1

VENTURE CAPITAL FIRMS’ DIVERSIFICATION INTO NEW GEOGRAPHIC MARKETS:

AN INTEGRATION OF INSTITUTIONAL AND SOCIAL NETWORK PERSPECTIVES

Pek-Hooi Soh School of Business

National University of Singapore 1 Business Link

Singapore 117592 Tel: (65) 6874-3180 Fax: (65) 6779-2621

Email: bizsohph@nus.edu.sg

Jane W. Lu Lee Kong Chian School of Business Singapore Management University

469 Bukit Timah Road Singapore 259756

Tel: (65) 6822 0758 Fax: (65) 6822 0777

E-mail: janelu@smu.edu.sg

January, 2005

2

VENTURE CAPITAL FIRMS’ DIVERSIFICATION INTO NEW GEOGRAPHIC MARKETS: AN INTEGRATION OF INSTITUTIONAL AND SOCIAL NETWORK PERSPECTIVES

ABSTRACT Integrating institutional and social network perspectives, this study examines the dual forces of institutional influences and network influences on the new market entry decisions of venture capital firms. In a sample of 2,130 venture capital firms and their investments over 1994-2003 in 88 geographic markets, we found support for both influences as the frequencies of entries by both other venture capital firms and co-investors in a geographic market enhance the propensity of focal firm’s entry into the same market. Further, firms central in co-investment networks are more likely to enter into new geographic markets. Finally, the centrality of a firm in its co-investment networks weakens institutional influences. These findings show the importance of legitimating signals arising from alternate sources in the institutional environment and how they interact and affect a firm’s diversification into new markets.

3

Strategic management scholars have long been interested in how firms draw on prior knowledge

and acquire information cues in order to reduce uncertainty surrounding their investment

decisions. Incumbents are likely to enter into new markets when they possess industry-

specialized supporting assets (Mitchell, 1989). As the number of large and profitable firms in a

new market increases, it signals the legitimacy of operating in that market and thus attracts other

firms to follow the entry (Haveman, 1993). Alternatively, firms may garner diverse experiences

from their network partners because more complex and useful tacit information about investment

decisions can be better conveyed through networks (Beckman & Haunschild, 2002). For

established firms, diversification into new markets is a strategy to change or expand the core

business domain of organizations (Fligstein, 1991). New market entry necessarily entails

significant technical and market uncertainty since firms may face the risk of assets being locked

into undesirable position and not appropriating the strategic value. Therefore, with relevant

knowledge and information signals, firms face a lower risk of losing the value of firm specialized

assets deployed in new markets, thus increasing the incentive to invest.

Existing studies have suggested two main explanations as to why firms diversify into

lines of business that are to any extent unrelated to their core activities. One explanation is the

rational-choice decision, the other is mimetic isomorphism. Rational-choice arguments suggest

that firms will be quick to expand when they are able to utilize existing capabilities needed to

survive in a new market (Brittain & Freeman, 1980), when they are under competitive pressure

to sustain their market dominance (Mitchell, 1989; Haveman & Nonnemaker, 2000), or when the

market success of new investments becomes more likely (Cohen & Klepper, 1996). The second

explanation focuses on the mimetic behaviour of firms in new market entry (Haunschild, 1993;

Haveman, 1993, Henisz & Delios, 2001). Neoinstitutional theory predicts that firms tend to

4

adopt the same investment strategies as other firms within the same industry or as their network

partners because of normative or informational influence flowing through the competitive or

cooperative interactions among firms. The increase in the number of other firms in a new market

will legitimate that market and signal the feasibility of entering that market, whereas the

experiences of network partners reduce the range of uncertainty.

Although the literature on diversification is substantial, most previous work has focused

on inducement effects on organizational change in established firms. Little attention has been

paid to the factors that drive the variation in perceived legitimation and access to experiences

generated by others. The institutional perspective has assumed that the availability of

information about potential investment opportunities is not a constraint and that organizations are

more attentive to investment decisions made by highly visible firms. In contrast, network studies

have shown how attributes of social structure can influence and constrain accessibility and

quality of information (Burt, 1992; Uzzi, 1996). Besides the legitimating and signalling effects

from other firms, information arising from the social networks does direct a firm’s attention to

experiences associated with the decisions of its partners to enter into new markets.

This paper investigates how firms simultaneously respond to alternate information

sources arising from the investment patterns of other firms and network partners, and how

attributes of social structure shape their responses. We argue that firms differ in their strategic

assessment of the risks and costs associated with potential market entry. We do not suggest that

their internal decision rules differ but that these firms vary in their attention to and perception of

external information sources regarding the investment opportunities. Under high uncertainty,

firms economize on search costs and vary in their degree of interaction with others within the

same industry and network. Some firms will interact more intensively than others with certain

5

other firms in the same organizational environment. Consequently, differential access to

uncertainty-reducing information will lead to different risk assessment of the same investment

opportunities. We propose that a firm’s first entry decision in a new market will be jointly

influenced by the percentages of other firms and partners in that market, but the signalling effect

of other firms is dampened as the centrality of the firm increases.

Our predictions found support in an examination of the investment decision of 2,130

venture capital firms to enter into 88 new geographic markets worldwide from 1994 to 2003. The

decision of a venture capital firm to invest in a new venture in a different nation is similar to the

decision of an existing firm to enter a new business domain. In both cases, information must be

gathered on the nature of potential new markets. Unlike established firms which operate in

particular product markets, venture capital firms do not face the risk of cannibalization of

existing assets and structural inertia which would otherwise counter their incentives to enter into

new markets. To a greater extent, venture capital firms rely on knowledge and information from

diverse sources to inform them of the risks surrounding the new ventures in new markets.

Therefore, venture capital industry provides an appropriate empirical context for this study of

informational influences.

The study contributes to a greater understanding of how institutional and network forces

influence firms’ diversification into new markets and have implications concerning firms’ ability

to take advantage of alternate sources of information about risky investments.

THE INVESTMENT DECISION OF VENTURE CAPITAL FIRMS

For more than forty years, the Venture Capital (VC) industry has played a notable role in

shaping the landscape of new enterprise formation. Venture capitalists provide funds and assist

in the formation of new ventures. The investment decision is presumably made based on the

6

technical merits of the business proposals. This rational investment rule predicts that venture

capital firms are likely to invest in projects that are expected to produce acceptable rate of return

(Brealey & Myers, 1996). Prior studies have found that the attractiveness of industry or

geographic market and technology, the ability and experience of management team/founder,

stage of start-ups, amount of capital required, control, liquidity and exit options are regarded as

important investment decision criteria (Bygrave, 1987; Hall & Hofer, 1993; Macmillan, Siegel &

Narasimha, 1985).

While prior studies provide parsimonious theoretical explanation of investment decisions

by venture capital firms, they tend to focus on individual economic exchanges to predict the

investment decisions based on the maximization of efficiencies. Such focus has been criticized

for offering an under-socialized view of organizational activities (Granovetter, 1985). This

criticism is especially relevant in the decision making of venture capital firms because new firms

typically represent risky and unproven organizational propositions. It is difficult to assess the

quality of new ventures. The decision becomes more uncertain when venture capital firms do not

have any experience in a new product or geographic market. As a result, venture capital firms

tend to focus on certain geographic and/or product areas (Sorenson & Stuart, 2001). Despite of

this general tendency, venture capital firms do diversify into new markets. This gives arise to an

interesting question: how do venture capital firms gather and assess information about new

markets in which they have little experience?

Institutional theory and network theory point to two major sources of uncertainty-

reducing information within the VC community. First, the investment patterns of other venture

capital firms in the same market. Second, the social networks in the VC community, which are

built from syndicated investments. The former exerts influences on venture capital firms’ new

7

market entry decision through a mimetic process. The latter has been found as the prime factor

in diffusing information about potential investment opportunities (Sorenson & Stuart, 2001).

To reduce the investment risk in new ventures, venture capital firms actively cultivate

networks comprised of financial institutions, universities, large corporations and other

organizations. These networks and the norm of information sharing enable venture capital firms

to monitor existing investments and to identify new deals. However, access to information about

investment opportunities is dispersed in the VC community because the social interactions vary

in the degree to which they generate credibility, trust and reciprocity that facilitate information

transfer. Moreover, the information transmitted through indirect ties is filtered and constrained

by the ability of these ties acting as information processing units (Ahuja, 2000). Thus, the extent

of information flows between venture capital firms may enhance or dampen the signalling effects

of entry patterns of other firms. In the following, we will draw upon institutional and social

network theories to explain how alternate information sources shape the decision of firms to

enter into new markets.

Institutional Influences

Institutional theorists argue that the organisation-environment relationship is

“institutionally embedded” (Meyer & Rowan, 1977). Each organisation is nested in a context of

many other organisations (Granovetter, 1985) and its own internal institutional environments

(Zucker, 1977). The institutional environment is the fundamental driving force behind

organizational activities because of an organization’s desire to fit with its institutional

environment (Martinez & Dacin, 1999). In pursuing a fit with the institutional environment,

organizations tend to conform to institutional pressures from other organizations. This

isomorphic tendency often leads to uniformity in decisions and homogeneity in organizational

8

form (DiMaggio & Powell, 1983). Inter-organizational mimetic behavior occurs by a process

called mimetic isomorphism.

The concept of mimetic isomorphism is centred around the idea that the likelihood a firm

will copy a decision or mimic an organizational form increases with the frequency that other

organizations in a firm's environment implement that decision or use that organizational form

(DiMaggio & Powell, 1983). Imitation comes about because prior decisions or actions by other

organizations increase the legitimacy of similar decisions and actions, something particularly

important in the face of high uncertainty (Tolbert & Zucker, 1983; DiMaggio & Powell, 1983;

Haunschild & Miner, 1997). As a result, there is a tendency to imitate structures and practices

that have been adopted by large numbers of organizations. It is the purest form of mimetic

isomorphism, because it is the sheer number of other adopters that forms the decision base for a

firm and determines the desirability of a structure, practice or decision.

Considerable empirical support exists for this type of imitation. Haunschild and Miner

(1997) found that the likelihood of using a particular investment banker in acquisitions was

related to the number of other acquiring firms that had previously used that banker. In other

examples, the proportion of prior entrants/adopters has been found to affect a firm's market entry

decisions (Haveman, 1993), plant location decisions (Henisz & Delios, 2001) and its use of

structures such as the M-form (Fligstein, 1985) and matrix management (Burns & Wholey,

1993). If frequency-based imitation is a relevant notion in the investment strategy of venture

capital firms, the proportion of other venture capital firms that invested in a given geographic

market in the past should be positively related to a venture capital firm’s entry into that market.

Hypothesis 1: The greater the frequency of investments made by other venture capital firms in a geographic market, the greater a venture capital firm's propensity to enter the same market.

9

Network Influences

In making a decision to invest in a new venture, a venture capital firm requires very good

access to information about the venture in question. The focal firm needs to know about the

kinds of risks associated with the venture in the potential market and the degree of risks that can

be managed. Bygrave (1987) observed that information sharing in the VC community is in fact

an important activity for discovering investment opportunities and closing deals. Venture capital

firms often turn to informants in the networks established from syndicated investments and

acquire the requisite information for investment decision. The informants are often the co-

investors of the firm in past transactions (Bygrave, 1987; Sorenson & Stuart, 2001). Past

transactions between co-investors create significant trust that facilitates the transfer of relevant

and reliable information about risks.

How networks of co-investors guide a firm’s investment decision can be understood by

examining the kinds of risk and uncertainty in investing in new ventures. Investments in new

ventures are highly risky because no proven track record is presented about the entrepreneurial

team, the products, and the potential market. Similarly, if a firm has no experience in managing a

new venture in the particular market, information asymmetric problems may prevent the firm

from appraising the viability of the venture (Shane & Cable, 2002). Information asymmetry in

new ventures occurs because entrepreneurs often possess more information than the potential

investors about the prospects of their business and the competence of founding teams.

Information asymmetry becomes a problem when entrepreneurs are reluctant to fully disclose

their information in order to prevent others from pursuing the same opportunities. Without prior

investment experience and complete information from the entrepreneurs, a firm has to turn to its

informants in the networks for their experiences in dealing with similar decisions.

10

To further understand how network information can reduce the uncertainty about the

potential investment opportunities and shorten the window of consideration for such investments,

we need to examine the three aspects of information that underlie the investment decisions:

currency, timeliness, and credibility. First, the information flows in the network can keep its

members updated on latest market and political developments in the host countries of new

ventures, including both market opportunities and potential economic or political problems.

Next, the potential markets are full of rapid changes which present both opportunities and

threats. It is crucial for venture capital firms to have timely information about such changes and

act on them. Co-investment partners with extensive contacts can provide one another with timely

information, enabling firms to be proactive in their investment decisions. Finally, specific

information conveyed by partners and referrers has more credibility than general information

acquired through market intelligence.

To sum up, a firm's co-investment network provides good access to information well

beyond what the firm could acquire if acting independently in the VC community. By

participating in the co-investment network, a firm is able to gain access to the relevant

investment experiences of other firms. Given the informational influences from the co-investors,

it is likely that the firm will follow the investment entry patterns of these firms, who may in turn

influenced by their partners. As a result, we expect that a venture capital firm’s entry into a

potential geographic market should be positively related to the proportion of its co-investors that

invested in that market.

Hypothesis 2: The greater the frequency of investments made by co-investors in a geographic market, the greater a venture capital firm's propensity to enter the same market. However, the extent of social interactions is dissimilar across firms. A focal firm's

11

location in a network reflects the degree of accessibility to all other firms in the same network.

The informational benefits stemming from varied network locations have been well explored in

social network analysis (Wasserman & Faust 1994). Studies have argued that central firms have

superior access to information (Gulati, 1999; Sorensen & Stuart, 2001). Central firms can be

information brokers, mediating the flows of information between all other pairs of actors in the

network (Brass & Burkhardt, 1993). Moreover, firms that connect closely to all others on their

shortest paths will gain access to information about the market and its competitors more

efficiently. With fewer intermediate partners, such firms depend less on specific others to gain

information about opportunities and threats. Central firms are likely to have a larger "intelligence

web” through which they can acquire valuable information and have access to this information

earlier than other network partners (Gulati & Gargiulo, 1999). Therefore, we expect that a firm’s

centrality is positively correlated with its propensity to enter into new markets.

Hypothesis 3: The greater the centrality of a venture capital firm in the co-investment network, the greater the firm's propensity to enter a new market.

Given their superior positions in co-investment networks, central firms have full access to

the informational benefits from co-investment networks. The easy access to abundant, timely

and credible information on investment opportunities residing in co-investment networks will

make central firms to focus more on information from co-investment networks while

downgrading the informational signals arising from institutional forces. Therefore, we expect

that the centrality of a venture capital firm will reduce the firm’s tendency to follow the

institutional signals created by other venture capital firms.

Hypothesis 4: The positive relationship between other venture capital firms’ frequency of past investments into a geographic market and a venture capital firm's current decision to enter the same market will be weakened the more central a firm is in the co-investment network.

12

Figure 1 below summarizes the conceptual model in our study, illustrating both

institutional and network forces that drive venture capital firms’ decisions on entering new

geographic markets.

--------------------------- Insert Figure 1 here

---------------------------

METHOD Sample and Data

We gathered the data on venture capital investments from the Venture Economics

database by Thompson Financial (http://www.ventureeconomics.com/). This database provides

information on the US, European, and Asian private equity markets from 1962 onwards. It has a

coverage of more than 95% of the venture capital industry in the US. It provides comprehensive

information by VC firms, funds, portfolio companies, disbursement, etc.

We test our hypotheses using the disbursement information from 1994 to 2003. During

this period, 2,130 firms invested in portfolio companies over 88 geographic markets. VC firms

typically raise their funds from private and public pension funds, endowment funds, foundations,

institutional investors and wealthy individuals. They are interested in new ventures which have

the potential for high growth or high rewards. However, new ventures are highly risky. In order

to mitigate the risk of venture investment, VC firms develop a portfolio of companies and often

co-invest with other firms. The investments made by VC firms are not long term but the idea is

to wait for the portfolio companies to reach a sufficient size and credibility so that they can be

sold to corporations (mergers and acquisitions or M&A) or to the institutional public-equity

markets (initial public offering or IPO).

There are several types of VC firms, some serve as the general partner for the investors

13

whose funds are organized as limited partnerships, whereas others may be affiliates or

subsidiaries of a commercial bank, investment bank, insurance company, or other non-financial,

industrial corporations. VC firms may be generalist or specialist investors depending on their

investment strategy. Generalists tend to invest in various industry sectors or geographical

locations, whereas specialists invest in fewer industry sectors or locations. Typically, a VC firm

may look at a few hundred investment opportunities before investing in only a few selected

companies with viable business propositions. The firm constantly keeps in touch with other firms

in the VC community in order to identify new investment opportunities and it will perform due

diligence to assess the technical and business merits of the potential companies. Once an

investment decision is made, the firm will commit a certain amount of fund that is to be

disbursed to the target company through a series of tranches. On the other hand, the portfolio

company may raise and receive several rounds of venture financing from the same VC firm or

multiple VC firms in its life as needed.

We focus our analysis of investment decisions made by 2,130 independent VC firms. The

details of each investment include date, amount, investment stage, the profiles of both VC firm

and target company. In total, we have about 530,004 usable records for our analyses. Using the

datasets, we further identified for each firm the set of co-investors who had jointly invested and

disbursed funds in the same target companies in the same investment rounds.

Variables

This study’s dependent variables are the investment decisions to enter one of the eighty-

eight geographic markets for the first time. We defined geographic markets by nations. We

coded this new market entry decision for each year since the foundation year of each venture

capital firm. We coded this information as a dummy variable which takes a value of 1 when a

14

venture capital firm makes an investment in a new geographic market in a particular year, and 0

otherwise.

We have constructed three independent variables for this study. They are:

Other firm's entry pattern by geographic market. We measured this pattern of entry by

calculating the number of other venture capital firms that have made new entries in each

geographic market as a percentage of the total number of venture capital firms, for each year

since the foundation of the focal venture capital firm.

Co-investors’ entry pattern by geographic market. We measured this pattern of entry by

calculating the number of co-investors that have made new entries in each geographic market as

a percentage of the total number of venture capital firms that have made new entries in the same

geographic market, for each year since the foundation of the focal venture capital firm.

Centrality in the co-investment network. We computed degree centrality for each venture

capital firm in our sample by counting its accumulated total number of co-investors, for each

year since the foundation of the focal venture capital firm.

We have controlled for factors that are known to affect VC firms’ investment decisions.

At the VC firm level, we controlled for venture capital firm’s size (as measured by the total

amount of capital under management) and firm age. At the geographic market level, we

controlled for market attractiveness (GDP of geographic markets and population of geographic

markets) and market risk (political hazards in the geographic markets) (Henisz, 2000). We also

controlled for the density of portfolio companies in each geographic market (as measured by the

total number of portfolio companies in each geographic market).

Statistical Model

Given the cross-sectional time-series data, we used a pooled time-series model. We

15

selected the panel logit model because the dependent variable is binary data and the model

allows for random effects to treat firm heterogeneity explicitly. Logit models use logistic

function and employ maximum likelihood estimation technique. The random errors of logit

models contain unobservable firm effects that are randomly and normally distributed. Random

effects models are used because we are interested to make inferences about the population from

the cross-sectional units.

RESULTS

Table 1 reports the descriptive statistics for all the variables of this study. As described

earlier, the choice set for each VC firm was 88 geographic markets (countries) for each year. We

coded 1 when a VC firm first entered a geographic market. Given the extensive choice (88) and

the focus on first entry over 10-year period of time, the dependent variable (first entry into a

geographic market) and two entry pattern variables (other firms’ entry pattern and co-investors’

entry pattern) had very small value. In terms of the third independent variable, degree centrality

in co-investment networks, the average is 10 co-investors with a minimum of 0 and a maximum

of 300 co-investors.

----------------------------------- Insert Tables 1 & 2 here

----------------------------------- We tested our hypotheses using five regressions which were developed in a hierarchical

manner. We first developed our base model including all the control variables. We then entered

each independent variable one by one, followed by the introduction of interaction term in the

fifth model. We summarize the results in Table 2. Model 1 is the baseline model that included

only the control variables. All the control variables are significant. As expected, the number of

portfolio companies in a certain geographic market increases the new entries into this market.

The propensity of new entries in a geographic market is negatively associated with firm age but

16

positively associated with firm size in term of capital under management. In the meantime,

market attractiveness, as measured by GDP and GDP per capita, enhances the propensity of new

market entry while country risk reduces such propensity.

Model 2 tested Hypothesis 1 which predicts that the more other VC firms enter into a

geographic market, the more likely that a VC firm will make a new entry into that market. The

positive and significant coefficient of percentage of other firms in geographic markets provides

strong support to our Hypothesis 1. In a similar manner, we examined Hypothesis 2 by adding

the percentage of co-investors in geographic markets to Model 1, out baseline model. The

coefficient of this variable was positive and significant in Model 3, suggesting that the more co-

investors enter into a geographic market, the more likely a VC firm will diversify into the same

market. Our Hypothesis 2 is also supported.

Hypothesis 3 focuses on the role of the positions that VC firms occupy in co-investment

networks and predicts that network centrality enhances a VC firm’s propensity of new market

entry. Model 4 added the degree centrality measure to our baseline model. The positive and

significant sign of degree centrality measure supports our Hypothesis 3. Hypothesis 4 further

predicts that a VC firm’s network position reduces the influences of other firms on the VC firm’s

new market entry decision. Model 5 entered the interaction terms between degree centrality and

the percentage of other firms in geographic markets. Consistent with our Hypothesis 4, the

coefficient estimation of this interaction term was negative and significant. Hypothesis 4 is

supported.

Models 6, 7 and 8 present full models and check the robustness of the above hypothesis

tests. Model 6 entered both percentage of other firms in geographic markets and percentage of

co-investors in geographic markets. The signs and significance levels of these two independent

17

variables remained the same in Model 6 as in Models 2 and 3 respectively. Model 7 further

added degree centrality and presents the full model for all the main effect hypotheses. All the

main effects remain the same as in their respective models. Finally, Model 9 added interaction

term between percentage of other firms in geographic markets and degree centrality. Both the

interaction effect and main effect remain robust.

Following Aikon and West (1991), we constructed Figure 2 to illustrate the interaction

effect between other firms’ entry into geographic markets and network position of the focal firm.

Based on the results of Model 8, we plotted the relationship between other VC firms’ entry into

geographic markets and the propensity of the focal VC firm to enter the same market, across VC

firms with low, medium and high degree of network centrality. All three curves in Figure 2 had

positive slope, suggesting that the more other VC firms entering into a geographic market, the

more likely the focal VC firm will enter the same market. This general trend is consistent with

our Hypothesis 2. The relative slopes among the three curves illustrates the interaction effect

between other VC firms’ market entry pattern and VC firms’ network position as measured by

centrality. As shown in Figure 2, the lower the centrality of a VC firm, the steeper the slope.

The differences in the steepness of the slope clearly indicate that VC firms who are central in co-

investment networks are less likely to follow the market entry pattern of other VC firms.

----------------------------------- Insert Figure 2 here

-----------------------------------

DISCUSSIONS AND CONCLUSIONS

In this paper, we examined the dual forces of institutional influences and network

influences on market entry decision of venture capital firms. We found that both institutional

influences and network influences have significant effects on the market entry of venture capital

18

firms. Specifically, venture capital firms are more likely to enter a new geographic market as the

more other venture capital firms or the more their co-investors enter the market. Our findings

highlight the importance of both influences in venture capital firms’ market entry decisions.

We further investigated the roles played by a venture capital firm’s position in the co-

investment network in the firm’s market entry decision. We found that the more central a

venture capital firm in its co-investment network, the more likely it will enter new geographic

markets. At the same time, the more central a venture capital firm’s position in its co-investment

network, the less likely the firm’s decision on market entries will be influenced by other venture

capital firms’ market entry pattern. We argue that differences in the informational influence of

institutional patterns and social network patterns of investments in geographical markets stem

from variation across VC firms in their degree of interaction with others in the VC community.

Different level of interaction leads to differential attention to and perception of external

information signals regarding the investment opportunities. The differing effects of institutional

influences on firms with differing positions in the co-investment network illustrate the superior

informational advantages residing in the co-investment networks. The access to such network

resources reduces the value of market signals as presented in the entry pattern of other venture

capital firms.

Our findings have important implications for research and for practitioners. We

contribute to network theory by integrating institutional perspectives into predictions based

solely on network theory. The prominent influences from both institutional forces and network

forces suggest that our framework presents a more complete account of venture capital firms’

market entry decisions and that it is important to incorporate both influences in the studies on

venture capital firms’ investment decisions.

19

Further, we advance institutional theory by challenging the implicit assumption that firms

have the same access to information. The significant interaction between institutional influences

and a firm’s network position illustrates the importance to investigate the differences in

information access because such differences can have an impact on the strength of institutional

influences. Future studies could further the exploration on the conditions under which

institutional influences differ. Such exploration will lead to better understanding of the

conditional influences of institutional forces.

Finally, we add to the literature on venture capital firms by identifying two new key

influences on venture capital firms’ investment decisions. The significant influences of both

institutional forces and network forces suggest the important role played by social considerations

in addition to technical considerations. Further, the differing effects of institutional influences

on venture capital firms with different network positions illustrate the value of co-investment

networks. Venture capitalists should explore ways to better position themselves in the co-

investment networks to reap informational benefits in the co-investment networks.

20

REFERENCES

Agarwal, S. & S. N. Ramaswami, 1992. Choice of foreign market entry mode: Impact of ownership, location and internalization factors. Journal of International Business Studies, 23: 1-27.

Ahuja, G. 2000. Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45, 425-455.

Aikon, L. S., and West, S. G. 1991. Multiple Regression: Testing and Interpreting Interactions. New York: Sage.

Anderson, E. & H. Gatignon. 1986. Modes of entry: A transaction cost analysis and propositions. Journal of International Business Studies, 17: 1-26.

Barkema, H.G., J.H. Bell, & J.M. Pennings. 1996. International expansion through start-up or acquisition: A learning perspective. Academy of Management Journal, 41: 7-27.

Beckman, C. & P. Haunschild, 2002. Network learning: The effects of partners’ heterogeneity of experience on corporate acquisitions. Administrative Science Quarterly, 47: 92-124.

Berger, P.L., & T. Luckmann. 1967. The social construction of reality. New York: Doubleday.

Brass, D. J., & M. E. Burkhardt, 1993. Potential power and power use: An investigation of structure and behavior. Academy of Management Journal, 36: 441-470.

Brittain, J. & J. Freeman, 1980. Organizational proliferation and density-dependent selection. In John R Kimberly & Robert Miles (eds.), The Organizational Life Cycle: 291-338. San Francisco: Jossey-Bass.

Burns, L. & D. Wholey. 1993. Adoption and abandonment of matrix management programs: Effects of organizational characteristics and inter-organizational networks. Academy of Management Journal, 36: 106-138.

Burt, R. 1992. Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press.

Bygrave, W.D. 1987. Syndicated investments by venture capital firms: A networking perspective. Journal of Business Venturing, 2:139-154.

Cohen, W.M., & S. Klepper, 1996. A reprise of size and R&D. The Economic Journal, 106: 925-952.

Coleman, J. 1990. Foundations of social theory. Cambridge, MA: Harvard University Press.

Datta, D.K., P. Herrmann, & A. Rasheed. 1997. Antecedents and consequences of foreign market entry: What do we know? The Academy of International Business 1997 Annual Meetings, Mexico.

21

DiMaggio, P.J. & W.W. Powell. 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48: 147-160.

Fligstein, N. 1991. The structural transformation of American industry: The causes of diversification in the largest firms, 1919-1979, in Walter W. Powell and Paul J DiMaggio (eds.), The New Institutionalism in Organizational Analysis: 311-336. Chicago: University of Chicago Press.

Fligstein, N. 1985. The spread of the multidivisional form among large firms, 1919-1979. American Sociological Review, 50: 377-391.

Goodrick, E. & G.R. Slancik, 1996. Organizational discretion in responding to institutional practices: Hospitals and caesarean births. Administrative Science Quarterly, 41: 1-28.

Granovetter, M. 1985. Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91: 481-510.

Granovetter, M. S. 1982. The strength of weak ties: A network theory revisited in

Marsden, P.V.& N. Lin. Social Structure and Network Analysis, 105-130. Beverly Hills, CA: Sage.

Gulati, R. & M. Gargiulo, 1999. Where do interorganizational networks come from? American Journal of Sociology, 103: 177-231.

Gulati, R. 1999. The influence of network resources and firm capabilities on alliance formation. Strategic Management Journal, 20: 397-420.

Hall J. & C. Hofer, 1993. Venture capitalists' decision criteria in new venture evaluation. Journal of Business Venturing, 8 (1): 25-43.

Haveman, H. 1993. Follow the leader: Mimetic isomorphism and entry into new markets. Administrative Science Quarterly, 38: 593-627.

Haveman, H. & Nonnemaker, 2000. Competition in multiple geographic markets: The impact on growth and entry. Administrative Science Quarterly, 45: 232-267.

Haunschild, P. 1993. Interorganizational imitation: The impact of interlocks on corporation acquisition activity. Administrative Science Quarterly, 38: 564-592.

Haunschild, P. & A. Miner. 1997. Modes of inter-organizational imitation: The effects of outcome salience and uncertainty. Administrative Science Quarterly, 42: 472-500.

Henisz, W. J. 2000. The institutional environment for economic growth. Economics and Politics, 12: 1-31.

Henisz, W. J. & A. Delios, 2001, Uncertainty, Imitation, and Plant Location: Japanese Multinational Corporations, 1990-1996. Administrative Science Quarterly, 46: 443-475.

22

MacMillan I. R. Siegel, & S. Narasimha, 1985. Criteria used by venture capitalists to evaluate new venture proposals. Journal of Business Venturing, 1(1):119-129.

March, J.G. 1981. Decisions in organizations and theory of choice. In A. Van de Ven and W. F. Joyce, editors, Perspectives on organization design and behavior: 205-244. New York: Wiley.

Martinez, R.J. & M.T. Dacin. 1999. Efficiency motives and normative forces: Combining transaction costs and institutional logic. Journal of Management, 25: 75-96.

Mezias, Stephen J. 1990. An institutional model of organizational practice: Financial reporting at the Fortune 200. Administrative Science Quarterly, 35: 431-457.

Meyer, J., & B. Rowan. 1977. Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology, 83: 340-363.

Mitchell, W. 1989, Whether and when? Probability and timing of incumbents’ entry into emerging industrial subfields. Administrative Science Quarterly, 34: 208-230.

Nahapiet, J. & S. Ghoshal, 1998. Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 2: 242-266.

Reagans, R. & B. McEvily, 2003. Network structure and knowledge transfer: The effect of cohesion and range. Administrative Science Quarterly, 48: 240-267.

Roberts, P. & R. Greenwood. 1997. Integrating transaction cost and institutional theories: Toward a constrained-efficiency framework for understanding organizational design adoption. Academy of Management Review, 22: 346-373.

Scott, W.R. 1995. Institutions and organizations. Thousand Oaks, CA: Sage.

Shane, S. & D. Cable, 2002. Network Ties, Reputation, and the Financing of New Ventures. Management Science, 48: 364-381.

Sorenson, O. & T. E. Stuart, 2001. Syndication networks and the spatial distribution of venture capital investments. American Journal of Sociology, 106, 1546-1588

Tolbert, P. & L. Zucker. 1983. Institutional sources of change in the formal structure of organizations: The diffusion of civil service reform, 1880-1935. Administrative Science Quarterly, 28: 22-39.

Uzzi, B. 1996. The sources and consequences of embeddedness for the economic performance of organizations: The network effect. American Sociological Review, 61: 674-698.

Wasserman, S., & K. Faust, 1994. Social network analysis: Methods and applications. New York: Cambridge University Press.

23

Yiu, D. & S. Makino, 2002. The Choice between joint venture and wholly owned subsidiary: An institutional perspective. Journal of the Institute of Management Sciences, 13, 667-683.

Zucker, L.G. 1987. Institutional theories of organization. Annual Review of Sociology, 13: 443-464.

Zucker, L.G. 1977. The role of institutionalization in cultural persistence. American Sociological Review, 42: 726-743.

24

FIGURE 1: Investment Decision of Venture Capital Firm

Market Entry

Decision (Focal VC Firm)

Centrality in Co-

Investment Network(Focal VC Firm)

Entry Patterns of Other VC Firms

Entry Patterns of

Co-Investors

H1 +

H3 +

H2 +

H4 -

25

TABLE 1: DESCRIPTIVE STATISTICS AND CORRELATIONS Variables Mean s.d. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 1. First entry into a geographic market 0.0057 0.0750 1 2. Total number of portfolio companies in

geographic markets 8.1208 9.1321 -0.0175 1 3. Firm size 420.7649 1,209.6600 0.0280 0.1497 1 4. Firm age 83.4968 349.9160 0.1880 -0.0028 -0.0046 1 5. GDP 6.36x1011 1.49 x1012 0.1524 0.0015 0.0017 0.7535 1 6. GDP per capita 16,709 14,347 0.0529 0.0027 0.0007 0.1867 0.3545 1 7. Political risk 0.4148 0.1687 0.0009 0.0021 0.0009 -0.0171 0.0540 0.2603 1 8. Percent of other firms in geographic markets 0.0221 0.1138 0.1769 0.0010 0.0017 0.8700 0.7860 0.1616 -0.0143 1 9. Percent of co-investors in geographic markets 0.0004 0.0385 0.0442 0.0031 0.0159 0.0041 0.0047 0.0035 0.0025 0.0056 1 10. Degree centrality in co-investment network 10.1628 23.1230 0.0273 0.1702 0.2619 0.0189 -0.0051 -0.0047 -0.0060 -0.0046 0.0145 1

Notes: 1) All descriptive statistics reported for non-transformed values. 2) Significant at the 0.001 level (two-tailed test) when Pearson correlations > |0.0032|

26

TABLE 2: Entry Into New Geographic Marketsa,b,c

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Constant -6.011***

(0.070) -6.194***

(0.075) -6.078***

(0.071) -6.019***

(0.070) -6.293***

(0.075) -6.190***

(0.075) -6.221***

(0.075) -6.290***

(0.075) 1. Total number of portfolio companies

in geographic marketsc 0.001***

(0.000) 0.000***

(0.000) 0.001***

(0.000) 0.001***

(0.000) 0.000***

(0.000) 0.000***

(0.000) 0.000***

(0.000) 0.001***

(0.000) 2. Firm sizec 0.000***

(0.000) 0.000***

(0.000) 0.000***

(0.000) 0.000***

(0.000) 0.000***

(0.000) 0.000***

(0.000) 0.000***

(0.000) 0.000***

(0.000) 3. Firm agec -0.051***

(0.003) -0.050***

(0.003) -0.052***

(0.003) -0.060***

(0.004) -0.060***

(0.004) -0.050***

(0.003) -0.061***

(0.004) -0.060***

(0.004) 4. GDPc 0.000***

(0.000) 0.000* (0.000)

0.000* (0.000)

0.000*** (0.000)

0.000*** (0.000)

0.000* (0.000)

0.000* (0.000)

0.000*** (0.000)

5. GDP per capita 0.000*** (0.000)

0.000*** (0.000)

0.000*** (0.000)

0.000*** (0.000)

0.000*** (0.000)

0.000*** (0.000)

0.000*** (0.000)

0.000*** (0.000)

6. Political risk -0.716*** (0.145)

-0.376** (0.149)

-0.530*** (0.141)

-0.724*** (0.145)

-0.414** (0.150)

-0.398** (0.149)

-0.376** (0.149)

-0.438** (0.150)

7. Percent of other firms in geographic markets (H1)

2.583*** (0.102)

2.412*** (0.153)

2.584*** (0.102)

2.794*** (0.103)

2.397*** (0.153)

8. Percent of co-investors in geographic markets (H2)

5.723*** (0.446)

5.785*** (0.449)

5.542*** (0.453)

5.761*** (0.451)

9. Degree centrality in co-investment network (H3)

0.010*** (0.001)

0.014*** (0.001)

0.011*** (0.001)

0.014*** (0.001)

10. Percent of other firms x Degree Centrality (H4)

-0.038*** (0.003)

-0.039*** (0.003)

No. of groups 2130 2130 2130 2130 2130 2130 2130 2130 No. of observations 530,004 530,004 530,004 530,004 530,004 530,004 530,004 530,004 Log-likelihood -15400.96 -15303.08 -15464.77 -15317.55 -15072.026 -15197.25 -15102.47 -14968.39 Model chi-square 296.10 306.23 60.45 218.31 253.48 52.56 84.10 90.91

a *** p < .001; ** p < .01; * p < .05; all one-tailed tests. b Cell entries are unstandardized coefficient estimates. Numbers in parantheses are standard errors. cLogarithmic transformation.

27

FIGURE 2: Institutional Influences and Network Position in Venture Capital Firms’ Investment Decision

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

0% 20% 40% 60% 80% 100%

Other VC Firms' Entry (% of All VC Firms)

Prob

abili

ty o

f Ent

ry

centrality (low)centrality (medium)centrality (high)