Contagion Effects of National Innovative Capacity

Contagion Effects of National Innovative Capacity: Comparing structural equivalence and cohesion models Hung-Chun Huang* , Hsin-Yu Shih, and Ya-Chi Wu Department of International Business Studies, National Chi Nan University, Taiwan

FINAL VERSION Forthcoming in Technological Forecasting & Social Change Originally published in Technological Forecasting & Social Change © 2010 Published by Elsevier Inc..

This document is the author’s final manuscript version of the journal article, incorporating any revisions agreed during the peer review process. Some differences between this version and the publisher’s version remain. You are advised to consult the publisher’s version if you wish to cite from it.

Please cite this article as: H.-C. Huang, et al., Contagion effects of national innovative capacity: Comparing structural equivalence and cohesion models, Technol. Forecast. Soc. Change (2010), doi:10.1016/j.techfore.2010.07.017

*

Address correspondence to Hung-Chun Huang, Department of International Business Studies, National Chi Nan University, 1, University Rd., Puli, Nantou 54561, Taiwan.

Tel.: 886-49-2911249 Fax: 886-49-2912595 E-mail: anfang886@gmail.com 1

Abstract The effective promotion of national innovation performance is a crucial component of national innovation policy. This study examines network contagion effects of national innovative capacity via the international diffusion of embodied and disembodied technology by two different social network models: the cohesion model, based on diffusion by direct communication, and the structural equivalence model, based on diffusion by network position similarity. This investigation then utilizes data of 42 countries during 1997 to 2002 to empirically examine their network relationship.

The analytical results demonstrate that international technology diffusion influences national innovation performance through contagion effects, but that the international similarity of national innovative capacity performance is more accurately predicted by network position than by interactions with others; and this study result provides a new perspective for science and technology policy makers.

Keywords National innovative capacity; embodied form; disembodied form; contagion effect; trade flows; patent citation

2

1. Introduction Technological innovation is an indispensable method for promoting national competitiveness, regardless of the current national competitiveness of the country in question. Suarez-villa[1] proposes essential concept of national innovative capacity (NIC) and a measure of it in terms of patenting rates. Therefore, Furman et al.[2] theorizes that national innovative capacity reflects more fundamental determinants of innovation, offering countries means of influencing national innovative capacity.

Technology capacity is closely related to national cultural [3, 4] and social setting [5-7]. However, healthy innovation infrastructure is essential but insufficient by itself to support the environment required to achieve continuous innovation [8]. Thus, countries can acquire innovative capacity by obtaining foreign advanced technologies [9, 10]. In most countries, the main sources of technological progress leading to productivity gain are required abroad rather than domestically, as demonstrated by recent research[11]. International interactions affect countries in terms of their economic performance, politics and culture [4, 12]. Theories of interdependence support mutual interdependence between nations as a result of close interactions, leading to political reciprocity and complicity [13-15]. Therefore, when a country determines its national technology development policy, its decisions depend not only on its own situation [16], but also on the advice or experience of other nations [14].

On the other hand, due to intensive globalization, global innovation performance becomes an important issue [17]. Related investigations on R&D management stress the need for interaction between developers and global users of new technology to enhance development and execution processes [18, 19]. However, previous studies (e.g. [4, 15]) in terms of national innovativeness propose various contingency perspective on international cooperation. Therefore, whether the international social

3

proximity influences national innovative capacity remains uncertain. Meanwhile, obtaining foreign advanced technologies involves several different channels [20, 21].

Weather these alternate channels exist differential impact on national innovative capacity is uncertain. Therefore, exactly which international relationships and diffusive channels exert the greatest effect must be identified.

Although current literature provides numerous important insights, many of the questions raise above remain unanswered. Particularly, while these questions related to multilateral interactivity are best understood as social network issue, few explicit social network analyses of these questions exist. The related ideas of international activity via network analysis have been extensively employed to study international R&D networks [22, 23], national technological systems [24] and international technology diffusion [25]. Therefore, this work examines social contagion effects, using cohesion and structural equivalence models to explore the critical mechanisms influencing national innovation performance. Thus, this study has the following research objectives: First, to assess the effects of contagion on the performance of national innovative capacity through international diffusion of embodied and disembodied

technology.

Second,

to

provide

authority

utilizing

extensive

consideration on the influence upon national innovative capacity to devise an enhanced policy for enhancing national competitiveness.

The remainder of this investigation is organized as follows. Section 2 reviews the literature, focusing on the concept of national innovative capacity, international technology diffusion containing embodied and disembodied forms and social contagion effects, as well as the cohesion and structural equivalence models.

Subsequently, section 3 presents the research hypotheses related to the testing and comparison of the various contagion effects. Next, Section 4 introduces the 4

measurements and models of social network analysis employed to investigate the mechanisms of international technology diffusion. Section 5 then empirically tests the research hypotheses, and discusses the theoretical and managerial implications.

Finally, section 6 presents conclusions and remarks.

2. National innovative capacity influenced by international technology diffusion This study employs the contagion effects derived from social network analysis to examine national innovation performance influence by international technology diffusion. Therefore, this section reviews the relevant literature concerning the national innovative capacity, defines the international diffusion of embodied and disembodied technology, and social contagion effects.

2.1. National Innovative Capacity National innovative capacity has been defined as the institutional potential of a country to sustain innovation. Numerous scholars have clearly defined this concept(e.g. [1, 2] ) and a suitable measure based on patent [26]. 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 [2]. The capability of national technology results from the hybrid of the improvement, creation and application of knowledge and technology in enterprises, the interaction among industry, and operation in social system [27, 28]. Thus, national innovation performance is closely related to national cultural [3, 4], social setting [5], social capital [6, 7], and knowledge diffusion network[16]. Measuring national innovation output includes patents, publications in scientific journals, copyrights, trademarks, etc.

All of these are products of innovation efforts, and even represent direct indicators of innovative output [2]. Therefore, international patents are the most useful available

5

measure for comparing innovation output across countries and over time [8, 26, 29].

Furthermore, patents for the national implications not only demonstrate technological capability changes [16, 26, 29-31] but also present national innovativeness [32-34].

Therefore, patents are acknowledged to provide a reliable and unbiased indication of national innovation effort [26, 35, 36]. Thus, based on previous studies (e.g.[1, 2, 4, 16, 26, 37]), patent output can represent innovative output. Additionally, Furman & Hayes [8] 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 [36] even considers international patents “the only observable manifestation of inventive activity with a well-grounded claim for universality.� 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)[38]. However, healthy innovation infrastructure is essential but insufficient by itself to support the environment required to achieve continuous innovation [8]. Lanjouw and Mody[39] using patent data to investigate flows of technology across both developing and developed countries find inventive capabilities are increasing in both sides.

Furthermore, recent research has demonstrated that in most countries the main sources of technological progress leading to productivity gain are from abroad rather than domestically[11]. Countries can acquire innovative capacity by obtaining foreign advanced technologies [9, 10]. Therefore, national innovative capacity might be influenced by international technology diffusion.

2.2. International Technology Diffusion Diffusion is a process that involves spreading certain innovation information by participants in a social system through particular channels [40]. Diffusion is an 6

exceptional form of communication, and involves participants providing and sharing information. Diffusion thus can refer to the dissemination of knowledge, technology transfer or deployment [5]. Technology diffusion was influenced by innovations and technical updates over time. Based on technology diffusion, Vernon[41] argues international product life cycle theory; however, this theory focus 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 [42-44] divides international technology diffusion into rent spillover and pure knowledge spillover.

The type of rent spillover, referring to the price of new products for which innovation technology knowledge exists, 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 [45] to supply their innovation capacity. The activities of the international diffusion of embodied technology are observable based on trade flows and foreign direct investment [43, 46]. Moreover, most related studies (for example [10, 11, 47, 48]) 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 the capital

7

accumulation and the 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 [49] demonstrate a positive relationship between trade and national competitiveness using exploring the dynamic configuration of global economics through trade flows. Coe et al. [50] find it better to measure trade-related spillover using trade in capital goods rather than total trade. Hence, this work adopts imports of machinery and equipment for diffusing information on embodied technology to investigation. Countries exchanging goods through international trade generate rent spillover.

The type of 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 (R&D) 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 and diffusion via numerous noncommercial channels. Thus this kind of diffusion can be called active technology spillover or the disembodied form of international technology diffusion, measured in the form of formulas, blueprints, drawings, patent citations, and so on [51]. The advantages of innovation activities are reflected in the process of commercialization[45]; restated, an effective method of measuring national competitiveness in disembodied form is through patent citation frequency. Pure knowledge spillover results from disembodied knowledge flows, including licensing, patent citations, or outsourcing agreements. Griliches [43] suggests that patent citations can be measured as a disembodied form of diffusion. Moreover, Helleiner

8

[52] indicates that based on the definition of a patent, technology includes not only legally guaranteed patents and trademarks but also the sophistication technique for tangible merchandise.

According to Jaffe et al.[53], Eaton & Kortum[54], and Hu & Jaffe[55] international patenting is a proxy of the channel for the international diffusion of disembodied technology. Patents can indicate intellectual property and measure technology innovation performance [26, 30, 31]. Numerous researchers have taken frequency of patent citations as an indicator of national innovation competitiveness (e.g.[42, 56]) , with the importance of a patient increasing with 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 to investigation. Countries citing their patents to others generate pure knowledge spillover.

2.3. Contagion Effects Lundvall [57] 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. This if this study observes the influence of social proximity on national innovation performance, it can identify a similar mode of international interaction. Social influence occurs when actor behavior, attitudes, or beliefs involuntarily follow those of others in the same social system [58]. Contagion is often used to describe the processes involved in social influence. Restated, social contagion arises from actor proximity in social structure using one another to manage the uncertainty of innovation [59]. Social contagion is based on the interpersonal synapse over which innovation is transmitted. The related ideas of contagion effect on innovation diffusion have been extensively employed to study electronic commerce 9

diffusion[60], science and engineering technology diffusion[61, 62], efficiency learning and diffusion networks[63]. This study employs network analysis to examine and compare the influence of contagion effects on national innovation performance.

Social influence theory involves two processes: communication and comparison.

Communication based on social influence involves direct contact between ego and alter[59]. 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 [64].

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 [65], but also resulting in network constraints among them. Some social network researchers interpret cohesion from a group perspective. Festinger [66] 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 policies similarity between partner countries [14]. This study thus examines the influence of cohesion mechanism on national innovative capacity performance.

The other process of contagion is social comparison. In searching for a social identity, before the ego acts, it observes alters’ acts, and then corrects its actions. Ego 10

compares himself with those alters who he sees as similar in network aspects [64].

The comparison is actuated if actors are competing [59]. 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 [67]. The concept of social equivalence exhibits another socialization process. The actors in the structural equivalence mode exhibit a similar pattern of relations to other actors in the social configuration [65], despite not necessarily having direct ties with one another. The similarity of patterns arising from social context creates powerful internalized pressures with which actors must comply [14]. Therefore individuals encountering uncertainty may refer to structurally equivalent actors to simulate appropriate responses. Burt [59] 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 national innovative capacity on performance.

3. Hypotheses This work examines the performance of national innovative capacity using social contagion effects, via the cohesion and structural equivalence models, and through international diffusion of embodied and disembodied technology. Regarding the cohesion model, primarily the significant alter within a cohesive group influence actors who directly contact one another. In the structural equivalence model, the alter in the similarity of network position influence actors and they may not have direct interactions with. Hence, the hypotheses in this study employed the contagion model to examine embodied and disembodied technology diffusion.

3.1. Cohesion Model

11

The social influence process of cohesion model is focused on the interaction between the ego and alter. When actors encounter tough questions or deal with something, their attitude and conduct will lean towards alters within the same group.

The cohesion model incorporates the opinions, behaviors, attitudes, and policies connecting actors. Therefore, the policy making of a given country promptly follows that of an alter country, since both share a common assessment of the costs and benefits of interaction [59]. Consequently, this study assumes that countries belonging to the same group can use cohesion mechanisms to influence national innovation performance. Thus the hypotheses are: Hypothesis 1: The performance in terms of national innovative capacity of a focal country is positively and significantly influenced by the performances of other countries within a cohesive group formed via international diffusion of embodied technology.

Hypothesis 2: The performance in terms of national innovative capacity of a focal country is positively and significantly influenced by the performances of other countries within a cohesive group formed via international diffusion of disembodied technology.

3.2. Structural Equivalence Model Burt [68] argued that ego behavior is predicted more accurately by structural network position than by interactions with others. Because of competition, competitors can readily follow changes made by egos [64]. 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 behavioral paradigm. Therefore, the more similar the structural position of the ego to

12

alters, the more likely that alters will substitute for the position of the ego [59].

According to Burt, this study determines that actors within the structural equivalence model are competitive with each other. Burt 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. This study thus hypothesizes the following: Hypothesis 3: The performance in terms of national innovative capacity of a focal country is positively and significantly influenced by the performances of other countries occupying structurally equivalent positions defined via international diffusion of embodied technology.

Hypothesis 4: The performance in terms of national innovative capacity of a focal country is positively and significantly influenced by the performances of other countries occupying structurally equivalent positions defined via international diffusion of disembodied technology.

3.3. Comparison Although two different contagion mechanisms may exist in social proximity, numerous scholars argue that ego behavior is more likely to be affected by alter having the same network position than by alter interacting with each other [14, 59, 60, 62]. Consequently, the contagion effect of the structural equivalence model should exceed that of the cohesion model. The hypotheses used to compare the performance of national innovative capacity are examined below: Hypothesis 5: In terms of international diffusion of embodied technology, national innovative capacity is more similar between countries with social proximity of structural equivalence than between countries with social proximity of cohesion.

13

Hypothesis 6: In terms of international diffusion of disembodied technology, national innovative capacity is more similar between countries with social proximity of structural equivalence than between countries with social proximity of cohesion.

4. Methodology 4.1. Measurement of international technology diffusion This work employs social contagion effects to examine spillovers in international technology diffusion. Since total national R&D expenditure is positively and significantly related to international technology diffusion [4, 16, 44, 69], Xu & Wang[70] and Shih & Chang[25] 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. Equation (1) shows the formula used to calculate international technology diffusion.

ITDij = rij × RDi .........................................................................(1) Here, ITDij represents the degree of technological knowledge diffusion from country

i to country j, RDi is the R&D expenditure of country i; and rij represents the fraction of knowledge spillover from country i to country j. Distinguishing embodied and disembodied forms of technology diffusion requires establishing two weighting formulas, rE ,ij ,t and rD ,ij ,t . This study defines the embodied form of diffusion as rE ,ij ,t [25].

rE ,ij ,t =

M ij ,t l

i ≠ j,

l

∑∑ M i =1 j =1

i, j, l = 1,2 … ,42 ...........................(2)

ij ,t

Mij,t represents country j importing capital goods from country i during year t; 14

l stands for numbers of countries, from 1 to 42. Regarding trade flows in this study,

the quantity of machinery and equipment imports in one country is multiplied by total R&D expenditure in another country, and it imports from 42 countries while they export to this country so it forms 42 by 42 matrix. Hence, this study assumes that if a certain country imports more capital goods from other country, the net importer nation will benefit through embodied technology diffusion.

Regarding the disembodied technology diffusion, patent citations represent the linkage to prior knowledge; restated, the frequencies with which a certain country cites patents from another country represent the density of pure knowledge spillovers between the two countries. Thus, the weight of disembodied technology diffusion is

rD,ij,t, defined as: rD ,ij ,t =

Cij ,t l

i ≠ j,

l

∑∑ C i =1 j =1

i, j, l = 1,2 … ,42 .................................(3)

ij ,t

Cij,t denotes the frequencies of country j citing patents from country i during year t; l represents individual countries by number, from 1 to 42. Patent citations are measured by the citation frequencies and owing to the reference, a given country cites patents from 42 countries while they are cited by this country, it also constitutes 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.

4.2. Contagion Effects

Numerous researchers are interested in the contagion process of the innovations diffusion [60, 62]. Actors tend to be affected by the opinions and behaviors of significant others belonging to the cohesive group or occupying a position of

15

structural equivalence. This influence process is the contagion effect.

Burt [59] designed a theoretical framework for the contagion effects of cohesion and structural equivalence in the social network by observing the diffusion of medical innovation. Thus, this study adopts the social contagion model devised by Burt [59] to forecast international technology diffusion among countries. yi is defined as the patent output in country i, and represents the realization of national innovative activities in country i. y*i denotes the expected patent output in country i based on the response to other countries and ε represents the residual term. Weight wij is the crucial term in this study, which can recognize the contagion effects of cohesion and the structural equivalence model by operating wij, and it measures the social proximity of country i to country j relative to its social proximity to other countries, except that country i reveals the degree of closeness between countries i and j compared to other countries within the network. The contagion effects equation is as follows: ⎛ ⎞ yi = ρ ⎜⎜ ∑ wij y j ⎟⎟ + ε ⎝ j ⎠

j≠i

( )

or yi = ρ yi* + ε , j ≠ i ..................(4)

Here, yi* = ∑ wij y j , j ≠ i j

wij =

( proximity i to j ) v , k ≠ i ........................................................(5) ∑ ( proximity i to j)v k

The magnitude of exponent v can be measured as the degree to which country i is reactionary in relying on other countries [25, 59]. This work operates the contagion effects of the cohesion and structural equivalence models via wij., and thus the two models can measure the social proximity of contagion effects by equation Eqs. (4) and (5). If social proximity is measured by trade flows or frequency of patent citations between countries i and j, then wij represents the cohesion model. On the other hand,

16

if social proximity is measured via the similarity of relation between country i and country j, then wij represents the structural equivalence model. Since yi* = ∑ wij y j , j

the meaning of yi* is different from the relationship between actors. Consequently, if

wij is measured using the cohesion model, then yi* represents the degree to which country i responds to the performance behavior of trading or citation partners.

Conversely, if wij is measured using the structural equivalence model, then yi* denotes the response of country i to the performance of competitors. The relationship between yi and yi* represents the degree to which social contagion process influences international technology diffusion.

This study employs the two types of diffusion mechanism, cohesion and structural equivalence models. As for the cohesion model, the weight matrix W is measured using the row data, representing the effects of social contagion on national innovative capacity of importing or patent citing countries. In this study ITDij denotes the degree of technological knowledge diffusion via export value or frequency of patent citations from countries i to j; conversely, if the weight matrix W is measured using the normalized column data, this operation exhibits the effect of social contagion to national innovation performance from exporting or cited countries.

The degree of technological knowledge diffusion via import value or frequency of patent citations from countries i to j is represented by ITD ji . Summing the row ( ITDij ) and column data ( ITD ji ) can investigate the influence on the performance behavior of national innovative capacity of the degree of technological knowledge diffusion via trading or citing partners. According to Burt [59], the exponent v frames the scope of the influencing process on ego conception, and a high value indicates a strong 17

relationship between the closet alters. The weight of influence wij is constituted as follows:

(ITD + ITD ) = ∑ (ITD + ITD ) v

C ij

w

ij

ji

v

ik

, k ≠ i ...................................................... (6)

ki

k

As for the structural equivalence model, measuring the extent to which country i and country j requires examining Euclidean distance, which is the most common method used by sociologists to measure degree of structural equivalence, the value of which ranges between zero and one. In this particular case, 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. Here, Ri denotes the summation of the degree of technological knowledge diffusion via export values or frequency of patent citation to each country in row i, and Ci represents the summation of the degree of technological knowledge diffusion via import values or frequency of patent citation from each country in column i. If

ITDik ITD jk ITDki ITDkj and , then countries i and j are structurally equivalent, = = Ri Rj Ci Cj demonstrating that the degree of technological knowledge diffusing occurring through their exports or patent are cited the duplicate proportions of outcomes to every other country; and the degree of technological knowledge diffusion occurring through their imports or patent cite the duplicate proportions of input from each country.

Consequently, the following is used to measure Euclidean distance.

⎡ ⎛ ITD ITD jk ik − d ij = ⎢∑ ⎜ Rj ⎢ k ⎜⎝ Ri ⎣

2

⎞ ⎛ ITDkj ⎟ + ∑ ⎜ ITDki − ⎟ ⎜ Cj k ⎝ Ci ⎠

⎞ ⎟ ⎟ ⎠

2

1

⎤ 2 ⎥ , i ≠ k ≠ j ...... (7) ⎥ ⎦

After identifying the structural equivalence between countries i and j, this study 18

applies the value of Euclidean distance d ij to the weight wij. The weight wij of the structural equivalence defined by Burt [59]. d max i represents the maximum distance between country i and other countries in the global network. Shih [60] suggests that the proximity of sector i to j can be represent as the extent to which

d ij is smaller than d max i . Similarly, if the extent to which d ij is smaller among countries than d max i , it demonstrates the proximity of country i to j.

(d max − d ) = ∑ (d max − d ) v

SE ij

w

i

ij

v

i

, k ≠ i .........................................................(8)

ik

k

4.3. 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. The social contagion effects 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[2]. Trade flow data are mainly obtained from Global Trade Information Services, Inc.. However, data on imports are more accurate than those on exports [25, 49, 71], and this study adopts an importing dataset. Furthermore, Coe et al. [50] 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 of patents granted by the United States Patent and Trademark office, and frequencies of patent citations are obtained from the NBER Patent Citations Database [38]. 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 and cited. As for the total R&D expenditure of each country, this investigation refers to World Competitiveness 19

databank, IMD. PATENTS represents the number of patents granted in year t+3 by USPTO due to the average lag between the application and approval accounted by USPTO and between the measures of innovative capacity and the observed realization of innovative output [2]. Thus, this study focuses on patent output during 2000 to 2005.

Considering the completeness of data collection, this investigation selects 42 countries as the sample owing to materials for some countries being absent.

Appendix 1 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 1 shows both embodied and disembodied diffusion countries.

5. Results and discussion

This study examines national innovation capacity using social contagion effects, the cohesion model and the structural equivalence model via international diffusion of embodied and disembodied technology. Equation (4) tests contagion effects, and this study examines the intensity of such effects on national innovative capacity at the global level, as follows: Equation (6) is applied in Eqn. (4) to examine the cohesion model; Eqn. (7) and (8) are incorporated into Eqn. (4) to analyze the structural equivalence model. Table 1 reveals that all models are significant. As for the relations between the contagion effects and patent output in each country as demonstrated by embodied spillover via cohesion mechanism (model 1) and disembodied spillover via cohesion mechanism (model 3), the cohesion mechanism exhibits unexpected negative effects via embodies and disembodies technology diffusion. However, regarding the structural equivalence models, embodied spillover via structural

20

equivalence mechanism (model 2) and disembodied spillover via Structural equivalence mechanism (model 4), demonstrate positive and significant relationships between the contagion effects and NIC performance in each country. This study infers that countries lean more towards influencing national innovative capacity through mimicking the behavior of competitors than that of communication partners.

Furthermore, comparison of the contagion effects in Table 1 reveals that the structural equivalence mechanism through embodied technology diffusion outperforms the cohesion mechanism in terms of influencing national innovative capacity, supporting hypothesis 5. Regarding disembodied technology diffusion, the structural equivalence mechanism remains more positive and significant than the cohesion mechanism in terms of influencing national innovative capacity, supporting hypothesis 6. Thus, the study findings support hypotheses 3, 4, 5 and 6, and do not support hypotheses 1 and 2.

Table 1 about here These empirical results confirm that the contagion effect in this study appears inconsistent with that in previous studies [14, 25, 59, 62]. This study discusses the reason for this finding, thus providing a broader perspective on exploring the influence of national innovative capacity via two mechanisms of the contagion effect.

Table 2 lists variable sources and definitions.

Table 2 about here 5.1. The effects of Contagion on NIC performance

International technology diffusion in the cohesion mechanism, the embodied technology diffusion negatively influences national innovation capacity performance, which appears inconsistent with previous research. Theoretically, international 21

technology diffusion positive affects both ego and alter countries [11, 72]. However, in this study, international technology diffusion negatively affects innovative capacity.

The reverse effects are observed when this investigation include developed and developing countries and those countries develop new-to-the-world technology differently[2]. On the one hand, developing countries import embodied technologies form developed countries to upgrade their productivity and increase efficiency [21]; furthermore, developed countries export numerous types of machinery and equipment to developing countries, while developed countries increase a positive effect to developing countries innovative capacity. Therefore, lower innovative capacity countries achieve economic growth and changes in productivity efficiency through the embodied technology of the more innovative capacity partner. However, the rent of embodied technology transfers to domestic innovative capacity, and is affected by import country absorptive capabilities[73]. Products just partially contain essential knowledge and techniques on manufacturing, and it cannot learn the technology completely. Acquiring knowledge involves not simply purchasing or trading goods, but rather systematic and purposeful knowledge-based learning and construction [74].

Developing countries do not exert a valid influence on innovative activity via the embodied technology of developed partner countries, but such technology does increase their production efficiency [21, 75]. At the global level, the several higher innovative capacity countries flow their technology into numerous lower innovative capacity countries, leading to technology diffusion negatively impacting cohesion mechanism based on NIC performance. Additionally, the technological-gap theory[76] and product life cycle theory[41] regard technological diffusion as hierarchical diffusion. According to international technology diffusion with global stratification patterns [25, 41, 47, 50, 77], the findings of this study are consistent with those of previous works.

22

Contrarily, the structural equivalence mechanism via embodied technology significantly and positively influences NIC performance. Embodied spillover via Structural equivalence mechanism (Model 2) represents countries that are more inclined to utilize mimicking behavior with structurally equivalent competitors through trading embodied technological commodities. This mechanism demonstrates that a ego countries and the alter countries are competitors; restated, they may not communicate directly via embodied technology exchanges, but their similar network position leads them to communicate indirectly by trading with third parties [59].

Owing to the existence of structural equivalence, a given country can mimic the technology of a competitor country with a similar network position, thus influencing their national innovative capacity. On the one hand, countries with similar network positions employ similar capabilities to acquire new technologies. On the other hand, while trade action from competitors results in more innovative outputs, and owing to competition, a focal country has a similar reaction and then increases its innovative output, thus influencing national innovative capacity.

This study compares two contagion mechanisms, namely cohesion versus structural equivalence, to determine whether national innovation capacity performance is more similar between countries with social proximity and structural equivalence than between those with social proximity of cohesion, as stated in Hypothesis 5. A standard error is calculated for the difference between two standardized coefficient estimates of structural equivalence and cohesion models, with a t test applied to assess the significance of the difference. The standardized coefficient estimate measured by the structural equivalence model significantly exceeds that measured by the cohesion model, and the R2 of the former model is significantly larger than that of the latter model.

23

The analytical results support

Hypothesis 5, indicating that the structural equivalence model exerts a more significant contagion effect than does the cohesion model on the diffusion of embodied technology affecting global NIC structures. Restated, NIC performance is triggered more by the influence of competitors than that of cohesion partners.

In terms of embodied diffusion, countries prefer to learn from the experiences of others with a similar network position, since such learning can positively influence national innovation performance; countries can react to competitors who are structurally equivalent in terms of embodied technology diffusion. Policy-making as a means of influencing innovative capacity cannot be performed in isolation and decisions of other cohesive or structurally equivalent countries should be considered in policy-making. This result is consistent with that of Koka et al. [14], namely that countries seeking to develop a profitable trading policy must ensure their policies fit those of other related countries.

For diffusion of disembodied forms, the standardized coefficient of the cohesion model is negative and significant. Like embodied technology diffusion, disembodied technology is significantly diffused with global stratification patterns, and this result can be discussed from two perspectives. First, this investigation at the global level included developing countries and developed countries; it may exhibit the reverse effect of cohesion mechanism due to the large differences in innovative capacity between developing and developed countries. Second, as discussed above, owing to low innovative capacity and insufficient intellectual property, developing countries must cite more cohesive partner’s disembodied technology from to apply their R&D and promote their technological advances[21]. Consequently, at the global level, the strong relationship within cohesive groups has side effects on innovative capacity. Therefore, the effect of disembodied technology diffusion among countries 24

within a cohesive group exerts a negative influence on innovative capacity.

For structural equivalence mechanisms, the standardized coefficient is positive and significant. Due to the multi-collinarity between patent output and patent citation, the structural equivalence model has higher explanatory power. However, the diffusion of the structural equivalence mechanism remains an important issue requiring discussion. A country that is structurally equivalent not only has a similar network position to a competitor but also similar technological capabilities to acquire the knowledge of their competitors; disembodied technology via structurally equivalent mechanism is more easy 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, a 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 a ego country, their conduct positively affects innovation capacity. Consequently, alter countries, as the role of competition in the same network position, provide a ego country with positive feedback regarding national innovation capacity performance via international technology diffusion.

By comparing two contagion mechanisms of disembodied technology diffusion, as presented in Hypothesis 6, this investigation applied a t test to assess the significance of the difference. The standardized coefficient estimate measured using the structural equivalence model significantly exceeds that measured using the cohesion model, and the R2 of the former model significantly exceeds that of the latter model. Analytical results still support Hypothesis 6, indicating that the structural 25

equivalence model yields more significant contagion effect than the cohesion model on the diffusion of disembodied technology affecting NIC in a global structure. That is, national innovative capacity performance is influenced more by competitive countries than cohesion partner countries.

In terms of disembodied diffusion, the 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 to domestic inventors, but also when a country can absorb and apply technology from abroad.

Additionally, direct learning regarding explicit knowledge of foreign competitors increases domestic technological capability and can be actively adopted for innovation efficiency.

5.2. The differential impact of embodied and disembodied technology diffusion

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 two technological spillover via cohesion mechanism (Model 1 and Model 3), involving the coefficient of cohesion model, or two technological spillover via structural equivalence mechanism (Model 2 and Model 4), involving the coefficient of structural equivalence model, demonstrates that disembodied diffusion influences national innovative capacity significantly more than embodied diffusion does. This result indicates a difference in spillover rigidity between embodied and disembodied technology diffusion. Notably, rent spillovers resulting from embodied technology diffusion are more rigid than pure knowledge spillovers resulting from disembodied technology diffusion.

26

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 efficient [21]. Restated, embodied technology diffusion is rigid to knowledge spillover, which is a relatively weak form of international technology diffusion that influences national innovative capacity.

Direct acquisition of foreign technological knowledge involves explicitly using disembodied knowledge in the forms of formulas, blueprints, drawings, and patent applications [78]. Pure knowledge spillover occurs internationally if technological knowledge is obtained from overseas for less than the original cost to 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. Disembodied technology diffusion is less rigid for knowledge spillover, and is termed active technology spillover. More specifically, disembodied technology diffusion influences national innovation capacity more significantly than does embodied technology diffusion.

6. Conclusion

Since globalization, numerous forms of international cooperation, such as global supply chains and globalized R&D, exogenously affect a country towards technological progress, as well as affecting economic growth via international technology diffusion. Consequently, it is important to understand the mechanisms of international technology diffusion, to identify key influences on national innovation performance. This work examines the influence on national innovative capacity of 27

network contagion effects of cohesion and structural equivalence mechanisms via international diffusion of embodied and disembodied technology.

Two types of potential knowledge generated by R&D activities are rent spillovers and pure knowledge spillovers. Rent spillovers result from embodied knowledge flows, while pure knowledge spillovers result from disembodied knowledge flows.

However, exogenous potential knowledge is influenced by the network structure of technology diffusion within the international economy. Two different diffusion mechanisms, cohesion and structural equivalence, are used to examine the contagion effects of two socially proximate actors, and those the performance of one actor in terms of innovation ability can be expected to trigger that of the other actor. The cohesion mechanism, which is based on diffusion via direct contact and communication, examines the influence of cohesion partners on innovative capacity performance. Relatively, the structural equivalence mechanism, which is based on diffusion by similarity of network position, examines imitation between competitors resulting from conformity to prevalent norms within structurally similar sectors.

This work employed the contagion effects to examine international technology diffusion via embodied and disembodied technology, and empirically tested and compared the two mechanisms. Social network analysis has been successfully applied to study the contagion effects of international technology diffusion and identify the contagion effect that most accurately predicts those countries mimetic behavior. From the empirical results, this investigation identifies the distinguishable influence pattern on the performance of national innovative capacity between countries with different technological diffusion forms and social proximity.

First, embodied or disembodied technology diffusion through structural equivalence mechanisms significantly influences national innovation capacity. More 28

specifically, NIC are affected more by structurally equivalent countries than cohesion countries. That is, country becomes more inclined to take same network position countries as a paradigm via international technology diffusion based on the environment in which they are developing.

Second, embodied or disembodied technology diffusion via cohesion mechanisms negatively affect national innovation capacity, which can be considered international technology diffusion via global stratification patterns. Restated, merely utilizing the technology of a cohesion partner without absorbing the embodied or disembodied technological rent spillover into domestic innovative activity will less affect national innovative performance.

Third, embodied and disembodied technology diffusions significantly influence national innovative capacity. Embodied technology is rigid to knowledge spillover and more strongly influences productivity changes than does national innovative capacity. Relatively, disembodied technology is less rigid to knowledge spillover and increases domestic technological knowledge able to be adopted for innovation, and moreover affects technical change.

In terms of international technology diffusion, policy makers should refer to their development plans to assist with tuning these technology diffusion mechanisms.

Participating in international cooperation deploys their “international relationship� strategy to influence their innovativeness. Furthermore, national expenditure on research and development can be considered to reflect national intention to develop specific national technological capabilities. However, without considering global network position of ego country, this expenditure is likely to become less effective.

Despite its contributions, this study has certain limitations, and these limitations

29

of should be acknowledged to identify future research directions. This work provides some suggested directions for future research. Suggestions include the following: The ego behavior and opinions are not merely determined by exogenous mechanisms (the behavior and opinions of others), but also by endogenous mechanisms (reaction to various other constraints and opportunities granted by the ego conditions). This process is typically modeled in sociology as an autocorrelation model. Owing to the lack of an endogenous mechanism, this study highlights the need for further research on the autocorrelation model of international technology diffusion to simultaneously consider exogenous and endogenous mechanisms. Second, this work explores the social contagion effect at the global level, but 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.

Acknowledgements

The authors thank Professor Daim and two anonymous referees for their insightful and valuable comments on the paper. Les Davy, Pai-Yu Liu, Cheng-Chi Hsieh, and Ted Knoy are thanked for their professional assistance. Also, this research was supported by a grant from the National Science Council of Taiwan under Contract No. 97-2410-H-260-011-MY3. This support is gratefully acknowledged.

References

[1]

L. Suarez-Villa, and S. A. Hasnath, “The effect of infrastructure on invention : Innovative capacity and the dynamics of public construction investment,�

30

[2] [3]

[4]

[5] [6]

[7]

[8]

[9] [10] [11] [12]

[13] [14]

[15]

Technological Forecasting and Social Change, vol. 44, no. 4, pp. 333-358, 1993.

J. L. Furman, M. E. Porter, and S. Stern, “The determinants of national innovative capacity,” Research Policy, vol. 31, pp. 899-933, 2002.

M. Coccia, “Democratization is the driving force for technological and economic change,” Technological Forecasting and Social Change, vol. 77, no. 2, pp. 248-264, 2010.

P. van Hemert, and P. Nijkamp, “Knowledge investments, business R&D and innovativeness of countries: A qualitative meta-analytic comparison,” Technological Forecasting and Social Change, vol. 77, no. 3, pp. 369-384, 2010.

L. Tornatzky, and M. Fleisher, The Processes of Technological Innovation, Lexington, MA: Lexington Books, D.C. Heath and Company., 1990.

R. Landry, N. Amara, and M. Lamari, “Does social capital determine innovation?

To what extent?,＂ Technological Forecasting and Social Change, vol. 69, no.

7, pp. 618-701, September 2002, 2002.

R. Rutten, and F. Boekema, “Regional social capital: Embeddedness, innovation networks and regional economic development,” Technological Forecasting and Social Change, vol. 74, no. 9, pp. 1834-1846, 2007.

J. L. Furman, and R. Hayes, “Catching up or standing still? National innovative productivity among ‘follower’ countries, 1978-1999,” Research Policy, vol. 33, pp. 1329-1354 2004.

R. J. Barro, and X. Sala-i-Martin, “Technological diffusion, convergence, and growth,” Journal of Economic Growth vol. 2, no. 1, pp. 1-26, 1997.

G. M. Grossman, and E. Helpman, Innovation and Growth in the World Economy, Cambridge, MA: MIT Press, 1991.

W. Keller, “International technology diffusion,” Journal of Economic Literature, vol. XLII, pp. 752-782, 2004.

B. Clarysse, and U. Muldur, “Regional cohesion in Europe? An analysis of how EU public RTD support influences the techno-economic regional landscape,” Research Policy, vol. 30, no. 2, pp. 275-296, 2001.

M. Staniland, What is political economy? A study of social theory and under-development, New Haven, CT: Yale University, 1985.

B. R. Koka, 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.

S. Kuhlmann, and J. Edler, “Scenarios of technology and innovation policies in Europe: Investigating future governance,” Technological Forecasting and Social 31

[16]

[17]

[18] [19] [20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

[28]

Change, vol. 70, no. 7, pp. 619-637, 2003.

M.-C. Hu, and C.-Y. Tseng, “Technological interdependence and knowledge diffusion in the building of national innovative capacity: The role of Taiwan's chemical industry,” Technological Forecasting and Social Change, vol. 74, no. 3, pp. 298-312, 2007.

T. Könnölä, J. Smith, and A. Eerola, “Introduction: Future-oriented technology analysis -- Impacts and implications for policy and decision making,” Technological Forecasting and Social Change, vol. 76, no. 9, pp. 1135-1137, 2009.

E. von Hippel, The Sources of Innovations, New York: Oxford University Press, 1988.

L. Dahlander, and D. M. Gann, “How open is innovation?,” Research Policy, vol.

39, no. 6, pp. 699-709, 2010.

A. Kraay, I. Soloaga, and J. Tybout, Product quality, productive efficiency, and international technology diffusion: evidence from plant-level panel data: Paper Presented at the NBER Summer Institute, 2001.

J. W. Kim, and H. K. Lee, “Embodied and disembodied international spillovers of R&D in OECD manufacturing industries,” Technovation, vol. 24, pp.

359-368, 2004.

N. Arranz, and J. C. F. deArroyabe, “Governance structures in R&D networks: An analysis in the European context,” Technological Forecasting and Social Change, vol. 74, no. 5, pp. 645-662, 2007.

N. Arranz, and J. C. F. deArroyabe, “Joint R&D projects: Experiences in the context of European technology policy,” Technological Forecasting and Social Change, vol. 73, no. 7, pp. 860-885, 2006.

P.-L. Chang, and H.-Y. Shih, “Comparing patterns of intersectoral innovation diffusion in Taiwan and China: a network analysis,” Technovation, vol. 25, no. 2, pp. 155-169, 2005.

H.-Y. Shih, and T.-L. S. Chang, “International Diffusion of Embodied and Disembodied Technology: A Network Analysis Approach,” Technological Forecasting and Social Change, vol. 76, no. 6, pp. 821-834, 2009.

G. Park, and Y. Park, “On the measurement of patent stock as knowledge indicators,” Technological Forecasting and Social Change, vol. 73, no. 7, pp.

793-812, 2006.

S. Lall, Technological Change and Industrialization in the Asian Newly Industrializing Economics: Achievements and Challenges, p.^pp. 13-68: Cambridge, 2000.

M. Karaoz, and M. Albeni, “Dynamic technological learning trends in Turkish 32

manufacturing industries,” Technological Forecasting and Social Change, vol.

72, no. 7, pp. 866-885, 2005.

[29] Z. Ma, Y. Lee, and C.-F. P. Chen, “Booming or emerging? China's technological capability and international collaboration in patent activities,” Technological Forecasting and Social Change, vol. 76, no. 6, pp. 787-796, 2009.

[30] M. Coccia, “Technometrics: Origins, historical evolution and new directions,” Technological Forecasting and Social Change, vol. 72, no. 8, pp. 944-979, 2005.

[31] N. Thumm, “Europe's construction of a patent system for biotechnological inventions:: An assessment of industry views,” Technological Forecasting and Social Change, vol. 69, no. 9, pp. 917-928, 2002.

[32] A. L. Porter, J. David Roessner, X. Y. Jin et al., “Changes in national technological competitiveness: 1990, 1993, 1996 and 1999,” Technology Analysis and Strategic Management, vol. 13, no. 4, pp. 477-496, 2001.

[33] D. Archibugi, M. Denni, and A. Filippetti, “The technological capabilities of nations: The state of the art of synthetic indicators,” Technological Forecasting and Social Change, vol. 76, no. 7, pp. 917-931, 2009.

[34] Y. G. Lee, and Y. I. Song, “Selecting the key research areas in nano-technology field using technology cluster analysis: A case study based on National R&D Programs in South Korea,” Technovation, vol. 27, no. 1-2, pp. 57-64, 2007.

[35] Z. Griliches, “Patent Statistics as Economic Indicators: A Survey,” Journal of Economic Literature, vol. XXVIII, pp. 1661-1707, December 1990, 1990.

[36] M. Trajtenberg, Patents as Indicators of Innovation, Cambridge (MA): Harvard University Press. , 1990.

[37] M.-C. Hu, and J. A. Mathews, “China's national innovative capacity,” Research Policy, vol. 37, no. 9, pp. 1465~1479, October, 2008.

[38] B. H. Hall, 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.

[39] J. O. Lanjouw, and A. Mody, “Innovation and the international diffusion of environmentally responsive technology,” Research Policy, vol. 25, pp. 549-571, 1996.

[40] E. M. Rogers, Diffusion of Innovations, New York: Free Press, 1985.

[41] R. Vernon, “International Investment and International Trade in the Product Cycle,” Quarterly Journal of Economics, vol. 153, pp. 190-207, 1966.

[42] Z. Griliches, “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.

33

[43] Z. Griliches, “Market value, R&D, and patents,” Economics Letters, vol. 7, pp.

183-187, 1981.

[44] Z. Griliches, "Productivity and technological change: some measurement issues," Technology and Productivity: The Challenge for Economic Policy, pp.

229-231: OECD, 1991.

[45] E. Bascavusoglu, "Patterns of technology transfer to the developing countries: differentiating between embodied and disembodied knowledge," TEAM and CNRS Working Papers, 2004.

[46] G. Papaconstantinou, N. Sakurai, and A. Wyckoff, “Domestic and international product-embodied R&D diffusion,” Research Policy, vol. 27, pp. 301-314, 1998.

[47] D. T. Coe, and E. Helpman, “International R&D spillovers,” European Economic Review, vol. 39, pp. 859-887, 1995.

[48] J. Eaton, and S. Kortum, “Trade in capital goods,” European Economic Review, vol. 45, no. 7, pp. 1195-1235, 2001.

[49] D. A. Smith, 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.

[50] D. T. Coe, E. Helpman, and A. W. Hoffmaister, “North-South R&D spillovers.,” The Economic Journal, vol. 107, pp. 134-149, 1997.

[51] K. E. Maskus, Encouraging International Technology Transfer, Geneva, Switzerland, 2004.

[52] G. G. Helleiner, “The Role of Multinational Corporation in Less Developed Countries’ Trade in Technology,” World Development, vol. 3, pp. 161-189, 1975.

[53] A. B. Jaffe, M. Trajtenberg, and R. Henderson, “Geographic localization of

[54]

[55]

[56]

[57]

knowledge spillovers as evidenced by patent citations,” Quarterly Journal of Economics, vol. 108 no. 3, pp. 577-98, 1993.

J. Eaton, and S. Kortum, “International patenting and technology diffusion: theory and measurement,” International Economic Review, vol. 40, pp. 537-570, 1999.

A. G. Z. Hu, 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.

D. Austin, “An Event Study Approach to Measuring Innovative Output: The Case of Biotechnology.,” American Economic Review, vol. 83, pp. 253-258, 1993.

B.-A. Lundvall, National Systems of Innovation: Towards a Theorem of Innovation and Interactive Learning, 1992, Ed. ed., London: Pinter Publications, 1992.

34

[58] R. Th. A. J. Leenders, Structure and Influence: Statistical Models for the Dynamics of Actor Attributes, Network Structure and Their Interdependence, Amsterdam: Thela Thesis Publishers, 1995.

[59] R. S. Burt, “Social contagion and innovation, cohesion versus structural equivalence.,” American Journal of Sociology, vol. 92, pp. 1287-1335, 1987.

[60] H.-Y. Shih, “Contagion effects of electronic commerce diffusion: Perspective from network analysis of industrial structure,” Technological Forecasting and Social Change, vol. 75, no. 1, pp. 78-90, 2008.

[61] S. Cantono, and G. Silverberg, “A percolation model of eco-innovation diffusion: The relationship between diffusion, learning economies and subsidies,” Technological Forecasting and Social Change, vol. 76, no. 4, pp. 487-496, 2009.

[62] J. Harkola, and A. Greve, “Diffusion of technology: cohesion or structural equivalence?,” in Academy of Management Meeting., Vancouver, 1995, pp.

422–426.

[63] Y. Chiffoleau, “Learning about innovation through networks: the development of environment-friendly viticulture,” Technovation, vol. 25, no. 10, pp.

1193-1204, 2005.

[64] R. Th. A. J. Leenders, “Modeling social influence through network autocorrelation: constructing the weight matrix,” Social Networks, vol. 24, pp.

21-47, 2002.

[65] R. E. Rice, 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.

[66] L. Festinger, S. Schachter, and K. Back, Social Pressures of an Informal Groups: A Study of Human Factors of Housing, New York: Harper, 1950.

[67] F. Lorrain, and H. C. White, “Structural equivalence of individuals in a social network,” Journal of Mathematical Sociology, vol. 1, pp. 49-80, 1971.

[68] R. S. Burt, "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.

[69] Z. Griliches, R&D and Productivity, the Econometric Evidence, p.^pp. 382, Chicago and London: University of Chicago Press, 1998.

[70] B. Xu, and J. Wang, “Capital goods trade and R&D spillovers in the OECD,” Canadian Journal of Economics, vol. 32, pp. 1258-1274, 1999.

[71] S. Kim, 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.

35

[72] J. Eaton, and S. Kortum, “Engines of growth: domestic and foreign sources of [73]

[74]

[75]

[76] [77] [78]

innovation,” Japan and the World Economy, vol. 9, pp. 235-259, 1997.

W. M. Cohen, and D. A. Levinthal, “Absorptive capacity: A new perspective on learning and innovation,” Administrative Science Quarterly, vol. 35, pp.

128-152, 1990.

D. J. Teece, “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.

E. Özçelik, 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.

M. V. Posner, “ International trade and technical change.,” Oxford Economic Papers, vol. 13, pp. 323-341, 1961.

P. A. Geroski, “Models of technology diffusion,” Research Policy, vol. 29, pp.

603–625, 2000.

G. Gong, 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.

Biographical Note Hung-Chun Huang is a Ph.D. candidate in department of the international business studies, National Chi Nan University of Taiwan and lecturers in the department of information management, Nan Kai University of Technology. Before returning to academia, his industrial background includes ten years' experience in the R&D team of information technology industry, high-tech team management and software system development for global supply chain management. His current research interests are in the areas of Technology Management and International Technology Diffusion. His papers have been published in a variety of conferences like PICMET (Portland International Center for Management of Engineering and Technology).

Hsin-Yu Shih is an associate professor at the Department of International Business Studies, National Chi Nan University of Taiwan. His previous articles have appeared in the Technological Forecasting and Social Change, Psychology and Marketing,

International Journal of Service Industry Management, Technovation, Journal of e-Business. His current research interests are in the areas of Technology Management and Network Analysis.

Ya-Chi Wu received a Master degree in business administration from National Chi 36

Nan University of Taiwan in 2008. She serves as a research assistant in graduate institute of nursing in Chang Gung Institute of Technology. Her current research interests are in the areas of Technology Management and Nursing.

37