpoints of Brem and Voigt’s (2009) that innovation outputs are influenced by both technical-push and market-pull factors. In addition, the empirical results of this study also support the hypothesis, namely the outputs of the national innovation system are influenced by the pulling force of the markets (Ibata-Arens, 2008; Lee & Park, 2006). From the statements above, it is knowable that the market environment provides a great framework condition available to contribute to innovation outputs, besides, in order to effectively promote the national innovation capability, the innovation policies should not be focused only on the R&D resources, but also need to lay stress on the integration of each policy and make an effective policy mix. Furthermore, in order to achieve specific policy objectives, the formulation process of each policy should be designed technically since the relations between different policies are mutually and closely connected. The coordination and supporting measures among policies have a great influence on policy outcomes.

On the relationships between innovation diffusion and innovation outputs, this study infers that innovation diffusion have a positive influence on innovation outputs, however, the empirical results show no any significant relationship between these two dimensions, the reason probably could be originated from the data integrity as well as the use of the popularity and application of ICT as the basis to measure innovation diffusion. Yet for national innovation systems, innovation diffusion should not only include the communication of information, but also the abilities to absorb and transform of knowledge. Besides, the innovation output is often knowledgeable, unique and with a high

-2 -1 0 1 2 3

Innovation demands

Innovation resources

Innovation outputs

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technological basis, therefore, the communication of information usually can only promote the accumulation and application of knowledge without any direct benefit on original innovation knowledge and innovation outputs. As for the issue that if there exists any other mediating and moderating variables is worth further research.

On the theoretical contribution, the previous relevant studies on the national innovation system were focused mostly on theoretical exploration (Chang & Shih, 2004; Hekkert et al.,2007 ; Hekkert & Negro, 2009; Kayal, 2008; Liu and White, 2001), yet there exists only few studies using different perspectives of mechanisms to analyze the process of the empirical structure of national innovation capability. Hence, this study adopts a structural approach to verify the effects on innovation outputs from different mechanisms, and explores the impacts from the mechanisms of innovation resources, innovation demands and innovation diffusion on innovation outputs from the policymaker's perspective. Also, this study seeks to clarify the connections and the functions among sub systems inside the national innovation system, and thereby expects to provide a certain extent of theoretical explanation.

In terms of practical advice, this study intends to provide the government with some valuable information and propose the structural model of national innovation capacity from the viewpoints of the innovation system, which is mainly related to the use of different mechanisms to promote the national innovation capacity. A very important issue for the current trend of growing global market competition is how the government shall improve the country's competitiveness and create the national innovation capacity amid different dimensions. In this study, we expect to bring forward a more clearstructural model with a clearer framework to systematically promote the national innovation capacity.

Limitations and suggestions for future research

First, this study focuses on adopting a structural model to analyze the links and relations between innovation resources, innovation demands, innovation diffusion and innovation outputs. However, we must take the time delay effect into consideration when it comes to the innovation inputs and innovation outputs of a national innovation system.

Furthermore, the outcomes of innovation resources, innovation demands and innovation diffusion might be different due to the changes of time and environment. Thus, we suggest that any further studies might be able to collect and analyze any practical data through other longitudinal research methods.

Secondly, this study chooses innovation resources, innovation demands and innovation diffusion as influence variables on innovation outputs. The outcomes of this study indicate that both innovation resources and innovation demands have a direct influence on innovation outputs. However, innovation diffusion has no significant influence on innovation outputs. In addition, this study proposes that the popularity and application of ICT is not the only indicator available to measure the innovation diffusion, the absorption and transformation of knowledge might also be the crucial factors for innovation diffusion. Based on the above reasons, this study suggests that the capacities of knowledge absorption and transformation can be discussed and tested in future researches.

Lastly, this study is a quantitative research for satisfying the research breadth but lacks deeper analysis of the subject. Therefore, case study and other research methods are recommended for future researches in order to promote the academic value of this study.

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ACKNOWLEDGEMENT

The authors would like to thank the National Science Council of the Republic of China (Taiwan) for financially supporting this research under Contract No. NSC 100-2410-H-492-001-.