Transnational Technical Communities in Transnational Entrepreneurship:

Conceptual Framework and Evidence

Akie Iriyama1 Katz Graduate School of Business

251 Mervis Hall University of Pittsburgh

Pittsburgh, PA 15260

Phone: 412-648-1646 Fax: 412-648-1693

Email: airiyama@katz.pitt.edu

Ravi Madhavan Katz Graduate School of Business

236 Mervis Hall University of Pittsburgh

Pittsburgh, PA 15260

Phone: 412-648-1530 Fax: 412-648-1693

Email: rmadhavan@katz.pitt.edu

Submitted for:

Presentation at NeXt Conference, 2008 &

Special Issue of Entrepreneurship Theory & Practice ‘Transnational Entrepreneurship and Global Reach’

1 Corresponding author.

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ABSTRACT

We propose a conceptual framework in which transnational entrepreneurship is embedded in global-reach human networks referred to as transnational technical communities. Transnational technical communities are emerging economic actors in the global business landscape and have unique attributes that distinguish them from other well-recognized economic actors such as MNCs. We hypothesize that transnational technical communities accelerate cross-border venture-related activities, especially when they possess an entrepreneurship orientation. A regression analysis of factors determining cross-border venture capital investments provides evidence supportive of our argument.

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INTRODUCTION

Transnational entrepreneurship is an emerging area with great promise, attracting

scholars from multiple disciplines and reflecting significant practitioner interest. Despite its

salience, our understanding of the mechanisms and drivers of entrepreneurial activities across

national borders is still at a nascent stage. Given the relative newness of the area, the

transnational entrepreneurship literature may need a broad framework, in addition to micro

views, to serve as a base for understanding. Responding such a need, and employing an

economic sociology (i.e., embededness) lens, we view transnational entrepreneurship as being

embedded in prior human networks of global reach. While such prior human networks have

traditionally taken the form of Diaspora networks (such as the overseas Chinese network), we

focus on a more contemporary variant, transnational technical communities (TTC).

TTCs comprise entrepreneurs, angel investors, venture capitalists, MNC managers, multi-

lateral organization administrators, technocrats, and industrial and academic researchers who

share a common interest in both a set of geographic regions (e.g., Taiwan and Silicon Valley)

and a technological domain (e.g., microelectronics) and a cultural and linguistic domain (e.g.

Mandarin). These people networks take on the characteristics of a technical “community of

practice” (Brown & Duguid, 1991) that span organizational boundaries as well as national

borders. TTCs are emergent economic actors on the world economic stage, joining other well-

recognized economic actors such as MNCs, but demonstrating unique attributes and mechanisms

(Saxenian, 2002b; Madhavan & Iriyama, 2006).

There is much anecdotal evidence for TTCs’ emergence. For example, Saxenian (2005:

51) describes that Nortel, which did not have a presence in India till the 1990’s, could build

alliance relationships with Indian software subcontractors because it had two Indian-born

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employees who actively worked to form these alliances. In this case, the two Indian-born

managers in Nortel are not transnational entrepreneurs, but they did belong to the Indian-US

software industry TTC, and thus could play a “connector” role between Indian start-ups and the

U.S.-based MNC. In another example, Arun Sarin, chief executive officer of the UK-

headquartered Vodafone Group, originally from India and educated and experienced in the U.S.,

decided to take Vodafone into the Indian market though a joint venture with local group Essar in

2006. It was thought by many that his Indian origin as well as personal relationships with Indian

TTC helped him launch Vodafone business in India. Furthermore, several analyses of the

economic aspects of TTCs have appeared over the last few years e.g., the Mexican community in

New York (Portes, 1996), the large Salvadoran immigrant populations in Los Angeles and

Washington, DC (Landolt, Autler, and Baires, 1999), and transnational communities within

professional service firms (Beaverstock, 2004). In a noteworthy contribution that highlighted the

technical aspect of such communities, AnnaLee Saxenian documented the role they play in the

industrial upgrading of countries such as Taiwan, China, and India (Saxenian, 2002a; 2002b;

2005; 2006). Similarly. Madhavan & Iriyama (2006) found TTC facilitates cross-border venture

capital investments.

Despite such descriptive studies and mounting quantitative evidence, the

conceptualization of TTC is yet to evolve significantly. In particular, TTC has not been

understood in the context of transnational entrepreneurship. Accordingly, the point of departure

for this study is the observation that transnational entrepreneurship relies on existing networks to

launch and grow new ventures. Thus, an accurate understanding of transnational

entrepreneurship is predicated on an understanding of TTCs. In this spirit, we undertake here a

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comparative investigation of TTCs so as to develop a critical insight into transnational

entrepreneurship studies.

In particular, it may be postulated that TTCs differ in terms of their entrepreneurial

orientation (e.g., Covin & Slevin, 1989). Extant field study research, media reports and current

opinion suggest some ways of arraying different TTCs along the dimension of entrepreneurial

drive. This study attempts such a comparative generalization of TTC and empirically examines

whether quantitative data supports the validity of our argument. Specifically, we propose that

TTC accelerates transnational venture business-related activities, in particular when they have

greater entrepreneurial orientation. A regression analysis examining determinants of cross-border

venture capital investment provides supportive evidence to our argument. We believe that this

study provides a clarification of a broad framework of TTC which promises to help further our

understanding of transnational entrepreneurship.

TRANSNATIONAL TECHNICAL COMMUNITIES

We view Transnational Technical Community (TTC) as a cross-border people network

comprising entrepreneurs, angel investors, venture capitalists, MNC managers, multi-lateral

organization administrators, technocrats, and industrial and academic researchers who share a

common interest in both a set of geographic regions in different countries (e.g., Hsinchu and

Silicon Valley) a technological domain (e.g., microelectronics), and a cultural and linguistic

domain (e.g. Mandarin). In understanding the construct, we acknowledge that TTC has some

overlaps with simple immigrant (ethnic) networks and in related fashion with the diaspora

construct. Yet, the TTC construct diverges from these conventional concepts of people networks

in critical respects. TTC differs from immigrant ethic networks, which latter have been studied

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extensively in sociology, leading to conclusions such as that ethnic networks embody social

capital that serves as a first step for immigrants’ job transition (Nee & Sanders, 2001), or for

immigrant entrepreneurs (Kloosterman & Rath, 2003). While such studies typically view

immigrants’ ethnic networks within a national border (e.g. Chinese community in the U.S.), TTC

is focused on a people network spanning national borders. TTC embraces, for example, people

who return from a foreign country to their home country and settle down there but still frequently

travel between those countries. Furthermore, TTC also includes – at least peripherally -- local

people in the home and host countries who may not necessarily have migrated but have direct

interest in and interaction with their counterparts in the other country. For example, local

entrepreneurs of Taiwan could become part of the extended TTC insofar as they share

technological, regional, and cultural/linguistic interests with other members of TTC – thus, a

Taiwanese entrepreneur who has no direct links with the U.S. may still benefit from technical

knowledge originating in the U.S. through the TTC. TTC is also distinct from diaspora.

Diaspora is a population considered ‘deterritorialised’ or ‘transnational’ -- that is, which has

originated in a land other than which it currently resides, and whose social, economic and

political networks cross the borders of nation-states or, indeed, span the globe (Vertovec, 1999).

As such, TTC may be a subset of a diaspora, but with the unique characteristic of being a

technical community - TTC typically comprises a set of highly educated individuals who share a

keen technological interest –e.g., in microelectronics – as well as common cultural, geographical,

and linguistic interests. In any community, members typically share relational, affective, and

cognitive linkages that facilitate collaborative economic actions (Mullen, 1991). While TTC may

demonstrate similar relational and affective linkages to those of broader immigrant networks and

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diaspora, it is the cognitive dimension represented by the shared technical interest that sets it

apart.

In this sense, TTCs, while admittedly often an ethnic network, also takes on the

characteristics of “community of practice” (Brown & Duguid, 1991, Lave & Wenger, 1991) – a

group of individuals engaging in, and contributing to, learning and knowledge-building in a

shared domain. While communities-of-practice have traditionally been analyzed as intra-firm

phenomena, variants with broader reach have been identified in the literature—e.g., external

communities of practice, defined as groups “whose members…regularly engage in sharing and

group learning based on common interests, mutual trust and collaboration.” (Dewhurst &

Cegarro Navarro, 2004: 322-323); or “networks of practice” that are “extended epistemic

networks” characterized by looser ties than inter-firm communities-of-practice (Brown &

Duguid, 2001: 205). Davenport and Prusak (1998) note that spontaneous and unstructured

knowledge transfers of knowledge routinely take place within and across organizational

boundaries, whether the process is actively managed or not. Lee & Cole (2003), in the same

spirit, have built a model of knowledge creation within individual communities that span

organizational boundaries. Similarly, Bouty (2000) found that interpersonal knowledge exchange

of R&D scientists across organizations foster innovations especially when they develop social

capital overtime. Resonating with their views, we see TTC as a community of practice among

individuals which span organizational boundaries as well as national boundaries.

Defined thus as a cross-border network of individuals with shared interests, TTC displays

several important characteristics. First, given its nature as a community of people, TTC is more

loosely bounded than formal organizations such as MNC. Membership in an informal

community of practice such as TTC is open (Lee & Cole, 2003), limited only by shared

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technological, regional, linguistic and cultural interests. TTC, thus, could comprise people in a

variety of roles: professionals, managers, entrepreneurs, investors, etc., including people with

simultaneous affiliations to other formal organizations (e.g., MNC managers, academic

researchers, venture capitalists, and administrators in the governments). Thus, a TTC is clearly

not a network that has a unitary structure; it may be more like a “swarm” than like an organism.

Accordingly, TTC is not mutually exclusive, but inclusive, with other economic organizations.

For example, Mr. Sarin, the CEO of U.K.-headquartered Vodafone, shares technological interest

domain (wireless telephone), regional interest domain (UK and India), and cultural & linguistic

interest domain (English and Hindi) with other members of the TTC who could be individual

Indian entrepreneurs, government administrators, and executives in local Indian companies.

Consequently, a variety of formal and informal organizations overlay TTC. We thus expect that

TTC plays a role of a boundary spanner among other international economic organizations

(Williams, 2002).

Social psychologists contend that social groups or communities vary along a continuum

of entitativity i.e., the extent to which a social aggregate is perceived as having the nature of

entity, of having real existence (Campbell, 1958; McCconell, Sherman & Hamilton, 1997). For

example, a group that is high in entitativity would be seen as possessing unity and coherence; in

this sense, a group of shoppers in a grocery store rates much lower in entitativity than members

of a college fraternity (McConnell, Sherman & Hamilton, 1997). The degree of entitativity is

influenced by various factors, which include the Gestalt principles such as similarity and

proximity (Campbell, 1958), organization among various elements of social entities,

interdependence among social entities, and expectations of behavioral consistency (Hamilton &

Sherman, 1996). Thus, an MNC may demonstrate a higher degree of entitativity than the TTC –

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we would expect a higher degree of decision-making coherence and behavioral consistency from

the former, to the extent that it is formally a unitary organization with a clear line of authority.

Similarly, the diaspora is arguably lower in entitativity than the TTC, lacking the clear focus

provided by shared technical interest. A key point to note is that, although we compare TTC

with other economic actors such as MNCs, we do not imply that TTC as a social group is at the

same level of entitativity as the latter.

Second, TTC may exist at multiple levels. A TTC thus could be a subset of another larger

TTC. For example, a TTC that comprises people with a shared interest in the microelectronics

industry may embed a smaller TTC of people interested primarily in the microprocessor industry.

Often, the broader informal TTC may embed a formalized organization intended to advance the

interests of the community. For instance, there are at least thirty immigrants’ professional and

networking associations in Silicon Valley (see, Saxenian, 2006, for a list). One example is The

Indus Entrepreneurs (TiE). TiE was formed by first successful Indian entrepreneurs in Silicon

Valley who saw Chinese Institute of Engineers (CIE, founded in 1979) as their role model. TiE,

which had about 2,000 members in 2004, seeks to foster entrepreneurship in later-generation

Indian immigrants by providing mentorship and resources. Furthermore, TiE helps the members

in Silicon Valley build business connections with India. One can recognize TiE as a formal

element embedded within the larger, looser, and informal TTC.

Summing up, TTC, as networks of individuals forming communities of practice that span

organizations and national borders, possesses unique characteristics compared with other

international economic actors.

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ENTREPRENEURIAL ORIENTATION IN TRANSNATIONAL TECHNICAL

COMMUNITIES

To situate TTC, a holistic concept by definition, better in the transnational

entrepreneurship literature, we introduce an additional dimension i.e., entrepreneurial orientation.

Entrepreneurial orientation refers to an actor’s (e.g. firm’s) strategic orientation, capturing

specific entrepreneurial aspects of innovativeness, proactiveness, and risk taking, autonomy, and

competitive aggressiveness (Lumpkin & Dess, 1996; Tang, Tang, Mariono, Zhang, & Li, 2008).

Since Covin & Slevin’s (1989) seminal study, entrepreneurial orientation has been a critical

construct in entrepreneurship research. Prior studies have found, for example, that

entrepreneurial orientation serves as a factor for organizational success, although often

depending on contexts (Lee, Lee & Penning, 2001, Lumpkin & Dess, 1996, Miller & Friesen,

1982). While researchers have extensively employed entrepreneurial orientation to describe

firms’ (or individuals’) posture, we extend this concept to the larger collectivity of a people

community viz. TTC. Our main proposition is that different TTCs have their own positions in the

spectrum of entrepreneurial orientation, which influences the development of transnational

entrepreneurship activities between nations.

TTCs, as a broad construct, could involve a large variety of members, and thus may vary

significantly in terms of what resources, norms, social capital, and dynamism they hold.

Accordingly, all TTCs do not demonstrate the same degree of entrepreneurial orientation.

Countries’ cultural, institutional, economic factors affect such differences. Saxenian (2005)

demonstrates that cultural and institutional factors lead to different dynamisms in development of

TTC for countries. For example, Taiwanese immigrants in the U.S. have strong entrepreneurial

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motivation, risk-preference, and tend start up their own businesses in U.S. and in their home

country. In contrast, people from Mainland China in 1990s were more risk-averse and tend not to

start their own businesses when they return to the home country. In case of Korea, their

institutional system fosters large corporations and those studying in foreign countries seek to

return to promotions within the sponsoring firm. Accordingly, they have more risk-averse

postures and demonstrate a lower degree of entrepreneurial orientation.

We propose that TTCs with different levels of entrepreneurial orientation result in

transnational entrepreneurship in different degrees. Table 1 summarizes the comparison between

TTC with a lower level of entrepreneurial orientation and that of a higher level. With smaller

levels of entrepreneurial orientation, TTC members tend to be more risk-averse and seek to

exploit their knowledge bases earned in a foreign country through working for large companies.

They pursue career promotion within a large corporation rather than starting-up their own

businesses. Accordingly, the development of cross-border venture-business activities between a

foreign country and a home country tends to be modest as people do not seek business

opportunities across national borders. MNCs play a key role in extending businesses beyond

borders, rather than do entrepreneurs or venture capitalists. In TTC with higher levels of

entrepreneurial orientation, by contrast, people more likely seek business opportunities across

national borders. They tend to exploit the information pool and social capital of their TTC to

reduce search costs and coordination costs in global reach. They thus serve as a “carrier wave”

across borders which mobilize goods, services and capital as well as circulate knowledge,

information, and business practices (Saxenian, 2006; Madhavan & Iriyama, 2006). In 1990s, for

instance, Taiwanese entrepreneurs and Taiwanese government administrators (both were in the

same TTC) collaborated, playing a significant role in reducing legal barriers for attracting risk

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capital into Taiwan. This contributed to the development of Hshinchu Industrial Park, which

came to be known as ‘Taiwan’s Silicon Valley’. In sum, while TTC is an important base for

transnational entrepreneurship, its influence is strengthened by the level of its entrepreneurial

orientation. In this study, we employ cross-border venture capital flows to gauge

entrepreneurship activities between countries, for reasons which we argue in the next section.

Consequently, we postulate that:

Proposition: TTC accelerates venture-related activities across national borders, especially when

it demonstrates a higher level of entrepreneurial orientation.

Hypothesis 1: Cross-border venture capital flow will be positively related to TTC strength.

Hypothesis 2: The TTC’s level of entrepreneurial orientation positively moderates the

relationship between TTC strength and cross-border venture capital flow.

EMPIRICAL ANALYSIS

Research Design

In order to test our hypotheses, we conducted a regression analysis with a panel dataset of

cross-border venture capital flows. Specifically, the regression examines whether the relationship

between venture capital outflow from U.S. to each recipient country and the extent of TTC

development between countries is positively moderated by of entrepreneurship orientation of

recipient nation affiliated with TTC.i

We set the sample period for venture capital flow between 1982 and 2002, keeping in

mind the availability of reliable data. Our sample includes all countries that have hosted venture

capital flow from the U.S. Because data for some variables (e.g. entrepreneurial orientation,

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TTC) are not available for all recipient countries, the number of host countries was reduced to

49. We finally obtained 982 country-year observations.

Variables and Data

To gauge the extent of entrepreneurship activities between countries, we employ

VCOUT, the aggregated value of venture capital flow from the U.S. to each recipient country

between 1982 and 2002. We employed this measure for a couple of reasons. First, cross-border

venture capital investment is among the most reasonable measures to capture venture business

activities across countries. A large number of transnational entrepreneurs and venture capitalists

who reside in a foreign country currently invest capital in start-ups in their host countries. At the

same time, venture capitalists who return to their home countries have come to invest capital in

foreign countries where they resided and developed business networks (or TTC) (Saxenian,

2006). Indeed, cross border venture capital outflow from U.S. has substantially expanded since

the 1990s as transnational entrepreneurs’ activities become salient (Madhavan & Iriyama, 2006).

In addition, cross-border venture capital investment data which is close to the population is more

easily available than other alternative measures such as the number of internationalized start-ups.

We sourced the cross-border venture capital flow data from the VentureXpert database compiled

by Thomson/SDC, one of the major databases on venture capital. This database collects

information on venture capital investments from various sources, including the investment

banking community, surveys of venture capital firms’ general partners and their portfolio

companies, government filings and industry associations. Previous academic work has often

utilized this database (e.g., Gompers and Lerner, 1999; Sorenson and Stuart, 2001). The

VentureXpert database provides us with the aggregated value of venture capital flow in Million

U.S. dollars from the U.S. to each recipient nation. We then adjusted the data to the base year

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2000 to account for inflation. The GDP deflator for the U.S. was obtained from International

Financial Statistics, compiled by the International Monetary Fund.

To measure the extent of TTC development between U.S. and other countries, we used

the data of each annual edition of the Statistical Yearbook of the Immigration and Naturalization

Service of the U.S. Department of Justice. from 1959 to 2003. ii This database defines

immigrants as persons lawfully admitted for permanent residence in the U.S. Note, therefore,

that aliens admitted to the United States for shorter duration, such as temporary workers and

students, are not regarded as immigrants. (However, foreign students and temporary workers

who choose to become permanent residents will be reflected at the point of their “adjustment of

status.”) Each volume contains data on immigrants in eight occupation groups: “professional

specialty and technical”, “executive”, “administrative, and managerial”, “sales”, “administrative

support”, “precision production, craft, and repair”, “operator, fabricator, and laborer”, and

“service”. We specifically focused on immigrants with “professional specialty and technical”

occupation as the foundation of the transnational technical community. Further, we adopted the

cumulative term to construct this variable. Intuition suggests that the immigrant flow for one

specific year may not significantly influence venture capital flow for, say, the next year. Rather,

a strong transnational technical community can only grow out of many years’ cumulative

immigration. We cumulated the number of professional specialty and technical immigrants from

each nation to the U.S. during the period from 1959 to the focal year of venture capital flow

(between 1982 and 2002). We selected 1959 as the starting point due to the availability of

reliable data of immigrants into the U.S. For most recipient nations of venture capital, the

number of immigrants from those nations into the U.S. was available since 1959, while data

availability was substantially poorer before 1959.iii, iv The 1980, 1981, and 2000 volumes of the

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Statistical Yearbook of the Immigration and Naturalization Service do not contain the breakdown

of immigrants by occupation. Also, the volumes for 1979 and 2002 were not available. We

addressed these missing years by interpolating the immigration data for each recipient nation.

One challenge in the present study is how best to construct the measure of

entrepreneurship orientation at the country level (EO). Prior empirical research operationalizing

entrepreneurship orientation focused on the firm or individual level. There have been scare

attempts to operationalize it at the country level. There are several possible indices to capture the

extent of venture business activities at the country level. One example is the Transnational

Entrepreneurship Activity (TEA) Index by the Global Entrepreneurship Monitor (GEM).

However, such a measure reflects the degree of actual business activities in a country and does

not exactly reflect entrepreneurship orientation that is a psychological posture of people in a

firm. The content measure of entrepreneurship (such as TEA) and the process of

entrepreneurship (such as entrepreneurial orientation) need to be clearly distinguished (Lumpkin

& Dess, 1996). Consequently, we constructed a measure to better reflect entrepreneurial

orientation at the country level.

Specifically, we employed Hofstede’s well known cultural indices (Hofstede, 1980).

Using a cultural measure is reasonable as national cultures reflect people’s psychological

posture, which is then associated with entrepreneurship orientation. Indeed Kreiser, Marino, &

Weaver (2002) found that individual entrepreneurship orientation differs by national culture. In

this study, we employed two of the four indices: uncertainty avoidance and individuality.

Uncertainty avoidance is inversely associated with entrepreneurship orientation as the former

reflects an individual’s risk averseness and the latter reflects risk-seeking. Also, we include

individuality as it is a close concept to autonomy in five key dimensions of entrepreneurship

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orientation (Lumpkin & Dess, 1996). Individuality as well as autonomy is an important aspect

for entrepreneurs to start-up their own business rather than working for large companies. To

calculate the entrepreneurial orientation measure, we applied the formula of Kogut & Singh

(1988). Algebraically, we built the following index:

{ } { }[ ]INDAVEi

IUAAVEii VARINDINDVARIUAIUAEO /)(/)(

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−+−=

where EOi stands for the index for entrepreneurship orientation of the ith country. IUA indicates

inverted uncertainty avoidance index (which is then multiplied by 100). IND is Individuality

Index. AVE indicates the average of all sampled countries. VAR is the variance of all countries

for IUA and IND indices.

We also included several control variables in the regression model. First, we included the

gross domestic product (GDP) for each recipient country to gauge the size of the economy. We

obtained the data for this variable (on 2000 constant price basis) from the World Development

Indicators of the World Bankv. The data were then adjusted to Million US dollars with the

exchange rate of each nation’s currency per U.S. dollar in 2000 (from International Financial

Statistics). We also included the outflow of FDI from U.S. to each recipient nation. Including

FDI enables us to account for two relevant influences- existing historically established channels

of investment. We obtained the data from the Bureau of Economic Analysis of the U.S.

Department of Commerce. The data were adjusted to account for inflation with the GDP deflator

(from the International Financial Statistics). We also included the geographic distance between

the U.S. and each recipient nation as another control variable (DIS). We obtained the data from

Haveman (2006) who provides the physical distance data between capital cities in kilometers.

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Further, the enrollment ratio of tertiary education of recipient country is included to capture

human capital element (EDU). The data was from the World Development Indicators. Finally,

because macro-economic or other time-variant factors that impact venture capital flow are

potentially enormous, we control for the year heterogeneity by employing a year dummy in all of

our models—1982 was set as base yearvi.

Model estimation

Since our sample consists of a pooled and cross-sectional data set, and all of the models

are based on panel data, OLS models will not estimate unobserved effects. A panel effect model

such as fixed effect model or random effect model allows us to control for unobserved

heterogeneity in the venture capital flow to each recipient nation. The present study employed

the random-effect model. We preferred the random effect specification to the fixed effect

specification for the following three reasons. First, the Hausman test did not reject the hypothesis

that the difference of coefficients was not systematic, pointing to the use of the random-effect

model. Second, a random effect GLS model is capable of producing estimators that are BLUE

(best linear unbiased estimates) since it controls for heteroscedasticity (Gujarati, 1995). Third,

use of the random effect model makes it possible to employ the EO variable, which is time-

invariant and thus cannot be used in fixed model estimation. Finally, we conducted the

Wooldridge test to check the serial correlation problem in the composite error term that is often

detected in the time-series data setting; the results did not suggest any serial correlation problems

in our empirical models.

RESULTS

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Table 2 summarizes the descriptive statistics and correlations. Table 3 demonstrates the

regression results. Model 1 includes control variables only. Model 2 adds TTC (Hypothesis 1)

and EO. Model 3 adds the interaction of TTC and EO (Hypothesis 2). As shown, the TTC

variable alone has a positive and statistically significant coefficient in both Model 2 (β=0.69,

p<.05) and Model 3 (β=1.36, p<.001). The results support Hypothesis 1. In addition, the

interaction of TTC and EO in Model 3 has a positive and statistically significant effect (β=1.14,

p<.01), supporting Hypothesis 2. It should be noted that the EO variable has a positive and

significant effect in Model 3. Therefore, each host country’s entrepreneurial orientation itself is

an important factor for transnational venture-related activities. For the control variables, GDP,

FDI, and EDU have positive and statistically significant coefficients. The DIS variable does not

have a significant coefficient.

DISCUSSION

As a nascent field, transnational entrepreneurship will inevitably witness the use of

multiple theoretical perspectives and empirical approaches. As part of this evolution, the

economic sociology perspective espoused here has much to offer. Given the general principle of

the social embeddedness of economic activity – including entrepreneurship – and the specific

phenomenon of TTCs as a new economic actor on the global scene, we have provided here a

relatively broad framework, suggesting that investigating transnational entrepreneurship in the

context of TTC will be a useful step in our overall understanding of this emerging research area.

This paper contributes to better situating transnational entrepreneurship in the broader

socioeconomic context of a globalizing world. In particular, we add a new dimension –

entrepreneurial orientation - to TTC. The regression analysis provides evidence supportive of our

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argument. Cross-border venture capital investments are facilitated not only by the development

of TTC and each host country’s entrepreneurial orientation, but also by the interaction between

them. Thus, TTC’s influence on transnational venture-related activities should be understood in

conjunction with entrepreneurial orientation.

Several intriguing future research directions emerge from this study, from among which

we touch upon four major possibilities. First, exploring the socioeconomic context of

international entrepreneurship is an important first step in understanding it. While all

entrepreneurship is socially embedded, the social context of international entrepreneurship is

especially intriguing – e.g., how does the budding international entrepreneur search the

opportunity space, decide among alternatives, syndicate resources, and grow the venture? What

are the technological and social networks that undergird this information search and sharing

process? Given the importance of people networks in entrepreneurship, the nuanced ways in

which the TTC serves as a communication vehicle in entrepreneurial search and discovery

processes would be worth studying. For instance, how do factor conditions and TTC orientation

interact in the global pattern of entrepreneurship? These types of questions can be fruitfully

addressed using the approach illustrated here.

Second, we believe that the idea of TTC should be applied to explaining other important

facets of transnational entrepreneurial activities or, perhaps, even broader entrepreneurial

activities. For example, future research should examine the impact of TTC on each recipient

country’s extent of domestic entrepreneurial activities. For example, how much does TTC impact

the new business creation in a recipient country? We might test such an idea to examine the

impact of TTC on the GEM TEA index. This idea might hold a potential implication for national

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policies as a recent research study has found that TEA could be an important predictor for

country’s economic growth (Stel, Carree & Thurik, 2005).

Third, we expect that there should be other important dimensions than entrepreneurial

orientation to understand TTC’s impact on international entrepreneurship. Among them is the

knowledge orientation of TTC. Attributes, mechanisms and impacts for other economic facets of

TTC might differ by the extent of the members’ educational level and knowledge domains. For

example, different TTCs might vary in terms of knowledge base, industry focus, language

orientation, etc. – such as the US-India Information Technology TTC or the US-Taiwan micro-

electronics TTC. These and other related factors would encourage or discourage the development

of transnational entrepreneurship, or shape the shape/ direction of entrepreneurship.

Fourth, given the broad view provided by the present paper, it is important to undertake

more micro-perspective analyses in the context of TTC. It would be enlightening, for example, to

examine the relationship of Taiwanese venture capitalists’ personal profiles to their transnational

entrepreneurial activities. Also, scrutinizing TTC networks across organization and national

boundaries at the individual level will provide micro-level evidences to verify or extend TTC

concept. Thus, micro-analyses employing individual level data or case studies are critical for the

future development of this construct in the context of transnational entrepreneurship.

In summary, we have proposed a conceptual framework in which transnational

entrepreneurship is embedded in global-reach human networks referred to as TTCs. TTCs are

emerging economic actors in the global business landscape and have unique attributes that

distinguish them from other well-recognized economic actors such as MNCs. We hypothesized

that TTCs accelerate cross-border venture-related activities, especially when they possess an

entrepreneurship orientation. A regression analysis of factors determining cross-border venture

20

capital investments provided evidence supportive of our argument. Accordingly, we have argued

that, as the nascent field of international entrepreneurship evolves, scholars should take note of

how international entrepreneurship is embedded in human networks such as TTCs.

21

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Table1. Configuration of Transnational Technical Communities

Low Entrepreneurship Orientation High Entrepreneurship Orientation

- TTC members tend to exploit their knowledge base by working in large firms

- They tend to pursue their career promotion in the firm. - Few returnees to home countries - TTC members of MNC might connect the

MNC to ventures in their home country - e.g., Mr. Sarin’s role in taking Vodafone to

India, Nortel’s alliances with Indian ventures

- TTC members’ risk postures facilitate their pursuing entrepreneurship opportunities across national borders.

- Many TTC members who work in a

foreign country return to their home country to exploit their knowledge base in the home country.

-Accelerates transnational

entrepreneurship business activities - e.g., Mutual cross-border venture capital

investments between Taiwan and U.S.

26

Table2. Descriptive Statistic and Correlations

Variable Mean S.D. GDP FDI EDU DIS TTC EO

VCOUT 65.6 299.1

GDP 381.8 777.0 0.25 ***

FDI 1163.0 3296.7 0.60 *** 0.24 ***

EDU 29.2 17.3 0.26 *** 0.19 *** 0.25 ***

DIS 8581.6 3834.6 -0.04 0.00 -0.10 ** -0.09 **

TTC 20.0 35.1 0.19 *** 0.09 ** 0.19 *** -0.05 0.14 ***

EO 1.0 2.3 0.01 -0.08 0.04 -0.02 0.18 *** -0.08 *

N = 982 †: p < .10, *: p < .05, **: p < .01, ***: p < .001 Mean and standard deviation of VCOUT, GDP, and TTC are divided by 1000 for viewability.

27

Table3. Results of Random Effect Regression: Determinants of Venture Capital Flow from U.S.

0.05 *** 0.05 *** 0.04 ***0.01 0.01 0.01

48.81 *** 47.81 *** 45.23 ***2.63 2.63 2.76

1.79 0.81 3.06

2.74 2.67 2.72

1038.22 † 1229.82 * 1309.83 *630.98 614.88 601.07

0.69 * 1.36 ***0.29 0.37

184.50 13937.72 *4371.38 6590.23

1.14 **0.42

-61966.93 -19691.22 -31117.98

42822.56 40866.49 40509.14

Wald χ2 0.45 0.46 0.46

Overall R2 676.87 *** 696.91 *** 716.62 ***

TTC

TTC × EO

Constant

FDI

DIS

EO

<1> <2> <3>

EDU

GDP

N=982 †: p < .10, *: p < .05, **: p < .01, ***: p < .001 The upper number of each cell indicates coefficient. The lower number indicates standard error. Recipient country effect and year effect are controlled.

28

i An alternative method is to examine entrepreneurial orientation both of U.S. and each recipient nation. As the one side of each country pair is consistently U.S. in our context, however, we focus on entrepreneurial orientation of recipient countries. ii We used the 2003 data to interpolate the missing data for year 2002. iii For a few nations, immigration data were not available for early sample periods (e.g. 1960’s). Since “not available” data implies that the number of immigrants was negligibly small, we regarded such data as zero and cumulated immigrant data only for available data points. iv Data for Taiwan and Mainland China were available only from 1979 because their data were not reported separately from each other up to then. We accumulated immigrant data from 1979 for each and neglected the data before 1979. Since this variable construction clearly underestimates the extent of cumulative immigration, we tested for sensitivity by specifying alternative regression models that omitted these nations from the sample. The omission did not appear to affect our main results. v Since the data for Taiwan were not available from the World Bank database, we obtained that data from the Taiwan Statistical Data Book complied by the Council of Economic Planning and Development of Taiwan. vi In addition to year heterogeneity, we considered the possibility that global venture capital flows were larger in certain industries as compared to others. Since we utilized aggregated venture capital flow data, we could not control for this by using dummy variables. Instead, we calculated industry shares of global venture capital flow and the U.S. domestic venture capital flow for eighteen key industries and compared them. Spearman’s rank correlations for industry share between global venture capital and domestic venture capital were more than 0.7 and significant at the 1% level for every observation year. Thus, we concluded that the structures of industry shares were mostly consistent between global venture capital flow and domestic venture capital flow.