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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: [email protected]
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: [email protected]
January, 2005
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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.
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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
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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
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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
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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
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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
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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.
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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.
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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
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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.
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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
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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
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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
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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
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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
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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
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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.
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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.
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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)