Terjesen Patel Fiet DSouza 2013 Technovation Normative rationality VC Financing

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Normative rationality in venture capital financing

Siri Terjesen a,n, Pankaj C. Patel b, James O. Fiet c, Rodney D’Souza d

a Department of Management and Entrepreneurship, Kelley School of Business, Indiana University, 1309 E. 10th St., Bloomington, IN 47405, USAb Department of Marketing and Management, Miller College of Business, Ball State University, Muncie, IN 47306, USAc College of Business, University of Louisville, Louisville, KY 40292, USAd Management and Entrepreneurship, Northern Kentucky University, Haile/US Bank College of Business, Highland Heights, KY 41099, USA

a r t i c l e i n f o

Available online 20 December 2012

Keywords:

Finite mixture regressions

Macroculture

Normative rationality

Venture capital firms

Venture capitalists

a b s t r a c t

We examine whether venture capitalists (VCs) make investments based on normative rationality,

which is derived from habitual and embedded norms and traditions indicative of a macroculture.

Syndication and social and professional relations facilitate the development of shared decision-making

frameworks. Using a four step methodology and a unique dataset of 139VC decisions and 82

independent VC assessments of those decisions, we find that the VC industry exhibits collective

investment decision-making preferences, reflecting normative rationality. We offer implications for

theory, practice, and future research.

& 2012 Elsevier Ltd. All rights reserved.

1. Introduction

New technology firms contribute to job creation and economic

growth and development (Kirchhoff, 1989; Kirchhoff and Phillips,

1988; Kirchhoff et al., 2007). According to Stouder and Kirchhoff

(2004: 352), ‘‘One main critical task facing entrepreneurs is to

acquire and manage the resources needed to starty [a venture],

especially financialy resources,’’ and venture capital (VC) is one

source of funding. Venture capitalists (VCs) also provide human

capital and social capital—key resources for firm survival

(National Venture Capital Association (NVCA), 2011). In the U.S.,

venture capitalist (VC)-backed firms account for 12 million jobs

and $3.1 trillion in revenue (NVCA, 2011), approximately 11% of

private sector employment, and 21% of gross domestic product.

VC decisions ultimately affect industry innovation and economic

growth (Lerner, 2002; Sorenson and Stuart, 2001), especially in

critical sectors such as technology (Chorev and Anderson, 2006;

Pandey and Jang, 1996) and life sciences (Platzer, 2009). VC firms

frequently work together in syndicates with two or more firms

investing in the same or in other investment rounds (Manigart

et al., 2006; Tian, 2012), often developing repeated patterns of

activities.

Scholars have long argued that the pure neoclassical economic

rationality perspective is insufficient to explain decision-making

(e.g., Kirchhoff, 1994). A large body of theory and empirical

research suggests the presence of institutional norms—that is

that decisions are based on what is considered acceptable or

legitimate in a specific environment, as well as on technology and

economic criteria (DiMaggio and Powell, 1983). In a decision-

making context, normative rationality describes those decisions,

which are embedded in norms and traditions (Oliver, 1997), and

thus may result in almost homogeneous decisions. There is

anecdotal evidence that suggests that the VC industry exhibits

normative rationality, which dictates how funding decisions are

and will be made; however, there are no known investigations of

this contention. There is evidence in the finance literature on

herding behavior in stock market investments (Kaplan and

Schoar, 2005), which suggests some plausibility for normative

rationality in highly uncertain decision-making contexts such as

VC investments.

This research attempts to answers the question: do individual

VCs make homogeneous decisions regarding the funding of

business plans? A better understanding of how VCs make deci-

sions could guide entrepreneurs when soliciting financial support

for their start-ups. If all VCs think and act alike with respect to

investment decisions, then an entrepreneur’s time would not be

well spent soliciting multiple VCs. Rather, an entrepreneur’s time

and resources would be better spent incorporating VC feedback

into a plan and then taking the revised plan to another VC.

This article proceeds as follows. First, we outline the theore-

tical background for the research question and discuss how VC

decisions reflect normative rationality. Next, we describe our

unique primary dataset of 139 business plans that were presented

to 82 VCs based on the East and West coasts of the U.S., and the

four-step methodology. Following a presentation of the results,

we conclude by discussing the limitations and the implications

for theory, practice, and future research.

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/technovation

Technovation

0166-4972/$ - see front matter & 2012 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.technovation.2012.10.004

n Corresponding author. Tel.: þ1 502 409 0634; fax: þ1 765 285 5117.

E-mail addresses: [email protected] (S. Terjesen),

[email protected] (P.C. Patel), [email protected] (J.O. Fiet),

[email protected] (R. D’Souza).

Technovation 33 (2013) 255–264


2. Theoretical background: VC investment as a context for

normative rationality

A normative rationality perspective is consistent with the

strategy literature on interorganizational macrocultures, which

are described as ‘‘relatively idiosyncratic, organization-related

beliefs that are shared among top managers across organizations’’

(Abrahamson and Fombrun, 1994: 730). A rich literature

describes how managers can develop shared mental models and

how decision-making can become routinized in groups and in the

industry (Porac and Thomas, 1990). Embedded ties facilitate trust,

fine-grained information transfer, and joint problem solving

(Uzzi, 1997). Repeated interactions regarding specific decisions

lead to collectively developed behavioral patterns. Strong and

long-lasting ties foster the development of social rules and

reciprocal trust, which, in turn, encourage communication among

parties and the creation of routines, collective languages, and a

collective culture (Coleman, 1990). Groups routinize their

decision-making patterns over time (McClelland, 1984), espe-

cially through repeated interactions (Gersick and Hackman,

1990) and this aids sensemaking through continuity and coordi-

nation (Weick, 1979).

There is anecdotal evidence to suggest that VCs may exhibit

normatively rational decision-making, which consists of decisions

that are embedded in historical and normative processes. The VC

industry places a high value on historical interactions. VCs prefer

to interact with individuals with whom they have a history and

they know well, e.g., certain entrepreneurs, lawyers, or other VCs

(Walske and Zacharakis, 2009). VCs are also more likely to

support venture teams with whom they have experienced success

in the past (Sorenson and Stuart, 2001).

Furthermore, an embedded macroculture develops and main-

tains VC industry norms. The majority of VC investments often

take place in syndicates, which are dense networks that are

structurally embedded and enable information to diffuse across

boundaries (Sorenson and Stuart, 2001). For example, of the

estimated 31,000 firms that received U.S. venture capital from

1980 to 2005, 70% garnered funds from two or more VCs (Tian,

2012). Among VC-backed firms holding an initial public offering

(IPO), two or more firms backed 88% of those that received

funding (Tian, 2012).

Through syndicate investing, VCs develop a web of relation-

ships based on past and current investments (Lerner, 1994),

which can lead to normative decision-making. Syndicates have

high degrees of reciprocity (Lerner, 1994) and repeat investments

(Bygrave, 1988), thus exposing participating VC firms to more

deals. In a syndicate, individual VC firms may alternate between

lead and non-lead roles over time (Bygrave, 1988), with the lead

firm usually contributing the most resources and having larger

equity stake (Wright and Lockett, 2003). Through syndication,

VCs share knowledge, contacts, and other resources (Bygrave,

1988). Thus syndication allows individual VCs to combine their

sector-specific and location-specific investment expertise to help

diffuse information across sector boundaries and diversify their

portfolios (Sorenson and Stuart, 2001). VC syndicate sanctions

include the damaging effects of reputation, withheld deal flow in

the future, and the threat of non-investment in subsequent

rounds (Wright and Lockett, 2003).

Embedded human capital structures facilitate the development

of a normative rationality (Oliver, 1997). The embeddedness in

the VC industry is also illustrated in the norms related to human

capital. The majority of VC firm employees receive MBAs from

a handful of premier institutions, namely Harvard, Stanford,

MIT, and Wharton (Smart et al., 2000). Furthermore, instruction

at these institutions comes from a limited set of experts, e.g.,

Georges Doriot at MIT (Bancroft, 2009; Roberts and Eesley, 2009).

Key VC employees can be hired away from other VC firms

(Bancroft, 2009), facilitating direct knowledge spillover. VC firms

hire entrepreneurs with experience working with VCs (Wetfeet,

2010). Also, key VC employees leave established firms to start

new ones (Bancroft, 2009; Walske and Zacharakis, 2009). This

human capital transfer is institutionalized outside the U.S. For

example, U.S. VCs trained VC managers in Asia (Bruton et al.,

2005) and established the early VC firms in Europe (Manigart,

1994). Worldwide, comparative studies indicate that the VC

industry is increasingly homogeneous in terms of experiential

background (Cornelius, 2005).

Social and professional relations, such as friendship ties, business

clubs, industry associations, and professional and occupational

associations facilitate normative decision-making, which occurs by

developing shared norms, embedding economic behavior, and facil-

itating trust (Oliver, 1997). VCs share extensive professional and

social ties (e.g., Bancroft, 2009; Shane and Cable, 2002). VCs have

high levels of relational embeddedness, which influence their

partner selections in inter-firm collaborations (Meuleman et al.,

2010). There are numerous professional VC associations at local (e.g.,

Silicon Valley) and national (e.g., National Venture Capital Associa-

tion for the U.S., Canadian Venture Capital Association for Canada,

European Venture Capital Association for Europe, and Australian

Venture Capital Australia for Australia) levels, which enjoy wide-

spread industry support (Bruton et al., 2005: 739) and participation,

thus reinforcing norms.

Industry homogeneity may also structure homogeneous,

industry-level decisions. As examples, individuals working in

the VC industry have high degrees of homogeneity in terms of

gender (male) (Brush et al., 2004), education, and work experi-

ence (Wetfeet, 2010); and these homogenous groups tend to have

higher levels of communication and lower levels of conflict

(Ancona and Caldwell, 1992), thus reinforcing norms. Investment

and hiring practices also reveal preferences for homogeneity: VCs

are also less likely to pursue markets that are geographically

distant to them (Dimov and De Holan, 2010). Also, VCs prefer

entrepreneurial teams with training and professional experience

similar to their own human capital (Franke et al., 2006).

The above discussion highlights the high levels of intercon-

nectivity in the VC industry (Bygrave, 1988) and suggests the

presence of a macroculture and the strong likelihood of normative

rationality in decision-making. The high levels of ambiguity and

uncertainty in VC investment decisions are also likely to result in

evolving cognitive frameworks that can become mutually con-

stitutive (Wright and Lockett, 2003; Weick, 1979). Research

indicates that VCs have a limited understanding of their own

decisions (Zacharakis and Meyer, 1998); thus prompting the

possibility that VCs are imitating other firms rather than making

independent, rational decisions. Thus, we expect:

Hypothesis: Individual venture capitalists will exhibit homoge-

nous investment decisions when presented with different investment

opportunities.

3. Data and analytical approach

We gathered data using individual VC investment decisions

because syndicate-level designs would have been confounded by

syndicate-related factors. This design meets the requirements for

a test of normative rationality that determines whether indivi-

duals independently make identical decisions after controlling for

potential economic rationality (D’Andrade, 1995; Ross, 2004).

We collected 70 funded business plans and 69 unfunded

business plans as initially submitted to VCs for possible funding

(this database is also used in Dos Santos et al., 2011). We asked

VCs for unfunded business plans, which had been given due

S. Terjesen et al. / Technovation 33 (2013) 255–264256


consideration and which had gone through the due diligence

process. This helped assure that the unfunded business plans,

which were analyzed, were thorough and complete. Later,

we used floor/ceiling analysis to further ensure that unfunded

business plans were of comparable quality as funded business

plans. Some of the business plans we collected had been modified

after an earlier rejection. If our sample would have included

revised plans (based on early VC feedback), which had then been

accepted by a different VC, we would not have known whether to

attribute VC funding success to the business idea or to the VCs’

combined thinking. We eliminated these revised plans. The plans

were for technology startups because technology-based indus-

tries receive the bulk of VC investment (NVCA, 2011).

For each business plan, individual VCs, not a syndicate, made

the funding decision. The 70 funded plans came from both the

U.S. East coast (28 plans) and West coast (42 plans). Of the 69

unfunded plans, 38 came from East coast VCs and 31 came from

West coast VCs. A total of 82 unique VCs (31 from the East coast,

51 from the West coast) provided funding decisions. Because

there are various funding sources including banks, private VC

firms, and corporate venture capital investors, we control for

these differences in our sample by only using private VC firms.

Our analysis followed the four steps depicted in Fig. 1: (1) free-

listing, (2) focus groups, (3) expert evaluation, and (4) statistical

analysis (see White et al., 2004 for a full description of the

methodology for Steps 1 and 2).

3.1. Step 1: free-listing

Criteria used in VC funding decisions were identified by

gathering data from 120 VCs and angel investors in the

U.S. Midwest. We adopted a form of free-listing data collection,

asking respondents for answers that represent pertinent elements

about a particular domain (Romney et al., 1986). Free-listing is

recommended when little is known about a domain because it

allows participants to provide information without researcher

bias (Weller and Romney, 1988b). Because the free-listing tech-

nique is problematic if respondents use different definitions for

the same term or use the same term with different meanings, we

combined free-listing with a content analysis of existing literature

of VC decision-making criteria (e.g., Macmillan et al., 1985;

Tyebjee and Bruno, 1984a,b). In total, 22 criteria believed to affect

VC funding decisions were identified. This list of terms and

definitions was distributed to 120 VCs, angel investors, and

commercial lenders at a venture club meeting in a Midwestern

city in the US. Angel investors were included because they

provide seed capital funding to start-ups that they believe will

be successful in obtaining VC funding. Commercial bankers also

provide early stage financing and their presence at the venture

club meeting suggests an interest in start-up financing (Gonzalez

and James, 2007). The 120 individuals were asked to identify

which criteria they use to make a decision to invest in a new

business and, if necessary, to add new criteria to the list and to

define these. Thirty-eight respondents did not fill out the ques-

tionnaire and the remaining twenty four responses were incom-

plete. This resulted in fifty-eight usable responses. Six additional

criteria were added, resulting in a total of 28 criteria. We

eliminated seven of the 28 criteria because they appeared on

few lists.

3.2. Step 2: focus groups

We presented the remaining 21 criteria to a focus group (Focus

Group A) of 12 individuals (none of whom were represented in

Step 1) that included VCs and angel investors from a Midwest

city, all of whom were lead investors in over fifty different

businesses. We asked Focus Group A to (1) determine whether

the terms and definitions of the criteria were consistent, (2)

Step 1: Free

listing

Sample: 120 VCs

and angel

investors in U.S.

Midwest.

Outcome: Together

with a content

analysis of existing

literature, 21

criteria important

for funding a

business plan to be

used in Step 2.

Step 2: Focus Groups

Sample: Focus Group A of

12 VCs and angel

investors in U.S. Midwest,

Focus Group B of 15 VCs

and angel investors in U.S.

Midwest.

Outcome:Meaning,

categorization, and

importance of criteria

identified in free?listing.

Seven criteria eliminated

due to limited role in

decision?making,

resulting in 14 criteria for

decision?making used in

Step 3: value added,

market size, competition,

timing, technology

advantages, intellectual

property, strategy, start?

up experience, industry

experience, leadership

experience, revenue sales,

strategic partners,

customer adoption and

margin analysis.

Step 3: Expert

Evaluation of

Business Plans

Sample: 70 funded and

69 unfunded business

plans of VCs from U.S.

West and East coasts.

All plans were first?

time start?up

investment decisions

made by a venture

capital firm (not as a

part of syndicate). 9

experts with extensive

VC experience and

~18 years of industry

experience.

Outcome: Blind

evaluation of each plan

by 3 experts using

criteria and weights

from Focus Groups,

deriving rating of each

plan’s criteria to be

used in Steps 4a & 4b.

Step 4: Statistical Analysis

Structural Inferences (4a)

Consensus Analysis

- DV: VC Investment Decision

- IV: Evaluations by experts

Structural Heterogeneity Inferences

(4b)

1) Residual Analysis



2) Finite Mixture regressions:

heterogeneity in investment

evaluations



Industry Heterogeneity Inferences

(4c)

Heterogeneity in decisions based on

VC firm characteristics


- IV: VC Team Characteristics

Mental Model: Assessing collective cognitive

structures (macroculture )Behavioral Model: Assessing decision behavior using

collective cognitive structures (macroculture)

Analytical Approach

Fig. 1. Analytical approach.

S. Terjesen et al. / Technovation 33 (2013) 255–264 257


weigh each criterion in terms of importance in funding decisions,

(3) identify scales to evaluate each criterion, and (4) group the 21

criteria into meaningful categories. We then presented the scales,

weights, and definitions for each criterion to another focus group,

Focus Group B, of 15 VCs not in group A. We asked Focus Group B

to validate/change the criteria and scales and to determine how

important each category was in funding decisions. Together with

the Focus Group A data, the criteria were weighted for their

importance in the VC decision-making process.

Because we had access to both funded and unfunded business

plans, we used only those criteria that could be verified externally

and also modified during contract negotiations, eliminating

criteria such as whether the business plans contained financial

projections, milestones, and exit strategies (Weller and Romney,

1988a). This resulted in a set of the following 14 criteria used

by experts in Step 3: value added, market size, competition,

timing, technological advantage, intellectual property protection,

strategy, start-up experience, industry experience, leadership

experience, revenue sales, strategic partners, customer adoption,

and margin analysis.

3.3. Step 3: expert evaluation of business plans

Our hypothesis focused on identifying the extent to which

criteria used for venture funding are similar across investors. The

objective of Steps 1 and 2 was to identify espoused criteria that

are commonly agreed upon by investors. In Steps 3 and 4, we

assessed whether such agreed-upon criteria are used in decision-

making. In our design choice, we could have VCs who actually

invest in ventures evaluate business plans, or have experts use

criteria identified in Steps 1 and 2 to evaluate business plans.

Using expert evaluations is necessary in the current context.

Our hypothesis posits VC decision-making would exert a

‘fixed’ effect. If VCs were driven by economic rationality, which

was contingent on prior experiences and VC firm resources, then

it would be a ‘random’ effect. Having an external expert evaluate

a business plan indicates the extent to which the ‘fixed’ effect was

uniform, based on criteria that are widely used in the industry.

Expert evaluations are necessary as they help assess the extent to

which decision outcomes are based on commonly held criteria.

Although individual VC assessments for each criterion would be

invariably different, expert evaluation helps assess the uniformity

of the applicability of the criteria based on reaching the same

decisions.

In step 3, nine experts evaluated the business plans using the

criteria and scales developed in the previous two steps. The nine

experts worked in a Midwestern city in the US, had experience

dealing with VCs and individual investors, and were well

grounded in the technology industries. The experts had an

average of 18 years of relevant industry experience in commu-

nication equipment, industrial electronics, semi-conductors, elec-

tromedical equipment, and/or computers and office equipment

and 14 years of investment experience as a venture capitalist,

angel investor, private investor, and/or serial entrepreneur. None

of these experts were involved in the previous steps. See Table 1

for the experts’ profiles.

This single blind study employed a balanced sample of funded

and unfunded plans, thereby reducing the likelihood that evalua-

tions could be correct by chance. Because most business plans

submitted to VCs are not funded, someone with little knowledge

of VC funding could be correct most of the time if he/she indicated

that each plan fared poorly on all criteria and should not be

funded. We specifically instructed experts to base the decision

strictly on the business plan content (and not on an assumption

about the likely distribution of funded versus unfunded plans)

and not to respond if they had prior knowledge of a particular

venture. Furthermore, to limit information from external sources,

we removed all identifying items from the business plans (e.g.,

names of company, management team, strategic partners, custo-

mers, and suppliers).

We randomly assigned the 139 business plans to the nine

experts, with each plan evaluated by three experts to limit

individual bias. Each expert evaluated two plans per week over

a 23-week period. In addition to rating each plan on the

established criteria, we asked each expert to indicate if a plan

should be funded. We used the expert rating of each plan’s

criteria in steps 4a and 4b. We made sure that the experts did

not have more information than what was available in the

business plans because the experts were very familiar with the

industries and we did not want them to be able to identify the

plans from sources other than those that we provided to them.

Inter-expert reliability was 0.93. The difference between

funded and unfunded business plans based on expert ratings

was significant (t-test: po0.01). Furthermore, a logistic regres-

sion of expert ratings explained 89% of the variance between

funded and unfunded plans. To further explore these findings, we

created a composite score using principal component analysis.

The reliability was 0.92.

Next, we determined whether the ratings for funded and

unfunded plans were unevenly distributed to create artificial

separation. We tested floor and ceiling effects to assess the

uniformity of scale ranges (Nunnally, 1978), ensuring that the

floor/ceiling effects were small (o15% of the sample) and that

skewness statistics were between �1 and þ1. The maximum

floor effect was 11.51% (intellectual property: 16/139) and the

maximum ceiling effect was 12.94% (market size: 18/139). The

skewness values ranged from 0.847 and �0.911, and were within

the recommended bounds.

3.4. Step 4: statistical analysis

We analyzed all data at the group level to assess the degree of

agreement (consensus analysis). The heterogeneity tests (residual

analysis and finite mixture regression) focused on the level

of disagreement (D’Andrade, 1995). Following consensus and

residual analyses, we used finite mixture regressions (FMR) to

explore the extent of heterogeneity among VCs’ funding decisions.

FMRs independently identify classes without external impositions

(McLachlan and Peel, 2000). We assessed the latent classes in the

VC evaluations. Despite the high inter-rater reliability and dis-

criminatory power, the expert evaluations might have been

unreliable if we had not tested all the VC criteria. We also

examined a set of VC firm characteristics and network positions

for possible correlation with funding decision patterns.

3.4.1. Consensus analysis and residual agreement

The consensus analysis focused on three issues. First, it

explored whether a particular ‘cultural model’ or shared knowl-

edge of a particular domain exists among a group of informants

(Borgatti, 1996, 1999). In survey research, homogeneity among

informant responses indicates consensus. Consensus analysis

related reliability testing applies to informants rather than to

survey items. Second, it compares the relationship between each

informant’s knowledge of a domain (his or her ‘cultural compe-

tence’) and the knowledge possessed by the aggregate (Ross,

2004). Cultural competence scores are estimated by factoring a

matrix of person-by-person similarity coefficients and are repre-

sented as proportions. Thus, an informant with an estimated

cultural competence of 0.7 commands 70% of that domain’s

knowledge (Borgatti, 1996, 1999). Third, without assuming the

correct answers a priori, it solicits the local or culturally correct



answers to survey questions (Batchelder and Romney, 1988).

An anthropologist can reach conclusions about the local under-

standing of VC investment criteria by interviewing a small

number of VCs, observing participants, and analyzing printed

material content. However, consensus analysis operationalizes

the findings by estimating the social distribution of the knowl-

edge, the average knowledge possessed by each respondent, and

the culturally ‘correct’ answers to the questions. Expert informants

are individuals with higher cultural competence scores. Experts

tend to agree with each other more frequently, creating more

homogeneous consensus (D’Andrade, 1995).

Consensus analysis weights an informant’s responses according

to individual competence (Romney et al., 1986). Using Bayesian

posterior probability rather than a majority rule to rate answers

ensured that the most competent informants received the most

credit for their responses.

To assess the level of homogeneity in decision criteria, we

conducted a consensus analysis at both ‘mental’ and ‘behavioral’

levels. The mental level represents similarity in decision criteria

among VCs from the criteria derived from Steps 1 (free-listing)

and 2 (focus-group) in Fig. 1. Although the mental model focuses

on preferences for funding, the behavioral level explains how

effectively mental models are represented or used in actual

funding scenarios. Overall, the mental level represents generally

accepted knowledge in the field, whereas, the behavioral model

assesses the extent to which the generally accepted decision

criteria from the mental model are used. Thus, we are able to

obtain robust consensus between the suggested criteria (the

mental model) and the actual criteria (the behavioral model).

Following Ross (2004), our consensus in mental and behavioral

models met the following conditions: (a) the first factor’s eigen-

value is greater than the ratio of 3:1, (b) factor loadings are

positive, and (c) the first factor explains significant amounts of

variance (at least 80%). Because respondents had a high level of

agreement between their mental and behavioral models, we

treated the factor loadings as competence scores to be analyzed

further (Borgatti, 1996; Ross, 2004).

The notion of residual agreement is important for under-

standing consensus and for studying differences in cultural

domains which could influence competence scores. For example,

experienced VCs may accept different levels of market and agency

risk compared to their less-experienced counterparts (Fiet, 1995).

Consequently, VCs from different firms may exhibit varied com-

petence loadings and be immersed in different cultural milieus.

If a large number of VCs possessed different investment

behaviors, the competence loadings on the second factor could

also be explored.

Because patterns can spread across loadings, residual agree-

ment must be calculated separately (Ross, 2004). The residual

analysis procedure is based on each individual’s agreement with

consensus responses (Nakao and Romney, 1984). We calculated

the VC-by-VC residual agreement by subtracting the agreement

predicted by the consensus (represented by the first factor

loadings) from the observed agreement in the data. We calculated

the predicted agreement matrix by multiplying the first factor

loadings of each pair of participants, resulting in an index of

agreement predicted by each participant’s knowledge of the

consensus. Subtracting the predicted agreement matrix from the

observed agreement matrix resulted in a matrix of agreement

that was not accounted for by the consensus, as represented by

the first factor. For example, if a VC agreed on 80% of the

responses with the general model and another VC agreed on

70% of the responses for the same model, the predicted agreement

between the two would be 56% (¼80%�70%). The predicted

agreements for all the pairs of the respective mental and beha-

vioral models produced a matrix of predicted VC agreement on

financing decisions. We then standardized the residual agreement

matrix, creating a matrix of values between zero and one. We

used an OLS regression of each VC firm’s age, size, and portfolio

size/fund managed between a dyad of VC firms in the residual

matrix.

3.4.2. Finite mixture regressions

Employed in fields as diverse as biology, medicine, physics,

economics, and marketing, finite mixture regression (FMR)

models are used when different groups and their membership

cannot be observed. FMR can analyze inter-relationships such as

macroculture when group memberships are simply unobservable

(McLachlan and Peel, 2000). Another application of mixture

models is market segmentation (Wedel and Kamakura, 2001)

for which they are considered to be more state-of-the art than

traditional cluster analysis and cluster-wise regression. Finite

mixture models with a fixed number of components are usually

estimated with the expectation-maximization algorithm within a

maximum likelihood framework (Dempster et al., 1977) and with

Markov Chain Monte Carlo sampling (Diebolt and Robert, 1994)

within a Bayesian framework. We used the flexmix package in R

for analysis.

No inferential technique exists for identifying the number of

segments in a whole, such as a macroculture; however a number

of indicators may be used to draw inferences about the number of

latent groups in a population. Information criteria are based on

assessing the degree of improvement in explanatory power

Table 1

Expert profiles.

Expert Core investment industries Involvement in

investment process

Years of investment

experience

Highest education

#1 Communication Equipment (SIC: 3661, 3663, and 3669) Venture Capitalist 11 MBA

#2 Industrial Electronics (SIC: from 3821 to 3826 and 3829)

and Semi-conductors (SIC: 3674)

Venture Capitalist 13 MBA

#3 Electromedical Equipment (SIC: 3844 and 3845) Angel investment 19 PhD Biochemistry

#4 Computers and Office Equipment (SIC: 3571, 3572,

and 3575)

Venture Capitalist 16 MBA



Private Investor/serial entrepreneur 14 PhD Mechanical Engineering

#6 Communication Equipment (SIC: 3661, 3663, and 3669) Serial Entrepreneur 12 MS Electrical Engineering

#7 Communication Equipment (SIC: 3661, 3663, and 3669) Venture Capitalist 10 PhD Electrical Engineering

#8 Electromedical Equipment (SIC: 3844 and 3845) Private Investor/serial entrepreneur 18 MBA



Private Investor/serial entrepreneur 15 MS Electrical Engineering



adjusted for the degrees of freedom required by additional

parameters. We used the Akaike’s information criterion (AIC)

and consistent Akaike’s information criterion (CAIC). The lesser-

known criterion is Entropy which explains the degree to which

segments are sufficiently distinctive and thus accounts for the

separation in estimated posterior probabilities. Higher estimation

implies a greater degree of separation. Normed Entropy Criteria

(NEC(S)) adjusts for over-parameterization relative to a one-

segment model:

NEC Sð Þ ¼ES

½ln LðSð ÞÞ�ln Lð1ð Þ�

ES ¼ 1�

P

N

n ¼ 1

P

S

s ¼ 1

�pnsln pns� �

N

ES is the entropy of segment S, and indicates separation between

segment S and the 1-segment solution; pns the posterior prob-

ability of subject n in segment s, and N the number of subjects;

L(S) the log-likelihood for S-segment and L(1) the log-likelihood of

a 1-segment solution. The model tests differences in entropy

between different segments, L(S), and 1-segment, L(1). The

number of segments retained, Sn in the finite mixture solution is

where the entropy between segment S and segment 1 is

minimized.

3.4.2.1. FMR for expert ratings. We conducted a FMR on plan

evaluations to determine if there were independent groups of

funded and unfunded plans. Because expert evaluations are

highly discriminatory, we wanted to know if subgroups existed

between the funded and unfunded groups identified by the FMR.

These results presented a record of actual funding (mental model)

rather than a retrospective rationalization of what the VCs said

(behavior model) was important to them.

3.4.2.2. FMR for VC firms. We then compared the funding decision

with VC firm characteristics and respective network positions

using Abell and Nisar’s (2007) criteria. If economic rationality

plays a critical role in decision-making, then VC firm charac-

teristics should explain significant variance in differentiating

funded and unfunded business plans. In other words, if VCs are

driven by idiosyncratic criteria, then based on certain firm

characteristics they would be more likely to fund a certain type

of business plan over the other. For example, VCs with high IPO

rates may be more inclined to fund high potential ventures.

Similarly, VCs with larger fund size may not fund ventures with

smaller initial funding, or VCs with smaller fund size may not be

able to fund ventures with larger initial investments. More

importantly, network positions could affect deal and informa-

tion flow, and therefore, affect identification of investment

opportunities and future growth of ventures. For example, VCs

with centralized network positions are more likely to focus

on ventures with strategic partners to create greater knowledge

complementarity; whereas, VC firms with limited experience

could rely more on venture team characteristics.

We identified 82 VC firm characteristics based on performance

data from 1994 to 2004 because the actual funding decisions were

made in 2004. The VentureXpert database contains comprehensive

information about ventures, buyouts, funds, private equity, firms,

executives, portfolio companies, and limited partners and has been

used in numerous studies (e.g., Sorenson and Stuart, 2001). We

gathered the following VC firm characteristics from Abell and Nisar’s

(2007) comprehensive review of past VC firm characteristics and

additional network characteristics which were found to be impor-

tant elsewhere in prior literature: (i) exit rate: the percentage of

portfolio companies exited; (ii) IPO rate: the percentage of portfolio

companies sold via IPO; (iii) M&A rate: the percentage of portfolio

companies sold via M&A; (iv) dollar exit rate: the percentage of

invested $ exited; (v) dollar IPO rate: the percentage of invested $

exited via IPO; (vi) dollar M&A rate: the percentage of invested $

exited via M&A; (vii) book/market ratio: the book/market ratio of

public companies in a sample fund’s industry of interest; (viii) VC

fund size: the amount of committed capital reported by a VC fund;

(ix) venture capital firm experience: the average number of years of

VC firm experience in VC industry; (x) partner experience: the

average number of years of VC firm partners’ experience in the VC

industry; (xi) corporate board director: a dummy variable, which

takes the value 1 if a VC firm has or had a seat on the board of

directors of a company, 0 otherwise (we normalized each of the

following network measures according to their theoretical max-

imum (e.g., the degree a VC firm could syndicate with other VC

firms)); (xii) degree: number of unique VC firms with which a VC

firm has syndicated with (regardless of syndicate role); (xiii)

indegree: the number of unique VC firms that led syndicates in

which a VC firm was a non-lead member; (xiv) outdegree: the

number of unique VC firms that have taken part as non-lead

investors in syndicates led by a VC firm; (xv) eigenvector: a VC

firm’s ‘‘closeness’’ to other VC firms; (xvi) betweenness: the number

of the shortest distance paths between other VC firms in a network

with which a VC firm interacts. We controlled for factors that might

promote heterogeneous investment preferences.

4. Results

As described above, we utilized three methodologies to test

our hypothesis that individual VCs exhibit homogenous invest-

ment decisions. Consensus analysis focuses on between mental

models (agreement with espoused decision-making criteria) and

behavioral models (the extent to which aspects of a behavioral

model are manifested in actual decision-making). Steps 1 and 2 in

Fig. 1 relate to mental models in the VC industry. If VCs draw on

common decision-making criteria, they must share a mental

model. If idiosyncratic VC resources drove VC decision criteria,

then a mental model would likely be fragmented because each

individual could draw on his or her unique decision-making

templates. Based on consensus analysis, Table 2(a) demonstrates

that there is a high level of agreement between both the mental

and behavioral models. Specifically, among the criteria shared by

investors in Step 1 and Step 2, there is a strong consensus in their

decision-making criteria. Specifically, for the mental model, the

ratio of the first to the second eigenvalue is 6.422, and the first

factor explains 81% of the variance. Therefore, the espoused

decision-making criteria are strongly shared among 147 investors

(120 investors in Step 1 and 27 investors in Step 2). In other

words, investors draw on similar decision-making criteria; how-

ever, Table 2(a) assesses whether these shared criteria are in fact

used in actual decision-making.

In the behavioral model, we used VC decisions as both out-

come and expert evaluations based on the mental model criteria

Table 2(a)

Consensus analysis (Step 4(a) in Fig. 1).

Model Ratio of first and second

eigenvalue

Percentage variance

explained by first

eigenvalue (%)

Average

competence

Mental 6.422:1 81 0.847

Behavioral

Funded 5.872:1 85 0.885

Unfunded 6.271:1 87 0.893

Combined 5.556:1 84 0.847



derived from Step 1 and Step 2. As listed in Table 2(a), the ratio of

first and the second eigenvalue for the overall behavioral model is

5.556 to 1, which indicates that the first factor explains 84% of

variance. This suggests that the mental model criteria are indeed

used in actual decision-making. We further assessed whether

these criteria were used when business plans were funded and

when they were not funded. The ratio of the first and second

eigenvalues for the funded business plans was 5.872 to 1; and for

unfunded business plans, it was 6.271 to 1. In other words, the

funding criteria were shared consistently across funded and

unfunded business plans.

Next, we assessed the average competence shared between

the mental and behavioral model. Recall that average competence

scores are based on the factor loadings of the criteria in the

mental and behavioral models (funded, unfunded, and combi-

ned)—higher scores indicate higher levels of knowledge shared

among the participants (Borgatti, 1996; Ross, 2004). High load-

ings and a significant difference with the other factors indicate

that VCs share commonly accepted cultural knowledge about

their investment domains. At the 99% confidence level, the

average competence for the mental model was 84.7% and for

the behavioral model it was 88.5% (funded plans), 89.3%

(unfunded plans), and 84.7% (combined pool of funded and

unfunded plans). Again, overall, the mental and behavioral

models show that investors not only share common espoused

criteria (mental model), but when faced with real life decisions

they also use decision criteria as espoused (behavioral model).

While Table 2(a) focuses on shared criteria, we further

explored whether investor firm characteristics drove ‘unshared’

(or, residual) variance. If investor firm characteristics were sig-

nificantly related to residual factors, in addition to shared criteria,

idiosyncratic factors could drive decisions making. Our residual

analysis (Table 2(b)) shows that VC firm characteristics do not

significantly explain the residual, supporting the conclusion that

there is no sub-group effect in VC decision-making.

Because mental and behavioral models indicate that investors

share espoused criteria and rely on these criteria when making

decisions, FMR enables us to further test whether the weights on

individual criteria for funding and not-funding a business plan are

consistent. FMR allows us to start with no presumption about the

underlying distribution of outcomes (funded/unfunded) or the

distribution of criteria across funded and unfunded business

plans. As shown in Table 3(b), without imposing criteria on

underlying groups in the sample, we found that a two-segment

solution provides the best fit. In other words, the two-segment

solution had the lowest log-likelihood (�119.783), AIC (120.604),

CAIC (129.627), and the highest entropy (0.920), NEC(S) (0.365),

and R2 (0.614). Next, based on the two identified segments, we

assessed business plan membership to each. Matching the seg-

ments with the actual funding decisions revealed a classification

of 65 funded plans (compared to 70 actually funded by VCs), and

67 unfunded business plans (compared to 69 actually unfunded

by VCs). In summary these results indicate a high degree of

agreement (six misclassifications out of 139) with the actual VC

decisions.

Next, because only five funded and two unfunded business

plans were misclassified (5.04% misclassification), we assessed

the relevance of funding criteria for each segment in Table 3(b).

We found that value added, competition, strategy, and margin

analysis were central to both funded and unfunded business

plans. However, the presence of strategic partners (b¼0.37,

po0.05) and customer adoption (b¼0.43, po0.05) were also

critical to funding. However, limited market size (b¼0.05,

po0.05), lack of start-up capital (b¼0.65, po0.05), industry

(b¼0.33, po0.01), and leadership experiences (b¼0.40,

po0.05) could lower the possibility of funding. Overall, although

funded and unfunded business plans shared some common

criteria, lack of experience and limited market size reduced the

likelihood of funding, whereas, the presence of strategic partners

and customer adoption were central to receiving funding.

In the next step, using the 16 VC firm characteristics identified

previously, we ran a finite mixture model (Table 3(a)) and found

no distinct groups. The log-likelihood (�112.986), AIC (�21.374),

and CAIC (258.450) were the lowest for one group solution.

As shown in Table 4(a), the Entropy and NEC(S) coefficients were

not so different as to be able to infer the presence of any

particular number of segments. One group solution added addi-

tional credence to the relevance of normative rationality. Because

Table 2(b)

Consensus analysis. Residual analysis on VC consensus model (Step 4(b), in Fig. 1).

Mental model Behavioral model

D Age (years) 0.154 0.110

ln (D portfolio size [$ million]) 0.232 0.085

ln (D portfolio size/fund manager

[$ million])

0.178 0.119

R2 0.142 0.117

Table 3(a)

Finite mixture regression for business plan evaluations (Step 4(b) in Fig. 1). Groups

based on finite mixture models.

Number of segments

1 2 3 4 5

Likelihood �141.659 �119.783 �165.464 �156.821 �175.354

AIC 137.188 120.604 143.391 140.282 144.951

CAIC 154.888 129.627 175.975 163.236 184.497

Entropy – 0.920 0.688 0.785 0.734

NEC(S) – 0.365 0.249 0.227 0.190

R2 0.424 0.614 0.672 0.687 0.704

Note: classification based on the two segment solution for funded and unfunded

business plans was 69 funded and 74 unfunded, thus indicating high discrimina-

tory power of the segment.

Table 3(b)

Finite mixture regression for business plan evaluations (Step 4(b) in Fig. 1). Finite

mixture estimation.

Two-segment solution

Segment 1: funded Segment 2: unfunded

Value added 0.17n 0.53n

Market size 0.81 0.05n

Competition 0.65n 0.12n

Timing 0.31 0.01

Technology advantages 0.53 0.09

Intellectual property 0.32 0.00

Strategy 0.42n 0.02n

Start-up experience 0.36 0.65n

Industry experience 0.32 0.33nn

Leadership experience 0.70 0.40n

Revenue sales 0.25 0.04

Strategic partners 0.37n 0.07

Customer adoption 0.43n 0.03

Margin analysis 0.14n 0.04n

Log-likelihood �122.62AIC 119.37CAIC 128.52R2 0.61

Note:n po .01.nn po .05.



VC firm characteristics were not fundamentally distinct when

comparing funded and unfunded business plans, firm specific

characteristics did not seem to drive the funding and non-funding

decisions in our sample. In other words, learning in VC firms

driven by exit rate, IPO rate, M&A rates, and network positions did

not distinguish actual funding criteria that were used. As an

added caution, we ran a logistic regression to identify any

differences in funding patterns and found no significant difference

in funding based on sixteen VC firm characteristics (see

Table 4(b)).

We found that VC firm characteristics played insignificant

roles in funding decisions. Past exit rate (b¼0.067, p40.10),

IPO rate (b¼0.154, p40.10), and M&A rate (b¼0.221, p40.10)

did not affect funding outcomes. Similarly, the dollar values of

exit, IPO, and M&A rate did not affect funding outcomes either. VC

firm fund size (b¼0.014, p40.10) and book to market ratio

(b¼0.051, p40.10), indicative of VC firm value, did not affect

funding criteria. Finally, the coefficients for VC experience

(b¼0.223, p40.10), partner experience (b¼0.024, p40.10), and

all the network positions were insignificant.

Overall, consensus analysis indicated that investors shared a

mental model and use similar funding criteria when making

actual funding decisions. Furthermore, using an unsupervised

approach under FMR, criteria differentiate funded from unfunded

business plans. More importantly, although VCs used some

criteria to differentiate funded from unfunded business plans,

they focused on venture team experience when not funding a

business plan. They placed greater weight on strategic partners

and customer adoption when funding a business plan. Based on

unique firm resources, VC firm characteristics could drive funding

decisions. However, neither FMR nor logistic regression showed

that firm characteristics drove funding decisions.

5. Discussion

Our finding that VCs exhibit homogeneous investment deci-

sions implies that norms pertaining to business plan evaluation

are readily diffused throughout the VC industry, leading to the

standardization of knowledge and the unquestioned acceptance

of strategic recipes among industry incumbents (Spender, 1989).

Although we have not delved into differences, these strategic

recipes may in time aggregate into macrocultures (Abrahamson

and Fombrun, 1994), which may dictate how venture decisions

across an industry come to resemble each other. To date, common

understandings of the qualities of successful business plans have

been mostly built on years of anecdotal evidence. In such a case,

industry knowledge can serve incumbent firms well. However,

strategic innovations may affect a firm’s viability: an incumbent’s

deviation from accepted business plan evaluation practices could

lead to a decline in performance.

Another theoretical implication of this research relates to the

likely importance of a shared VC learning capability. Specifically,

if as expected a common understanding of the essential attributes

of business plans will well serve VC firms (or at least those VC

firms focused on similar investment opportunities), then being

able to quickly and accurately acquire relevant industry knowl-

edge is a key success requirement among VCs. Industry experi-

ence, both at the firm level and at the individual plan evaluator

level, is critical as knowledge is sticky. The ability to acquire more

knowledge is a function of the amount of knowledge already

possessed (Cohen and Levinthal, 1990).

A final theoretical implication of this research is the explicit

nature of the industry knowledge that drives business plan

evaluations within the VC industry. Several researchers suggest

that the tacit knowledge embedded in a firm’s specific routines is

the type of knowledge that is most likely to lead to long term

success (Nelson and Winter, 1982; Nonaka and Takeuchi, 1995).

However, the suggestion from the current study that plan

evaluation best practices are easily known implies that tacit

knowledge may not play a large role in determining why VC

firms choose different investment opportunities. This is not to

suggest that tacit knowledge will not or cannot be an important

component of VC firm success; however, such knowledge

probably creates value for VC firms through activities and in

skill-based areas other than those pertaining to proficiency

in evaluating business plans. Taken together, we expand the

management/entrepreneurship branch of VC literature (Cornelius

and Persson, 2006).

Our study also offers important implications for practicing

entrepreneurs and VCs. First, as our study indicates that VCs tend

to rate new ventures similarly, an entrepreneur who fails to

receive VC backing for his/her plan should incorporate VC feed-

back into a plan before bringing the plan to the next VC. From a

VC perspective, almost homogeneous thinking suggests that some

entrepreneurs’ ideas may be universally rejected. Second, the

results from finite mixture estimation shows that funded and

non-funded business plans do not share a common set of decision

criteria. Lack of startup, industry, and leadership experience and

limited market size lead to the rejection of business plans, but

their presence does not necessarily increase the chances of

funding. This finding suggests that human capital and market

potential form the threshold criteria ‘‘to get a foot in the door.’’ In

other words, an entrepreneur with the best idea may still not

Table 4(a)

Finite mixture and logistic regression of VC firm characteristics (Step 4(c) in

Fig. 1). Groups based on finite mixture models.

Number of segments

1 2 3 4 5

Likelihood �112.986 �143.020 �151.533 �160.600 �165.227

AIC 219.374 221.116 221.426 229.438 231.691

CAIC 258.450 263.031 281.359 279.612 292.141

Entropy 0.564 0.519 0.533 0.543

NEC(S) 0.052 0.068 0.038 0.043

R2 0.524 0.553 0.622 0.674 0.755

Note: lower levels of AIC and CAIC are desirable; higher levels of entropy and

NEC(S) indicate a greater degree of separation among groups.

Table 4(b)

Finite mixture and logistic regression of VC firm characteristics

(Step 4(c) in Fig. 1). Logistic regression.

b (s.e.)

Exit rate 0.067 (0.073)

IPO rate 0.154 (0.254)

M&A rate 0.221 (0.182)

Dollar exit rate 0.100 (0.094)

Dollar IPO rate 0.042 (0.076)

Dollar M&A rate 0.031 (0.044)

Book/market ratio 0.051 (0.062)

VC fund size 0.014 (0.016)

VC experience 0.223 (0.348)

Partner experience 0.024 (0.052)

Corporate board director 0.013 (0.016)

Degree 0.011 (0.015)

Indegree 0.022 (0.016)

Outdegree 0.071 (0.038)

Eigenvector 0.038 (0.049)

Betweenness 0.211 (0.148)

Pseudo-R2 0.153



receive funding if he/she lacks the requisite human capital. Once

the threshold criteria is met, factors that uniquely drive funding

are strategic partners and product adoption by customers. The

results suggest that VCs use funding criteria in a sequential

pattern. Thus entrepreneurs must highlight their human capital

to receive further consideration from the VCs. Third, for the VC

industry, the findings suggest that shared decision criteria

increases legitimacy and lowers uncertainty and ambiguity, but

could limit the unique ‘random’ effects from VC firm resources

that help VC firms to establish a distinct competitive advantage

vis-�a-vis portfolio investments. While prior research focused on

cooperation among VC firms, our results indicate that VCs must

manage tradeoffs between external learning (from the industry)

with internal learning in order to develop unique knowledge that

can be leveraged to ‘‘hit more home runs’’ than competitors.

Fourth, in addition to sharing risk through syndicates, VCs could

assign specialization roles among members to increase cognitive

efficiency and develop more effective learning in uncertain

investment environments. Fifth, there is increasing evidence

of a ‘‘herd mentality’’ in VC investments. As normative rationality

would further catalyze such herding behavior, building from the

earlier discussion, focusing on internal firm resources and knowledge

could help VCs make more informed investment decisions.

The above implications should be considered with respect to

four limitations. First, although free listing is a widely accepted

technique in anthropology, it may be limited in eliciting the

actual attributes used for VC decision-making. Second, our infer-

ences on common normative frameworks are based on the

technology industries and may not be generalizable outside this

context. Third, the norms measured in this study are strictly

based on investment preferences. Norms in due diligence, infor-

mation seeking, and resource configuration may also be possible.

Fourth, our study design did not enable us to examine normative

rationality vis-�a-vis another type of decision-making, for example

neoclassical economic rationality.

6. Future research directions

In addition to the directions stemming from the above limita-

tions, the present study suggests a number of promising direc-

tions for future research. First, future studies could explore how

knowledge sharing and social interactions facilitate knowledge

convergence. Specifically, researchers could investigate whether

certain experiences (such as working together in the same firm or

studying on the same MBA program) are more likely to lead to

homogeneous thinking. Along this line, researchers could inves-

tigate syndication networks in more detail, particularly the

socialization and knowledge-sharing processes. Second, as the

VC industry varies across countries (e.g., Pandey and Jang, 1996;

Wonglimpiyarat, 2007) and there are vast differences in other

aspects of the national institutional environment, future researchers

could examine normative rationality in other national VC

environments. This line of work would extend Kirchhoff’s signifi-

cant interest in the phenomenon of entrepreneurship across

countries (Davidsson et al., 2002). Third, future research could

incorporate longitudinal exploration, examining and the evolution

of macroculture over time. In doing so, researchers would further

Kirchhoff’s calls to explore how thinking may evolve from pure

neoclassical economic rationality (e.g. Kirchhoff, 1994). Fourth, we

discussed how macroculture is driven at individual, firm, and

industry levels. Over time, individual learning is embedded into

firm related routines and processes which, in turn, could drive

convergence toward shared knowledge, beliefs, and values. As

firms in an industry become increasingly interdependent they are

more likely to share markets and common resource bases, which

could drive common managerial schemas and frameworks.

Although we cannot test these multilevel effects due to data

limitations, future research could focus on an omnibus test of

macroculture driven by shared belief and values at individual,

firm, and industry levels (we thank an anonymous reviewer for

this suggestion). Fifth, we observe that entrepreneurship research

borrows theories from economics, sociology, and finance to

explain the phenomenon. Kirchhoff (1994) was one of the first

to question the underlying assumptions of mainstream economic

theories and their relevance in entrepreneurship research. Con-

tinuing in the spirit of Kirchhoff’s (1994) call, we believe that

studies in entrepreneurship could benefit by exploring both main-

stream theories and more contextually relevant theories.

7. Conclusion

Beyond the economic and social embeddedness arguments

of venture finance, normative rationality influences venture financing

criteria. If rational financing processes or social embeddedness

processes affect venture finance, then one might expect consider-

able heterogeneity in values and behaviors due to the obvious

variety in VC firm characteristics. However, in this study,

we observe homogeneity in business plan values and VC firm

characteristics. Thus, we find evidence that a macroculture pre-

vails in the VC industry, which entails VCs sharing values and

behaviors in the decision-making process.

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