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Dancing with strangers? Initial trust and the formation of initial
ties between new ventures and corporate venture capitalists
Massimo G. Colombo*1
Benedetta Montanaro1
Kourosh Shafi2
1 Department of Management, Economics, and Industrial Engineering, Politecnico di Milano, Milan,
Lombardy, Italy
2 Department of Management, College of Business and Economics, California State University,
Hayward, CA, USA
*corresponding author. Email: massimo.colombo@polimi.it
ABSTRACT
This study proposes a hybrid model of initial trust formation that highlights the role of social
categorization and its interplay with both institutional trust and the individuating information about
the party. Using data on 1,474 CVC investments in European ventures and a case-control research
design, we find that ventures more likely form initial CVC ties with investors whose parent
companies are located in countries considered as more trustworthy. This effect is weaker but does
not disappear when social defenses safeguard ventures from misplacing trust, and when there are
social ties between CVC investors and ventures’ independent VC investors.
Keywords: Corporate venture capital, trust, interorganizational relationships, social categorization
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INTRODUCTION
In recent years, trust has taken center stage in management (Rousseau et al., 1998; McEvily et al.,
2003; Dirks et al., 2009) and entrepreneurship studies (Welter and Smallbone, 2006; Welter, 2012;
Scarbrough et al., 2013; Pollack et al., 2017). Trust involves confident expectations about the
intentions and behaviors of another party and the willingness to accept vulnerability (Rouseau et al.,
1998: 394).
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Firms favor collaborating with parties they trust because of the positive consequences
of trust on performance for interorganizational collaborations (Granovetter, 1985; Uzzi, 1997;
Poppo and Zenger, 2002; Lado et al., 2008; Luo, 2008).
Although evidence abounds on the primacy of trustworthiness as a partner selection
criterion, our understanding of the origins of trust remains limited (Poppo et al., 2008; Poppo et al.,
2016). According to the social exchange perspective (Cook and Emerson, 1987; Ring and Van de
Ven, 1994), experimenting with small-sized collaborations is crucial for generating information on
a partner’s trustworthiness.
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However, the mechanism for generating trust highlighted by this
perspective is unfeasible when initial transactions involve committing sizable resources, and a
party’s opportunistic behavior has detrimental effects on the other party.
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Under these
circumstances, which are fraught with great risk, firms must place initial trust in unfamiliar
partners, as the absence of trust would preclude collaboration materialization. Given the prevalence
of the formation of ties with new partners, it is imperative to explore the sources of initial trust in
interorganizational relationships because factors unrelated to direct interaction and first-hand
experience with the trustee must underlie trust at zero acquaintance (Dunning et al., 2014).
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Mayer et al. (1995: 712) defined trust as “the willingness of a party to be vulnerable to the actions of another
party based on the expectation that the other will perform a particular action important to the trustor,
irrespective of the ability to monitor or control that other party”.
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“Social exchange relations evolve in a slow process, starting with minor transactions in which little trust is
required because little risk is involved and in which both partners can prove their trustworthiness, enabling
them to expand their relation and engage in major transactions. Thus, the process of social exchange leads to
the trust required for it in a self-governing fashion” (Blau, 1968:454).
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An example is offered by new biotechnology ventures. These ventures typically lack necessary experience
and resources, and are often forced to ally with incumbent organizations, so as to transform their innovative
knowledge into commercially-viable products. In doing so, they need to take a “leap of faith” and make a
relatively large lump-sum initial commitment, fraught with fears of misappropriation (Diestre and
Rajagoplan, 2012).
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Theories of initial trust formation provide two distinct perspectives, namely an institutional
and a cognitive account. Proponents of institutional trust adopt an economic framework and
emphasize that the decision to trust an unfamiliar exchange party results from a choice based on
controlled information processing and rational, forward-looking calculations. A firm’s managers
judge an unfamiliar party as trustworthy and are willing to trust it if they anticipate that the penalty
costs the other party will incur upon engagement in opportunistic actions outweigh the benefits
(Coleman, 1990; Hardin, 1992; Williamson, 1993). This perspective gives precedence to
trustworthiness based on legal arrangements and social structures, potentially imposing sanctions on
the defecting party (Zucker, 1986; Fukuyama, 1995).
The cognitive perspective is anchored in social psychology thinking, underlining that
individuals frequently resort to simple heuristics (“leaps of faith”) to quickly and easily decide
whether to trust strangers (Lewicky and Brienfield, 2011). According to this perspective, social
categorization plays a crucial role in influencing inferences about strangers’ trustworthiness
(Brewer, 1981; Fiske and Neuberg, 1990; Nee et al., 2018; Pratt et al., 2019). Initial trust
originates from individuals’ socially informed beliefs regarding the honesty, good faith, and
reliability of unfamiliar third parties whose membership in a selected social category confers them
trustworthiness. Social categorization may involve placing the exchange party in the same social
category to which the focal individual belongs (“unit grouping,” Brewer, 1996), or placing it in a
general social category toward which the focal individual has positive prejudice (“stereotyping,” see
Tajfel, 1981; Fiske, 2000).
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Although these perspectives can simultaneously contribute to the generation of initial trust,
most studies have limited their focus to one or the other, thus neglecting the interplay between
different sources of initial trust. Moreover, the extent to which social categorization loses its
importance as a source of initial trust when objective individuating information that a party regards
as predictive of the trustworthiness of the target exchange party becomes available, is unclear.
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Brewer (1981: 356) made the theoretical claim that group membership “serves as a rule for defining the
boundaries of low-risk interpersonal trust that bypasses the need for personal knowledge and the costs of
negotiating reciprocity.” For empirical support, see the study by Tanis and Postmes (2005).
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These are important gaps because of the crucial role initial trust plays in promoting
interorganizational collaborations and influencing partner selection. To improve our understanding
about the sources of initial trust, we adhere to the view proposed by McEvily (2011) that the
decision to trust an unfamiliar exchange party is a hybridization of elements from calculative and
heuristic decision-making. We focus on one instance of stereotypes that has important economic
implications, as previous studies have shown, that regarding the country of origin (i.e., the degree to
which citizens of one country express attitudes of general trustworthiness toward citizens of another
country. See e.g., Al-Sulaiti and Baker, 1998; Guiso et al., 2009; Bottazzi et al., 2016). We argue
that country-of-origin stereotypical trust is an important source of initial trust that favors the
formation of collaborative relationships between unfamiliar partners. However, its positive effect is
reduced when institutional trust reassures prospective partners of their limited vulnerability to
misplaced trust. The same occurs when individuating information about the trustworthiness of a
prospective partner becomes available.
To provide context to this issue, we examine the formation of initial ties between new
ventures and corporate venture capital (CVC) investors, that is, the minority equity investments of
typically large-sized companies in new ventures (Dushnitsky, 2012). Usually, the first collaboration
that new ventures have with established companies is with a CVC investor, and the trustworthiness
of CVC investors is admittedly a fundamental issue for the entrepreneurs of these ventures. Indeed,
CVC ties involve a critical tension for new ventures between value creation and misappropriation,
referred to in the literature as the “swimming with sharks” dilemma (Katila et al., 2008). On one
hand, new ventures seek access to the complementary resources possessed by established
companies for value creation (Park and Steensma, 2012; Alvarez-Garrido and Dushnitsky, 2016).
On the other hand, collaborating with these companies puts their knowledge at risk of
misappropriation (Alvarez and Barney, 2001; Diestre and Rajagopalan, 2012). For these reasons,
the formation of initial CVC ties provides an ideal testbed for investigating the origins of initial
trust and its influence on ventures’ selection of unfamiliar partners in interorganizational
collaborations. Additionally, it allows us to investigate how this relationship is moderated by factors
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that reflect institutional trust, and the individuating information that new venture entrepreneurs can
use to assess CVC investors’ trustworthiness.
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We argue that country-of-origin stereotypical trust is positively associated with the
likelihood of tie formation between new ventures and unfamiliar CVC investors (i.e., investors with
which the focal venture has no prior ties). Moreover, in line with the view that the influence of
stereotypical trust on CVC tie formation depends on both the extent of institutional trust and the
individuating information available to new ventures, we posit that the positive effect of
stereotypical trust on the likelihood of forming initial ties with a CVC investor is weaker in the
following circumstances: (i) when new ventures enjoy the legal defense offered by a strong
intellectual property protection (IPP) regime (Dushnitsky and Shaver, 2009; Colombo & Shafi,
2016); (ii) new ventures enjoy the social defense provided by a high-status independent venture
capital (IVC) investor that occupies a central position in the network of VC syndicates (Hallen et
al., 2014; Pahnke et al., 2015); (iii) the target CVC investor has a high status (Pollock et al., 2015);
and (iv) the target CVC investor has direct or indirect social ties with the IVC investors to which the
focal venture is affiliated (Meuleman et al., 2017; Kim et al., 2019).
To test our predictions, we use a sample of 1,331 new ventures located in European Union
(EU) countries and the United Kingdom (UK), that received VC from one or more CVC investors
in the period 1998–2018. In line with our hypotheses, we find that country-of-origin stereotypical
trust has a statistically significant positive effect on initial CVC tie formation. This effect weakens,
but does not disappear, if ventures are backed by a high-status IVC investor; that is, if new ventures
rely on high institutional trust based on social sanctions. The positive effect of country-of-origin
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We are aware that one observes CVC tie formation only when both the focal venture and the focal CVC
investor agree to form the tie. However, in this paper, we follow a prominent stream of literature initiated by
Katila et al. (2008) and Dushnitsky and Shaver (2009) and focus our theory development on the venture side.
The main reason is that country-of-origin stereotypical trust is unlikely to play any major role in CVC
investors’ decisions. In fact, CVC investors have access to a broad set of information sources on new
ventures, including their own previous experience with venture investing in particular countries. They also
use contractual and non-contractual mechanisms to deter new ventures’ opportunistic behaviors. In particular,
they have the resources to pursue legal issues if necessary. Still, other studies have considered the country-
level stereotypical trust on the investor side. The theoretical arguments would be similar to those used by
Bottazzi et al. (2016) to examine the investments made by IVC investors. In the empirical section, we duly
consider the CVC side. Particularly, in the specification of the empirical matching model, we control for
several factors that may influence the propensity of CVC investors to invest in a focal venture. In a robustness
check, we also include in the model specification the country-of-origin stereotypical trust of CVC investors.
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stereotypical trust also weakens when the individuating information about the focal CVC investor
conveyed through direct or indirect social ties between this CVC investor and the IVC investors
backing the focal venture, reassures the entrepreneurs about CVC investor trustworthiness.
Conversely, the strength of the IPP regime and the focal CVC investor’s high status do not seem to
influence the relationship between country-of-origin stereotypical trust and the likelihood of CVC
tie formation.
This study makes several contributions to existing literature. First, we contribute to the
literature that considers the role of initial trust in promoting interorganizational collaborations with
unfamiliar partners. We propose a hybrid model of trust formation that combines calculative and
heuristic sources of trust, focusing on country-of-origin stereotypical trust—a prominent example of
initial trust based on social categorization—and point to the boundary conditions related to its
interplay with institutional trust and the availability of individuating information on the exchange
party. Second, this study contributes to the literature on the role of trust in entrepreneurship.
Contrary to most previous studies, we focus on the entrepreneurs’ perspective, highlighting how
their perceptions of the trustworthiness of unfamiliar CVC investors influence initial CVC tie
formation. We also contribute to the literature on CVC. By focusing on country-of-origin
stereotypical trust, we extend prior studies that have investigated new ventures’ use of legal and
social defenses to protect against the misappropriation risks posed by CVC investors (Katila et al.,
2008; Dushnitsky and Shaver, 2009; Hallen et al., 2014; Colombo & Shafi, 2016). We also expand
the evidence of studies underpinning the role of social ties to CVC investors in providing
individuating information about their trustworthiness (or lack thereof) (Kim et al., 2019).
Lastly, this study has important practical implications. It helps entrepreneurs select the
“right” CVC investor by carefully evaluating the context surrounding CVC investments and the
reliable individuating information available on the prospective CVC investors. Moreover, we
highlight the importance for CVC investors interested in forming an initial CVC tie with a focal
startup to convince entrepreneurs of their own trustworthiness. Finally, our study raise policy
makers’ awareness of the crucial role played by an institutional environment favorable to the
development of initial trust between entrepreneurs and CVC investors.
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BACKGROUND
Ties between new ventures and CVC investors
This study examines initial CVC tie formation from the perspective of new ventures. Although
CVC ties present an important opportunity for new ventures, they also present unique challenges. In
selecting CVC partners, new ventures face a tension between value creation and value
misappropriation, which is also labeled, as aforementioned, the “swimming with sharks” dilemma
(Katila et al., 2008; Dushnitsky and Shaver, 2009; Hallen et al., 2014; Colombo & Shafi, 2016. See
Jeon and Maula, 2022 for a review). On the one hand, through these interorganizational
partnerships, new ventures can access the valuable complementary resources of CVC investors’
parent companies (e.g., manufacturing facilities, distribution channels, and marketing capabilities)
and improve their performance (Park and Steensma, 2012; Alvarez-Garrido and Dushnitsky, 2016).
On the other hand, by investing in new ventures, established companies aim to accelerate their
innovation discovery processes. CVC is a complement to (and sometime even a substitute for) their
innovative efforts in domains as diverse as new technologies, products, and business models
(Dushnitsky and Lenox, 2005; Wadhwa and Kotha, 2006; Keil et al., 2008; Basu et al., 2011; Maula
et al., 2013; Smith and Shah, 2013). However, CVC investors may draw insights from the
technologies developed by new ventures in ways that are not aligned with, or that even are
detrimental to, the interests of new ventures (Kim et al., 2019). Anticipating the potential high risk
in forming ties with CVC investors regarding the misappropriation of their knowledge, new
ventures often become worried about approaching CVC investors.
We claim that the initial trust of ventures’ entrepreneurs in a focal unfamiliar CVC investor
obviates the “swimming with sharks” dilemma and encourages tie formation with this investor.
Without initial interorganizational trust in place, it is unlikely that new ventures engage in any
formal commitment to collaborate (Zaheer and Harris, 2005).
Initial trust and its sources
In the organizational context, initial trust refers to trust that may exist when two parties that are
potential partners in an interorganizational collaboration are unfamiliar with each other and have no
experience of previous collaborations. McKnight et al. (1998) proposed a framework that
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distinguishes between personality-, institution-, and cognition-based initial trust (for a review, see
McKnight and Chervany, 2006). Personality-based trust is the general tendency to trust others, and
is mostly developed during childhood. Because we are interested in interorganizational
relationships, we view personality-based trust as an idiosyncratic factor, and thus use the parts of
McKnight et al.’s (1998) framework most applicable to initial interorganizational relationships:
institutional and cognitive trust.
The institutional perspective assumes that the decision to trust another party is the result of
a rational choice based on controlled information processing and forward-looking calculations that
attempt to maximize gains or minimize losses (Coleman, 1990; Hardin, 1992; Williamson, 1993).
According to this perspective, a firm trusts another party when it anticipates that the latter has
incentives to honor and fulfill trust, as it is in its own economic interest to behave in this way.
Specifically, an unfamiliar prospective partner is considered trustworthy when the penalty costs
from legal or social sanctions (e.g., damage to reputation) of defection exceed the benefits it would
reap from behaving opportunistically (Zucker, 1986; Shapiro, 1987; Lane and Bachmann, 1998).
The cognitive perspective brings to the fore the affective, motivational, and social factors
that influence initial trust (McAllister, 1995). It emphasizes the reliance on heuristics based on
simple and rapid information cues to judge whether an unfamiliar exchange party is trustworthy
(Brewer, 1981; Lewicki and Briensfield, 2010; Nee et al., 2018; Pratt et al., 2019). Cognitive trust
emerges when an agent’s expectations about the trustworthiness of a stranger which lead to the
intention to trust, are based on salient information on the trustee’s membership in a social,
organizational, or professional category. One well known category-based trust is shared
membership in a group. Individuals tend to attribute positive characteristics, including
trustworthiness, to other members of the same social group since in-group members share similar
values and characteristics (Brewer, 1996). As perceived similarity among group members increases,
the transfer of trust occurs more readily (Williams, 2001). Therefore, the awareness of category
membership confers people with depersonalized trust and influences the socially-informed
judgment about the trustworthiness of others (Kramer, 1999). The list of categories connotative of
trust-related expectations include for example gender (Orbell et al., 1994) and ethnicity (Bengtsson
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and Hsu, 2015). Social categorization based on stereotypes is another prominent example of simple
heuristics leading to initial trust (Tajfel, 1981; Fiske, 2000). The term stereotype refers to the
placement of an agent in a general category wherein all members have similar attributes.
Stereotypes are social categories that can be used to infer (un)favorable attributes for agents in that
category (Foddy et al., 2009). The shared beliefs regarding a stereotype may form the basis of a
diffuse trust expectation because the stereotyped target is considered to possess the generic features
of all members of that category. The generalized representation of the key characteristics of another
organization forms the basis for presumptive trust (Kramer and Lewicki, 2010).
The cognitive perspective may be viewed as a response to concerns regarding the behavioral
assumptions that agents resort to rational calculations and their expectations are driven purely by
economic interests. March (1994) observed that the rational choice model overstates the cognitive
capabilities of decision-makers and the degree to which they would engage in accurate calculations
for making decisions. Thus, an inclusive reconciliation of the institutional and cognitive perspectives
is a promising way to move the discourse on initial trust forward.
In this study, in accordance with McEvily (2011), we view initial trust as a hybrid concept
combining elements of calculative and heuristic decision-making in varying proportions.
Accordingly, we examine how social categorization based on country-of-origin stereotypes can lead
to initial trust, which, in turn, may favor initial tie formation between new ventures and CVC
investors. A party’s perception of the adherence of another party to the focal party’s core values and
norms based on the other party’s membership of a social group, triggers the category-based perception
of the other party’s trustworthiness (Williams, 2001). While social group membership may be based
on various characteristics, like gender, ethnicity, education or professional background, previous
studies that we will survey in the next section point to the country of an economic actor (in our case,
the parent company of a CVC investor) as a prominent characteristic that influences its categorization
by other economic actors (in our case, ventures’ entrepreneurs) as more or less trustworthy.
We also discuss how institutional trust based on the presence of effective legal and social
defenses, and the availability of individuating information on unfamiliar CVC investors substitute for
country-of-origin stereotypical trust, thus reducing its positive effect on CVC tie formation.
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HYPOTHESES
Country-of-origin stereotypical trust and CVC tie formation
One category of stereotyping that can lead to initial trust between unfamiliar agents is that
associated with their nation. Nations have character stereotypes, namely, shared beliefs toward the
personality traits of their citizens (Terracciano and McCrae, 2007), which in turn form the basis of
stereotypical trust (Kramer and Lewicki, 2010). In other words, country-of-origin stereotypical trust
is a social aggregate construct that expresses the trust-related expectations of people from one
nation toward those of another. Inglehart (1991) observed that country-of-origin stereotypical trust
correlates with common language and absence of historical conflicts (for more factors, see Delhey,
2007; Guiso et al., 2009).
Previous studies have shown that country-of-origin stereotypical trust influences political
choices and has political consequences, shaping public opinion about international relations
between countries (Brewer et al., 2004), and the integration of, for example, the EU (Genna, 2009).
It also influences consumer purchase decisions. Consumers in each country evaluate the quality of
products in line with country-of-origin stereotypes and make purchase decisions accordingly (Al-
Sulaiti and Baker, 1998), especially when they are novices or have ambiguous information about
the attributes of the product (Maheswaran, 1994). More important for the purpose of the present
study, country-of-origin stereotypical trust positively impacts economic transactions between firms.
Guiso et al. (2009) show that higher bilateral trust leads to more international trade flows between
two countries, more portfolio investment, and more foreign direct investment.
Zaheer and Zaheer (2006: 25) were the first to propose that “regardless of the general
symmetry or asymmetry in levels of trust deriving from the institutional and cultural environments
in which partners are embedded, there could be specific trust asymmetry arising from legitimacy
spillovers from the country of origin of partners in an international collaboration, such that firms
from countries that are viewed by nationals of the focal country as untrustworthy will be seen as
untrustworthy as well.” Inspired by this view, Ang et al. (2014) found that high-tech companies
which are wary of misappropriation risks, invest more in Chinese regions where local people are
regarded as more trustworthy. MacDuffie (2011) suggested that the historical mistrust between
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Japan and China at the national level informs perceptions of trustworthiness between Japanese
automakers and Chinese suppliers (for further evidence, see Ertug et al., 2013). More pertinent to
the present study, Bottazzi et al. (2016) showed that IVC investors are more inclined to invest in
start-ups located in countries whose citizens are judged as more trustworthy by citizens of the
countries of the IVC investors.
Therefore, we expect that entrepreneurs’ perceptions of trustworthiness rooted in the
country of origin spill over to interorganizational relationships, including those between their new
ventures and CVC investors. This leads to Hypothesis H1:
H1. A new venture is more likely to form an initial tie with a CVC investor if the parent
company of the CVC investor is located in a country whose people are perceived to be
trustworthy by people in the country of the new venture.
Moderating role of institutional trust based on legal and social sanctions
Institutions are rules or habits with normative content that enable and constrain action;
noncompliance with these rules and habits ensues sanctions that include loss of legitimacy or
reputation (Nooteboom, 2007; Bachmann and Inkpen, 2011). Institutional trust arises when social
behavior is monitored and sanctioned by legal, political, and social systems (Zucker, 1986;
Williamson 1993; Fukuyama, 1995).
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Impersonal arrangements in institutional structures reduce the
risk of misplaced trust by imposing formal and informal rules, including legal regulations, codes of
conduct, corporate reputation, industry standards, and informal norms of behavior set by
professional associations (Lane and Bachmann, 1996; Bachmann and Inkpen, 2011). These
arrangements are collectively-accepted, valid, explicit or implicit rules of behavior of actors
participating in the system. Not only do institutions provide actors with conduct guidelines, but they
also restrict and sanction actors’ misbehavior.
We argue that institutional trust substitutes for country-of-origin stereotypical trust, thus
reducing its positive effect on CVC tie formation. We consider two prominent sources of
institutional trust, namely legal and social sanctions. In the presence of these sanctions,
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It should be clear that the object of trust is not an institution (e.g., trust in police), but that institutions serve
as the foundation for trusting behavior. We use the term “institutional trust” as Williamson (1993) does.
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entrepreneurs are more inclined to rely on calculative rather than heuristic decision-making and give
less weight to stereotypical trust. The advantage of stereotypes lies in the reduction of cognitive
effort, but their drawback is that they may lead to inaccurate judgment (Bodenhausen and Wyer,
1985). Therefore, entrepreneurs are less likely to use country-of-origin stereotypical trust when the
cognitive effort involved in making inferences about the low probability of CVC investor
misbehavior is small.
Legal sanctions. Law is a formal institution that defines the appropriate rules of behavior and
necessary sanctions that a party incurs if it violates an agreement and breaches trust. Indeed, the
regulatory environment, which is an important part of formal institutions, shapes “the rules of the
game” (North, 1990). Legal provisions such as contract law or IPP law align the expectations of two
parties long before they engage in business transactions, provide structural assurance, and thus deter
opportunistic behavior. In particular, the legal protection of IP provides firms with a shield against
knowledge misappropriation, arguably encouraging risky investments in the production of
innovative knowledge (Levin et al., 1987; Hu and Png, 2013), and expanding markets for
innovation, such as licensing markets (Dechenaux et al., 2008; Huang and Murray, 2009).
In the context of ties between new ventures and CVC investors, knowledge
misappropriation risk is a major impediment to tie formation. CVC investors, by pursuing open
innovation strategies, wish to draw insights from the technologies developed by new ventures.
These strategic objectives may be in conflict with and detrimental to the interest of the target
venture if the CVC investor manages to “imitate the innovation [developed by the new venture],
and leave the entrepreneur empty-handed” (Dushnitsky and Shaver, 2009, p. 1046). Accordingly,
Kim et al. (2019) showed that ventures that innovate by building on the technology of incumbent
firms, and are thus particularly susceptible to the misappropriation risks posed by these firms, avoid
forming CVC ties with them if their entrepreneurs are knowledgeable about the opportunistic
tendencies of these incumbent firms. Meanwhile, a strong IPP regime provides new ventures with a
shield against misappropriation risks (Katila et al., 2008). Scholars have thus demonstrated that the
likelihood of same-industry CVC tie formation, which potentially involves high misappropriation
risks, increases substantially when new ventures benefit from the legal protection of a strong IPP
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regime (Dushnitsky and Shaver, 2009; Colombo & Shafi, 2016). Based on these arguments, we
expect punishment through strong IPP enforcement to be an important source of initial institutional
trust in CVC tie formation.
Having highlighted the role of a strong IPP regime, we elaborate on the interplay between
stereotypical trust and strong IPP regimes. In accordance with the “default hypothesis” of
stereotypes, our hybrid model of initial trust formation indicates that entrepreneurs will use country-
of-origin stereotypes as a basis for judgment about the trustworthiness of prospective CVC
investors as a last resort—that is, when no other easily accessible and reliable information with
more direct relevance is available. Hence, when information about IPP regime strength allows
entrepreneurs to easily make reliable calculations about the low probability of CVC investors’
misappropriating their ventures’ technical knowledge, heuristics based on country-of-origin
stereotypes will have limited influence on the decision to trust and form an initial tie with
unfamiliar CVC investors. This leads to hypothesis H2:
H2. The positive effect of country-of-origin stereotypical trust on initial tie formation
between a new venture and a CVC investor is weaker when the IPP regime is strong
rather than weak.
Social sanctions. Firms value their reputation for integrity as a form of capital, as it makes them
more attractive business partners and facilitates collaboration with other parties (Rindova et al.,
2005; Lange et al., 2011; Stern et al., 2014). Accordingly, they are unlikely to engage in
opportunistic or unethical behaviors if these behaviors can erode their reputation and cause a loss of
future business opportunities (Sullivan et al., 2007). From this perspective, reputation can be relied
upon as an informal institutional mechanism because opportunistic behavior erodes the actor’s
reputation (Bachmann and Inkpen, 2011). In other words, incentives associated with reputation for
integrity maintenance are a form of social structural control.
However, this reasoning is based on the premise that an actor’s misbehavior is observed and
sanctioned by other actors in the same social community. In the context of ties between new
ventures and CVC investors, it is questionable whether misbehavior by CVC investors damages
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their reputation. Accordingly, the reputation-based social sanctioning that new ventures can employ
to protect against knowledge misappropriation by CVC investors becomes more effective if the
focal venture is affiliated with a high-status IVC investor, that is an IVC investor that is central in
the network of VC co-investments (Hallen et al., 2014). Central IVC investors can leverage their
central position to effectively broadcast alleged misbehavior by CVC investors and cause serious
damage to their reputation, which in turn renders CVC investors less attractive partners in future
deals. Like IPP laws that act as arbitrators with binding rules and punishments for defectors, central
IVC investors are information-controlling intermediaries that credibly separate libel from legitimate
complaint. Thus, these investors can impose social sanctions through reputation loss in the case of
opportunistic behavior by CVC investors.
Having highlighted the role of affiliations with high-status IVC investors, we now focus on
how the availability of the related social sanctioning mechanism influences the relationship between
country-of-origin stereotypical trust and CVC tie formation. Consistent again with the “default
hypothesis” of stereotypes, entrepreneurs will be more inclined to use country-of-origin stereotypes
as a basis for judgment about prospective CVC investor trustworthiness when their venture is not
benefiting from affiliations with high-status IVC investors. Meanwhile, when ventures are backed
by high-status IVC investors, entrepreneurs are confident that the fear of social sanctioning deters
potential misbehavior from CVC investors. Hence, they are more likely to discount their own
stereotypical beliefs, as reliable information facilitating calculative decision-making is available.
This means that reliance on country-of-origin stereotypical trust declines with the increased
accessibility to institutional trust based on social sanctions. Hence, we propose hypothesis H3:
H3. The positive effect of country-of-origin stereotypical trust on initial tie formation
between a new venture and a CVC investor is weaker when the new venture is backed
by higher-status IVC investors.
Moderating role of individuating information on CVC investors
As mentioned, stereotypes represent information that can be easily identified, recalled, predicted,
and reacted to. They serve the function of simplifying information processing and saving cognitive
effort, but may generate incorrect expectations about the attributes and behaviors of unfamiliar
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exchange parties. Therefore, according to the “default hypothesis,” when individuals make
inferences about people’s attributes or about the causes of their behavior, they will typically rely
less (or not rely altogether) on stereotypes if they have the motivation and capacity to easily and
quickly collect reliable individuating information on the target’s attributes and behavior; this is
because such information allows for forming a more accurate judgment about the party’s
trustworthiness (Locksley et al., 1980, 1982; Glick et al., 1988; Krueger and Rothbart, 1988;
Beckett and Park 1995). There are two main sources of individuation information. Individuals may
directly collect information about a focal party’s characteristics that they perceive as rather accurate
indications of its trustworthiness; or they may rely on referrals by another party, whose judgment
about the trustworthiness of the focal party is perceived as reliable.
Here, we examine individuating information about prospective CVC investors which
influences ventures’ entrepreneurs judgement about their trustworthiness. This individuating
information may relate to characteristics of CVC investors that are directly observable by
entrepreneurs. The “status” of the CVC investor, reflecting its perceived position in the social
hierarchy based on its centrality in the network of VC co-investments (Sauder et al., 2012), is a
prominent example of this type of individuating information. However, individuating information
on prospective CVC investors may also be provided by referrals from third parties, such as the IVC
investors to which a focal venture is affiliated, that are considered as reliable information sources by
the venture’s entrepreneurs. These IVC investors may have direct first-hand experience of the
trustworthiness of prospective CVC investors because of previous co-investments. If they do not,
they may rely on the referrals of other trustworthy parties, notably the IVC investors that co-
invested with both the IVC investors to which the focal startup is affiliated and the prospective
CVC investors. In other words, the direct and indirect social ties between these IVC investors and
prospective CVC investors are an important indirect channel allowing entrepreneurs to collect
individuation information about prospective CVC investors. This individuating information
independent of the channels used to collect it, reassures entrepreneurs of the trustworthiness of the
prospective CVC investors. Under these conditions, we expect country-of-origin stereotypical trust
to have a more limited effect on CVC tie formation.
16
CVC investors’ status. High-status organizations are categorized as more trustworthy than are low-
status organizations. Status is a valuable social asset that can only be built through a slow process of
social collaboration (Sauder et al., 2012), making it an investment worthy of protection and
accumulation. Because untrustworthy partners are deselected over time and the portfolios of
trustworthy collaborations, on average, survive (Vanneste et al., 2014), high-status organizations are
viewed as desirable and trustworthy by many other organizations, as they have received or
reciprocated many collaborative invitations from other organizations (Sorenson and Stuart, 2001).
The status of an organization not only indicates the past endowment of trustworthiness
placed on it by many other organizations through collaboration, but also leads to expectation
formation about its future actions. High-status organizations face a higher anticipated opportunity
cost of damage to their reputation for integrity than low-status ones do if they were to engage in
opportunistic behavior (Brass et al., 1998). This is because high-status organizations are positioned
centrally in the network. Hence, information on the activities and behavior of high-status
organizations is rapidly and possibly even redundantly disseminated in the network to a large
number of actors (Raub and Weesie 1990).
7
Moreover, the activities of high-status organizations are
largely visible (Rhee and Haunschild, 2006), given that they receive wide analyst coverage (Shen et
al., 2014), as well as media attention (Castellucci and Ertug, 2010). In such a situation, all else
being equal, the perception of monitoring by other organizations (i.e., leading to a greater
anticipated likelihood of getting caught) deters high-status organizations from engaging in
opportunistic behavior (McCabe and Trevino, 1993; Brass et al., 1998).
The high status of a focal CVC investor provides easily accessible information that
entrepreneurs can use to make reliable judgments about its trustworthiness. Therefore, we leverage
the “default hypothesis” to postulate that the positive effect of country-of-origin stereotypical trust
on initial CVC tie formation is weaker when target CVC investors have a high status. This is
7
These ideas are consistent with Podolny’s (1993) evidence, who finds that high-status investment banks are
subject to less due diligence than their low-status counterparts when they are chosen to lead a syndicate to
underwrite corporate securities.
17
because entrepreneurs are less likely to resort to stereotyping for trust judgments when they are
aware of the target CVC investors’ high status, as entrepreneurs know that such status reduces the
uncertainty about the CVC investors’ intentions and future behaviors. This leads to hypothesis H4:
H4. The positive effect of country-of-origin stereotypical trust on initial tie
formation between a new venture and a CVC investor is weaker when the target
CVC investor’s status is higher.
Social direct and indirect ties between CVC investors and IVC investors. Social network scholars
have long recognized that participation in social networks provides actors with easy and timely
access to reliable referrals to other participants in the network (Burt, 1992). Direct and indirect
social ties created through previous collaborations between firms are effective information conduits
that convey reliable individuating information about firm attributes and behaviors (Gulati, 1995a;
Gulati and Gargiulo, 1999). Prior collaborations between two firms create a channel through which
each collaborator learns the competencies and reliability of the other. In this way the repeated
collaborations between two firms create and sustain trust (Podolny, 1994; Gulati, 1995b; Baum et
al., 2005). Even if a focal firm does not have direct social ties with another firm, it may obtain
reliable referrals about this firm through a common partner in previous collaborations. Moreover,
when two firms share a common partner, this suggests that they are considered as trustworthy by
this common partner (Gulati, 1995a; Gulati and Gargiulo, 1999). Syndicated VC investments create
a network of social ties which VC investors rely on for acquiring reliable information about other
VC investors. This mechanism builds trust between VC investors that are directly or indirectly
connected with each other. Accordingly, VC investors exhibit a marked preference for co-investing
with members of their previous syndicates, or with VC investors with whom they share a common
co-investor (Sorenson and Stuart, 2001, 2008; Zhelyazkov and Gulati, 2016; Meuleman et al.,
2017).
A focal CVC investor may have direct social ties with the IVC investors to which a focal
venture is affiliated through their common participation in previous syndicated investments in other
ventures. Through their participation in these syndicates, these IVC investors develop professional
18
relationships with the CVC investor and become familiar with its attributes and behaviors, including
its trustworthiness as a collaboration partner. IVC investors have a high stake in the success of their
portfolio ventures, and share the same concerns as new ventures about the selection of trustworthy
and competent CVC investors during follow-on VC rounds. Therefore, IVC investors will pass their
first-hand information on the focal CVC investor to venture entrepreneurs, and advise these
entrepreneurs about the wisdom of forming a tie with this investor. The individuating information
on the prospective CVC investor that entrepreneurs obtain from the IVC investors that back their
ventures, makes entrepreneurs less inclined to rely on simple heuristics based on social
categorization to decide whether to trust the CVC investor. Under these circumstances, country-of-
origin stereotypical trust becomes less salient in favoring CVC tie formation. This leads to
hypothesis H5:
H5. The positive effect of country-of-origin stereotypical trust on initial tie formation
between a new venture and a CVC investor is weaker when the target CVC investor has
direct social ties with the IVC investors to which the venture is affiliated
Even if a focal CVC investor does not have direct social ties with the IVC investors to
which a focal venture is affiliated, it may have participated in syndicated investments with other
IVC investors that have direct social ties with the focal venture’s IVC investors through previous
co-investments. Then, referrals from common syndication partners provide the IVC investors to
which the focal venture is affiliated with reliable information on the trustworthiness of the CVC
investor; this is expected to occur because the IVC investors connected through past co-investments
could jeopardize their valuable direct social ties in case they provided inaccurate information to
their co-investors.
For reasons such as those described above, IVC investors tend to share information on CVC
investors obtained from their partners of previous syndicated investments with venture
entrepreneurs and use it to advise them about CVC tie formation. The availability of this
individuating information makes venture entrepreneurs less inclined to rely on social categorization
19
based on country-of-origin stereotypes when deciding on CVC tie formation. Based on these
arguments, we derive hypothesis H6:
8
H6. The positive effect of stereotypical trust on initial tie formation between a new
venture and a CVC investor is weaker when the venture is affiliated with IVC
investors that have indirect social ties with the target CVC investor
METHODS
Data
To address our research questions, we use the VICO 5.0
9
database, which contains longitudinal data
on 35,374 VC-backed ventures located in EU countries, the UK, and Israel. This database is unique
in that it has a comprehensive and extensive coverage of VC-backed firms’ information gathered
through combining multiple secondary data sources: Thompson Eikon, Crunchbase, and the Zephyr
and Orbis databases managed by Bureau van Dijk. The VICO database includes new ventures that
were independent at foundation, were less than ten years old at the first round of VC financing, and
received their first VC round between 1998 and 2018. The VICO database excludes leveraged
8
The individuating information on the trustworthiness of an unfamiliar CVC investor provided by the IVC
investors (based on their referrals from direct and indirect social ties with the CVC investor) to which the new
ventures are affiliated may be positive or negative. In both cases, these social ties will have a negative
moderating effect of on the relation between country-of-origin stereotypical trust and CVC tie formation. For
example, Zhelyazkov and Gulati (2016) argue and document empirically that a focal VC investor’s
unexpected withdrawal from a VC syndicate can lead to disrupting the relationships with other syndicate
members, and reduce these firms’ willingness to syndicate future investments with the withdrawing VC
investor. If a focal CVC investor withdrew from previous syndicates in which it co-invested with the IVC
investors to which a focal venture is affiliated, or with other VC investors that co-invested with these IVC
investors, the negative referrals from IVC investors would reduce the willingness of new venture
entrepreneurs to form an initial tie with the focal CVC investor. Similarly, Kim et al. (2019) showed that, if a
focal venture has a technological link with the parent company of a focal CVC investor, and this parent
company revealed tendencies to misappropriate other firms’ knowledge, the presence of direct social links
between the IVC investors to which the new venture is affiliated and the CVC investor reduces the
probability that the venture forms a tie with the CVC investor. In both situations, the effect of country-of-
origin stereotypical trust on the formation of CVC ties will be weakened. Importantly, in our sample of CVC
investments, the effect of these negative referrals is likely to be negligible. This is because withdrawals from
syndicates by the CVC investors under consideration in our study are extremely rare. Indeed, we detected
only 88 withdrawals, corresponding to 3.7 percent of the realized CVC ties. Furthermore, we do not have the
data necessary to assess the incidence in our sample of the phenomenon highlighted by Kim et al. (2019).
However, we expect it to be much smaller than in their sample comprising 29 CVC investors, as CVC
investors’ parent companies are much smaller on average in our sample (i.e., the average value of total asset
in our sample is 3.2 billion Euro, while it is 21.6 billion US$ in their sample).
9
VICO 5.0 (http://risis.eu/data/vico-dataset/) is a proprietary database developed at Politecnico di Milano
with the support of the RISIS and RISIS2 projects, that were funded by the European Commission under the
FP7 and Horizon 2020 programs.
20
buyouts, real estate, distressed buyouts, and other private equity investments. To alleviate concerns
about survivorship bias, the database contains both surviving ventures (i.e., IPOed or remained
privately held and independent) and non-surviving ventures (i.e., those that were acquired, went
bankrupt, or terminated operations) by the end of the observation period.
The VICO database provides detailed data on these new ventures, including address,
industry of operation (at the 3-digit level of the NACE Rev. 2 classification), and longitudinal
accounting data. In addition, for each VC round, VICO contains data on the year of the round,
identity of the VC investors, and investment amount. Additionally, we collected patenting data from
the European Patent Office (EPO) for every venture included in the VICO database (source:
Patstat). Moreover, for all CVC investors, we obtained the geographic coordinates of their parent
companies from Google Maps API Web Services.
We exclude from our initial sample all ventures located in countries that were not included
as respondents in the Eurobarometer survey, from which we obtained our measure of country-of-
origin stereotypical trust (more information on this survey below). This results in a sample of
28,785 ventures (based in Austria, Belgium, Denmark, Finland, France, Greece, Germany, Ireland,
Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the UK) that received 40,457 VC
rounds between 1998 and 2018 from 9,913 known investors. Of these, 1,302 were CVC investors.
They invested in 4,072 ventures in 5,208 VC rounds. Because we are interested in investigating the
initial ties between ventures and CVC investors, we exclude 1,039 investments. Moreover, since the
Eurobarometer survey only provides data on the trust perceived by respondents toward people
originating from 25 countries (i.e., all the respondent countries previously reported, along with the
Czech Republic, Hungary, Japan, Norway, Poland, Russia, Slovakia, Switzerland, Turkey, and the
United States of America [U.S.]), we discarded all investments performed by CVC investors for
which country-of-origin stereotypical trust was not available, obtaining a sample of 1,129 CVC
investors investing in 3,882 ventures in 4,181 VC rounds.
After excluding observations with missing accounting and ownership data of CVC
investors and those with missing accounting, industry, and location information on invested
ventures, the sample used in this study includes 1,331 ventures that received 1,363 VC rounds from
21
479 CVC investors from 1998 to 2018. The total number of CVC investments was 1,454 because
some ventures received multiple CVC investments (i.e., investments from different CVC investors)
in the same round.
Measures
Dependent variable
To investigate which CVC investors the ventures form initial ties with, we perform a dyad analysis
of the probability that venture j accepts an investment offer from CVC investor i in a given VC
round. We employ a case-control research design to create a dyad-level dataset. The approach is to
build case-control groups. Each group includes a single realized CVC tie formed between venture j
and CVC investor i, and a set of counterfactual ties between venture j and other CVC investors that
could have been realized but were not (non-realized ties). We compare realized CVC investment
ties with their counterfactual non-realized ties (for a similar approach, see Sorenson and Stuart
2008; Zhelyazkov and Tatarynowicz, 2021).
For each of the 1,454 CVC-venture realized ties in our sample, we identify counterfactual
non-realized ties by matching each invested venture with all prospective CVC investors that could
have invested in the focal venture but did not. We define prospective CVC investors as those that
invested in another venture in the same industry as the venture of the realized tie (using the 2-digit
NACE Rev. 2 primary code) and in the same period (i.e., a three-year period around the investment
date).
10
This process results in 53,243 non-realized ties. Given our interest in the first time a new
venture forms a tie with a CVC investor (e.g., an initial tie), we remove all dyads between ventures
and CVC investors that had formed a tie in a previous VC round. Accordingly, we remove 691
dyads and obtain 52,552 non-realized ties. The dependent variable, Realizedij, takes the value of one
if venture j received CVC from investor i in a given round, and zero otherwise. To reduce the
problem possibly generated by the large number of zeros, for our main estimates we match the
10
More precisely, we considered 2-digit NACE Rev. 2 primary codes, with two exceptions: biotechnology
(72.11) has been combined with pharmaceuticals (21), and software publishing (58.2) has been combined
with computer programming activities (63). As a robustness check, we considered as prospective CVC
investors only those that invested in another venture in the same industry as the venture of the realized tie and
in the same year. Our results do not change.
22
realized ties with randomly selected unrealized ties (from the pool of all potentially realizable but
unrealized ties) with a maximum ratio of 1:5, and resort to bootstrapping (for a similar approach,
see Zhelyazkov and Tatarynowicz, 2021)
11
. With this adjustment, we get a final sample of 8,091
ties (1,454 realized ties and 6,637 unrealized ones).
Explanatory variable
Trust. Consistent with prior research (Guiso et al. 2009, Bottazzi et al. 2016), we obtain our
measure of country-of-origin stereotypical trust from a survey conducted by Eurobarometer in 1996
(i.e., the year with the last publicly accessible survey containing the forthcoming trust-related
question). The Eurobarometer surveys, promoted by the European Commission, assess public
opinion on issues ranging from individual national priorities to integrated European organizations.
About a thousand individuals from each European country responded to each survey. The question
of interest to our research is how much respondents trust their fellow citizens and the citizens of
each of the other countries in the EU (and several other non-EU countries, such as the U.S.).
Specifically, respondents were asked the following question: “I would like to ask you a question
about how much trust you have in people from various countries. For each, please tell me whether
you have a lot of trust, some trust, not very much trust, or no trust at all.” First, we recode the
answers to the trust question from 1 (i.e., no trust at all) to 4 (i.e., a lot of trust), and then calculate
the mean value of the responses for each pair of countries. Trust is the mean level of trust that
citizens in the country where the ventures are located have in people from the country where the
parent companies of the CVC investors are located.
It is worth mentioning two reassuring points regarding the interpretation and quality of this
measure. Given the explicit emphasis on individuals from a specific country as the target of trust,
stereotypical trust reflects the trustworthiness of these individuals as perceived by individuals from
another specific country, rather than the dispositional trusting behavior of individuals from the latter
country toward a generic citizen of a different country (Guiso et al. 2009). Moreover, Peabody
11
As explained in the robustness checks section, we also match the realized ties with randomly selected
unrealized ties (from the pool of all potentially realizable but unrealized ties) with a ratio of 1:10, in
addition to running our estimates on the full sample as well (thus including all potentially realizable but
unrealized ties).
23
(1985) found that Europeans generally agreed with the national character stereotypes of European
nations (as well as of the U.S.).
Moderating variables
IPP regime. The strength of the IPP regime depends on the industry in which the focal new venture
operates.
12
According to Dushnitsky and Shaver (2009), industries with effective legal defenses
provided by IPP laws include pharmaceuticals, biotechnology, biological products, chemical
products, surgical instruments, and other medical equipment. We set IPP regime to one in these
industries, and zero otherwise.
IVC centrality. To benefit from social sanctioning, new ventures affiliate to a high-status IVC
investor that occupies a central position in the network of syndicated VC investments. As is
commonly reported in the literature (Podolny 2001; Pollock et al. 2015), to measure the centrality
of the VC investor, we use the eigenvector centrality from the syndication network of co-
investments among VC investors. We take several steps to operationalize VC centrality, following
the methodology discussed in Hallen et al. (2014). First, using the VICO database, we create an
adjacency matrix for each year that considers two VC investors as adjacent if they have syndicated
a round of VC in a venture in the previous five years. Next, we compute the eigenvector
corresponding to the largest eigenvalue of the adjacency matrix for that year. To allow for
comparability across years, we normalize the measure by the maximum obtained in each year (our
results are robust to this normalization). Finally, we define IVC centrality at each round as the
maximum normalized eigenvector centrality of all participating IVC investors up to the focal round
in the new venture.
CVC centrality. To measure CVC investor status, we use the normalized eigenvector centrality of
CVC investors (obtained from the same procedure used to compute IVC centrality). Although both
IVC centrality and CVC centrality are based on eigenvector centrality, they differ in that IVC
centrality is a venture-specific characteristic calculated at the venture-round level, while CVC
12
IPP regime strength also varies by country (e.g. Park, 2008). However, all ventures considered in this study
are located in European countries, across which the strength of the IPP regime does not vary greatly.
24
centrality in a given year takes the same values across all dyads in which a given CVC investor is
involved.
Direct social ties. To control for prior collaborations between a focal CVC investor i and the IVC
investors to which a focal venture j is affiliated, we count the number of VC investments that CVC
investor i and the aforementioned IVC investors have previously syndicated together, using VICO
as the data source. Because of the highly skewed distribution of this variable, we generate a dummy
equal to one when the number of co-investments is higher than or equal to one (i.e., when the CVC
investor has at least one direct social tie with one or more IVC investors backing the focal venture),
and zero otherwise.
Indirect social ties. We create a dummy variable equal to one when CVC investor i has no direct
ties with the IVC investors backing focal venture j, but there is a co-investment history (i.e., at least
one common investment) between (at least one of) these IVC investors and (at least one of) other
IVC investors that previously syndicated one or more VC investments with the focal CVC investor
i, but did not invest in venture j (e.g., Sorenson and Stuart, 2001, 2008).
13
Control variables
We insert several controls into the model specification. Venture age is the (natural logarithm of the)
number of years since the founding of the venture and controls for venture maturity. We also
control for venture citation-weighted patent stock (Citation weighted patent stock) since CVC
investors are keen to open a window on new ventures’ technological resources. To create this
measure, we first count the number of annual successful (i.e., granted) patent applications for each
venture in the EPO, dated at the application year. We then weight each successful patent application
by its forward citations five years after filing, and then use a 15 percent knowledge-depreciation
rate (Hall et al., 2005) to capture the economic importance of patents. As geographical distance
influences the likelihood of tie formation between VC investors and new ventures (Sorenson and
13
We also measure the presence of indirect social ties by creating an alternative dummy which is not mutually
exclusive with the variable Direct social ties; this dummy takes the value of one whenever there are one or
more previous syndicated VC investments between (at least one of) the IVC investors backing venture j and
(at least one of the) other IVC investors that previously syndicated one or more VC investments with CVC
investor i, regardless of the presence of direct ties. Results are fully consistent with the main estimates.
25
Stuart, 2001), in the set of control variables, we include Distance, which measures the (natural
logarithm of the) geographical distance in thousands of kilometers between the focal venture and
the headquarters of the CVC investor’s parent company. Industry overlap is set to one if venture j
and the parent company of CVC investor i operate in the same industry, and zero otherwise. CVC
investors in the same industry as the focal venture are more valuable but, at the same time, more
dangerous partners for the venture (Dushnitsky and Shaver, 2009). We define industries based on
the 3-digit NACE Rev. 2 industry classification codes. If there are more industry codes for one
venture or parent company of the focal CVC investor, we set Industry overlap to one if there is any
match in the industry classification codes. In accordance with the spirit of Casciaro & Piskorski wrk
(2005), we also capture the interdependence between venture j and CVC investor i as reflected in
CVC transactions. More precisely, Interdependence is the sum, in a three year window before each
focal tie (either realized or unrealized), of i) the ratio between the number of CVC investments in
ventures in sector j by CVC investors whose parent companies are in sector i and the total number
of CVC investments in ventures in sector j; and ii) the ratio between the number of CVC
investments in ventures in sector j by CVC investors whose parent companies are in sector i and the
total number of CVC investments by CVC investors whose parent companies are in sector i. Round
is the ordinal count of the current financing round and controls for the investment stage. Prior CVC
investors is a dummy variable with a value set to one if the focal venture received a CVC
investment in any prior round, and zero otherwise; backing by CVC investors may influence the
focal venture’s attractiveness to other CVC investors. Last, we consider the relative availability of
CVC and IVC (CVC to IVC inflow) by calculating the ratio of the annual number of CVC
investments to the annual number of IVC investments in each country year.
RESULTS
Table 1 displays the descriptive statistics and correlation matrix of all variables (at the dyad level).
The mean of Realized is 0.027, which is equal to the ratio of 1,454 realized initial CVC ties to the
54,006 total (realized and non-realized) ties included in the dataset. In 7.2 percent of the dyads, the
ventures and CVC investors’ parent companies are in the same industry; in 6.6 percent of the dyads,
ventures are in industries with strong IPP regimes; in 2.4 and 3.7 percent of the dyads, there are
26
direct and indirect social ties between the focal CVC investor and IVC investors backing the focal
venture, respectively. The correlations between variables are mostly low, and computations of
variance inflation factors and condition indices do not suggest concerns of multicollinearity, as none
of these values are close to the cutoffs of 5 and 10 (Belsley et al., 2005).
Insert Table 1 about here
Tables 2a and 2b report the results of the estimates of the conditional logit model with
venture fixed effects to control for the non-independence of observations in different rounds of a
given venture. To further problems possibly arising from the small number of realized ties
compared with non-realized ties in the dataset (King and Zeng, 2001), the estimates reported in
Table 2a and 2b are based on a simulation in which we resort to bootstrapping and repetitively
(n=1,000) and randomly draw out of the set of counterfactual ties 5 non-realized CVC ties (without
replacement) for each realized CVC tie, instead of using all potential but non-realized CVC ties.
Table 2c reports the average marginal effects (AMEs) of Trust on the logarithm of the odds ratios in
the models reported in Table 2a and 2b with interaction terms.
14
The AMEs are graphically shown
in Figure 1a-1e.
Insert Tables 2a, Table 2b and Table 2c about here
Insert Fig. 1 about here
Model I presents the baseline regression with only control variables. These results are
similar to those of previous studies on CVC tie formation (e.g., Dushnitky and Shaver, 2009;
Colombo & Shafi, 2016; Kim et al., 2019). The coefficient of CVC centrality is positive and
significant (p<0.01), indicating that high-status CVC investors are more attractive partners. The
coefficient of Industry overlap is positive and significant (p<0.05), suggesting that new ventures
seek complementary assets offered by same-industry companies, despite the increased
14
As with the fixed effects logit estimator, the conditional logit estimator
gives us the effect of each
independent and control variable xt on the log-odds ratio,
. We cannot estimate the partial
effects on the response probabilities unless we assume a certain value for c. Because the distribution of ci is
unrestricted—in particular, E[ci] is not necessarily zero—one would not know what to provide for c.
Furthermore, it is also not possible to estimate average partial effects, as doing so implies finding
E[ , a task that requires specifying a distribution for ci, that again we do not know. Hence, we can
express our results only in terms of effects on the log of odds ratio (Wooldridge, 2010).
27
misappropriation risks engendered by these ties. In a similar way, we find a positive and significant
(p<0.01) coefficient also for the variable Interdependence, suggesting that CVC investors are
generally more likely to invest in ventures for which the level of interdependence between the CVC
investors’ parent companies and invested ventures is high. The variable Direct social ties has a
positive and significant coefficient (p<0.01), confirming that social ties created through previous
co-investments between CVC investors and the IVC investors to which ventures are affiliated are
effective information conduits for prospective CVC investors’ characteristics, and increase the
likelihood of CVC tie formation. The coefficient of Citation weighted patent stock is positive and
significant (p<0.01). The coefficient of Distance is negative and significant (p<0.01) but becomes
positive and significant (p<0.01) when we introduce Trust in the model specification (see Models
II-VII). The remaining controls do not show significant coefficients at conventional confidence
levels.
Regarding the relationship between country-of-origin stereotypical trust and the likelihood
of CVC tie formation, in Model II, Trust has a positive and significant coefficient (p<0.01). The
AME of Trust on the logarithm of the odds ratio is positive, significant (p<0.01), and equal to 3.05.
Hence, we can reject the null hypothesis of H1.
In Models III–IV in Table 2a and Models V–VII in Table 2b, we test H2–H6, which predict
a negative moderating role of IPP regime, IVC centrality, CVC centrality, Direct social ties and
Indirect social ties on the positive association between Trust and the probability of initial CVC tie
formation. In Model III, the coefficient of the interaction term between Trust and IPP regime is
negative but not significant (p=0.922). As reported in Table 2c, when IPP regime is equal to 0,
indicating that ventures are in an industry with weak IPP regime, the AME of Trust is equal to
3.104 (p < 0.001); when IPP regime is equal to 1, indicating that ventures are in an industry with
strong IPP regime, the AME of Trust is approximately 1.25 times smaller (i.e., it is equal to 2.484,
p<0.001). The difference between the two AMEs, however, is not significant (p=0.286). Hence, H2
is not supported, suggesting that effective legal defenses are not enough to weaken the positive
association between country-of-origin stereotypical trust and initial CVC tie formation.
28
To test H3, we interact Trust and IVC centrality (Model IV, Table 2a). The coefficient of
the interaction term is negative and significant (p<0.01). The AME of Trust is always positive and
significant, but decreases with increasing values of IVC centrality, as illustrated in Fig. 1b. For
example, as shown in Panel B of Table 2c, when IVC centrality increases from its minimum to its
mean, the AME of Trust diminishes from 3.409 (p<0.001) to 3.071 (p<0.001), and the difference
between the two AMEs is significant (p<0.01). When IVC centrality increases from its mean to its
mean plus one standard deviation, the AME of Trust becomes 1.2 times smaller, decreasing to
2.564 (p<0.001), and the difference between the two AMEs is again significant (p<0.01). Thus,
these results confirm H3 and indicate that country-of-origin stereotypical trust plays a less
important role in initial CVC tie formation when ventures can rely on effective social defenses.
To test H4, we interact CVC centrality and Trust (Model V, Table 2b). The coefficient of
the interaction term is negative but not significant (p=0.701). As shown in Fig. 1c, the AME of
Trust remains almost constant, independent of the values of CVC centrality. As shown in Panel C of
Table 2c, with CVC centrality set at the minimum value, the AME of Trust is equal to 3.077
(p<0.01); with CVC centrality set at a value equal to one standard deviation above the mean, the
AME of Trust is 3.053 (p<0.01). The differences between the AMEs of Trust at different values of
CVC centrality (minimum, mean, and mean plus one standard deviation) are not significant at
conventional confidence levels. Overall, these results do not provide empirical support for H4,
suggesting that the positive association between Trust and the likelihood of initial CVC tie
formation is not affected by CVC investors’ high status.
To test H5, we interact Direct social tie and Trust (Model VI, Table 2b). The coefficient of
the interaction term is negative and significant (p<0.01). The AME of Trust computed at different
values of Direct social tie is presented in Panel D of Table 2c and in Fig. 1d. When Direct social tie
goes from 0 to 1, the AME of Trust goes from 3.194 (p<0.001) to 1.421 (p<0.001), becoming 2.25
times smaller. The difference between the two values is significant (p<0.01). These findings
confirm H5, that is, the influence of country-of-origin stereotypical trust on initial CVC tie
formation is reduced when there are direct social ties between CVC investors and the IVC investors
to which the ventures are affiliated.
29
We test H6 in a similar manner, interacting Indirect social tie and Trust (Model VI, Table
2b). The coefficient of the interaction term is negative and significant (p=0.051). As shown in Panel
D of Table 2c and in Fig. 1e, when Indirect social tie is equal to 0, the AME of Trust is equal to
3.095 (p<0.001); when Indirect social tie is equal to 1, the AME of Trust is equal to 1.956
(p<0.001), and the difference between the two values is (weakly) significant (p<0.1). These results
support H6, showing that the presence of indirect social ties between CVC investors and the IVC
investors to which ventures are affiliated reduces the influence of country-of-origin stereotypical
trust on initial CVC tie formation.
Finally, Model VIII presents the full model including all the interaction terms; the results
are similar to those illustrated above.
ROBUSTNESS CHECKS
Insert Tables 3 about here
We perform several robustness checks. These are reported in Tables A1, A2, A3 and A4 in
the Appendix. First, we run our estimates on the full sample constituted by 1,454 realized ties and
52,552 unrealized ones (Model I, Table A1), and on a subsample created by matching realized ties
with randomly selected unrealized ties (from the pool of all potentially realizable but unrealized
ties) with a maximum ratio of 1:10 (Model II, Table A1). Results remain consistent with the ones
reported above.
15
Second, we include an additional set of control variables that might impact initial CVC tie
formation and be related to initial trust levels (Model I, Table A2). We insert in the model
specification CVC size, measured as the (natural logarithm of the) ratio of the sales of the parent
company of the focal CVC investor to the average sales of all firms in the industry (defined at the 3-
digit level of the NACE Rev. 2 classification; Source: Orbis database), and CVC subsidiary, a
dummy variable that equals one if the CVC program is a wholly owned subsidiary, and zero
15
Out of the full sample 466 groups (11,318 observations) were dropped because of all positive outcomes,
arriving to a final pool of 42,688 observations (out of which 1,361 realized and 41,327 unrealized ties). Due
to computational limitations and the substantial machine time required to run the robustness checks on the 1:5
sample with the bootstrapping procedure, we opted for a more computationally efficient approach. For these
reasons, we have performed all the additional robustness checks reported below on the full sample constituted
by 1,454 realized ties and 52,552 unrealized ones.
30
otherwise. We expect CVC investors belonging to larger companies to be more attractive to new
ventures. We also expect less fear of knowledge misappropriation if the CVC program is run by a
legally independent subsidiary. We also insert in the model specification two other controls that
were considered by Dushnitsky and Shaver (2009) and the replication study conducted by Colombo
& Shafi (2016): the interaction term IPP x Industry Overlap, as a strong IPP regime reduces the risk
of knowledge misappropriation, making same-industry CVC investors relatively more attractive to
new ventures compared to what happens under a weak IPP regime; the squared term Round2 to
account for non-linearity in the association between ventures’ financing stage and initial CVC tie
formation. The estimates illustrated earlier remain generally unchanged.
Third, we explore the robustness of our findings regarding one of the assumptions of this
study: that entrepreneurs of a new venture perceive a CVC investor as more trustworthy if the
headquarters of the CVC investor’s parent company is located in a country whose people are
perceived as more trustworthy by people from the home country of the new venture. For example, a
CVC investor belonging to a U.S. parent company is perceived by entrepreneurs of new ventures
looking for an initial CVC tie as a U.S. investor, independently of the location of the CVC
operations and the home countries of the CVC investment managers. First, in our dataset, a
substantial number of CVC investments (19%) were made through an internal organizational unit
(typically the unit in charge of corporate development) and not through a legally autonomous
subsidiary. In these cases, the CVC investor’s location coincides with that of the parent company.
Further, out of the 453 CVC investors in our sample that have a legally autonomous VC subsidiary,
the VC subsidiary is in a different country from the parent company’s home country in only 53
cases. In the remaining 88 percent of cases, the parent company’s home country is the same as that
of the CVC subsidiary. Therefore, discrepancies between the home country of the parent companies
and the one of the CVC subsidiaries are unlikely to generate a serious bias in our estimates.
More importantly, previous studies show that CVC investment managers play a key role in
shaping their employers’ investment strategies. For example, Dokko and Gaba (2012) show that the
CVC investors’ goals (strategic vs. financial) and investment practices (early-stage vs. late-stage
investments, and industry diversification vs. concentration of investments) depend on the career
31
experiences of their investment managers. In this study, we assume that while looking for a CVC
investor, entrepreneurs of new ventures are poorly informed about the home country of CVC
investment managers or, if they are informed, this information has a second-order influence
compared to the home country of the parent company of the CVC investor. Although this
assumption is plausible, whether the home country of CVC investment managers can bias the
detected link between country-of-origin stereotypical trust and initial CVC tie formation remains to
be empirically explored. To investigate this issue further, we collected data on CVC investors’
investment managers from different sources, including Pitchbook, Crunchbase, CVC investors’
websites, and LinkedIn. We then used an algorithm to determine the nationality of these individuals
based on their names (using NamSor API, accessible at https://www.namsor.com). We calculated a
new variable, Investment manager-level trust, which captures the average level of stereotypical trust
from the new venture’s country toward the home countries of all investment managers of a focal
CVC investor, and added it to the model specification. As is apparent from the estimates in Model
II of Table A2, this variable has a positive and significant coefficient (p<0.01), but our main
findings remain qualitatively similar.
Fourth, in additional estimations (see Table A3 in the Appendix), we include into the model
specification the investment amount (Round size) because ventures with greater financial resource
needs might be under pressure to collaborate with CVC investors (Katila et al., 2008). We have data
on Round size for approximately two-thirds of the observations; missing values were imputed as a
time-variant function of the company’s age, nation, financing round, and industry. The estimates
(reported in Model I, Table A3) remain unchanged, suggesting negligible omitted variable bias
associated with the financial needs of new ventures.
Fifth, we insert into the model specification variables regarding ease of communication,
such as common native language and common spoken language, as obtained from the study by
Melitz and Toubal (2014) (Model II, Table A3). We also include two dummy variables that are
equal to one if new ventures and CVC investors’ parent companies are in the same country
(Domestic) or in neighboring countries (Neighboring countries), and zero otherwise (Model III,
Table A3). These variables exhibit positive and significant coefficients, as expected (with the
32
exception of Common spoken language), but the main estimates regarding the effect of country-of-
origin stereotypical trust remain the same even with these checks. We also control for cultural
distance, as it has been shown to be negatively associated with interorganizational trust (Luo 2002).
We use the six dimensions used in the study by Hofstede (2010) to measure cultural distance (i.e.,
power distance index, individualism vs. collectivism, uncertainty avoidance index, masculinity vs.
femininity, long-term orientation vs. short-term orientation, and indulgence vs. restraint) between
the venture’s country and the CVC investors’ country (Model IV, Table A3). The estimates again
remain unchanged.
Sixth, further robustness checks (see Table A4 in the Appendix) control for other
institutional variables that may affect partner selection (Roy and Oliver 2009, Cumming et al.
2010). These controls include the difference between the home countries of new ventures and those
of CVC investors in the following dimensions: civil vs. common law, rule of law, efficiency of the
judicial system, risk of contract repudiation, and risk of expropriation, as in the study by La Porta et
al. (1998) (Model I, Table A3). The estimates remain unchanged. We include also the fixed effects
of the countries of the parent companies of CVC investors (Model II, Table A4), and the results
continue to be similar to the main estimates.
Seventh, regarding our implicit assumption that the home countries of entrepreneurs of new
ventures coincide with the countries in which these ventures are located, it relies on the evidence
provided by previous studies. Specifically, entrepreneurs are inclined to establish ventures in their
home region, and relocation abroad is a rather rare event, at least in Europe.
16
To address this issue,
we conduct a series of additional analyses. First, we explore the extent to which the founding teams
of the ventures under consideration in this paper comprise a mix of nationalities. Although we do
16
Previous studies have provided limited but converging evidence showing that entrepreneurs are affected by
a local bias. For example, Michelacci and Silva (2007) showed that entrepreneurs are inclined to establish
their businesses in their home region. Dahl and Sorenson (2012) found that ventures perform better when they
are located in the home region of their founders. Using a sample of Italian companies, Bertoni et al. (2019b)
confirmed the local bias of high-tech entrepreneurs and documented that relocation from the home country is
very rare. As to this latter aspect, Colombo et al. (2019) used the VICO database (the same database used in
the present study) and focused the attention on 332 companies founded in 2003 or 2004, for which they were
able to track the location between foundation and 2009 (see the robustness checks section of their paper).
During the observation period, only 90 companies relocated (27.1%), and none relocated abroad.
33
not have data on the composition of the entrepreneurial teams of all ventures included in the VICO
database, we have data on the country of origin of all founders of 383 European VC-
backed ventures included in this database. Among 270 of these ventures, most founders are from
the same country as the one in which the ventures are located. Among the remaining 113
ventures, 63 are located in the UK. Thus, apart from the UK, most founders in our sample are likely
to be from the country where their ventures are located, as implicitly assumed in this study. Then,
we split the sample and run the estimates separately for ventures located in the UK and the other
ventures. Regarding other ventures, all the main estimates hold, except for the interaction term
between Trust and IVC centrality, which becomes non-significant. Conversely, the main estimates
cannot be replicated in the sample comprising UK-based ventures (the results are available from the
authors upon request). This evidence reassures us that using the country where ventures are located
as a proxy for the home country of entrepreneurs is a feasible assumption in our dataset and does
not generate any serious bias. The exception is countries such as the UK, where the number of
immigrant entrepreneurs looking for VC is very large.
Eight, we insert among the controls the variable CVC trust, capturing how trustworthy
citizens of the country of the parent companies of CVC investors consider citizens of the country of
the new ventures. From a theoretical perspective, we are interested in how new ventures’
(admittedly high) concerns about knowledge misappropriation risks are reduced by country-of-
origin stereotypical trust. Nevertheless, it may be the case that the likelihood of initial CVC tie
formation also depends on CVC investors’ country-of-origin stereotypical trust. This variable
is available only for investors in Europe in our sample. Our results (Model III, Table A4) show that
the coefficient of this variable is positive and significant (p<0.01), and that the country-of-origin
stereotypical trust originating from the new ventures remains significant.
DISCUSSION AND CONCLUSION
Inspired by the literature on categorization thinking in social psychology, this study investigates
whether stereotypical trust influences individuals’ decisions to engage in business collaborations
with an unfamiliar partner, and the interplay of such trust with institutional trust and with the
availability of individuating information on the prospective partner. We specifically examine the
34
initial tie formation between new ventures and CVC investors, and how it is influenced by
entrepreneurs’ country-of-origin stereotypical trust in unfamiliar CVC investors. For this purpose,
we consider a sample of 1,454 initial CVC investments, made by 479 CVC investors in the period
1998–2018, in 1,331 ventures located in EU countries and the UK.
We use a case-control methodology and estimate conditional logit models to highlight the
(allegedly positive) effect of entrepreneurs’ country-of-origin stereotypical trust on CVC initial tie
formation and the boundary conditions of this effect. We find that entrepreneurs of new ventures are
more likely to establish initial ties with CVC investors if the parent companies of these investors are
in countries where citizens are perceived as more trustworthy by people from the countries of the
new ventures. This result suggests that entrepreneurs rely on simple and quick heuristics based on
social categorization to judge about the trustworthiness of unfamiliar CVC investors. Moreover, the
effect of entrepreneurs’ country-of-origin stereotypical trust on initial CVC tie formation is weaker
in the presence of a high level of institutional trust based on effective social defenses, as these
defenses reduce the ventures’ vulnerability to misplaced trust. The effect of country-of-origin
stereotypical trust is also reduced when new venture entrepreneurs obtain individuating information
about prospective CVC investors, reassuring them of the investors’ intentions and future behaviors.
Accordingly, country-of-origin stereotypical trust plays a less important role in favoring initial CVC
tie formation if the IVC investors to which new ventures are affiliated have direct and/or indirect
social ties with the prospective CVC investors; this enables these IVC investors to provide
entrepreneurs of new ventures with reliable information about the trustworthiness of the CVC
investors. Conversely, we find that the strength of the IPP regime and the high status of a focal
CVC investor do not influence the association between country-of-origin stereotypical trust and
initial CVC tie formation. It is possible that the strength of the IPP regime in the industry in which
ventures operate does not reassure entrepreneurs about the limited vulnerability of their ventures to
knowledge misappropriation and other forms of opportunistic behavior on the part of CVC
investors. It is also possible that collecting and processing information on CVC investors’ status
requires great cognitive effort from entrepreneurs, and needs knowledge about the organization of
the VC industry that they rarely possess. Notably, when institutional trust or individuating
35
information are present, the positive effect of country-of-origin stereotypical trust on the formation
of initial CVC ties is reduced, but does not vanish entirely.
This study makes three main contributions to the literature. First, we contribute to the
literature on the underlying origins of initial trust between strangers (McKnight et al., 1998;
McKnight and Cervany, 2006), and its role in encouraging initial interorganizational relationships
(Zaheer et al., 1998; Poppo et al., 2008, Poppo et al., 2016). We take inspiration from McEvily
(2011), and propose a hybrid model of initial trust that combines calculative decision-making
(based on deliberate information processing) and heuristic decision-making (based on quick and
simple cues) in varying proportions. In many situations, individuals must decide whether to place
trust in strangers, but the calculation of the costs and benefits of trust requires great cognitive effort,
and may be unreliable because of a lack of relevant individuating information. Under these
circumstances, individuals rely on social categorization to form trusting beliefs (Brewer, 1996;
Williamson, 2001). Nee et al. (2018) showed that individuals use the experience accrued in
relational exchanges as a reference point that influences their judgment of strangers’
trustworthiness. Pratt et al. (2019) emphasized that initial trust often requires a “leap of faith” based
on the extrapolation of weak and indirect evidence. Our study extends this perspective by
investigating the boundary conditions that make individuals (in our case, entrepreneurs) inclined to
rely on stereotypes as a source of initial trust, as reflected in their propensity to form initial ties with
unfamiliar parties (in our case, CVC investors) belonging to more or less trustworthy social
categories (countries of origin). We document that in making judgments about the trustworthiness
of an unfamiliar exchange party, individuals rely less on the social category to which this exchange
party belongs when they expect to be less vulnerable to the exchange party’s misbehavior because
of the presence of institutional trust. The same occurs when individuating information makes them
more confident about the exchange party’s good intentions. Still, it is important to emphasize that
albeit the influence of country-of-origin stereotypical trust in favoring initial CVC tie formation
weakens through the presence of institutional trust and individuating information, it does not
vanish. In other words, stereotypical trust is sticky (for a similar argument in a different context, see
Pratt et al., 2019).
36
Second, we contribute to studies examining how trust lubricates entrepreneurial activity
(Welter and Smallbone, 2006; Welter, 2012; Scarbrough et al., 2013; Pollack et al., 2017),
especially transactions with financial resource holders, such as VC investors. This line of research
has highlighted investors’ trust in entrepreneurs as an essential consideration in funding decisions,
as trust mitigates the adverse selection and moral hazard problems involved in funding
entrepreneurs (e.g., Harrison et al., 1997; Maxwell and Levesque, 2014; Bottazzi et al., 2016;
Müller and Wöhler, 2023). Typically, entrepreneurs have an informational advantage vis-à-vis
investors, and may exaggerate the prospects of their ventures, or hide negative information to attract
investors. Researchers also show that after the investment is done, entrepreneurs can act
opportunistically, exacerbating moral hazard problems (Amit et al., 1998). Notwithstanding, while
investors’ trust in entrepreneurs has been thoroughly shown by academicians to play a crucial role
in facilitating investment transactions, fewer scholars have delved into the role of entrepreneurs’
trust in investors as an important antecedent of funding (for exceptions, see Busenitz et al., 1997;
Strätling et al., 2012). If investors are not judged as trustworthy by entrepreneurs, the likelihood for
initial investments to materialize is considerably reduced, independent of investors’ positive
attitudes. To the best of our knowledge, no previous studies have investigated the origins of
entrepreneurs’ trust in unfamiliar CVC investors. Specifically, entrepreneurs may be wary of
investors’ inclination to misappropriate their ventures’ technological knowledge (Dushnitsky and
Shaver 2009), or to let such knowledge leak to competitors (Pahnke et al. 2015), which may induce
entrepreneurs to decline investors’ offers. Our study offers insights into the situations wherein
entrepreneurs may be more likely to trust and collaborate with unfamiliar, potentially dangerous
investors. In highlighting the interplay between the institutional and cognitive accounts of initial
trust, we also answer the call made by past studies on trust in entrepreneurship for “greater
conceptual clarity with respect to the various forms of trust and the interrelationships between
them” (Welter and Smallbone, 2006: 472).
Finally, this study contributes to the literature on the drivers of CVC tie formation. These
ties are characterized by “tensions related to corporate investors posing a threat (to entrepreneurs)
by behaving opportunistically versus providing an opportunity as a valuable partner to startups”
37
(Jeon and Maula, 2022: 5). Previous studies have highlighted that in the absence of effective legal
or social defenses (i.e., a low level of institutional trust), the fear of misappropriation of the
ventures’ knowledge makes entrepreneurs wary of accepting investment offers made by potentially
dangerous CVC investors (Katila et al., 2008; Dushnitsky and Shaver, 2009; Hallen et al., 2014;
Colombo & Shafi, 2016), despite the potential to access the valuable resources that CVC ties may
provide (Park and Steensma, 2012; Alvarez-Garrido and Dushnitsky, 2016). Accordingly, Kim et
al. (2019) showed that entrepreneurs will avoid forming CVC ties if the following two conditions
are met: the innovation projects of their ventures build on the technology of the parent company of
a focal CVC investor, a situation which entails a great risk of knowledge misappropriation; and
entrepreneurs have reliable individuating information on the CVC investor’s opportunistic
tendencies. Entrepreneurs can obtain individuating information on the CVC investor through direct
social ties, if they were previously employed by the investor’s parent company. The social ties
between CVC investors and the IVC investors to which ventures are affiliated that are generated by
previous co-investments are also precious conduits for individuating information. The current study
contributes to this discourse by considering the individuating information about a focal CVC
investor that IVC investors can collect not only through direct but also indirect social ties with this
investor (e.g., when the CVC investor shares common partners with the IVC investors in previous
syndicated investments, Meuleman et al., 2017). Our work shows that when entrepreneurs have
access to reliable individuating information on prospective CVC investors through these channels,
they use it to infer the trustworthiness of CVC investors, and concomitantly give less weight to
heuristics based on social categorization (in our case, country-of-origin stereotypes). Heuristics
based on social categorization are also less influential when effective social defenses reduce
entrepreneurs’ concerns about the vulnerability of their ventures to CVC investors’ misbehavior.
Meanwhile, in the absence of institutional trust and individuating information on CVC investors,
social categorization is a fundamental source of initial trust and has a strong influence on initial
CVC tie formation.
Despite these contributions, the current study is not without limitations that also offer
opportunities for future research. First, we did not measure trust by posing direct questions to new
38
venture entrepreneurs. We assume that high initial trust facilitates the establishment of
interorganizational ties, an assumption commonly employed in prior literature using secondary data
(Gulati 1995a). Second, we consider stereotypical trust based on the country-of-origin of the parent
companies of CVC investors. Yet, social categorization may be based on other dimensions, such as
entrepreneurs’ identification with particular social groups (Tajfel, 1981), their previous experience
of successful exchanges (Nee et al., 2018), or the extrapolation of evidence from other contexts
(Pratt et al., 2019). Moreover, the propensity of entrepreneurs to rely on social categorization to
decide on the trustworthiness of CVC investors may vary by personal characteristics (e.g., gender)
that may influence their attitude toward taking risks. Third, we focus on individuating information
on CVC investors based on their high status and direct and indirect social ties to the IVC investors
backing a focal venture. Meanwhile, Kim et al. (2019) considered the information entrepreneurs
collect from CVC investors as former employees of their parent companies. Future researchers can
extend the analysis to other sources of individuating information, such as venture board members,
key employees, and important customers.
Fourth, to measure country-level stereotypical trust, we assume that the entrepreneurs’
home countries coincide with those in which their ventures are located. This assumption does not
consider immigrant entrepreneurship. The additional analyses performed reassure that our results
are robust. However, they also demonstrate that the findings of the current study are not applicable
to countries such as the UK, where the number of immigrant entrepreneurs is large. If data on the
home country of entrepreneurs were available on an adequate scale, scholars could examine the
extent to which the presence of different nationalities in entrepreneurial teams favors or hinders the
formation of initial trust toward CVC investors originating from different countries.
Fifth, we assume that entrepreneurs have positive expectations about the benefits of
forming ties with CVC investors that they view as trustworthy. Future research could explore the
effect of initial trust on the reported positive effects of CVC investments on new venture
performance (Chemmanur et al., 2014; Colombo and Murtinu, 2017). For example, high level of
initial trust between a new venture and a CVC investor may not only be necessary to establish a
collaboration in the first place, but may also facilitate the interaction and coordination between the
39
new venture and the CVC investor, thereby resulting in a more positive “treatment effect” of CVC
investment on new ventures’ performance (see Hegde and Tumlinson, 2004, for a similar argument
relating to ethnic similarity between entrepreneurs and VC investment managers).
Finally, we consider the dyadic relationship between new ventures and CVC investors, and
assume that IVC investors ensure the social protection of new ventures and provide entrepreneurs
with individuating information on prospective CVC investors. However, IVC investors may also
play more active roles. If new ventures have already received a round of VC financing, three parties
are potentially involved in CVC tie formation: the new venture, the CVC investor, and the IVC
investors on board from the previous rounds. To reduce the complexity of the analyses, we scale the
situation down to a two-party context. Future research could extend the analysis and investigate
whether judgments made by IVC investors about the trustworthiness of CVC investors influence
CVC tie formation in IVC-backed ventures. Another potential topic of scrutiny is whether the
different moderators delineated in this work still apply to similar scenarios with more players.
This study has important implications for entrepreneurs. Accepting an investment offer
from an unfamiliar CVC investor is a crucial strategic decision for entrepreneurs, and it is
influenced by their expectations of the intentions and future behavior of the CVC investor. In
making this decision, entrepreneurs need to carefully evaluate the context surrounding the CVC
investment, as well as the individuating information on the prospective CVC investor provided by
reliable sources. If their ventures are not protected by strong legal or social defenses, and the
available individuating information on the CVC investor is difficult to interpret and does not allow
them to make reliable calculations (i.e., then reassuring them that the probability of CVC investors’
misbehavior is low), entrepreneurs may not have other options but to resort to simple and quick
heuristics in their decision-making by placing the focal CVC investor in a social category (e.g., by
using country-of-origin stereotypes). However, they must be aware that the drawback is the
possibility of misjudging the trustworthiness of CVC investors, which may lead to negative
implications for their relationships.
Our study also has important implications for CVC investors and policymakers. While CVC
investors are concerned with entrepreneurs’ trustworthiness for obvious reasons, our study suggests
40
that it is important for these same investors to also convey reliable information about their own
trustworthiness to entrepreneurs. In this way, CVC investors can reduce entrepreneurs’ cognitive
burden in making decisions regarding initial CVC tie formation and limit the probability that, by
using simple heuristics based on social categorization, entrepreneurs mistakenly judge the
prospective CVC investors as untrustworthy and reject their investment offers. Similarly,
policymakers must be aware of the importance of rules and regulations and of their effective
enforcement in creating an institutional environment favorable for the development of initial trust
between entrepreneurs and CVC (and other) investors. Indeed, this study suggests that the absence
of institutional trust exacerbates the funding gap faced by new ventures. Our estimates show that in
absence of social defenses and of reassuring individuating information on CVC investors,
entrepreneurs become inclined to rely on simple heuristics to decide whether to accept an offer from
a CVC investor. This may lead entrepreneurs to misjudge the trustworthiness of the investor, and
ultimately reject a potentially value-creating offer, or even accept a potentially detrimental
investment.
Acknowledgments
We are indebted with Gary Dushnitsky, Armin Schwienbacher, acting as the associate editor, and
two anonymous reviewers for their constructive comments and helpful suggestions.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
Funding
We acknowledge the financial support provided by the RISIS 2 project, funded by the European
Union’s Horizon 2020 Research and Innovation Program under grant number 824091, the PRIN
2022 project “Digitization and the inclusiveness of entrepreneurial finance”, Prot. 2022Y3AWN5,
financed by the Italian Ministry of University and Research, and the GRINS project, financed by
PNRR (Piano Nazionale di Ripresa e Resilienza, Missione 4 (Infrastruttura e ricerca), Componente
2 (Dalla Ricerca all’Impresa), Investimento 1.3 (Partnership Estese), Tematica 9 (Sostenibilità
economica e finanziaria di sistemi e territori)).
41
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46
Table 1 – Descriptive statistics and correlation matrix
Variables
Mean
SD
Min
Max
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(1) Realized
0.027
0.162
0
1
1.000
(2) Trust
2.798
0.377
1.685
3.691
0.155*
1.000
(3) IPP regime
0.066
0.247
0
1
0.023*
-0.024*
1.000
(4) IVC centrality
0.225
0.341
0
1
0.008
-0.013
0.086*
1.000
(5) CVC centrality
0.085
0.187
0
0.984
0.074*
0.014*
0.023*
-0.001
1.000
(6) Direct social ties
0.024
0.153
0
1
0.120*
0.100*
0.048*
0.127*
0.195*
1.000
(7) Indirect social ties
0.037
0.188
0
1
0.026*
0.051*
0.071*
0.159*
0.260*
0.252*
1.000
(8) Industry overlap
0.072
0.258
0
1
0.016*
-0.014*
0.181*
0.027*
0.120*
0.048*
0.058*
1.000
(9) Round
1.274
0.82
1
16
0.004
-0.006
0.097*
0.220*
0.013
0.031*
0.065*
0.019*
1.000
(10) Venture age (years)
3.349
3.413
0
29
0.006
-0.036*
0.026*
0.064*
0.026*
-0.001
0.023*
0.004
0.355*
(11) Citation weighted patent stock
0.062
1.544
0
127.98
0.008
0.004
0.013
0.002
0.007
0.003
-0.002
0.015*
0.119*
(12) Prior CVC investors
0.037
0.19
0
1
0.005
0.026*
0.101*
0.129*
-0.011
0.028*
0.070*
0.007
0.504*
(13) Distance (km)
4353.5
2526.5
291.52
12325
-0.013
-0.178*
0.096*
0.025*
0.092*
0.038*
0.044*
0.180*
0.011
(14) CVC to IVC inflow
0.185
0.077
0.022
0.6
-0.018*
0.154*
-0.068*
-0.053*
-0.048*
0.016*
0.009
-0.008
-0.044*
(15) Domestic
0.156
0.363
0
1
0.228*
0.620*
-0.037*
-0.008
0.022*
0.137*
0.063*
-0.040*
-0.005
(16) Interdependence
0.308
0.274
0
2
0.112*
0.020*
-0.071*
-0.031*
-0.053*
-0.003
-0.023*
-0.317*
-0.035*
Variables
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(10) Venture age
1.000
(11) Citation weighted patent stock
0.051*
1.000
(12) Prior CVC investors
0.174*
0.051*
1.000
(13) Distance
0.005
0.010
0.012
1.000
(14) CVC to IVC inflow
-0.094*
-0.017*
0.025*
-0.015*
1.000
(15) Domestic
-0.025*
-0.006
0.004
-0.194*
0.100*
1.000
(16) Interdependence
-0.037*
0.002
-0.027*
-0.045*
0.010
0.050*
1.000
N = 54,006 (1,454 realized ties and 52,552 unrealized ones)
* p<0.01
47
Table 2a – Regression results: conditional logit model for the likelihood of initial CVC investor-new venture
dyad formationa – Models I-IV
Model I
Model II
Model III
Model IV
Trust
3.049***
(0.172)
3.104***
(0.179)
3.409***
(0.215)
Trust × IPP regime
-0.621
(0.581)
Trust × IVC centrality
-1.407***
(0.376)
CVC centrality
1.013***
(0.172)
1.010***
(0.193)
1.019***
(0.195)
1.029***
(0.195)
IVC centrality
-0.568
(0.535)
-0.541
(0.642)
-0.515
(0.613)
3.723***
(1.268)
Direct social ties
1.606***
(0.161)
1.124***
(0.193)
1.129***
(0.194)
1.134***
(0.189)
Indirect social ties
0.226
(0.202)
0.024
(0.222)
0.032
(0.222)
0.059
(0.217)
Industry overlap
1.178***
(0.122)
1.182***
(0.146)
1.188***
(0.146)
1.186***
(0.146)
Interdependence
2.346***
(0.103)
2.307***
(0.121)
2.298***
(0.122)
2.310***
(0.123)
Round
-0.180
(0.214)
-0.178
(0.303)
-0.183
(0.286)
-0.187
(0.271)
Venture age
0.281
(0.434)
0.177
(0.538)
0.175
(0.526)
0.216
(0.524)
Citation weighted patent stock
1.022***
(0.097)
0.962***
(0.125)
0.965***
(0.124)
0.963***
(0.114)
Prior CVC investors
0.397
(0.434)
0.841
(0.642)
0.835
(0.625)
0.777
(0.612)
Distance
-0.406***
(0.085)
0.280***
(0.107)
0.268**
(0.108)
0.268**
(0.107)
CVC to IVC inflow
-0.762
(2.583)
-0.221
(2.734)
-0.260
(2.679)
0.122
(2.629)
Log pseudo-likelihood
-1767.333
-1405.209
-1404.112
-1396.280
Wald χ 2 test, H0: coefficients of
variables=0
(degrees of freedom)
943.900***
(12)
910.323***
(13)
883.622***
(14)
939.702***
(14)
Pseudo R2
0.176
0.345
0.345
0.349
No. of observations=6,555. Out of the full sample (1,454 realized ties and 6,637 unrealized ones), 466 groups (1,536
observations) were dropped because of all positive outcomes. IPP regime is reported only in the interaction with Trust
because it is an attribute of the ventures and not of the dyad, thus it is omitted by the conditional logit with ventures’
fixed effects.
*** p<0.01, ** p<0.05, * p<0.1
a Robust standard errors appear in parentheses.
48
Table 2b – Regression results: conditional logit model for the likelihood of initial CVCinvestor-new venture dyad
formationa – Models V-VIII
Model V
Model VI
Model VII
Model VII
Trust
3.077***
(0.178)
3.194***
(0.172)
3.095***
(0.173)
3.446***
(0.221)
Trust × IPP regime
-0.475
(0.606)
Trust × IVC centrality
-1.009**
(0.413)
Trust × CVC centrality
-0.198
(0.516)
0.663
(0.543)
Trust × Direct social ties
-1.773***
(0.494)
-1.733***
(0.556)
Trust × Indirect social ties
-1.140*
(0.583)
-1.233**
(0.562)
CVC centrality
1.600
(1.572)
0.979***
(0.195)
1.003***
(0.194)
-0.992
(1.676)
IVC centrality
-0.536
(0.642)
-0.487
(0.653)
-0.586
(0.628)
2.516*
(1.346)
Direct social ties
1.125***
(0.193)
6.657***
(1.603)
1.118***
(0.195)
6.525***
(1.795)
Indirect social ties
0.023
(0.222)
0.033
(0.218)
3.503*
(1.827)
3.836**
(1.758)
Industry overlap
1.184***
(0.145)
1.181***
(0.148)
1.188***
(0.145)
1.190***
(0.147)
Interdependence
2.309***
(0.121)
2.322***
(0.123)
2.311***
(0.122)
2.317***
(0.124)
Round
-0.178
(0.303)
-0.204
(0.298)
-0.194
(0.300)
-0.231
(0.261)
Venture age
0.182
(0.538)
0.281
(0.541)
0.247
(0.531)
0.350
(0.512)
Citation weighted patent stock
0.961***
(0.124)
0.967***
(0.125)
1.089***
(0.126)
1.100***
(0.119)
Prior CVC investors
0.835
(0.643)
0.813
(0.631)
0.814
(0.642)
0.771
(0.596)
Distance
0.278***
(0.107)
0.260**
(0.107)
0.275**
(0.107)
0.242**
(0.109)
CVC to IVC inflow
-0.218
(2.737)
0.031
(2.704)
0.104
(2.716)
0.616
(2.544)
Log pseudo-likelihood
-1.405.109
-1.393.231
-1.402.490
-1.383.776
Wald χ 2 test, H0: coefficients of
variables=0
(degrees of freedom)
914.649***
(14)
925.524***
(14)
1068.828***
(14)
961.110***
(18)
Pseudo R2
0.345
0.350
0.346
0.355
No. of observations=6,555. Out of the full sample (1,454 realized ties and 6,637 unrealized ones), 466 groups (1,536
observations) were dropped because of all positive outcomes. IPP regime is reported only in the interaction with Trust
because it is an attribute of the ventures and not of the dyad, thus it is omitted by the conditional logit with ventures’
fixed effects.
*** p<0.01, ** p<0.05, * p<0.1
a Robust standard errors appear in parentheses.
49
Table 2c – Average Marginal Effecta of Trust on the logarithm of odds ratio in models with interaction terms, at
different values of moderator
Average Marginal
Effects
Difference
between Average
Marginal Effects
Panel (A)
Table 2a - Model III
1. IPP regime=0 (weak IPP regime)
3.104***
(0.179)
2. vs 1. -0.621
(0.581)
2. IPP regime=1 (strong IPP regime)
2.484***
(0.562)
Panel (B)
Table 2a - Model IV
1. IVC centrality at minimum
3.409***
(0.215)
2. vs 1. -0.34***
(0.090)
2. IVC centrality at mean
3.071***
(0.180)
3. vs 2. -0.50***
(0.135)
3. IVC centrality at mean + one S.D.
2.564***
(0.106)
Panel (C)
Table 2b - Model V
1. CVC centrality at minimum
3.077***
(0.178)
2. vs 1. -0.024
(0.062)
2. CVC centrality at mean
3.053***
(0.171)
3. vs 2. -0.044
(0.114)
3. CVC centrality at mean + one S.D.
3.009***
(0.121)
Panel (D)
Table 2b - Model VI
1. Direct social ties=0
3.194***
(0.171)
2. vs 1. -1.773***
(0.494)
2. Direct social ties=1
1.421***
(0.491)
Panel (E)
Table 2b - Model VII
1. Indirect social ties=0
3.095***
(0.173)
2. vs 1. -1.140*
(0.583)
2. Indirect social ties=1
1.956***
(0594)
*** p<0.01, ** p<0.05, * p<0.1
a Robust standard errors appear in parentheses.
50
Table A1 - Regression results: Robustness checksa
Model I
Model II
Trust
3.716***
(0.201)
3.636***
(0.209)
Trust × IPP regime
-0.521
(0.472)
-0.629
(0.534)
Trust × IVC centrality
-1.382***
(0.384)
-1.139***
(0.395)
Trust × CVC centrality
0.628
(0.474)
0.509
(0.497)
Trust × Direct Social Ties
-1.420***
(0.424)
-1.418***
(0.470)
Trust × Indirect Social Ties
-1.164**
(0.519)
-1.042*
(0.571)
CVC centrality
-0.816
(1.463)
-0.473
(1.544)
IVC centrality
3.512***
(1.286)
2.866**
(1.352)
Direct social ties
5.625***
(1.356)
5.590***
(1.503)
Indirect social ties
3.585**
(1.624)
3.176*
(1.781)
Industry overlap
1.014***
(0.115)
1.104***
(0.132)
Interdependence
2.029***
(0.094)
2.197***
(0.104)
Round
-0.157
(0.125)
-0.115
(0.248)
Venture age
-0.012
(0.433)
-0.184
(0.517)
Citation weighted patent stock
1.053***
(0.091)
1.070***
(0.132)
Prior CVC investors
0.838*
(0.467)
1.000*
(0.601)
Distance
0.283***
(0.091)
0.286***
(0.102)
CVC to IVC inflow
0.748
(1.688)
-0.247
(2.697)
Log pseudo-likelihood
-2962.937
-1877.089
Pseudo R2
0.242
0.317
Wald χ 2 test, H0: coefficients of variables=0
(degrees of freedom)
1410.227***
(18)
1050.145***
(18)
Number of observations
42,688
10,675
51
Table A2 - Regression results: Additional robustness checksa
a Robust standard errors appear in parentheses; *** p<0.01, ** p<0.05, * p<0.1
Model I
Model II
Trust
3.504***
(0.256)
3.528***
(0.233)
Trust × IPP regime
-0.009
(0.608)
-0.501
(0.493)
Trust × IVC centrality
-1.334***
(0.492)
-1.472***
(0.422)
Trust × CVC centrality
-0.139
(0.601)
0.895*
(0.504)
Trust × Direct Social Ties
-0.602
(0.510)
-1.203***
(0.420)
Trust × Indirect Social Ties
-1.093**
(0.545)
-1.154**
(0.491)
CVC centrality
1.632
(1.842)
-1.456
(1.550)
IVC centrality
3.984**
(1.699)
4.376***
(1.600)
Direct social ties
2.818*
(1.624)
4.819***
(1.346)
Indirect social ties
3.140*
(1.689)
3.485**
(1.529)
IPP Regime X industry overlap
-0.135
(0.376)
CVC size
-0.021
(0.105)
CVC Subsidiary
0.013
(0.034)
Investment manager-level trust
0.250***
(0.092)
Industry overlap
1.242***
(0.170)
0.995***
(0.127)
Interdependence
2.105***
(0.137)
2.024***
(0.110)
Round
6.140**
(2.546)
0.204
(0.241)
Round squared
-0.725**
(0.314)
Venture age
-1.530**
(0.773)
-1.020*
(0.543)
Citation weighted patent stock
1.145***
(0.194)
0.513*
(0.294)
Prior CVC investors
-2.182
(1.651)
1.121*
(0.627)
Distance
0.311**
(0.146)
0.335***
(0.103)
CVC to IVC inflow
2.787
(3.289)
0.456
(1.965)
Log pseudo-likelihood
-1359.449
-2315.772
Pseudo R2
0.234
0.250
Wald χ 2 test, H0: coefficients of variables=0 (degrees of freedom)
808.22***
(22)
989.243***
(19)
Number of observations
14,841
29,712
52
Table A3 – Regression results: Additional robustness checksa
* p<0.10, ** p<0.05, *** p<0.01. a Robust standard errors appear in parentheses.
Model I
Model II
Model III
Model IV
Trust
3.727***
(0.201)
0.883***
(0.236)
0.933***
(0.243)
2.870***
(0.257)
Trust × IPP regime
-0.529
(0.473)
-0.393
(0.407)
0.026
(0.396)
-0.442
(0.405)
Trust × IVC centrality
-1.393***
(0.384)
-1.362***
(0.325)
-1.153***
(0.316)
-1.353***
(0.328)
Trust × CVC centrality
0.633
(0.475)
0.626
(0.443)
0.728*
(0.411)
0.776*
(0.453)
Trust × Direct social ties
-1.415***
(0.424)
-1.413***
(0.392)
-1.338***
(0.375)
-1.372***
(0.382)
Trust × Indirect social ties
-1.171**
(0.519)
-0.985**
(0.447)
-0.908**
(0.442)
-1.118**
(0.475)
IVC centrality
3.544***
(1.276)
3.707*** (1.118)
3.060***
(1.111)
3.602***
(1.129)
CVC centrality
-0.827
(1.466)
-0.784
(1.363)
-1.009
(1.263)
-1.290
(1.394)
Direct social ties
5.609***
(1.355)
5.451***
(1.247)
5.243***
(1.198)
5.381***
(1.215)
Indirect social ties
3.606**
(1.623)
2.976** (1.408)
2.721**
(1.386)
3.436**
(1.490)
Industry overlap
1.012***
(0.115)
1.065***
(0.117)
1.137***
(0.118)
0.998***
(0.115)
Interdependence
2.026***
(0.095)
2.098***
(0.097)
2.074***
(0.101)
2.053***
(0.095)
Round
-0.145
(0.125)
-0.072
(0.129)
-0.153
(0.143)
-0.116
(0.126)
Venture age
-0.052
(0.425)
-0.095
(0.446)
0.154
(0.521)
-0.040
(0.436)
Citation weighted patent stock
1.038***
(0.092)
0.962***
(0.092)
0.884***
(0.094)
0.948***
(0.091)
Prior CVC investors
0.786*
(0.459)
0.787
(0.490)
0.711
(0.540)
0.791*
(0.479)
Distance
0.284***
(0.091)
-0.169*
(0.102)
0.672***
(0.109)
0.160*
(0.092)
CVC to IVC inflow
0.348
(1.694)
0.960
(1.709)
0.878
(1.970)
0.810
(1.567)
Round size
6.92e-06*
(3.6e-06)
Common native language
2.634***
(0.238)
Common spoken language
0.287
(0.353)
Neighboring countries
0.484***
(0.129)
Domestic
2.133***
(0.142)
Power distance index
-0.170***
(0.055)
Individualism vs. collectivism
0.008 (0.033)
Masculinity vs. femininity
-0.141***
(0.018)
Uncertainty avoidance index
-0.035
(0.054)
Long-term orientation vs. short-term or.
-0.029
(0.018)
Indulgence versus restraint
0.053
(0.039)
Log pseudo-likelihood
-2958.344
-2814.683
-2773.453
-2886.244
Pseudo R2
0.242
0.280
0.291
0.262
Wald χ 2 test, H0: coef. of var.=0 (d. of freedom)
1407.69***(19)
1731.163***(20)
1727.518***(20)
1593.302***(24)
Number of observations
42,675
42,688
42,688
42,688
53
Table A4 – Regression results: Additional robustness checks a
* p<0.10, ** p<0.05, *** p<0.01; a Robust standard errors appear in parentheses.
Model I
Model II
Model III
Trust
3.334***
(0.222)
4.399***
(0.224)
2.874***
(0.445)
Trust × IPP regime
-0.655
(0.455)
-0.253
(0.489)
-1.246*
(0.644)
Trust × IVC centrality
-1.315***
(0.377)
-1.591***
(0.381)
-1.170**
(0.590)
Trust × CVC centrality
0.652
(0.467)
0.442
(0.499)
0.616
(0.674)
Trust × Direct social ties
-1.382***
(0.412)
-1.502***
(0.448)
-3.290***
(0.532)
Trust × Indirect social ties
-1.019**
(0.504)
-1.041**
(0.515)
-2.191***
(0.826)
IVC centrality
3.313***
(1.266)
4.254***
(1.287)
2.615
(2.349)
CVC centrality
-0.902
(1.438)
-0.302
(1.544)
-0.583
(2.155)
Direct social ties
5.501***
(1.318)
5.822***
(1.433)
11.971***
(1.751)
Indirect social ties
3.110**
(1.578)
3.185**
(1.621)
7.111***
(2.686)
Industry overlap
1.023***
(0.116)
1.071***
(0.117)
0.902***
(0.150)
Interdependence
2.050***
(0.096)
2.097***
(0.098)
2.039***
(0.118)
Round
-0.146
(0.127)
-0.142
(0.130)
-0.185
(0.424)
Venture age
0.018
(0.447)
0.005
(0.448)
-0.682
(0.810)
Citation weighted patent stock
0.922***
(0.092)
0.944***
(0.093)
0.533 (0.575)
Prior CVC investors
0.793
(0.491)
0.765
(0.483)
1.477**
(0.656)
Distance
0.367***
(0.090)
-0.299*
(0.155)
-0.054
(0.130)
CVC to IVC inflow
0.764
(1.731)
0.637
(1.979)
2.680
(2.658)
Civil vs. common law
-0.126
(0.105)
Rule of law
-0.258***
(0.052)
Efficiency of judicial system
-0.044
(0.029)
Risk of contract repudiation
0.025
(0.212)
Risk of expropriation
0.448
(0.493)
CVC trust
1.755***
(0.348)
CVC’s country fixed effect
No
Yes
No
Log pseudo-likelihood
-2889.224
-2869.548
-1902.794
Pseudo R2
0.252
0.266
0.316
Wald χ 2 test, H0: coeff. of
var.=0 (d. of freedom)
1473.209***
(23)
1423.112***
(37)
798.812***
(19)
Number of observations
42,231
42,688
27,151
54
FIGURES
Figure 1a. Average Marginal Effects of Trust (with 95% CIs) on the logarithm of odds ratio in Model
III, Table 2a, at different values of moderator (effects on linear predictions).
Figure 1b. Average Marginal Effects of Trust (with 95% CIs) on the logarithm of odds ratio in
Model VI, Table 2a, at different values of moderator (effects on linear predictions).
Figure 1c. Average Marginal Effects of Trust (with 95% CIs) on the logarithm of odds ratio in
Model V, Table 2b, at different values of moderator (effects on linear predictions).
55
Figure 1d. Average Marginal Effects of Trust (with 95% CIs) on the logarithm of odds ratio in
Model VI, Table 2b, at different values of moderator (effects on linear predictions).
Figure 1e. Average Marginal Effects of Trust with 95% CIs on the logarithm of odds ratio in
Model VII, Table 2b, at different values of moderator (effects on linear predictions).