PICMET ’10 OUTSTANDING STUDENT PAPER AWARD The number of students doing significant research in the area of Engineering and Technology Management was demonstrated by the number of nominations received. The selection of the award winner was difficult because of the excellent quality of all the submissions, but one paper stood out for its contributions to the field of Engineering and Technology Management. AUTHOR:
ADVISOR & CO-AUTHOR:
Dr. Hsin-Yu Shih
National Chi Nan University, Taiwan
“Constructing National Innovative Capacity in Globalization: The Network Autocorrelation Perspective”
Abstract: Globalization has highlighted change in national technology capability. Exogenous factors drive a country towards technological progress, and drive economic growth via international technology diffusion. Previous studies have stressed that innovative capacity is determined by regional or local social systems. This paper reconsiders these studies and develops a new perspective of evaluating national innovative capacity. This method employs a network autocorrelation model which simultaneously considers both endogenous determiners and exogenous influence on national innovative capacity. Data from 42 countries from 1997 to 2002 are utilized to empirically examine their network relationship and innovation performance. The analytical results demonstrate the effect of domestic determiners within a global context and show that their differential context attribute influence on national innovative performance is influenced more by network positioning than by network partnership. They furthermore exhibit important differences between the alternate channels of international technology diffusion and their differential effects on innovative performance. This finding provides a new perspective for science and technology policy makers.
Please cite this article as: Huang, H.-C., H.-Y. Shih, Y.-C. Wu, "Constructing national innovative capacity in globalization: The network autocorrelation perspective." Technology Management for Global Economic Growth (PICMET), 2010 Proceedings of PICMET '10:.
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand ÂŠ 2010 PICMET
Constructing National Innovative Capacity in Globalization: The Network Autocorrelation Perspective Hung-Chun Huang, Hsin-Yu Shih, Ya-Chi Wu National Chi Nan University, International Business Studies Dept., Taiwan Abstract--Globalization has highlighted change in national technology capability. Exogenous factors drive a country towards technological progress, and drive economic growth via international technology diffusion. Previous studies have stressed that innovative capacity is determined by regional or local social systems. This paper reconsiders these studies and develops a new perspective of evaluating national innovative capacity. This method employs a network autocorrelation model which simultaneously considers both endogenous determiners and exogenous influence on national innovative capacity. Data from 42 countries from 1997 to 2002 are utilized to empirically examine their network relationship and innovation performance. The analytical results not only demonstrate the effect of domestic determiners with differences global context and their differential context attribute influence on national innovative performance more by network positioning than by network partnership, but also exhibit important differences between the alternate channels of international technology diffusion and their differential effects on innovative performance. This finding provides a new perspective for science and technology policy makers.
I. INTRODUCTION National innovative capacity is generally regarded as a countryâ€™s institutional potential to sustain innovation, and has been studied by numerous scholars. Suarez-Villa  proposed this essential concept and a measure of it in terms of patenting rates. Furthermore, Furman et al. theorize that national innovative capacity reflects more fundamental determinants of innovation, offering countries a means of influencing national innovative capacity. Technology is closely related to national cultural and social settings . Prior study stressed the innovative capacity determined by regional or local social systems. However, a healthy innovation infrastructure is essential but insufficient by itself to support the environment required to achieve continuous innovation . Related investigations of R&D management stress the need for interaction between developers and global users of new technology to enhance development and execution processes [5-8]. Consequently, can an endogenous perspective fully determine national performance in terms of innovative capacity? International interactive behaviors effect countries in terms of their economic performance, politics and culture primarily due to the development of international cooperation, such as global supply chains and globalized R&D. Therefore, when a country develops its science and technology policy, its decisions depend not only on its own situation, but also on the advice or experience of other nations. Theories of interdependence support mutual interdependence between
nations as a result of close interactions, leading to political reciprocity and complicity [9, 10]. However, do these international activities affect national innovative capacity? What kinds of international relationships have a greater effect? The existing literature provides many important insights, but many of the questions we raise above still remain unanswered. In particular, prior study has not adequately dealt with the many important differences between the alternate channels of international technology diffusion and their differential impact on national innovative capacity. Meanwhile, changes in national technology capability caused by globalization are beginning to show. Exogenous factors drive a country towards technological progress, as well as driving economic growth via international technology diffusion. Importantly, while this phenomenon related to indigenous technological development is best understood as a social network issue, there exist few explicit social network analyses of these questions. Remedying both of these deficiencies should be an important goal of future research. This paper is organized as follows. Section 2 summarizes the central issues of national innovative capacity related to the issues of international technology diffusion. Section 3 presents research hypotheses related to the testing and comparison of the various model effects. Section 4 examines the measurements and models of social network analysis employed to investigate the autocorrelation model. Section 5 then empirically tests the research hypotheses, and discusses the theoretical and managerial implications. Section 6 offers some conclusions. II. LITERATURE REVIEW This section introduces endogenous and exogenous perspectives of national innovative capacity along with the international diffusion of embodied and disembodied technological influence on NIC performance. A. The Determinants of National innovative capacity The National Innovative System is the interactive network among public, private, and academic sectors related to the production and diffusion of innovation to constitute the direction of development of national innovative capacity. National innovative capacity has been defined as the institutional potential of a country to sustain innovation. On the basis of this idea, numerous scholars (e.g. ) have studied how endogenous growth theory works and coordinates the elements of NIC. Furman et al.  definitively proposed the determinants of national innovative
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand © 2010 PICMET capacity, claiming it is based on three distinct areas; quality of the common innovation infrastructure, quality of the cluster-specific innovation environment, and quality of linkages. More specifically, common innovation infrastructure includes the stock of knowledge, GDP per capita, the amount of scientific and technical skills devoted to the production of new technologies, R&D personnel (FTE), R&D expenditure, national investments and policy choices (openness), expenditures on higher education, intellectual property protection and openness to international competition. Private R&D funding signifies the cluster-specific innovation environment and university R&D performance reveals the linkages of the common innovation infrastructure and industrial clusters, whose intensity will influence the extent to which the potential for innovation evoked by the common innovation infrastructure is translated into specific innovative outputs in a nation’s industrial clusters. Furthermore, pervious studies (e.g. [1, 2] ) have developed a suitable measure based on patenting rates as the indicator of NIC. Accordingly, patents are acknowledged to provide a reliable and unbiased indication of national innovation effort [12, 13]. One of the clearest indicators of innovation performance is the rate of take-up of patents issued by the US Patent and Trademarks Office (USPTO). Innovative capacity primarily depends upon the technological level and sophistication of an economy, and the investments and policy choices of both institutions and the private sector . Consequently, measuring national innovation output includes patents, publications in scientific journals, copyrights, trademarks, etc. All of these are products of innovation efforts, and copyrights and trademarks even represent direct indicators of innovative output. This work, based on previous studies (e.g.[1, 2, 14]) therefore determines patent output as innovative output. Additionally, Furman & Hayes  note that PATENTS correlated positively with the true level of new-to-the-world innovative output in their model, and that it appears to be the best available indicator for comparing national innovation output across countries over time. Trajtenberg  even considers international patents “the only observable manifestation of inventive activity with a well-grounded claim for universality.” Therefore, measuring international patents is the most useful available measure for comparing innovation output across countries and over time . Consequently, this study adopts USPTO patenting activities by sample countries to measure NIC. B. The exogenous influence of the global network Globalization; global outsourcing, global design and global supply chains carry out international science and technology diffusion. More specifically, diffusion is a process that involves spreading certain innovation information by participants in a social system through particular channels . Diffusion is an exceptional form of communication, and involves participants providing and sharing information. Diffusion thus can refer to the dissemination of knowledge,
technology transfer or deployment . Technology diffusion is influenced by innovations and technical updates over time. Vernon argues international product life cycle theory based on technology diffusion; however, this theory focuses upon production sites shifting process and trade flow rather than the influence of technology diffusion on innovation capacity. Countries acquire innovation technology in two main ways; enforcing domestic technology development and innovation capacity, and obtaining foreign advanced technologies via international technology diffusion. Griliches  divides international technology diffusion into rent spillover and pure knowledge spillover. Rent spillover refers to the price of new products for which innovation technology knowledge exists, and cannot fully reflect the high quality of knowledge innovation in the process of commercialization. A country purchasing intermediate products at certain price that does not mirror their actual value can enjoy the benefits of R&D conducted by other countries; that is, the purchasing country employs passive technology spillover or embodied technology diffusion  to supply their innovation capacity. The activities of international diffusion of embodied technology are observable based on trade flows and foreign direct investment [19, 20]. Moreover, most related studies (e.g. [21-25]) demonstrate a significant positive relationship between total factor productivity and international trade for a given nation as evidence of international research spillover. New growth theory argues that the marginal profit from capital investment is not certain to decrease over time, and accumulated capital can sustain long-term GDP per capita; this theory also deems knowledge to be the public goods in capital accumulation and creation of an increasing rate of return via the spread of information. A nation benefits from spillover through trade partner investment knowledge. Consequently, knowledge capital and R&D activities benefit national economic growth. Smith & White  demonstrate a positive relationship between trade and national competitiveness by exploring the dynamic configuration of global economics through trade flows. Coe et al.  find it better to measure trade-related spillover using trade in capital goods rather than total trade. This work investigates imports of machinery and equipment for diffusing information on embodied technology. Countries exchanging goods through international trade generate rent spillover. Pure knowledge spillover, as well as the inherent knowledge simulated and adopted by others, emerge primarily by externalities in the form of flows of research and development personnel, mobility of knowledge, dissemination via cooperation, international technology learning or the direct purchase of foreign technology knowledge. Such knowledge spillover makes leaders of enterprises or nations reluctant to accept unavoidable spread via numerous noncommercial channels. Thus this kind of diffusion can be called active technology spillover. The disembodied technology diffusion measured in the form of formulas, blueprints, drawings, patent citations, and so on
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand © 2010 PICMET . The advantages of innovation activities are reflected in the process of commercialization; that is to say, an effective method of measuring national competitiveness in a disembodied form is through patent citation frequency. Pure knowledge spillover results from disembodied knowledge flows, including licensing, patent citations, or outsourcing agreements. Griliches  suggests that patent citations can be measured as a disembodied form of diffusion. Moreover, Helleiner  indicates that based on the definition of a patent, technology includes not only legally guaranteed patents and trademarks but also the sophisticated techniques necessary to produce tangible merchandise. According to Jaffe et al., Eaton & Kortum, and Hu & Jaffe international patenting is a proxy for the channel of international diffusion of disembodied technology. Patents can indicate intellectual property and measure technology innovation performance [33, 34]. Numerous researchers have taken frequency of patent citations as an indicator of national innovation competitiveness (e.g.[35, 36]) , with the importance of a patent increasing with the frequency of citations. Patient citations thus are measurable innovation indicators of national competitiveness. Hence, this study adopts patent citations as a means of disembodied technology diffusion. Countries citing their patents in relation to others generate pure knowledge spillover. C. Perspective of Network Autocorrelation Although embodied and disembodied technology diffusion effect a national innovative capacity performance, previous studies have not adequately dealt with the many important differences between the alternate channels of international diffusion and their differential influence on national innovative capacity. Furthermore, while the questions related to external environments are best understood as global network issues, there exist few explicitly social network analyses of these questions. In network analysis, Leenders  proposes a type of process typically modeled as a network autocorrelation. The behavior and thought of actors are not only determined by the constraints and the opportunities via a social system (local affects) but also affected by others (interactive affects). The local affects are indigenous determinants of NIC and interactive affects are social influences of globalization. Social influence occurs when actor behavior, attitudes, or beliefs involuntarily follow those of others in the same social system. Numerous researchers are interested in the contagion process of the innovations diffusion (e.g. [38-40]). Actors tend to be effected by the opinions and behaviors of significant others belonging to the cohesive group or occupying a position of structural equivalence. This influence process is known as the contagion effect. Contagion is often used to describe the processes involved in social influence [41, 42]. Social influence theory involves two processes: communication and comparison . Communication based on social influence involves direct contact between ego and alter. Cohesion is the most
common approach to operating a communication process in social network analysis. While the ego hesitates to make a decision, he will seek alters who he trusts for consultation, mostly owing to the relationship of cohesion between them. The more intimate and frequent interactions between ego and alter, the greater the influence of alter on the opinion and behavior of the ego . The frequency, intensity, and closeness of interaction among cohesive actors leads to increased recurrence of action than it does among non-cohesive actors, not only increasing the opportunity to transmit social clues , but also resulting in network constraints among them. Some social network researchers interpret cohesion from a group perspective. Festinger  defines cohesion as “the result of all the forces acting on all members to remain in a group.” Actors in cohesive groups exhibit greater behavioral conformity and accordant relationship than those in less cohesive groups. Social structure is a configuration of social relations among actors where the relations involve exchange of cherished items that can be tangible (substance) or intangible (knowledge, information). Because of exchange, international trade yields increased opportunities for information sharing and thus government policy similarity between partner countries . This study thus examines the influence of cohesion mechanisms on national innovative capacity performance. Another contagion process is social comparison. Ego compares himself with those alters who he sees as similar in network aspects . The comparison is actuated if actors are competing . Therefore, the comparison is most frequently operated using the concept of equivalence. Equivalent actors are similarly embedded in the network. The most comprehensive conception of equivalence is structural equivalence . The actors in the structural equivalence mode exhibit a similar pattern of relations to other actors in the social configuration , because individuals encountering uncertainty may refer to structurally equivalent actors to simulate appropriate responses. Burt  proposes that decision-makers are socialized via the symbolic role-playing of placing themselves in the position of others. This study thus applies the structural equivalence model to examine the influence of national innovative capacity on performance. Lundvall  argues that the production and diffusion of new knowledge occurs in the mutual learning of members, and that is conducive to the development and diffusion of new technology. The network autocorrelation model illustrates the ego’s decision as deriving from his own status, education, and income (intrinsic opinion), along with interaction amongst significant others (contagion). This study observes both the determinants of national innovative capacity and the influence of social proximity on national innovation performance; it not only can adequately explore the global formulation of nation innovative capacity, but in doing so can identify a similar mode of international interaction.
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand ÂŠ 2010 PICMET III. HYPOTHESES A. The Determinants of National Innovative Capacity In an autocorrelation model, the egoâ€™s behavior is not only influenced by others (interactive affects), but by the ego's intrinsic conception and status (local affects). This study therefore adopts the Furman et al.  proposal, whereby the determinants of national innovative capacity are used to examine how innovative output is influenced by local affects. This study thus hypothesizes the following: Hypothesis 1: National innovative capacity is significantly determined by local affects. Hypothesis 1a: National innovative capacity is positively and significantly affected by the quality of the common innovation infrastructure. Hypothesis 1b: National innovative capacity is positively and significantly affected by the quality of the cluster-specific innovation environment. Hypothesis 1c: National innovative capacity is positively and significantly affected by the quality of linkages. B. The autocorrelation model The social influence process of cohesion mechanism is focused on the interaction between the ego and alter. When the ego encounters tough questions, its attitude and conduct will lean towards alters within the same group. The cohesion model incorporates the opinions, behaviors, attitudes, and policies connecting actors. That is to say the policy making of a given country tends to promptly follow that of an alter country, since both share a common assessment of the costs and benefits of interaction . Consequently, this study assumes that countries belonging to the same group can diffuse, embodied and disembodied technology through cohesion mechanisms and then further influence national innovation performance. On the other hand, Burt  argues that ego behavior is predicted more accurately by structural network position than by interactions with others. Due to competition, competitors can readily follow changes made by egos . Actors accept innovations when they see them being applied by others structurally equivalent to themselves. Owing to similarity, actors become aware of competition, and then take others as a behavioral paradigm. Therefore, the more similar the structural position of the ego to alters, the more likely that alters will substitute for the position of the ego . According to Burt, this study determines that actors within the structural equivalence model are competitive with each other. Burt  and Shih  applied structural equivalence to the study of industrial structures, and also concluded that actor adoption behavior is triggered by structurally equivalent others within the network. Consequently, this study assumes that among countries that are structurally equivalent, embodied and disembodied technology can be diffused through structural equivalent mechanisms and influence national innovation performance. This study thus hypothesizes the following:
Hypothesis 2: National innovative capacity is not only determined by local affects but also influenced by the interactive proximity of embodied technology. Hypothesis 2a: The interactive proximity of embodied technology by cohesive partner countries exerts positive and significant influence on national innovative capacity. Hypothesis 2b: The interactive proximity of embodied technology by structurally equivalent competitive countries exerts positive and significant influence on national innovative capacity. Hypothesis 3: National innovative capacity is not only determined by local affects, but also influenced by interactive proximity of disembodied technology. Hypothesis 3a: The interactive proximity of disembodied technology by cohesive partner countries exerts positive and significant influence on national innovative capacity. Hypothesis 3b: The interactive proximity of disembodied technology by structurally equivalent competitive countries exerts positive and significant influence on national innovative capacity. C. Comparison Although two different contagion mechanisms may exist in social proximity, numerous scholars argue that ego behavior is more likely to be effected by the alter having the same network position than by alters interacting with each other [10, 38-40]. Consequently, the interactive affects of the structural equivalence mechanism should positively and significantly exceed the cohesion mechanism. Furthermore, embodied and disembodied technologies provide important alternate channels of international technology diffusion. This study adequately deals with their differential impact on national innovative capacity. Embodied and disembodied technology generate differential knowledge and technology spillover to influence innovative capacity performance, however the differential form of knowledge conducts distinguishable learning efficiency to generate innovation. Consequently, this study assumes that NIC performance is affected by an autocorrelation model, with the disembodied technology diffusion producing significant influence on national innovation performance, much more than embodied technology does. The hypotheses used to compare the performance of national innovative capacity are examined below: Hypothesis 4: In terms of embodied technology diffusion, national innovative performance is influenced more by similarities between countries with structurally equivalent proximity than with cohesive proximity. Hypothesis 5: In terms of disembodied technology diffusion, national innovative performance is influenced more by similarities between countries with structurally equivalent proximity than with cohesive proximity. Hypothesis 6: National innovative performance is more significantly affected by disembodied technology diffusion than by embodied technology diffusion.
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand ÂŠ 2010 PICMET IV. METHODOLOGY
A. Data This investigation employs a sample of 42 countries over the period from 1997 to 2002, ranked according to the Global Competitiveness Index of the World Competitiveness Databank. This study mainly refers to the network autocorrelation model, so the dataset is inclusive of local affects and interactive affects. The dataset of local affects is based on the determinants of national innovative capacity proposed by Furman et al. . They include three factors;, the quality of common innovation infrastructure, the cluster-specific innovation environment, and quality of linkages amounting to 13 elements. Due to the significance of variables and the collection of dataset, this study applies 8 elements as local affects, including GDP per capita, GDP, total R&D personnel (FTE), R&D expenditure, openness, higher education expenditure as a percentage of GDP, R&D expenditure funded by industry as a percentage of GDP and R&D expenditure performed by university as a percentage of GDP. Moreover, Furman et al. proposes patent output as the outcome of national innovative capacity, and thus this work chooses patents output as the NIC performance. The interactive affects dataset contains four categories: bilateral trade in exports and imports, frequency of patent citations, aggregate R&D expenditure and international patents granted in year t+3. Trade flow data is mainly obtained from Global Trade Information Services, Inc.. However, data on imports are more accurate than those on exports [26, 48, 49], and this study adopts an importing dataset. Furthermore, Coe et al.  found that it is better to measure trade-related spillover using trade in capital goods than total trade. For frequencies of patent citations, the dataset consists of patents granted by the United States Patent and Trademark office, and frequencies of patent citations are obtained from the NBER Patent Citations Database . Owing to technical difficulties in analyzing raw data, this investigation gathers data for the periods from 1997~2002 and contains frequencies of inter-country patent citing as cited. As for the total R&D expenditure of each country, this investigation refers to World Competitiveness databank, IMD. PATENTS represents the number of patents granted in year t+3 by the USPTO due to the average lag between the application and approval by the USPTO and between the measures of innovative capacity and the observed realization of innovative output . Considering the completeness of data collection, this investigation selects 42 countries as the sample, owing to materials for some countries being absent. Appendix A lists the countries studied in this work. The initial levels of innovative productivity and the legacy of historical situations of each country represent different influences on the performance of national innovativeness , and thus Appendix A shows both embodied and disembodied diffusion countries. Appendix B lists variable sources and
B. Measurement Leenders proposes that egoâ€™s ideas and conduct are not solely determined by significant others (interactive affects), but by response to various confinements and opportunities bounded in the social system (local affects). This type of influence process in sociology is typically constructed in an autocorrelation model on the following equation: yi U yi XE H , â€Śâ€Śâ€Śâ€Śâ€Śâ€Śâ€Śâ€Śâ€Śâ€Śâ€Ś(1)
Here, XE denotes actorsâ€™ intrinsic opinion which would be shown in the absence of social influence. The difference between interactive and local affects is reflected in the external part (matrix W) and the internal part ( XE ). C. Local affects In the local affects section of XE , this study adopts the elements of Furman et al.  suggestion of a local affects model; including GDP per capita, R&D personnel (full-time equivalent), international trade openness, higher education share as a percentage of GDP, R&D funded by private sector as a percentage of R&D expenditure and R&D performed by universities as a percentage of R&D expenditure. Testing the significance of local affects on the performance of national innovative capacity is based on the following equation: yi XE H , â€Śâ€Śâ€Śâ€Śâ€Śâ€Śâ€Śâ€Śâ€Ś(2) The above parameter estimates and infers on the basis of the autocorrelation model hinging upon the selected specification of weight matrix W. This matrix represents the influence process assumed to be present in the network and can operate in many different ways. D. Interactive affects and International technology diffusion Burt  designed a theoretical framework for the contagion effects of cohesion and structural equivalence in the social network by observing the diffusion of technological innovation. Thus, this study adopts the social contagion model devised by Burt  to forecast international technology diffusion among countries. Since total national R&D expenditure is positively and significantly related to international technology diffusion [17, 51], Xu & Wang and Shih & Chang propose that international technology diffusion is measured based on national R&D expenditure, which must be multiplied by a weighted coefficient. This study considers total national R&D expenditure when measuring the degree of international technology diffusion. Regarding the embodied technology diffusion, this works employs trade flows as an interactive measurement. For example the quantity of machinery and equipment imported in one country is multiplied by the total R&D expenditure in another country, and it imports from 42 countries while those
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand ÂŠ 2010 PICMET countries export to this country, forming a 42 by 42 matrix. Shih & Chang propose that if a certain country imports more capital goods from another country, the net importer nation will benefit through embodied technology diffusion. In terms of disembodied technology diffusion, patent citations represent the linkage to prior knowledge; that is, the frequencies with which a certain country cites patents from another country represent the density of pure knowledge spillovers between the two countries. Patent citations are measured by citation frequencies, and owing to this reference, if a given country cites patents from 42 countries while their patents are cited by these other countries, this also constitutes a 42 by 42 matrix. This study thus assumes that when a given country cites more patents from other countries, the patent citing nation will benefit from disembodied technology diffusion. On the other hand, this study employs two types of diffusion mechanisms, cohesion and structural equivalence models, to investigate the differential effects in embodied and disembodied technology. As for the cohesion model, the weight matrix between ego countries and alter countries is measured by row and column data. If the weight matrix is measured using the row data, the effects of social contagion on national innovative capacity from importing or patent citing countries is represented. Conversely, if the weight matrix is measured using the normalized column data, this operation shows the effect of social contagion to national innovation performance from exporting or cited countries.
Summing the row and column data can investigate the influence on the performance behavior of national innovative capacity from trading or citing partners. As for the structural equivalence model, measuring the relationship between ego countries and alter countries requires examining Euclidean distance. This is the most common method used by sociologists to measure degree of structural equivalence, with a value ranging between zero and one. When this distance equals zero it means that the two actors are precisely structurally equivalent. Since the structural equivalence model measures the relations of the actors in terms of trading or patent citations, row data and column data are included in the Euclidean distance equation. The mathematical expression of international technology diffusion refer Huang & Shih works. V. RESULTS AND DISCUSSION This study argues the determinants of national innovative capacity are not merely from local affects; the original conception and development constrained by the social system, but also from interactive affects; the conception and development by the social contagion theory. The research hypotheses were tested by regression analysis based on the model in (1) and (2). The influences of local affects are examined by the (2) and result show in table 1. Model 1 and model 2 present local affects and a network autocorrelation model is presented from model 3 to model 9.
TABLE 1 RESULTS OF REGRESSION ANALYSIS
Quality of the common innovation infrastructure GDP per capita GDP R&D personnel (FTE) R&D $ Openness Ed share Quality of the cluster-specific innovation environment Private R&D funding Quality of linkages Univ. R&D performance Contagion effects Embodied via Cohesion Embodied via Structural equivalence Disembodied via Cohesion Disembodied via Structural equivalence Adjusted R2 Significance Observations
Dependent variable=(PATENTS)j,t+3 model 4 model 5 model 6 model 7
-0.052*** 0.418*** -0.124*** 0.678*** 0.040** 0.008
0.964 0.000 252
0.501 0.000 252
Note: 1. Numbers represent standardized beta coefficients.
0.550 0.000 252
0.575 0.000 252
0.613 0.000 252
2. *, p<0.05; **, p< 0.01; ***, p< 0.001
0.666 0.000 252
-0.429*** -0.384*** 0.449***
0.683 0.000 252
0.834 0.000 252
0.837 0.000 252
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand ÂŠ 2010 PICMET A. Local affects Furman et al.  proposes the determinants of NIC, which are tested by (2) and the result in model 1. This model provides an explanation power of 96.4%, which is significant support for hypothesis 1 ; the local affects in these models are significant overall, but some individual impacts are negative and even insignificant. In quality of the common innovation infrastructure, Furman et al.  suggests all variables except openness should be positive determents for NIC. However the empirical result showed GDP per capita, R&D personnel, and openness not consistent with pervious studies, therefore this result does not support hypothesis 1a. Additionally, in terms of the cluster-specific innovation environment and quality of linkages, previous studies indicate the variable of private
R&D funding and university R&D should be positive determents of NIC, however this empirical result does not support hypothesis 1b and hypothesis 1c. Therefore, to ensure model precision, this study uses co linearity statistics ensuring the model precision and finds model 1 indicating serious multicollinearity. Belsley et al.  suggest that if the value of the variance inflation factor (VIF) is more than 10, it has co linearity with other variables. As a result of Table 2, two variables, GDP and R&D $ are more than 10. This work also demonstrates the correlation analysis from Table 3 to determine that the variables of GDP and R&D $ are highly correlated with each other, and with R&D personnel (FTE). So this study deletes these two variables as an adjustment to the local affects model.
TABLE 2 CORRELATION ANALYSIS Std. variables N Mean 1 2 3 4 5 Deviation 1. GDP per capita 252 15241.89 11311.60 1 2. GDP 252 688963.20 1572249.13 0.377** 1 3. R&D personnel (FTE) 252 165982.89 276543.22 0.124* 1 0.716** 4. R&D $ 252 14739.13 42661.07 0.415** 0.992** 0.687** 1 5. Openness 252 86.55 67.45 0.127* -0.265** -0.317** -0.234** 1 6. Ed share 252 55.47 17.10 0.164** -0.093 -0.043 -0.110 0.084 7. Private R&D funding 252 47.01 16.64 0.496** 0.305** 0.216** 0.337** 0.056 8. Univ. R&D performance 252 25.35 14.23 -0.031 -0.236** -0.44** -0.231** 0.164 Note: ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
1 0.099 -0.070
TABLE 3 COLLINEARITY STATISTICS Dependent variable=(PATENTS)j,t+3 Collinearity Statistics Tolerance VIF GDP per capita 0.560 1.787 GDP 0.012 84.574 R&D personnel (FTE) 0.368 2.716 R&D $ 0.012 82.324 Openness 0.827 1.210 Ed share 0.877 1.140 Private R&D funding 0.572 1.749 Univ. R&D performance 0.632 1.581 Observations 252
Due to the muliticollinearity problem, the adjusted R square of model 1 is extremely high. While this work deletes variables with collinearity to make the model more precise, the adjusted R square of model 2 is lowered down to 50.10%. Moreover, once the explanatory power of model 2 is dropping down to a half; some variables are turned into insignificant (eg. openness, private R&D funding and Univ. R&D performance) and negative (eg. Ed share). This result implicates mere local affects are insufficient to determine NIC. Consequently this study tries to compensate for this effect by simultaneously taking local affects and interactive affects to interpret NIC. Results are shown from model 3 to model 9. However, the empirical result of hypothesis 1a, 1b, and 1c do not support Furman et al.  study. This study suggests
that pervious studies take 17 countries 1 as a sample to develop an NIC preferred model, yet those sample countries are developed countries; those countries have well developed infrastructures and healthy cluster-specific environments. However, this study examines 42 countries which include developed and developing countries, as Furmanâ€™s purposed the theories of new-to-the-world innovation production should apply all over the world. Therefore, the global economic differential development attributes the empirical result of some individual impacts on model 1 inconsistently with the prior study. Moreover, this result also implies that pervious studies imperfectly interpret NIC determinants on a global level. Meanwhile, comparing model 2 and model 5 or model 2 1
The sample country by Furmanâ€™s study was list in Appendix A.
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand ÂŠ 2010 PICMET and model 8, the results show a rising trend; this supports hypotheses 2 and 3. These results show that national innovative capacity is not solely determined by local affects but is also influenced by social proximity of international technology diffusion. B. The effects of autocorrelation model As for the relations between the autocorrelation model and NIC performance in each country as demonstrated by models 3 and 6, the cohesion mechanism exhibits unexpected negative effects via embodied and disembodied technology diffusion, this does not support hypotheses 2a and 3a. However, regarding the structural equivalence mechanism, models 4 and 7 demonstrate positive and significant relationships between the interactive affects and NIC, supporting hypotheses 2b and 3b. This result infers that countries lean more towards influencing national innovative capacity through mimicking the behavior of competitors than mimicking partners. Furthermore, comparison of the contagion effects in Table 1 reveals that influence on national innovative capacity via embodied technology is greater in terms of the structural equivalence mechanism than is the cohesion mechanism, supporting hypothesis 4. On the other hand, regarding disembodied forms, the structural equivalence mechanism retains a more positive and significant influence on national innovative capacity than does the cohesion mechanism, supporting hypothesis 5. In addition, by determining embodied and disembodied technology generates differential knowledge and technology spillover further influencing innovative performance, this study assumes that NIC performance is mainly effected by disembodied technology diffusion, rather than by embodied technology diffusion, supporting hypothesis 6. 1) Embodied technology diffusion autocorrelation model The results of embodied technology diffusion show the autocorrelation model rising gently. Most individual impacts are positive and even significant in terms of local affects. Notably, since interactive affects combine with local affects, in models 3, 4 and 5, the major R&D expenditure variables in the common innovation infrastructure, the cluster-specific innovation environment and quality of linkages obviously influence NIC performance, since these variables turn positive and significant. This result implies that international technology diffusion replenishes the model of national innovative determinants. However, the interactive effect of model 3, embodied technology diffusion via cohesion mechanism, is negative and significant, which appears inconsistent with previous studies. Theoretically, international technology diffusion positively effects both ego and alter countries [24, 55]. The reverse effects are observed when this investigation includes developed and developing countries, and those countries develop new-to-the-world technology differently. At the global level, several higher innovative capacity
countries spread their technology into numerous lower innovative capacity countries, leading technology diffusion to negatively impact NIC performance. That is to say, previous studies regard technological diffusion as global stratification patterns (e.g. [16, 21, 27, 49, 56, 57]). Therefore, the result of these findings is consistent with those of previous works. Furthermore, the rent of embodied technology transforming to innovative capacity is affected by the import countryâ€™s absorptive capabilities . Products only partially contain essential knowledge and techniques on manufacturing, and cannot transfer the technology completely. Acquiring knowledge involves not simply purchasing or trading goods, but rather systematic and purposeful knowledge-based learning and construction . Developed countries export numerous types of machinery and equipment to developing countries, contributing a positive effect to developing countriesâ€™ innovative capacity. Therefore, lower innovative capacity countries achieve economic growth and changes in productivity efficiency through the embodied technology of their more innovative partners. Developing countries do not exert a valid influence on innovative activity via the embodied technology of developed partner countries, but such technology increases their production efficiency [61-63]. The embodied technology autocorrelation model via structural equivalence mechanism diffusion displays positive but significant influence on NIC performance. Model 4 represents countries that are more inclined to utilize mimicking behavior with structurally equivalent competitors through trading embodied technological commodities. This mechanism demonstrates that ego countries and alter countries are competitors . Owing to the existence of structural equivalence, a given country can mimic the technology of a competitor country with a similar network position. Conversely, while trade action from competitors results in more innovative outputs owing to competition, a focal country has a mimic similar reaction . This result infers that embodied technology diffusion via structural equivalence gives negative results between competitors given the spatial limitations of indigenous innovative performance; mimicking behavior can not significantly replenish notional innovation capacity. This study compares two interactive affects of the autocorrelation model, to determine whether NIC performance is more similar between countries with structural equivalence proximity than with cohesion proximity, as stated in hypothesis 4. The model shows that R square should identify effectiveness . Thus, the R square of model 4 is larger than that of the model 3. Furthermore, in model 5 the structural equivalence coefficient is more positive and significant than the cohesion mechanism. The analytical results support hypothesis 4; the structural equivalence mechanism exerts a major interactive effect on the NIC performance. Restated, NIC performance is triggered more by competitors than cohesion partners. This result is
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand ÂŠ 2010 PICMET consistent with that of Koka et al. , namely that countries seeking to develop a profitable trading policy must ensure their policies fit those of other related countries. This study find that countries prefer to learn from the experiences of others with a similar network position, since such learning can positively influence national innovation performance; restated, countries can react to competitors who are structurally equivalent in terms of embodied technology diffusion. 2) Disembodied technology diffusion autocorrelation model The autocorrelation model with disembodied technology diffusion shown in model 6, 7 and 8, which explanatory power rise more obviously than embodied technology does. Most individual impact on local affects remain positive and significant, especially the major R&D expenditure variables. This result strongly infers that disembodied technology diffusion mainly interprets NIC performance. However, the standardized coefficient of interactive affects via the cohesion mechanism is negative and significant, and this empirical result not support hypothesis 3a. But the result of autocorrelation model 6 rises upward to 66.6%. This result significantly correlates international technology diffusion with global stratification patterns. Due to the large differences in innovative capacity between developing and developed countries, low innovative capacity countries depend upon their cohesive partnerâ€™s disembodied technology to promote their technological advances Consequently, the strong relationship within cohesive groups has reverse effects on innovative capacity. Restated, the effect of disembodied technology diffusion among countries within a cohesive group exerts a negative influence on innovative capacity. However, the essence of disembodied technology diffusion shows that pure knowledge spillover is more directly effects innovation capacity. Thus this autocorrelation model 6 affects innovation performance more than previous models. Additionally, this influence of interactive effects also shows prominently in structural equivalence mechanisms. In model 7, structural equivalence mechanisms, the standardized coefficient is positive and significant. A country that is structurally equivalent not only has a similar network position to a competitor but also a similar technological environment to acquire the knowledge of their competitors; disembodied technology via structurally equivalent mechanism is easier to diffuse. Since disembodied technology diffusion is termed an active technology spillover, direct learning or purchase of foreign technological knowledge involves explicitly using disembodied knowledge in the form of patent applications. While the actions of competitor countries stimulate increased patent output and raise national competitiveness, an ego country in the same network position performs similar and active R&D to increase their innovation activity. When other countries remain in a position of structural equivalence with an ego country, their conduct positively affects innovation capacity. Consequently, alter countries, following the role of
competition in the same network position, provide an ego country with positive feedback regarding national innovation performance via international technology diffusion. By comparing two interactive affects of disembodied technology diffusion as presented in Hypothesis 5, this investigation compares the results of model 7 and model 6. Furthermore, in model 8 the coefficient estimates of structural equivalence more positively and significantly than cohesion mechanisms do. This supports Hypothesis 5, indicating the autocorrelation model of structural equivalence mechanism yields more significant contagion effect than cohesion mechanisms. That is, national innovative performance is influenced more by the competitive countriesâ€™ innovative performance. Alter countries with similar network positions remain the main influences on national innovative capacity of ego countries. However, international pure knowledge spillover proves effective not only when technology is obtained from abroad for less than the original cost of domestic inventors, but also when a country can absorb and apply technology from abroad. Additionally, direct learning regarding explicit knowledge of foreign competitors replenishes indigenous technological capability and can actively be adopted for innovation efficiency. C. Comparison for embodied and disembodied technology Empirically, embodied and disembodied diffusion are not easily distinguishable, but the measurement in terms of empirical data can capture and differentiate either embodied or disembodied diffusion. Comparing the models 5 and model 8, it is demonstrated that disembodied technology diffusion autocorrelation model influences NIC more significantly than embodied technology diffusion does. Furthermore, comparing model 8 results of 83.4% with model 9 with 83.7% shows a slightly higher result, though it is total model. This result infers that the better model is model 8; national innovative capacity simultaneously determined by the indigenous innovation environment and influence on disembodied technology diffusion. More specifically, this result indicates a differential rigidity between two forms of technology diffusion. Notably, embodied technology diffusion is more rigid to rent spillovers than disembodied technology diffusion is to pure knowledge spillovers. Utilizing specialized and advanced intermediate products that have been invented overseas demonstrates the implicit usage of technological knowledge embodied in foreign intermediate goods for producing final output. Furthermore, the technological knowledge embodied in trading intermediates is not available to domestic inventors. Embodied technology diffusion is thus considered a passive technology spillover that primarily influences changes in productivity efficiency . Restated, embodied technology diffusion is rigid to knowledge spillover, which is a relatively weak form of international technology diffusion that influences national innovative performance. ! Disembodied technology diffusion is less rigid for
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand ÂŠ 2010 PICMET knowledge spillover, and is termed active technology spillover. Direct acquisition of foreign technological knowledge involves explicitly using disembodied knowledge in the forms of formulas, research papers, and patent applications . Furthermore, disembodied technology diffusion provides a cost effective channel to replenish NIC. Pure knowledge spillover occurs internationally if technological knowledge is obtained from foreign for less than the original cost of domestic inventors. Direct learning regarding foreign technological knowledge increases the domestic technological stock of knowledge that can be actively adopted for innovation, and that influences technical change. VI. CONCLUSIONS This section will quickly review the main contributions of this study. It will have summarized the limitation of the research method proposed and results obtained. It will conclude by suggesting new research directions. As the importance of countries upon the international innovative competition increases, so does the need to identify major determiners and influence mechanisms on national innovation performance. Consequently, this work takes the perspective of a network autocorrelation model to explore the endogenous determiners and exogenous influence on national innovative capacity. First, this study reconsiders the Furman et al.  propose the determinants of national innovative capacity, because the empirical result implies that merely local affects used to determine national innovative capacity results in imperfect results. More specifically, previous studies pay less attention to exogenous technological development infulence on national innovative capacity, without adequately dealing with the differing effects of exogenous technology. Through an autocorrelation model that simultaneously considers local affects and interactive affects, this study reconstructs the model of determents and influence on national innovative capacity, and examines the important differences between the alternate channels of international technology diffusion and their differential effects on innovative performance. Second, the important differences between the alternate channels of international technology diffusion on national innovative capacity show that disembodied technology diffusion more effectively replenishes the indigenous technology environments than embodied technology does. This empirical result infers that embodied technology is rigid to knowledge spillover and more strongly influences productivity changes than innovative performance. Comparatively speaking, disembodied technology is less rigid to knowledge spillover with regards to influence on national innovation performance, and moreover affects technical change. Therefore, this channel differential provides policy alternatives in national science and technology development. Third, in a global network context, differential interactive
proximity effects differentiate national innovative capacity. The cohesion proximity negatively affects national innovation capacity, inferring international technology diffusion via global stratification patterns. As a result, merely utilizing the technology of a cohesion partner without absorbing the embodied or disembodied technological rent spillover will more deeply embed a country into a large exchange system. Additionally, interactive environments in structural equivalence proximity significantly influence national innovation capacity. Countries are affected more by structurally equivalent competitors than cohesion partners. That is, countries become more inclined to take competitors as a paradigm via international technology diffusion based on the environment in which they are developing. Based on the empirical findings, national innovative capacity is simultaneously determined by domestic R&D resource allocation and influenced by international technology diffusion. The findings of this study imply that there are two points for countries interested in innovative capacity gain. First, tuning of the endogenous and exogenous factors can be an important strategic principle for technological growth. Restated, while a country can allocate domestic R&D resource for technology development, policy makers should refer more to their global network position than to network cohesion to deploy their international cooperation. Therefore, indigenous technological capability corresponds with foreign technology acquisition to increase innovation. Second, national innovative capability is more significantly affected by foreign disembodied technology. Consequently, acquiring competitor countriesâ€™ disembodied technology is more effective to influence on innovative capability gain. Despite its contributions, this study has certain limitations, and these limitations should be acknowledged to identify future research directions. This work provides some suggested directions for future research. Suggestions include the following: this work explores the network autocorrelation model, simultaneously considering local affects and social contagion effects in a global context; it does not individually examine the actions of focal countries at the block level (e.g. core, semi-periphery and periphery). Global stratification patterns can be made more specific if researchers focus on the interactions between certain countries and others. Finally, this study focuses on social contagion effects to the exclusion of other social network analysis. A useful direction for future works would be to apply more indicators and conceptions of social network analysis to analyze the data. ACKNOWLEDGMENTS The authors would like to thank Les Davy, National Chi Nan University Department of computer Science and Information engineering, for his editorial assistance. Also, this research was supported by a grant from the National Science Council of Taiwan for financially supporting this research under Contract No. 97-2410-H-260-011-MY3. This
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand © 2010 PICMET support is gratefully acknowledged. REFERENCES 
Suarez-Villa, L., “Invention, inventive learning, and innovative capacity,” Behavioral Science, vol. 35, no. 4, pp. 290-310, 1990.  Furman, J. L., M. E. Porter, and S. Stern, “The determinants of national innovative capacity,” Research Policy, vol. 31, pp. 899-933, 2002.  Tornatzky, L., and M. Fleisher, The Processes of Technological Innovation, Lexington, MA: Lexington Books, D.C. Heath and Company., 1990.  Furman, J. L., and R. Hayes, “Catching up or standing still? National innovative productivity among ‘follower’ countries, 1978-1999,” Research Policy, vol. 33, pp. 1329-1354 2004.  Aoki, M., and N. Rosenberg, The Japanese Firm as an Innovating Institution: CEPR Publication, Center for Economic Policy Research, Stanford University, 1987.  Marquis, D., The Anatomy of Successful Innovations: Ballinger Publishing Co., 1988.  Tushman, M., “Managing Communication Network in R&D Laboratories,” Sloan Management Review, pp. 37-49, 1979.  von Hippel, E., The Sources of Innovations, New York: Oxford University Press, 1988.  Staniland, M., What is political economy? A study of social theory and under-development, New Haven, CT: Yale University, 1985.  Koka, B. R., J. E. Prescott, and R. Madhavan, “Contagion Influence on Trade and Investment Policy: A Network Perspective,” Journal of International Business Studies, vol. 30, no. 1, pp. 127-147, 1999.  Nasierowski, W., and F. J. Arcelus, “Interrelationships among the elements of national innovation systems: a statistical evaluation,” European Journal of Operational Research, vol. 119, pp. 235-253, 1999.  Griliches, Z., “Patent Statistics as Economic Indicators: A Survey,” Journal of Economic Literature, vol. XXVIII, pp. 1661-1707, December 1990, 1990.  Trajtenberg, M., Patents as Indicators of Innovation, Cambridge (MA): Harvard University Press. , 1990.  Hu, M.-C., and J. A. Mathews, “China's national innovative capacity,” Research Policy, 2008.  Rogers, E. M., Diffusion of Innovations, New York: Free Press, 1985.  Vernon, R., “International Investment and International Trade in the Product Cycle,” Quarterly Journal of Economics, vol. 153, pp. 190-207, 1966.  Griliches, Z., "Productivity and technological change: some measurement issues," Technology and Productivity: The Challenge for Economic Policy, pp. 229-231: OECD, 1991.  Bascavusoglu, E., "Patterns of technology transfer to the developing countries: differentiating between embodied and disembodied knowledge," TEAM and CNRS Working Papers, 2004. Griliches, Z., “Market value, R&D, and patents,” Economics Letters, vol. 7, pp. 183-187, 1981.  Papaconstantinou, G., N. Sakurai, and A. Wyckoff, “Domestic and international product-embodied R&D diffusion,” Research Policy, vol. 27, pp. 301-314, 1998.  Coe, D. T., and E. Helpman, “International R&D spillovers,” European Economic Review, vol. 39, pp. 859-887, 1995.  Eaton, J., and S. Kortum, “Trade in capital goods,” European Economic Review, vol. 45, no. 7, pp. 1195-1235, 2001.  Keller, W., "The geography and channels of diffusion at the world’s technology frontier," National Bureau of Economic Research Working Paper No. 8150, 2001.  Keller, W., “International technology diffusion,” Journal of Economic Literature, vol. XLII, pp. 752-782, 2004.  Grossman, G. M., and E. Helpman, Innovation and Growth in the World Economy, Cambridge, MA: MIT Press, 1991.  Smith, D. A., and D. R. White, “Structure and dynamics of the global economy: network analysis of international trade, 1965-1980,” Social Forces, vol. 70, pp. 857-893, 1992.  Coe, D. T., E. Helpman, and A. W. Hoffmaister, “North-South R&D spillovers.,” The Economic Journal, vol. 107, pp. 134-149, 1997.
 Maskus, K. E., Encouraging International Technology Transfer, Geneva, Switzerland, 2004.  Helleiner, G. G., “The Role of Multinational Corporation in Less Developed Countries’ Trade in Technology,” World Development, vol. 3, pp. 161-189, 1975.  Jaffe, A. B., M. Trajtenberg, and R. Henderson, “Geographic localization of knowledge spillovers as evidenced by patent citations,” Quarterly Journal of Economics, vol. 108 no. 3, pp. 577-98, 1993.  Eaton, J., and S. Kortum, “International patenting and technology diffusion: theory and measurement,” International Economic Review, vol. 40, pp. 537-570, 1999.  Hu, A. G. Z., and A. B. Jaffe, “Patent citation and international knowledge flow: the cases of Korea and Taiwan,” International Journal of Industrial Organization, vol. 21, pp. 849-880, 2003.  Mogee, M. E., “Using Patent Data for Technology Analysis Planning,” Research Technology Management, vol. 34, no. 4, pp. 43-49, 1991.  OECD, Oslo Manual, Proposed Guidelines for Collecting and Interpreting Technological Innovation Data: OECD, 1997  Griliches, Z., “Issues in assessing the contribution of research and development to productivity growth,” The Bell Journal of Economics, vol. 10, no. 1, pp. 92-116, 1979.  Austin, D., “An Event Study Approach to Measuring Innovative Output: The Case of Biotechnology.,” American Economic Review, vol. 83, pp. 253-258, 1993.  Leenders, R. T. h. A. J., “Modeling social influence through network autocorrelation: constructing the weight matrix,” Social Networks, vol. 24, pp. 21-47, 2002.  Harkola, J., and A. Greve, “Diffusion of technology: cohesion or structural equivalence?,” in Academy of Management Meeting., Vancouver, 1995, pp. 422–426.  Shih, H.-Y., “Contagion effects of electronic commerce diffusion: Perspective from network analysis of industrial structure,” Technological Forecasting & Social Change, vol. 75, no. 1, pp. 78-90, 2008.  Burt, R. S., “Social contagion and innovation, cohesion versus structural equivalence.,” American Journal of Sociology, vol. 92, pp. 1287-1335, 1987.  Leenders, R. T. A. J., Structure and Influence: Statistical Models for the Dynamics of Actor Attributes, Network Structure and Their Interdependence, Amsterdam: Thela Thesis Publishers, 1995.  Leenders, R. T. h. A. J., Longitudinal behavior of network structure and actor a tributes: modeling interdependence of contagion and selection, New York: Gordon and Breach, 1997.  Rice, R. E., and C. Aydin, “Attitudes towards new organizational technology: Network proximity as a mechanism for social information processing,” Administrative Science Quarterly, vol. 36, pp. 219-44, 1991.  Festinger, L., S. Schachter, and K. Back, Social Pressures of an Informal Groups: A Study of Human Factors of Housing, New York: Harper, 1950.  Lorrain, F., and H. C. White, ȸStructural equivalence of individuals in a social network,” Journal of Mathematical Sociology, vol. 1, pp. 49-80, 1971.  Lundvall, B.-A., National Systems of Innovation: Towards a Theorem of Innovation and Interactive Learning, 1992, Ed. ed., London: Pinter Publications, 1992.  Burt, R. S., "A note on cooptation and definitions of constraint," Social structure and network analysis, P. V. Marsden and N. Lin, eds., Beverly Hill: Sage Publications., 1982.  Kim, S., and E.-H. Shin, “A longitudinal analysis of globalization and regionalization in international trade: a social network approach,” Social Forces, vol. 81, no. 2, pp. 445-470, 2002.  Shih, H.-Y., and T.-L. S. Chang, “International Diffusion of Embodied and Disembodied Technology: A Network Analysis Approach,” Technological Forecasting & Social Change, vol. 76, no. 6, pp. 821-834, 2009.  Hall, B. H., A. B. Jaffe, and M. Trajtenberg, "The NBER patent citations data file: lessons, insights and methodological tools," National Bureau of Economic Research Working Paper No. 8498., 2001.  Griliches, Z., R&D and Productivity, the Econometric Evidence, p.^pp. 382, Chicago and London: University of Chicago Press, 1998.
PICMET 2010 Proceedings, July 18-22, Phuket, Thailand © 2010 PICMET  Xu, B., and J. Wang, “Capital goods trade and R&D spillovers in the OECD,” Canadian Journal of Economics, vol. 32, pp. 1258-1274, 1999.  Huang, H.-C., and H.-Y. Shih, “National Innovative Capacity in theInternational Technology Diffusion: the Perspective of Network Contagion Effects.,” in The 2009 conference of Portland International Center for Management of Engineering and Technology, PICMET'09, Portland, Oregon, US, 2009, pp. 2699-2710.  Belsley, D. A., E. Kuh, and R. E. Welsch, Regression Diagnostics: Identifying influential data and sources of collinearity, New York John Wiley, 1980.  Eaton, J., and S. Kortum, “Engines of growth: domestic and foreign sources of innovation,” Japan and the World Economy, vol. 9, pp. 235-259, 1997.  Kojima, K., Direct Foreign Investment: a Japanese Model of Multinational Business Operations, London: Croom Helm press., 1978.  Geroski, P. A., “Models of technology diffusion,” Research Policy, vol. 29, pp. 603–625, 2000.  Cohen, W. M., and D. A. Levinthal, “Absorptive capacity: A new perspective on learning and innovation,” Administrative Science Quarterly, vol. 35, pp. 128-152, 1990.  Breiger, R., S. Boorman, and P. Arabie, “An algorithm for clustering
relation data with applications to social network analysis and comparison with multidimensional scaling,” Journal of Mathematical Psychology, vol. 12, pp. 328-383, 1975.  Teece, D. J., “Capturing value from knowledge assets: the new economy, markets for knowhow, and intangible assets,” California Management Review, vol. 40, no. 3, pp. 55-79, 1998.  Özçelik, E., and E. Taymaz, “Does innovativeness matter for international competitiveness in developing countries?: The case of Turkish manufacturing industries,” Research Policy, vol. 33, no. 3, pp. 409-424, 2004.  Kim, J. W., and H. K. Lee, “Embodied and disembodied international spillovers of R&D in OECD manufacturing industries,” Technovation, vol. 24, pp. 359-368, 2004.  Pack, H., and K. Saggi, “Inflows of Foreign Technology and Indigenous Technological Development,” Review of Development Economics, vol. 1, no. 1, pp. 81-98, 1997.  Hogg, R. V., and E. A. Tanis, Probability and statistical inference, 7 ed.: Pearson Education, Limited, 2005.  Gong, G., and W. Keller, “Convergence and polarization in global income levels: a review of recent results on the role of international technology diffusion,” Research Policy, vol. 32, pp. 1055-1079, 2003.
APPENDIX A. COUNTRIES OF NETWORK AUTOCORRELATION MODEL Argentina Australia* Austria* Chile China Colombia Germany* Greece Hong Kong Indonesia Ireland Italy* Netherlands* New Zealand* Norway* Russia Singapore South Africa Switzerland* Taiwan Thailand * Country is the sample by Furman et al. (2002) study.
Belgium Denmark* Hungary Japan* Philippines South Korea Turkey
Brazil Finland* Iceland Malaysia Poland Spain* United Kingdom*
Canada* France* India Mexico Portugal Sweden* United States*
APPENDIX B. VARIABLES AND DEFINITIONS Variable Innovative output Patents j,t+3
Full variable name
International patents granted in year t+3 Quality of the common innovation infrastructure GDP per capita j,t GDP Per Capita GDP j,t
R&D personnel (FTE) j,t Aggregate R&D Personnel Employed R&D $ j,t
all types of patents granted by USPTO in USPTO patent database country j in year (t+3) Gross Domestic Product per capita, constant price in 2000 US$ Gross Domestic Product, constant price in 2000 US$ Full time equivalent scientists and engineers devoted in R&D in all sectors
Aggregate R&D Expenditure
World Economic Indicators (WDI) World Economic Indicators (WDI) UNESCO Institute for Statistics S&T database OECD Science & Technology indicators IMD World Competitiveness Report
Total R&D expenditures in year 2000 millions of US$ Openness j,t Openness to international trade Exports plus Imports divided by GDP is Penn World Tables and investment the total trade as a percentage of GDP Ed share j,t Share of GDP spent on secondary Public spending on secondary and tertiary World Economic Indicators (WDI) and tertiary education education divided by GDP Quality of the cluster-specific innovation environment Private R&D funding j,t Percentage of R&D funded by R&D expenditures funded by industry UNESCO Institute for Statistics S&T private industry divided by total R&D expenditures database OECD Science & Technology indicators Quality of linkages Univ. R&D Percentage of R&D performed by R&D expenditures performed by UNESCO Institute for Statistics S&T performance j,t universities universities divided by total R&D database OECD Science & Technology expenditures indicatorsġ Contagion effects Embodied Embodied spillover via Cohesion Interaction within cohesive group via Global Trade Information Services, Inc. via Cohesion mechanism trade flows (GTI) Disembodied via Disembodied spillover via Interaction within cohesive group via NBER Patent Citations Database Cohesion Cohesion mechanism patent citations Embodied via Structural Embodied spillover via structural Relation in Structural equivalence via Global Trade Information Services, Inc. equivalence equivalence mechanism trade flows (GTI) Disembodied via Disembodied spillover via Relation in Structural equivalence via NBER Patent Citations Database Structural equivalence structural equivalence mechanism patent citations
PICMET ’10 PORTLAND INTERNATIONAL CENTER FOR MANAGEMENT OF ENGINEERING AND TECHNOLOGY
TECHNOLOGY MANAGEMENT FOR GLOBAL ECONOMIC GROWTH Editors Dundar F. Kocaoglu Timothy R. Anderson Tugrul U. Daim
Co-editors Antonie Jetter Charles M. Weber
PICMET ’10 OUTSTANDING STUDENT PAPER AWARD, Globalization has highlighted change in national technology capability. Exogenous factors drive...
Published on Nov 5, 2010
PICMET ’10 OUTSTANDING STUDENT PAPER AWARD, Globalization has highlighted change in national technology capability. Exogenous factors drive...