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  1. 1. 1 SOCIAL NETWORKS, UNCERTAINTY, AND MARKET CHOICE Isin Guler Boston University Mauro F. Guillén The Wharton School of the University of Pennsylvania February 2006 Version Funding from the Mack Center for Technological Innovation at the Wharton School is gratefully acknowledged. We thank Simone Polillo for his help in the calculation of the network variables.
  2. 2. 2 SOCIAL NETWORKS, UNCERTAINTY, AND MARKET CHOICE Abstract We argue that higher-status organizations, those facing a greater network constraint, and those whose network partners have already expanded abroad are more likely to pursue opportunities in foreign markets. We also argue that social networks affect attitudes towards uncertainty in foreign markets. Higher-status firms and those more network-constrained seek to reduce levels of uncertainty, while firms whose partners have expanded to high-uncertainty markets exhibit more tolerant attitudes towards uncertainty. Using data on the foreign investment and syndication experiences of 1,010 American venture capital firms over the 1990-2002 period, we find robust support for the predictions involving social status and partners’ expansion, and some evidence for the impact of network constraint.
  3. 3. 3 The structural approach to economic sociology posits that actors in general, and organizations in particular, make decisions that reflect their relationships to others and their overall position in social networks, and that these variables affect their performance. Research has established that firms imitate the decisions of others they are connected to (Burns and Wholey 1993; Davis 1991; Davis and Greve 1997; Haunschild 1993; Hong and Page 2001; Westphal, Gulati, and Shortell 1997), learn from their network partners (Beckman and Haunschild 2002; Powell, Koput, and Smith-Doerr 1996; Uzzi 1997), enhance their performance or chances of survival if they develop links to legitimate actors (Baum and Oliver 1992; Stark 1996; Stuart, Hoang, and Hybels 1999; Uzzi 1997), and are affected by each other’s competitive moves (Baum and Korn 1999). A growing body of empirical evidence also supports the ideas that the centrality and the structural holes that characterize an actor’s network position affect its decisions, pattern of behavior, and performance (Burt 1992; Burt 1997; Castilla 2005; Hochberg, Ljungqvist, and Lu 2005; Piskorski and Anand 2005; Podolny 2001; Podolny, Stuart, and Hannan 1996; Sorenson and Stuart 2001). In recent years, organizational and economic sociologists have started to assess whether similar network dynamics and effects occur when it comes to the international expansion of organizations. Research inspired by neo-institutional theory has found that firms imitate their relevant peers when expanding abroad as a way of mitigating the uncertainty surrounding the establishment of operations in a foreign country (Guillén 2002; Henisz and Delios 2001; Henisz and Macher 2004; Martin, Swaminathan, and Mitchell 1998; Westney 1993). Knowledge flows between organizational subunits located in different countries have been studied in order to
  4. 4. 4 understand the cross-border effects of different types of social linkages (Hansen 1999; Kostova 1999; Kostova and Roth 2002). The existing literature, however, has not explored the effect of structural position on foreign market entry, especially as it relates to the pervasive problem of uncertainty, which becomes exacerbated when organizations seek to enter unknown foreign countries. In a pioneering paper, Podolny (2001) argued and demonstrated empirically that organizations exhibit different attitudes towards uncertainty depending on their social status and network constraint, leading them to make different investment decisions. If structural position has such a distinct impact on organizational decision making mainly due to the way in which uncertainty is perceived and coped with, then the decision to enter specific foreign markets must also have something to do with social networks given that countries differ massively in terms of the level of uncertainty (Henisz and Delios 2001). This paper exploits the cross-national variation in uncertainty to develop a theory of organizational entry into new markets that is informed by social networks. We strive to make two separate contributions. First, we show how social network theory can enrich our understanding of the international expansion of organizations, thus extending the applicability of sociological theory to the study of foreign investment, a phenomenon that sociologists have mostly studied at the macroeconomic level of analysis as opposed to at the organizational level (Alderson 2004; De Soysa and Oneal 1999; Dixon and Boswell 1996; Firebaugh and Beck 1994; Kentor 1998; Kentor and Boswell 2003). Second, we improve on existing social network research by conceptualizing and measuring how structural position interacts with uncertainty to influence organizational decisions.
  5. 5. 5 THEORY Our point of departure is Podolny’s (2001) dual predictions about the impact of social networks on egocentric and altercentric uncertainty. Egocentric uncertainty refers to the unknowns surrounding how inputs are transformed into outputs, while altercentric uncertainty refers to the lack of information about the focal actor’s quality as perceived by potential exchange partners. Podolny’s first prediction was that high-status actors prefer to operate in markets characterized by low egocentric uncertainty, in which social status confers no obvious advantage because what matters is whether contextual conditions make it possible to transform inputs into outputs and to profit from such a conversion. By contrast, actors find their social status valuable when addressing the altercentric uncertainty faced by potential exchange partners. Podolny’s second argument was that actors embedded in networks rich in structural holes are in a better position to reduce egocentric uncertainty because they can choose markets and market segments that offer the best opportunities for profitable economic action. Using data on the domestic investments of U.S. venture capital firms, he found that high-status firms preferred to focus on late-stage venture financing, whereas firms rich in structural holes sorted into early- stage venture financing, a set of results he interpreted as being consistent with status being more useful to tackle altercentric uncertainty and structural holes being more useful to address egocentric uncertainty. We use Podolny’s logic to examine how the structural position of organizations in their home country’s social network affect the decision to pursue opportunities in foreign markets. Regardless of the industry, most organizations never operate in foreign markets, and when they do their home-country experience casts a long shadow over their decisions. The literature on international business has long highlighted the fact that the decision for the firm to go abroad is a
  6. 6. 6 momentous one (Aharoni 1966; Caves 1996; Dunning 1993). Before entering the first foreign market, the firm is exposed to just one institutional environment (Guillén and Suárez 2005; Westney 1993). Committing resources to an overseas market represents a qualitative change in the firm’s strategy (Guillén 2002; Henisz and Delios 2001). As documented in the international business literature, firms ponder very carefully which foreign markets to enter, deciding to operate in some of them but not in others for a variety of reasons, including geography, culture, politics, and economics (Caves 1996; Dunning 1993). We argue that organizations do not make the decision to enter a foreign market in a vacuum, but rather based on how the social world around them is organized and structured. Following Podolny (2001, 2005), we further argue that the social networks in which organizations are embedded shape both the decision to go abroad and the choice of foreign market to enter, under the assumption that markets differ from each other in a number of ways, including uncertainty. Thus, our theoretical and empirical approach surmounts two problems that afflicted Podolny’s original analysis. First, while he could only observe the level of uncertainty when a deal occurred, our theoretical and empirical analysis takes into consideration the non-occurrence of events (i.e. non-entry into a new market). Second, while he conceptualized and measured uncertainty as an attribute of a deal (early versus late-stage venture financing), our approach is to conceptualize and measure uncertainty as an attribute of markets independent of whether organizations actually enter them or not. We begin the development of our hypotheses by establishing the main effects of social status, structural holes, and network partners. We then examine how heterogeneity among
  7. 7. 7 organizations in terms of these three variables affects their reaction to different levels of uncertainty in foreign markets. Social Status Social status is the standing, honor, worth, esteem, or prestige of an actor given his or her particular position in society, i.e. its relationships to others (Burt 1982; Jensen 2003). In a market, status is a signal of relative quality or of expected performance, and it can convey positive or negative features of the actor (Podolny 1993). Past performance or quality is an important determinant of reputation, but social status is more than just having a good name based on previous efforts or results (Podolny and Phillips 1996). One frequent way of assessing an organization’s social status involves examining the extent to which it relates to others in such a way that its structural centrality is enhanced (Podolny 2001). In other words, an organization’s status depends on the status of other organizations or actors it relates to. There are two mutually-reinforcing reasons why the higher the social status of the organization in its home country, the more likely it is to enter a foreign market. First, given that most organizations in any given industry are domestic in nature, we argue that higher-status organizations are more likely to break with the established norm in their industry or sector of activity than lower-status organizations. Positions of high status provide actors and organizations with visibility, and in turn, with easier and timelier access to resources (Burt 1982; Ibarra 1993; Powell, Koput, and Smith-Doerr 1996). Organizations with high status also learn about new opportunities earlier than those with lower status (Burns and Wholey 1993; Coleman, Katz, and Menzel 1966), and enjoy a larger set of alternatives to choose from, e.g. in terms of investment opportunities or partners (Ahuja 2000; Podolny 1993). Higher-status organizations tend to be
  8. 8. 8 more innovative, to exercise leadership, and to become role models that others imitate (DiMaggio and Powell 1983; Haveman 1993). Diffusion research shows that innovative practices travel faster once they are adopted by high-status actors (Burt 1987; Podolny and Stuart 1995; Strang and Tuma 1993; Stuart 2000). Moreover, early adoption of practices has been found to increase power and centrality (Burkhardt and Brass 1990). In short, prior research suggests a relationship between high status and the innovativeness of an organization. Given that foreign expansion in general, and entering for the first time a specific foreign market in particular, represent a strategic departure, we expect higher-status organizations to be more likely to take such a momentous step. The second reason why a higher-status organization is more likely to enter a foreign market has to do with altercentric uncertainty. In order to successfully operate in a foreign market organizations need to establish relationships with collaborators, suppliers and customers, who may experience a nontrivial amount of uncertainty as to whether the newly entering foreign organization is reliable and trustworthy, i.e. whether it can meet expectations and deliver on its promised outputs. As mentioned above, Podolny (2001, 2005) calls this type of uncertainty altercentric because it is experienced by the focal firm’s potential collaborators, suppliers or customers. When altercentric uncertainty is high, social status becomes a signal of the yet unobserved quality of the organization (Podolny 1993; Stuart, Hoang, and Hybels 1999). In particular, the organization’s association with other high-status actors affects how others evaluate the organization’s standing (Podolny 1994; Stuart 2000; Stuart, Hoang, and Hybels 1999). Research on a variety of industries has shown that higher-status organizations attain higher levels of innovation output, growth rates, sales margins, financial results, and market shares (Ahuja 2000; Benjamin and Podolny 1999; Hsu 2004; Owen-Smith and Powell 2004; Podolny 1993;
  9. 9. 9 Podolny and Stuart 1995; Podolny, Stuart, and Hannan 1996; Powell, Koput, and Smith-Doerr 1996). Moreover, social status in existing markets has been shown to be a transferable resource in entering new markets (Jensen 2003). In the absence of other, more reliable data about the newly entering firm, status in previously entered markets can serve as a proxy for the quality of the organization’s goods or services in the new market. These observations and empirical results are consistent with the well-established finding in the international business literature that firms entering a new market face a liability of “foreignness” in that they do not fully understand local practices and patterns of behavior, and face a hurdle in terms of conveying to local exchange partners their ability to operate successfully (Zaheer 1995). In such cases, status in the existing markets becomes a valuable resource to reduce the liability of foreignness. Thus, because higher-status organizations are both more likely to break with established norms in their home-country and they can more easily overcome the liability of foreignness by more forcefully signaling their quality to host-country actors, we predict that: Hypothesis 1: The higher the status of the organization in its home-country network, the more likely it will enter foreign markets. Following Podolny (2001), we further argue that higher-status organizations prefer to operate in markets in which egocentric, i.e. input-output, uncertainty is low given that, as noted above, in those markets social status does not represent an advantage:
  10. 10. 10 Hypothesis 2: The level of egocentric uncertainty in a foreign market reduces the positive effect of the organization’s social status on its likelihood of entering that market. Structural Holes An actor or organization is rich in structural holes when it has relationships with many alters who are themselves not connected to each other. Thus, an organization with many structural holes around it can profitably exploit its position as a broker in the network by regulating information flows and shaping how activities spanning the hole are to take place (Burt 1992; Burt 1997). Organizations embedded in networks replete with structural holes tend to have more “weak ties,” and benefit from access to diverse sources of information (Burt 2004; Granovetter 1973). Such organizations generate more innovative ideas, and enjoy more entrepreneurial opportunities (Burt 1992; Burt 1997; Walker, Kogut, and Shan 1997). Empirical research has found that individuals and firms with access to diverse information perform better than others (Burt 1997; Hargadon and Sutton 1997; Mizruchi and Stearns 2001; Nerkar and Paruchuri 2005; Podolny and Baron 1997; Soda, Usai, and Zaheer 2004). Organizations beset by declining performance have been found to seek new network partners or ties as sources of useful information and resources (Beckman, Haunschild, and Phillips 2004; Mizruchi and Stearns 1988). In contrast, an organization with many redundant ties, and few structural holes in its network, is more constrained in the extent to which it can benefit from its position in the network.
  11. 11. 11 Organizations surrounded by structural holes enjoy such an advantageous structural position that they have no imperative reason to change the nature of their day-to-day operations or to enter a different system of relationships. Organizations that are poor in structural holes in the home market are constrained in that they can find relatively few opportunities to prosper and grow by acting as brokers (Burt 1992). Research has shown that, although relationships evolve, network structures exhibit a significant amount of persistence over time (Powell, Koput, and Smith-Doerr 1996; Walker, Kogut, and Shan 1997). Networks evolve from stable relationships, and become embedded as organizations seek familiar partners (Granovetter 1985; Gulati and Gargiulo 1999; Powell 1990). Prior ties form the basis of future decisions (Gulati and Gargiulo 1999). The relative stability of network structures implies that organizations that have access to limited opportunities in their networks may look to new environments to find new opportunities. In a parallel argument, the international business literature has long argued that, under normal circumstances, firms tend not to pursue foreign opportunities until they find that the market in their home country no longer offers opportunities, i.e. has become “saturated” (Johanson and Vahlne 1977; Vernon 1966). Thus, we predict: Hypothesis 3: The greater the organization’s network constraint in its home country (i.e. the smaller the number of structural holes), the more likely it will enter foreign markets. As noted above, structural holes help organizations reduce egocentric uncertainty because such structurally advantaged actors can choose the markets and market segments that offer the best opportunities to profitably convert inputs into outputs (Beckman, Haunschild, and Phillips
  12. 12. 12 2004; Podolny 2001). Organizations with many structural holes in their network will prefer to operate in markets where there is high egocentric uncertainty, and exploit their advantageous network position. We further predict that organizations facing network constraints in the home country, while eager to pursue opportunities abroad, will avoid foreign markets they perceive as uncertain: Hypothesis 4: The level of egocentric uncertainty in a foreign market reduces the positive effect of the organization’s network constraint (i.e. lack of structural holes) on its likelihood of entering that market. Network Partners Neo-institutional theory and network analysis highlight that organizations interact with each other in a variety of ways, resulting in patterns of control, exchange, and imitation. Interconnected organizations share information and experience, might feel peer pressure to engage in mimetic behavior, are likely to find a common ground for justifying the adoption of similar practices or strategies, and may develop similar patterns of specialization (DiMaggio and Powell 1983; Tolbert and Zucker 1983). Information and stimuli flowing through inter- organizational networks provide actors with clues as to new opportunities for action or for exchange. They also contribute to forming a shared understanding of the norms of behavior that every organization in the network ought to observe. Several studies have shown that organizations sharing a director on their boards, holding a stake in each other’s equity or depending on the same sources for critical resources tend to adopt similar patterns of behavior (Burns and Wholey 1993; Davis 1991; Davis and Greve 1997; Galaskiewicz and Wasserman
  13. 13. 13 1989; Haunschild 1993; Hong and Page 2001; Rao and Sivakumar 1999; Westphal, Gulati, and Shortell 1997). Research on foreign expansion has also argued and demonstrated that interconnected organizations follow each other to locations abroad (Guillén 2002; Henisz and Delios 2001; Martin, Swaminathan, and Mitchell 1998). In addition to the normative effect of social networks, this line of research proposes that a focal organization connected to another with a presence in a foreign country may learn two kinds of precious information. First, the focal organization may not have realized the extent to which there is an opportunity in the foreign country (Aharoni 1966). Second, even if the focal firm is aware of the foreign opportunity it may not exactly know how to pursue it. Establishing foreign operations requires negotiations with governments, suppliers, distributors, and customers, and a labor force needs to be hired and trained. The access to the experience of other interconnected organizations with a presence in the foreign country facilitates the identification and pursuit of opportunities. Therefore, we propose that: Hypothesis 5: The greater the number of the organization’s partners in its home- country network which have already entered a specific foreign market, the more likely the focal organization will enter that market. Following the organizations literature on foreign market entry (Guillén 2002; Henisz and Delios 2001), we further argue that the presence of the firm’s home-country partners makes it easier for it to cope with egocentric uncertainty in specific foreign markets because the partners can help the firm obtain information as to how to operate most effectively even in markets in which input-output uncertainty is high:
  14. 14. 14 Hypothesis 6: The level of egocentric uncertainty in a foreign market increases the positive effect of the number of the organization’s home-country partners already in the foreign market on its likelihood of entering that market. DATA AND METHODS We test the effects of social networks and uncertainty on foreign market entry with information on U.S. venture capital firms and their investments abroad. Venture capitalists raise money from investors of various kinds, placing it into a fund which they use to acquire equity stakes in entrepreneurial ventures. At the end of a predetermined period—typically 7 to 10 years —the investments are liquidated and the proceeds returned to the investors, except for a management fee of about 20 percent. Venture capitalists provide entrepreneurs and their companies with funding, strategic advice, contacts, and reputation (Gompers and Lerner 2000; Gompers and Lerner 2001).1 One of the most distinctive characteristics of the venture capital industry is that it is “an intensely social business” (Freeman 2005:163). I particular, firms tend to syndicate their investments with others. They do so in order to share information, resources, expertise, and risks (Castilla 2005; Gompers and Lerner 2000; Gompers and Lerner 2001; Hochberg, Ljungqvist, and Lu 2005; Sorenson and Stuart 2001). The widespread practice of syndicating venture capital investments provides with a unique opportunity to measure the structural position of each firm in the social network in terms of centrality and structural holes. Previous research has used 1 We use the term “firm” solely to refer to venture capital firms and “company” to refer to portfolio companies (entrepreneurial ventures).
  15. 15. 15 syndication data to assess network positions and examine investment behavior in the context of the U.S. domestic market (e.g. Podolny 2001; Sorenson and Stuart 2000). By contrast, we use similar data to calculate the structural position of each firm in the domestic network—which accounts for more than 95 percent of all investments by U.S. firms—and then examine entry into foreign markets. We focus our analysis between 1990 and 2002. The U.S. venture capital industry grew significantly during this period, in terms of both capital available for investment and the number and amount of actual investment. Activity in the U.S. and abroad peaked in the year 2000, which lies within our period of observation. We compiled the venture capital investment data from the VentureXpert database provided by Venture Economics,2 which collects information through an annual survey of over one thousand private equity partnerships in the U.S. This database has been used extensively in venture capital research (Barry, Muscarella, Peavy, and Vetsuypens 1990; Gompers and Lerner 2000; Megginson and Weiss 1991; Sahlman 1990; Shane and Stuart 2001). Although it tends to oversample investments in California companies, most of the concerns about VentureXpert’s quality have to do with issues surrounding capital disbursed and valuations (Kaplan, Martel, and Stromberg 2005), which are not the subject of this paper. Given that our analysis focuses on the foreign investments of venture capital firms, we observed a sample of 1,010 U.S.-domiciled firms between 1990 and 2002. Each of these firms has a presence in the venture capital industry, although some of them also do other forms of later-stage private equity.3 In order to capture causal relationships between the dependent variable and the independent variables, we lag all independent variables by one year. We 2 VentureXpert includes “standard U.S. venture investing” in portfolio companies, as long as the company is domiciled in the U.S., at least one of the investors is a venture capital firm, venture investment is a primary investment, and it entails an equity transaction.
  16. 16. 16 therefore empirically examine investments over the twelve-year period between 1991 and 2002. As of the end of 2002, 216 of the 1,010 venture capital firms made 1,714 rounds of investment in 920 ventures located in 40 different foreign countries. The largest investors were Warburg Pincus, Advent International Corporation, and Japan/America Ventures. The distribution of rounds by investment stage is as follows: startup or seed (6 percent), early stage (22), expansion (51), later stage (7), buyout or acquisition (7), and other (6). We included all of these rounds in our primary analysis and then checked if excluding the latter three categories affected the results. We excluded from all analyses 17 investments in companies that had gone public before the U.S. venture capital firm invested. We did not exclude either the 794 firms that never invested abroad or the 55 countries in which no investments took place because doing so would introduce sample selection bias into our analyses. Dependent Variable and Unit of Analysis. The sample consists of firm-country-year observations. Each venture capital firm is at risk of entering a country during a given year. The dependent variable (event) equals one if firm i entered country j in year t. We have complete data for 517,981 firm-country-year observations. We estimated our models with two different samples. First, we dropped firm-country-year observations after first entry into country j. Second, we allowed for repeated entry into the same market to test for robustness. Once we excluded observations after first entry, the sample shrunk to 516,493 observations. In the 12-year 3 While the number of venture capital firms represented in the sample may seem high, it should be noted that not all firms are active during the entire period of observation. We checked the sensitivity of our results by excluding VC firms that made fewer than three investments in the US in each year. This resulted in a sample of 552 firms and 242,017 firm-country-years. The results of the analyses with the reduced sample are qualitatively similar to the ones reported here.
  17. 17. 17 time period, we observed 688 non-zero firm-country-year combinations, 465 of which were first entries into a market. Network Variables. Following the established literature, we measured social status using Bonacich’s (1987) eigenvector centrality measure. This measure takes into account the centrality of the actors with which the focal actor is connected (Bonacich 1987). It is therefore a better measure of status than alternative centrality measures, especially as it captures affiliation with other high-status actors (Benjamin and Podolny 1999; Jensen 2003; Nerkar and Paruchuri 2005; Podolny 1993; Podolny 1994; Sorenson and Stuart 2001). We only considered a tie to exist if two firms invested together in the same U.S. venture and in the same round. For each year t, we calculated the centrality score using the information for years t-2, t-1 and t. Other researchers have also calculated centrality in syndication networks using information for several years in order to obtain a more stable measure (Castilla 2005; Hochberg, Ljungqvist, and Lu 2005; Piskorski and Anand 2005). The centrality score ranges between zero (for isolated firms with no contacts), and one (for firms that syndicate with a large number of others). Thus, the centrality score for firm i in year t is: ci = α ∑ Aij c j j where α is the reciprocal of an eigenvalue, and A is the adjacency matrix denoting the existing ties between pairs of firms. The centrality of each firm is thus a function of the centrality of the other firms it is connected to. Isolated firms with no ties to others get a score of zero. We also follow the existing literature in measuring network constraint with Burt’s (1992) index of autonomy. Isolates get a score of zero, and the measure increases as the actor loses
  18. 18. 18 autonomy, i.e. the number of structural holes drops or the network constraint increases. For each year, we calculate: H i = 1 − ∑ ( pij ∑ piq p qj ) 2 j q where pij is the proportion of i’s network that relates to j, etc., and i ≠ j ≠ q. Finally, we measured the “pull” effect of syndication by counting the number of each firm i’s syndication partners. We constructed a measure of the extent to which the focal firm’s syndication partners have invested in each foreign country j as of t-1. We followed a three-step procedure. First, we identified the syndication ties between each pair of venture capital firms in our sample as of each year t-1. If the pair of venture capital firms did not invest together in the United States during t-1, we entered a value of zero. Otherwise, we entered a value of one. We organized this information as a matrix with 1,010 rows for each venture capital firm in our sample and 216 columns for each of the venture capital firms that invested abroad at least once. (Note that as far as the influence on foreign investing behavior is concerned, the network information on the non-investing venture capital firms is not relevant, thus reducing the complexity of our calculations.) Second, we identified the investments undertaken by each of the 216 venture capital firms in each of 95 countries, cumulative as of the end of year t-1. We arranged this information as a matrix with 216 rows for each foreign investing venture capital firm and 95 columns for each foreign country. Third, and taking advantage of the fact that the first matrix has a number of columns (investing firms) that is exactly the same as the number of rows in the second matrix (also investing firms), we multiplied the two matrices, yielding
  19. 19. 19 another matrix with the total of 1,010 venture capital firms as rows, and the 95 countries as columns. After repeating this operation for each lagged year t-1 between 1990 and 2001, we obtained a time-varying measure of the effect of syndication ties with previously investing firms in each country. It is important to note that this variable is uniquely defined and calculated for each venture capital firm-country-year combination. In our sample, it ranges between a minimum of zero (for combinations in which no syndication partner, if the firm had any, invested in the country as of t-1), and a maximum of twenty-five. Egocentric uncertainty. We measured egocentric uncertainty, that is, the unknowns associated with profitably transforming inputs into outputs from the point of view of the entering firm i in a foreign country j during year t-1 with the reverse-signed political constraint index, which captures the checks and balances on policymakers to unilaterally change the policy regime (Henisz 2000b). The likelihood of an unexpected change in the policy regime has been found to be an excellent indicator of uncertainty in the context of international business (Henisz and Delios 2001). Conceptually, a higher number of independent government branches that have veto power over a policy change in a country reduces uncertainty. This indicator ranges between 0 (most hazardous) and 1 (most constrained, i.e. stable). The political constraint index is historically highly correlated with the risk indexes included in the International Country Risk Guide (ICRG 1996), and with the seven-point index of executive constraints of the Polity Database, which is widely used by political sociologists and political scientists (Marshall and Jaggers 2000). Unlike these other indicators, however, the political constraint index is forward looking in that it assesses the possibility that policy will be constrained rather than the government’s historical record of doing so. So that it is increasing in uncertainty, we multiplied the index by minus one.
  20. 20. 20 Interaction terms. In order to reduce multicollinearity between main and interaction effects we centered each main effect before calculating the interaction term by subtracting its mean (Jacard and Turrisi 2003). Firm-Level Control Variables. We included in all models three firm-level controls. First, we included the number of ventures funded by the venture capital firm in the United States as of year t-1 in order to account for unobserved firm heterogeneity in terms of skills or capabilities, what researchers have called venture capital firm “sophistication,” as well as size (Bottazzi, Da Rin, and Hellman 2005; Gompers and Lerner 2000:236; Kaplan, Martel, and Stromberg 2005). Second, we also included the number of ventures funded by the venture capital firm in foreign countries as of year t-1, which controls both for unobserved firm heterogeneity in general, and for the propensity to invest abroad in particular. Third, we added to all models the number of countries entered by firm i up until year t-1. Country-Level Control Variables. We included controls for the size of the economy, measured as GDP in constant 1995 U.S. dollars (World Bank 2004). We controlled for the number of U.S. venture capital firms that had invested in ventures in the country up until year t-1 so as to capture unobserved heterogeneity in investment conditions in each country. Year Controls. All models include a dummy variable for each year. Estimation Method. We analyzed the likelihood of a venture capital firm entering a new country with a hazard rate model (Allison 1995; Tuma and Hannan 1984). The model estimates the likelihood of an event happening at time t given that the event has not yet occurred. We used a hazard rate model because it accommodates time-varying independent variables, and allows censoring in the data (Allison 1995). We model the hazard rate of entering a new country with the piecewise exponential model as implemented in Stata (Sorensen 1999). Piecewise
  21. 21. 21 exponential models are exponential hazard rate models, where the baseline hazard rate is allowed to vary in an unconstrained way at each predefined time period. The benefit of this approach is the ability to model the time dependence without the more restrictive assumptions of other parametric models. In order to estimate the hazard rate in each time period, we divided the data into yearly spells (Hannan and Freeman 1989), and treated the observations as censored unless an event (entry) occurred. Each of the time-varying independent variables were updated in each spell. The models were estimated using maximum likelihood estimation as implemented in Stata. RESULTS Table 1 and 2 present the descriptive statistics and the correlation matrix. As expected, entry is positively correlated with centrality and with the number of syndication partners already operating in the country; and it is negatively correlated with the level of political uncertainty in the country. Network constraint is negatively correlated with entry, which is not consistent with the hypothesized relationship. Correlations between most of the independent variables are low, reducing any concerns about potential multicollinearity. The only exception is the 0.71 correlation between centrality, and U.S. investment experience. Excluding this variable did not change the results of our analyses; we therefore report models with U.S. investment experience as a control. Table 3 presents the empirical results. Model 1 includes the controls only, while model 2 includes the main effects of centrality (H1), network constraint (H3), and syndication (H5). As predicted, more central (i.e. higher-status) firms, those facing a greater network constraint, and those whose syndicate partners have already expanded abroad are more likely to follow suit.
  22. 22. 22 Models 3 through 5 add the interaction term between each of the three main hypothesized effects and uncertainty. Model 6 includes the three interaction terms simultaneously. We obtain support for the prediction that higher-status firms are less likely to enter a foreign market as the level of uncertainty increases (H2) and for the prediction that firms whose syndication partners have already entered the foreign market are more tolerant towards the level of uncertainty (H6). By contrast, the interaction between network constraint and uncertainty is not significant (H4). Model 7 shows the results from the full specification using the information of repeated entries as opposed to just first entries. The same hypotheses receive support. The control variables in the full model behave as would be expected: market entries increase with the firm’s international experience, the size of the country, the number of firms already in the country, and the number of countries entered by the firm, and decrease with the firm’s U.S. experience. Figure 1 provides a visual assessment of the impact of uncertainty for different types of venture capital firms on their hazard of foreign market entry. It is based on the estimates from model 6 in Table 3. There are several important aspects to be noted. First, uncertainty is in general detrimental to foreign market entry, as indicated by the downward sloping curves. Second, at the low end of uncertainty, firms with a level of centrality one standard deviation above the mean have a multiplier of the hazard rate of foreign entry of 5.4, compared to about 4.0 for the average firm, and 2.9 for firms with a level of centrality one standard deviation below the mean. At high levels of uncertainty, the situation is reversed in that more central firms have a smaller multiplier than less central firms (as demonstrated by the cross-over of the various curves in Figure 1). Third, at the low end of uncertainty, firms with a number of partners in the foreign country equal to the mean plus one standard deviation have a multiplier of nearly 3.6, compared
  23. 23. 23 to roughly 4.0 for the average firm, and almost 4.4 for firms with a number of partners equal to the mean minus one standard deviation. As in the case of centrality, the situation reverses itself at higher levels of uncertainty: firms with a higher number of partners have a greater multiplier than those with a lower number of partners. DISCUSSION AND CONCLUSION Organizations make decisions as to which markets to enter based not only on the features of those markets—size, attractiveness, and level of uncertainty—but also depending on their own characteristics. Controlling for their size and experience, we have argued and demonstrated empirically that the structural position of organizations in the social networks in which they are embedded affects both their propensity to pursue foreign markets and their choice of specific markets to enter. In particular, we argued and found that uncertainty in the foreign market interacts negatively with organizational centrality and positively with the number of organizational partners with a presence in the foreign market. Thus, we find evidence for a modified version of Podolny’s (2001) argument in that social status helps firms overcome the liability of foreignness, i.e. the uncertainty that potential exchange partners perceive about the quality of the entering firm. We also find that from the perspective of the entering firm, political uncertainty in the host market renders its social status less useful. Our analysis and results demonstrate that sociological theories of networks have the potential of making important contributions to our understanding of key economic and organizational decisions. In the present era of globalization, foreign market entry has become one of the most important decisions facing organizations, given that operating in just one country tends to limit strategic options and financial performance. The finding that home-country social
  24. 24. 24 networks exert significant and large effects on the choice of foreign markets resonates with the literature on multinational corporations and foreign investment, which has always emphasized that firms carry with them the institutional blueprints and ways of operating developed in the home country. Thus, our paper shows that the fields of organizational sociology and international business can benefit from each other in terms defining important questions, formulating predictions, and testing them. It also demonstrates that the sociology of networks is of fundamental importance when it comes to understanding market processes. Our research also offers an avenue for enriching social network theory by conceptualizing and measuring how structural position interacts with uncertainty to influence organizational decisions. Our focus on foreign market choice provides a setting in which there is ample variation in uncertainty, a fact that we exploited to formulate and test predictions about the interaction between structural position and uncertainty. Although we did not find utterly robust empirical support, our research also indicates that network constraint in one market (e.g. the home country) prompts organizations to look for others (foreign markets). Relative to existing research, our theoretical and empirical approach has the advantage that we formulate and test arguments about uncertainty using a concept and a measure that is totally independent from the occurrence of events (i.e. market entries or deals). The research reported in this paper is limited in several respects. First, our empirical setting, venture capital, has certain peculiar features that make it harder to generalize our results. For instance, venture capital investments may differ from other types of foreign investments due to their relatively short time horizon (7-10 years), limited involvement in portfolio companies’ operations, and syndication practices. Future studies ought to examine if similar structural dynamics obtain in different types of industries. Second, lack of complete data prevented us from
  25. 25. 25 testing whether organizations replicate in new markets the social connections present in their home country. These and other limitations provide opportunities for further assessing how social networks and uncertainty affect the decisions of organizations in the marketplace.
  26. 26. 26 REFERENCES
  27. 27. 27 Table 1. Descriptive Statistics Variable Obs Mean Std. Dev. Min Max 1 entry 516493 0.001 0.030 0.000 1.000 2 Centrality 516493 0.018 0.034 0.000 0.230 3 Centrality (mean centered) 516493 0.000 0.034 -0.018 0.212 4 Network constraint 516493 0.149 0.254 0.000 1.000 5 Network constraint (mean centered) 516493 0.000 0.254 -0.149 0.851 6 Syndication 516493 0.047 0.378 0.000 25.000 7 Syndication (mean centered) 516493 -0.006 0.378 -0.053 24.947 8 Political uncertainty 516493 -0.568 0.278 -0.890 0.000 9 Political uncertainty (mean centered) 516493 0.000 0.278 -0.322 0.568 10 VC's international experience 516493 0.488 3.659 0.000 115.000 11 VC's US experience 516493 10.550 23.171 0.000 363.000 12 GDP constant USD (*10-12) 516493 0.381 1.148 0.001 8.980 13 All VC's experience in the country 516493 4.734 15.567 0.000 157.000 14 Number of countries VC entered 516493 0.741 4.420 0.000 40.000 Table 2. Correlations (N=516,493) Variable 1 2 3 4 5 1 entry 1.000 2 Centrality (mean centered) 0.030 1.000 3 Network constraint (mean centered) -0.006 -0.155 1.000 4 Syndication (mean centered) 0.099 0.200 -0.037 1.000 5 Political uncertainty (mean centered) -0.019 0.004 0.001 -0.072 1.000 6 VC's international experience 0.039 0.146 -0.037 0.037 0.007 7 VC's US experience 0.033 0.714 -0.107 0.219 0.004 8 GDP constant USD (*10-12) 0.013 0.000 0.000 0.044 -0.212 9 All VC's experience in the country 0.053 -0.034 0.015 0.258 -0.164 10 Number of countries VC entered 0.037 0.173 -0.028 0.056 0.006 6 7 8 9 10 6 VC's international experience 1.000 7 VC's US experience 0.286 1.000 8 GDP constant USD -0.005 -0.003 1.000 9 All VC's experience in the country 0.001 0.024 0.289 1.000 10 Number of countries VC entered 0.377 0.282 -0.003 0.010 1.000
  28. 28. 1 Table 3. Piecewise Exponential Hazard Models of Entry into a Foreign Market, 1991-2002 (1) (2) (3) (4) (5) (6) (7) Centrality (mean centered) 24.473*** 22.088*** 24.511*** 24.838*** 21.205*** 14.028** (1.857) (2.097) (1.860) (1.855) (2.244) (1.863) Network constraint (mean centered) 0.625** 0.616** 0.739* 0.634** 0.556 0.459+ (0.200) (0.200) (0.361) (0.200) (0.358) (0.271) Syndication (mean centered) 0.327*** 0.310*** 0.327*** 0.514*** 0.536*** 0.450** (0.021) (0.022) (0.021) (0.045) (0.046) (0.032) Political uncertainty (mean centered) -5.235*** -4.737*** -4.115*** -4.707*** -4.970*** -4.288*** -3.547** (0.450) (0.431) (0.455) (0.436) (0.454) (0.466) (0.341) VC's international experience 0.024*** 0.041*** 0.041*** 0.041*** 0.044*** 0.044*** 0.041** (0.004) (0.005) (0.005) (0.005) (0.005) (0.005) (0.003) VC's US investment experience 0.022*** -0.009*** -0.010*** -0.009*** -0.010*** -0.011*** -0.002 (0.001) (0.003) (0.003) (0.003) (0.003) (0.003) (0.002) -12 Constant GDP in country (*10 ) 0.010 0.065* 0.069* 0.065* 0.062 0.066* 0.107** (0.033) (0.032) (0.032) (0.032) (0.032) (0.032) (0.026) All VC's experience in country 0.038*** 0.031*** 0.032*** 0.031*** 0.030*** 0.031*** 0.030** (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.001) VC's prior countries invested 0.090*** 0.085*** 0.086*** 0.085*** 0.084*** 0.084*** 0.090** (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.004) Centrality * political uncertainty -19.928** -28.734*** -46.107** (7.478) (8.927) (7.777) Constraint * political uncertainty 0.639 -0.398 -0.877 (1.708) (1.708) (1.279) Syndication * political uncertainty 0.695*** 0.865*** 0.967** (0.159) (0.171) (0.118) Year dummies included included included included included included included Observations 516493 516493 516493 516493 516493 516493 517981 Number of entries 465 465 465 465 465 465 688 Log likelihood -1832.7 -1679.33 -1675.29 -1679.261 -1672.83 -1666.41 -1604.81 Standard errors in parentheses +p<0.10 *p<0.05 **p<0.01 ***p<0.001 Models 1-6: piecewise exponential hazard rate models, single entry Model 7: piecewise exponential hazard rate model, multiple entries
  29. 29. Figure 1: Multiplier of the Hazard Rate of Entry into a Foreign Market, by Political Uncertainty 6 From top to b ottom: 5 Centrality at μ+σ Syndication at μ-σ Centrality and syndication at μ Syndication at μ+σ Centrality at μ-σ 4 Multiplier of the Hazard Rate 3 2 1 0 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Uncertainty (mean centered)