Analyzing VC Influence on Startup Success:
They Might Not Be Good For You

Beth Hadleya1, Peter A. Gloorb, Stephanie L. Woernerc, Yuhong Zhoud
a MIT Department of Computer Science, Cambridge MA, bhadley@mit.edu
b MIT Center for Collective Intelligence, Cambridge MA, pgloor@mit.edu
c MIT Sloan School of Management, Cambridge MA, woerner@mit.edu
d 1. South China University of Technology, Guangzhou China 2. MIT Cambridge MA
yuhongz@mit.edu

Abstract We study the impact of venture capitalists on startup success usingsocial network analysis. Using multiple sources, we compile a unique dataset of
3199 US-based technology startups and their board members, from which we
generate and analyze the interlocking directorates network (formal network) andthe Twitter activity network (informal network). We define three metrics of
success: startup funding (collected from Crunchbase), annual sales (collected from
OneSource), and return-on-investment (annual sales / funding). We find thatstartups with more VCs on their board are more centrally located in the formal
network, tend to receive greater funding, have greater annual sales, yet a smaller
return-on-investment. We also find that VCs are significantly more central in theTwitter network than non-VCs, and they have greater Twitter popularity (numberof followers / number of people they follow). Interestingly, VCs tweet
significantly less than non-VCs. Our results indicate that VCs carry a significant
amount of capital, both financially as well as socially, which they transmit to thestartups they become involved with, however their active participation on theboards of startups leads to lower ROI.

1

Corresponding author: Beth Hadley, email: bhadley@mit.edu

2

https://www.irs.gov/uac/soi-tax-stats-individual-income-tax-statistics-2014-zip-code-data-soi

2

1 Introduction
There is much uncertainty involved with early stage technology startups (Giardino
et al 2015). Venture capitalists are faced with literally million dollar questions as
they seek to evaluate startups, to determine the potential of an investment.
Likewise, entrepreneurs are faced with equally valuable questions as they seek
venture capitalists from whom to not only gain funding, but also support andmentorship (Woike 2015).
Fundamentally, we aim to investigate the question of how venture capitalists, inparticular through their networking behavior, influence the success of a startup. To
answer this broad question, we adopt a network theory approach, in which weconstruct social networks of the board members of each startup. We choose to
study the board of directors of a startup, as board directors have a great deal of
influence over a startup due to their financial, intellectual, and social capital
resources. Furthermore, board members often sit on multiple boards, and therefore
may share their resources with multiple startups. The resulting network formed byboard membership represents a communication network, through which criticalresources and information flows.
Collaborative Innovation Networks (COINs) are self-organizing social systems
in which self-motivated individuals collaborate to achieve a common goal (Gloor
2005). We believe the board membership network bears some similar traits to a
COIN network - within this network, multiple players, namely VCs andentrepreneurs, strive to create something new - a new venture and financial gain.
That said, the extent to which such a network is actually "collaborative" remains
an open question. Specifically, we pose the question: to what extent do venture
capitalists in this network contribute to the overall success of the entrepreneurs
and their startups?
To investigate this question, we first construct a formal network, aninterlocking directorates network composed of companies (the nodes) and links
between two companies who share at least one board member (Mizruchi 1996).
We look at the position of successful startups in this network and compare this tothe position of startups with many VCs on their boards. Secondly, we construct aninformal social network composed of the Twitter activity of the individuals in ourformal network, and look at the behavior of venture capitalists in this network. By
looking at venture capitalists’ influence in these networks, we develop evidence
that informs our response to the ultimate research question - how do venture
capitalists influence the success of startups.

2 Background and Related Work
Venture capital (VC) is well established as one of the key driving forces in theAmerican entrepreneurial ecosystem (Insight 2007). According to the National

3

Venture Capital Association, in 2015 nearly $60 billion in venture capital was
deployed across 4,380 deals. More than 30% of those deals were to companies
receiving venture investment for the first time (Franklin & Haque, 2016). Thatsaid, research studies have not provided consistent conclusions regarding theimpact of VC investments on entrepreneurial firms, and whether this impact is a
net positive or negative (Bertoni 2011). Although the answer is likely, “itdepends”, the question is worthy of continued rigorous analysis.
Advocates for the positive influence of VCs claim that VCs serve three main
roles to identify and promote successful startups:
1. “Screening”: VCs choose to invest in high quality companies with promisingpotential. They are experienced at selecting for certain criteria that predict
success, such as technical expertise and founder commitment (Chan 1983,
Amit 1998).
2. “Monitoring”: VCs track the status of their portfolio companies, comparing
investments with market trends and opportunities. They protect the value of
their investments by adding credibility and prestige to those companies they
invest in (Lerner 1995, Kaplan 2003).
3. “Coaching”: VCs provide advice and support to their portfolio companies with
the intent of improving their chances of success and, in return, the return on
their investment. This may include connecting the firm with resources,
networking, assisting with recruitment, providing experience, advice, andmentoring (Hellmann 2002, Hellmann 2000).
Multiple studies have demonstrated the correlation between VC involvement
and startup success: VC-backed firms have faster growth, faster times-to-marketof their products, more patents, higher productivity, greater innovation, higher
efficiency, and are more likely to have a successful exit (IPO or acquisition)(Wright and Robbie 1998, Bernstein et al. 2015, Chemmanur et al. 2008). Thatsaid, many studies fail to isolate the influence of the VC themself on the startup,
as it is often difficult to decouple the effects of screening from those of monitoring
and coaching (Lahr and Mina, 2016).
However, reasons and explanations abound which indicate just the opposite;that venture capitalists can and do have a negative influence on startups and theirentrepreneurs. Foremost, a large body of research exists regarding conflicts
between startup entrepreneurs and VCs. Prior research has identified three mainareas of VC-CEO conflicts: conflicts of interests and unfavorable attributions,conflicts of inefficient collaboration, and conflicts of VC-CEO mismatch (Khanin2013). Conflict does not necessarily have a negative impact on the success of astartup (Higashide and Birley 2002). However, at high levels of occurrence andintensity, conflict is generally considered to be costly to those involved (Reve andStern 1989). Furthermore, the negative impacts of VC investments have been
demonstrated analytically for a number of geographies external to the US,
including China, France, and Singapore (Xi and Su-Sheng 2016, Pommet 2017,
Wang et al. 2003), although to the authors' knowledge no studies in the US havereached similar conclusions. Furthermore, there seems to be a growing sentiment

4

emanating from Silicon Valley that entrepreneurs should be dubious, or at aminimum cautious, of venture capitalist investment (Mullins 2014).
Additionally, a number of studies indicate that communication networks have astrong influence over the success of innovation in companies. In one study ofresearchers at biotech startups, it was shown that innovation success is correlatedwith real-world communication intensity (Allen et al 2016). One may wonder if
communication intensity is independent of geographic constraints, especially in
our world of digital communication tools. Another study, however, demonstrated
communication intensity among biotechnology companies was significantly
higher among companies that were geographically close (Allen et al 2009). A
third study analyzed the digital social networks of entrepreneurs, and found no
positive effects of virtual network size on entrepreneur success (Gloor et al 2016).
Quality of communication, not quantity, appeared to dominate in the digital space.
It remains unclear, however, how these findings regarding communication in thephysical vs. digital space relate to the entrepreneur-venture capital ecosystem.
In this research, we attempt to disentangle the conflicting messages comingfrom academia and industry regarding the impact of VCs on startup success. By
applying a unique approach, namely a network theory analysis of the board
membership network and Twitter social network, we believe our work provides aunique contribution to the literature.

3 Data and Methods
3.1 Network Construction
To construct and analyze our VC-startup networks, we compiled a unique dataset
from multiple sources. We limited the scope of our study to tech startups foundedin the US in the past five years. We extracted 3199 startups from the S&P Capital
IQ database (https://www.capitaliq.com), including a list of the board members ofeach startup as well as whether each individual was a VC or not. We constructed
the interlocking directorates network based on the 8474 total board members,
resulting in a network with 3199 nodes (1 per startup) and links between startups
that share at least one board member. We used the Condor software tool
(www.galaxyadvisors.com) to calculate three centrality measures on this network:
betweenness, degree, and closeness. Betweenness centrality measures the extent towhich a vertex lies on paths between other vertices. Closeness centrality measuresthe mean distance from a vertex to other vertices. Degree centrality measures thenumber of neighbors a node has. These standard measures of centrality generallyindicate how central a node is in the network, and therefore how critical toinformation flow that node is.
We also constructed an informal social network with which to compare against
the formal network, as well as inform our understanding of VC communication

5

behavior. We chose Twitter as the data source for this network as Twitter is thesocial media platform most extensively used by startups and investors, andbroadly used by the business community (Wu et al 2015). We foremost wrote
python scripts to query Twitter's Search API to identify the corresponding Twitter
handle (ID) for the 8474 people in our formal interlocking directorates network.
Our query searched for users by name who included one of their associatedcompany names in their Twitter description or included the keywords "vc","capital", or "partner". This resulted in 1271 matched individuals, whose accuracy
was manually verified on a 10% sample and found to be 87.4% accurate. We
found that 15% of the interlocking directorates network was composed of VCs,
whereas 23% of the Twitter network was composed of VCs. We believe thisrepresentation of VCs is reasonably similar, and relatively realistic.
We used Condor to automatically generate the Twitter network. Links in thisnetwork exist between a person who has tweeted to another person, or a person
who has retweeted another person’s tweet. We limited the network construction to
only the users’ past 100 tweets, which is most representative of an individual's
recent tweeting behavior. Due to technical limitations, we could not analyze morethan 100 tweets in the user's history. This produced a network of 45,521 nodes and168,326 links. We analyzed the Twitter network using standard centrality
algorithms including betweenness, degree, closeness, and reach-2. Reach-2 is arelatively new centrality metric, which indicates the number of nodes the ego canreach in 2 steps.
3.2 Startup Success Metrics
We defined three metrics as the dependent variables with which to evaluate startup
success. Foremost, we collected information about the total amount of funding(venture capital or otherwise) that the startup has received since its founding using
Crunchbase (www.crunchbase.com). Secondly, we collected information about
the startup's annual sales from OneSource (www.onesource.com). We found total
funding information for 1514 of the startups extracted from Capital IQ, and annual
sales information for 525 of these startups. Finally, we defined our third successmetric as return-on-investment, or annual sales / total startup funding. This iseffectively a measure of the efficacy of a startup in transforming dollars ofinvestment into revenue. We consider these three success metrics separately,
however we did observe a positive correlation between startup funding and annual
sales (c=0.51, p= 1.17E-36, n=525). We observed no significant correlation
between ROI and total funding.
Note that one of the fundamental challenges of conducting this kind of study is
not only in determining a good metric of startup success, but also acquiring
accurate data. We acknowledge that our metric choices (startup funding, annual
sales, and return-on-investment) are not absolute measures of startup success, yet
we are confident in the accuracy of our data and we are also confident that they atleast partially reflect overall startup success in a meaningful way.

6

4 Results
Using the dataset previously described, we conducted an analysis to measure theimpact of VC networks on startup success. We discuss a number of researchquestions.

4.1 VCs in the Formal Interlocking Directorates Network
Foremost, in order for us to make any conclusions regarding startup success, we
needed to locate successful startups within our networks. Therefore, weinvestigated the question: are more successful startups more centrally located in
the interlocking directorates network?
To answer this question, we looked for correlations between the various
centrality measures and our three dependent variables (funding, annual revenue,
and ROI). We observed strong positive correlations between total funding andannual sales and all three centrality measures (see Table 1).
Table 1. Correlations between centrality measures and funding & sales in formal networknr: Log of Total Fundingnr: Log of Annual Sales

Betweenness

1514 0.217**

525 0.232**

Degree1514 0.312**

525 0.298**

Closeness1514 0.220**** for p < 0.001

525 0.197**

Startups central in the interlocking directorates network have board members
who are highly connected, likely highly connected venture capitalists sitting on
multiple boards. We tested this hypothesis using a standard Pearson's t-test and did
indeed find VCs to be more central than non-VCs in our network (see Table 2).
Table 2. T-Tests on formal network centrality measures: VCs vs. Non-VCs
mean for
VCs

mean for
Non-VCsnp-value

Mean Betweenness Centrality

1662.56149.807350

9.13e-11

Mean Degree Centrality

3.40

2.87

1124

6.79e-09

VCs not only have on average more connections, but also much higher
betweenness centrality than non-VCs. Combined with our earlier observation
regarding successful startups being more centrally located, we therefore made thehypothesis that the more VCs on a startup's board, the more successful the startup
in terms of total funding and sales. Indeed, we found positive correlations between
VC board membership and total funding (c=0.29, p=6.02E-12, n=525) and7

between annual sales (c=0.21, p=1.99E-06, n=525). This is not surprising, as
venture capital firms that invest in startups often negotiate for board
representation, so a correlation between total funding and VC board membershipis to be expected.
We then looked at VC board membership and ROI, defined as the ratio of
revenue to funding. Surprisingly, we observed a negative correlation between
board membership and ROI (c=-0.10, p=0.02, n=525). We verified these resultswith a Welch Two Sample t-test, where we broke the dataset into two groups:
startups with VCs on their board, and startups without. All t-tests proved
statistically significant with p<0.005. We found that startups with VCs on theirboard earn on average $6.81M more annually in revenue than startups without
VCs on their board, and receive $15.7M more in funding. However, startups
without VCs conclusively experience higher ROI, on average 191% higher thanstartups with VCs. When sorted by ROI, the top 20% of startups have on average
18% VC board membership, whereas the bottom 20% of startups have on average
31% VC board membership. This is statistically significant (t-test p=0.0037,
n=525).

4.2 VCs in the Informal Twitter Social Network
We now turn to an analysis of the informal network, the Twitter network, as we
hypothesized that by analyzing a different, more informal, network ofcommunication, we would likely reach new insights regarding the influence of
VCs on the success of startups.
We began our analysis by first comparing the formal network with the informal
network, as both networks are composed of the same people. Interestingly, we
found little to no statistically significant correlations between these two networks,
neither in terms of centrality measures nor in terms of overlap of core individuals(only 3 people were among the most central in the top 100 of both networks
ranked by betweenness centrality and degree centrality). These top three
individuals (Jon Sakoda: New Enterprise Associates, Roger Lee: Battery
Ventures, Peter Levine: Andreessen Horowitz LLC) are all venture capitalists witha long history in the tech startup industry, sit on 12 or more boards (as comparedto the average number of boards someone sits on in our dataset which is 3.92) andhave an above-median number of Twitter followers.
Our comparison of the formal network with the informal network demonstrated
that the two are very different networks, with no clear correlation between
individuals’ position within each. That said, in our continued attempt to ascertain
the influence of venture capitalists on the success of startups, we reasoned that an
investigation of venture capitalists’ behavior and location in the Twitter network
would inform our understanding of their communication patterns and influence on
startup success.

8

From our previous study, we found venture capitalists to be centrally located in
the interlocking directorates network. This held true for our smaller network
composed of only the 1271 people we found Twitter usernames for. We expectedventure capitalists to be centrally located in the Twitter network as well.
Using a two-tailed unequal variance t-test, we observed VCs are indeed more
central in the Twitter network. We measured statistically significant differences ina number of centrality measures among VCs and non-VCs, with the VC groupconsistently more central than the non-VC group (see Table 3).
Table 3. T-Tests on formal & informal network centrality measures: VCs vs. Non-VCs
n = 1271
p-value T-Test
Betweenness (ID network)
4.19E-06
Degree (ID network)
2.44E-07
Closeness (ID network)
4.78E-16
Betweenness (Twitter network)
0.012
Closeness (Twitter network)
0.029
Degree (Twitter network)
0.011
Reach-2 (Twitter network)
2.49E-07
ID network: Interlocking Directorates network

Mean for VCs Mean for non-VCs
4299
6145
4.34
3.05
0.00010.00012243375
19803700.00360.0035
65.8
60.2
918
670

Given our observation that VCs are more central in the Twitter network, wewere curious to determine what about their Twitter behavior - and by extension
their communication behavior - influenced their network centrality.
We conducted t-tests between VCs and non-VCs on a number of Twitter usage
characteristics, including the number of followers the user has, the number ofpublic lists the user is on, the number of tweets the user has made, and the numberof people the user is following. To prevent distortion from outliers with very manyand very few followers, we performed our analysis on a truncated mean dataset
sample (we sorted the dataset by number of followers, and removed the top andbottom 5%). As a measure of the “popularity” of the user, we took the ratio of thenumber of users the person is following to number of followers the user has. The
lower this number, the more followers the user has in proportion to the number ofusers the user is following, and thus the more “popular” the user on twitter.
The results of this analysis are provided in Table 4. Generally, we discoveredthat VCs truly are more “popular” than non-VCs (the popularity ratio differs by
23% between the two groups, with a t-test significance of 0.012). Furthermore,
VCs have 22% more followers than non-VCs (although this result only has astatistical significance of 0.077). Additionally, VCs appear on 31% more public
lists than non-VCs.
VCs appear to have greater social capital on Twitter than non-VCs.
Interestingly, VCs tend to tweet less than non-VCs (VCs post & repost 38% lessthan non-VCs). However, they have a strikingly higher Twitter popularity ratio ascompared to non-VCs (a higher ratio indicates lower popularity). This means that
VCs truly do - at least in the digital social networking space - have a higher social
capital than non-VCs.

9
Table 4. T-Tests on Twitter characteristics: VCs vs. Non-VCs
Description

T-testMean(VCs)

Mean (nonVCs)

The number of followers this account currently has

0.07734032

3302

The number of public lists that this user is a member of

0.0127

194

149

The number of tweets (including retweets) issued by the user

0.0004

1845

2963

The number of users this account is following

0.3864

794

681

Twitter popularity (# following / # followers)[Lower number is more popular]

0.0119

0.454

0.588

4.3 Network Centrality and Financial Success
These results lead us to the conclusion that VCs are truly popular people guardians not only of money but also social status and information. Clearly, thisbears implications on the success of the startups they fund and sit on the boards of.
To this end, we wondered if we could determine a correlation between centrality
in the Twitter social network and some measure of financial success - either theirpersonal income and/or the funding of the startup(s) they are affiliated with.

4.3.1 Network Centrality and Income
Foremost, we investigated the correlation between an individual's’ position in
the network and their income, as measured by the average income of theirresidential zip code (we extracted this data from the public US IRS dataset from20142). We looked at both the formal and informal networks. We found thatgenerally, the more central someone is located in either the formal or informal
network, the higher his or her income. This was especially true with the measureof 0-2 in the Twitter network (r = 0.14, P = 0.000).
Table 5. Correlations between network centrality measures and someone's income
Correlation with

Log of Average Income

n=1172r

P

Betweenness (Twitter network)

0.079**

0.005

Closeness (Twitter network)

0.038

0.173

Degree (Twitter network)

0.079**

0.005

Reach-2 (Twitter network)

0.145**

0.000

Betweenness centrality (ID network)-0.0120.670

2https://www.irs.gov/uac/soi-tax-stats-individual-income-tax-statistics-2014-zip-code-data-soi

10
Degree centrality (ID network)

0.053

Closeness centrality (ID network)** for p<0.01* for p<0.05

0.066*
0.018
ID network: Interlocking Directorates network

0.061

This would imply that VCs, who are generally more central in both networks, tend
to have greater income. That said, we did not find a statistically significant
difference in income between VCs and non-VCs (t-test P = 0.25 n=830). Clearly,
VCs are not the only individuals in our networks who are generating personal
wealth (the entrepreneurs clearly are as well!).
Additionally, from our earlier analysis, we demonstrated that VCs have asignificantly higher social capital than non-VCs. We reasoned that perhaps thisratio would correlate with residential income. Indeed, we found just that. We
found a significant negative correlation between an individual's’ income (on thelogarithmic scale) and their ratio of # following / # followers. Because this ratio is
inversely proportional to social capital, the greater someone's Twitter social
capital, the greater their income (c=-0.105, p=0.00018, n=1172). Therefore, bytransitive reasoning, we conclude that VCs are not only socially prominent people,
their prominence is rewarded financially.
Our analysis so far enabled us to determine that individual VCs are more central
in both the formal and informal networks, which correlates with greater income
and a higher social capital.

4.3.2 Network Centrality and Startup Funding
Next, we investigated whether an individual's’ position in the Twitter network wasindicative of the financial success of the startup to whom that person is affiliated.
We hypothesized that those individuals who are more central in the network those with higher personal income - would be affiliated with more highly fundedstartups.
The Capital IQ database contained a listing of all the companies each individual
is affiliated with - either as a board member or employee. If a person was affiliatedwith a startup we analyzed in our dataset of 1514 startups (from the formalinterlocking directorates network), we had a funding amount for that startup. In
total, we found funding information for at least one affiliated company for 830
people in our dataset. Less than 10% of these people were affiliated with anotherstartup for which we had funding data, so we decided an accurate and comparable
measure would be to take the maximum startup funding of all startups for which
we had data for each person. We looked at the correlation between our centrality
measures and the log of the maximum affiliated startup funding for the 830 people
with such data. We found significant positive correlations between centrality
measures and startup funding, as seen in Table 6.

11
Table 6. Correlations between Twitter centrality measures and someone's max startup funding
Twitter network

n=830

r: Log of startup fundingp

Betweenness Centrality

0.098

0.005

Closeness Centrality

0.063

0.070

Degree Centrality

0.109

0.002

Reach-2 Centrality

0.244

0.000

All centrality measures except closeness exhibit a significant positive correlation
with their max affiliated startup funding. Interestingly, reach-2 displays the largest
correlation, at 0.24. A possible explanation for this is that in real life, people arevery likely to share leads and opportunities with their close friends (1 degree) andtheir friends of friends (2 degree). Degree is correlated (at 0.109) with funding, but
significantly less correlated than reach-2 (which is 0.24). We reason that degree isless strongly correlated than reach-2 because people really do use their friends-offriends network and don't depend merely on their closest contacts. Using simply
degree to indicate funding misses out on the real-world events that happen due tofriends-of-friends. Thus, the friends-of-friends network (reach-2) is really a muchbetter representation of how information and opportunities spread throughout the
network. It’s not who you know that counts, it’s who your friends know. Thisfinding is supported by the academic world; in fact, the strength of weak ties
(indirect connections, or friends-of-friends) was first presented in Granovetter’s
seminal work “The Strength of Weak Ties” (1973). With over 40,000 references
to date, Granovetter's work explains the prevalence of weak ties and their efficacy
in a variety of personal and professional contexts. Our research appears to affirm
that the VC-startup context is yet another domain where weak ties dominate.

5 Discussion
We’ve conclusively demonstrated that more VCs on a startup's board correlateswith more funding. This is reasonable, because when VCs commit funding to astartup, it is in their best interest to support that startup’s success, not justfinancially but also by providing advice and opportunities. VCs often request aboard seat to gain authority over the startup, and potentially influence decisions
that will yield the greatest return on the VC’s investment.
We’ve also conclusively demonstrated that more VCs on a startup's board
correlates with greater sales revenue. There are a variety of explanations for such a
correlation. Foremost, startup funding amount and sales amount are correlated,which is logical. The more funding the startup receives, the more resources it has
to generate revenue. Additionally, receiving funding from venture capital
investors is an indication that external parties place trust in the startup and expect
a return on their investment, so it is logical to expect such startups to generate
greater sales revenue.

12

Our result indicating that VC board membership correlates negatively with ROI
is somewhat surprising. Certainly, our analysis does not enable us to make any
claims regarding causation, so we are careful not to reach conclusions that makeany causal claims between VC participation and ROI. That said, the presence ofthe correlation is intriguing and we therefore provide the following discussion of
how to interpret this result.
From the startup’s perspective, startup funding is not free money, but rather
money that is traded for company equity and potentially decision-making power.
The more funding a startup receives, the less equity the founders and employees
themselves get to keep. Company sales, however, represent money being
generated by the company that contributes to a company’s valuation. A higher
ROI ratio (sales / funding ) indicates that the value generated by the startup itself the founders and the employees - is likely to stay within the startup and not be
diluted by external investors such as VCs who “purchased” equity via investment.
From the startup’s perspective, a low ROI indicates that the startup has taken on asubstantial amount of funding but not seen a relative level of sales. We haveshown that low ROI correlates with a greater percentage of VCs on a startup's
board. It is likely that in this early stage of startup creation, more VCs invest in astartup and gain board membership, yet the cash they pump into the company inthe form of investment is not matched in terms of startup revenue generated.
This is a disappointing realization, especially since VCs typically invest in
startups with high growth potential. They expect a rapid return on theirinvestment, and will typically do what it takes to direct a startup down this path torapid growth. That said, it is well known that venture capitalists invest in a large
suite of portfolio startups with the hope of just a small percentage yielding
massive returns. According to Dave McClure, a partner at the VC Firm 500
Startups, 50-80% of startups yield no exit or return. 15-25% yield a small return of
2-5x. 5-10% of investments might reach a valuation of $100 million with exits
yielding 10-20x. And unicorns are, of course, extremely rare (<1% reach $1
billion valuations returning 50x or more). In summary, McClure concludes that“…most startup investments fail, a few work out ok, and a very tiny few succeed
beyond our wildest dreams.” (McClure 2015). Perhaps this is simply the innate
process of VC funding, and our data analysis exposes the inefficiencies of thesystem.
On the other hand, startups with high ROI seem to have less VC members on
their board. These startups likely did not receive high amounts of funding, yet are
generating a disproportionately large amount of sales. Because they did notreceive a large amount of startup funding, they were not in a position to need toaccept VCs onto their board. This does seem to lead to the logical conclusion thatstartups without VCs - and therefore without VC funding - seem to do better interms of ROI, at least in the first 5 years. This concept has been written about
numerous times in a number of entrepreneurship blogs and articles, with theunderlying recommendation (as one article put it) “If you are looking towards
more measured growth for your startup, want to keep control or you're simply not13established yet, you probably want to avoid VCs” (Jee 2016). The evidence
certainly seems to indicate the avoidance of VC investment, if possible.

6 Limitations & Suggestions for Future Work

6.1 Demonstrating Causality
One of the fundamental limitations of our work is that our dataset included no
temporal data, and thus we were unable to investigate cause-and-effectrelationships. We were merely able to observe statistically significant correlations,and hypothesize about these correlations under the assumption that they may
indeed be related via causality. Having dependent variable data (startup funding
and startup revenue) over time would have facilitated a causal analysis. Aninteresting question to investigate would be to analyze ROI over the lifetime of astartup, and relate this to VC board participation. Our expectation would be that
ROI would increase as VCs became less present on the board over time. Turning
to the informal network, it would be interesting to analyze the social network
behavior of VCs vs. non-VCs over time, and relate this to the performance of thestartups they are involved with. One would expect VCs to gain popularity on
Twitter as they become more central in the formal network - and thus become
involved with more highly funded startups.

6.2 Revealing Real-World Communication Patterns
The two communication networks we analyzed in this work (interlocking
directorates and twitter) were only proxies for the real-world communication that
we assume occurs between VCs and entrepreneurs. Although we acknowledge ourchoice of networks as a limitation, we argue that both networks do reflect to somereasonable degree of accuracy the presence of communication between two
individuals. With the interlocking directorates network, we know that board
members meet annually and therefore have the opportunity to exchange
information. Via the twitter network, we know that if two people have exchanged
tweets or retweeted the same tweet, they have exchanged information. However,
our results indicate little overlap in individual centrality between the two
networks. This may indicate that the communication captured between these two
networks is of a very different nature. Fundamentally, we cannot claim that the
information exchanged in either network is strictly related to the status or successof the startup in which the individual is engaged. It is likely that the twitter14

network captures much "noisier" communications (non-professional
communications) whereas the interlocking directorates network is more
representative of professional communications. This may explain our observationof little overlap between the two networks. However, we are unable to know forsure.
An interesting possibility for future work would be to construct a
communication network among the individuals in our network which is actually
representative of the real-world, pertinent information that is communicated. Sucha network could be constructed by conducting a survey of the individuals in our
networks, requesting them to respond to information about their communication
patterns on a weekly basis (to whom did they converse, what was the nature of theconversation (business, personal, etc.) By constructing a third network based on
this data, we believe we could develop intriguing insights by comparing to ourformal and informal networks.

6. 3 More Advanced Analytics
We acknowledge that our analysis could be strengthened by building a multiple
regression model to predict startups' success. This is intended as a suggestion for
future work. Furthermore, in building such a model, it would be interesting tocontrol for additional variables present in our dataset, such as gender, VC/startup
geographic location, professional status, income bracket, and/or board
membership role.

7 Contributions & Conclusion

7. 1 Contributions
In this work, we make the following contributions:
1. Demonstrate a novel approach to studying startup success by creating andcomparing formal and informal networks based on startup board of director
membership.
2. Collect a substantial dataset from multiple sources (Capital IQ, Crunchbase,
OneSource, US Tax Data) which did not previously exist and which permitted
investigation of questions not previously analyzed in this way.
Given our analysis of this dataset, we provide evidence for the following
conclusions:15

1. Startups and individuals located more centrally in both the formal andinformal network are generally more successful - in terms of startup funding,revenue, Twitter popularity and personal income.
2. Startups with more VCs on their board tend to receive greater funding, havegreater annual sales, but a smaller return-on-investment (defined as revenue
funding).
3. VCs are significantly more central in both formal and informal networks
than non-VCs, and they have greater popularity (defined as ratio of followers tonumber of people you follow on Twitter). Interestingly, VCs tweet significantly
less than non-VCs, further proving the point VCs are inherently more “popular”
than non-VCs.
4. We found little overlap between the interlocking directorates network andthe twitter communication network, indicating that these are two rather distinct
communication networks despite the fact that centrality in both networks is anindicator of success - both startup success and personal success.

7.2 Conclusion
In our study of the influence of venture capitalists via formal and informal
networks, we have determined that VCs play a central role in the success of astartup. Our work reveals intriguing comparisons between two distinct
communication networks - one composed of startup board membership andanother composed of twitter social networks. From our analysis, it is clear thatVCs can be considered the keepers of funding and information, and therefore holdconsiderable power and influence in the tech startup ecosystem.

References
Allen, T J, P A Gloor, A Fronzetti Colladon, S L Woerner, and O Raz. 2016. “The Power ofReciprocal Knowledge Sharing Relationships for Startup Success.” Journal of Small Businessand Enterprise Development 23 (3): 636–51. doi:10.1108/JSBED-08-2015-0110.
Allen, T J, O Raz, and P A Gloor. 2009. “Does Geographic Clustering Still Benefit High Tech
New Ventures?” ESD-WP-2009-1. Working Paper Series. Cambridge, MA: Massachusetts
Institute of Technology Engineering System Division.
Amit, R., Brander, J., Zott, C. (1998) Why do venture capital firms exist? Theory and Canadianevidence”. Journal of Business Venturing 13, 441.
Bernstein, Shai, Xavier Giroud, and Richard R. Townsend (2015) The impact of venture capitalmonitoring. The Journal of Finance16
Bertoni, Fabio and Colombo, Massimo G. and Grilli, Luca (2011) Venture Capital Financing and
the Growth of High-Tech Start-Ups: Disentangling Treatment from Selection Effects).
Research
Policy,
Vol.
40,
No.
7,
2011.
Availableat
SSRN:
https://ssrn.com/abstract=1102233.2005
Chan Y.S. (1983) On the positive role of financial intermediation in allocation of venture capitalin market with imperfect information. Journal of Finance, 35(5): 1543-1568
Chemmanur, Thomas J. and Krishnan, Karthik and Nandy, Debarshi K. (2008) How Does
Venture Capital Financing Improve Efficiency in Private Firms? A Look Beneath theSurface. US Census Bureau Center for Economic Studies Paper No. CES-WP- 08-16
Giardino C., Bajwa S.S., Wang X., Abrahamsson P. (2015) Key Challenges in Early-Stage
Software Startups. In: Lassenius C., Dingsøyr T., Paasivaara M. (eds) Agile Processes in
Software Engineering and Extreme Programming. XP 2015. Lecture Notes in BusinessInformation Processing, vol 212. Springer, Cham
Gloor PA (2005) Swarm creativity: Competitive advantage through collaborative innovation
networks. Oxford University Press, New York
Gloor, P A, S L Woerner, D Schoder, K Fischbach, and A Fronzetti Colladon. 2016. “Size Does
Not Matter – In the Virtual World. Comparing Online Social Networking Behavior with
Business Success of Entrepreneurs.” International Journal of Entrepreneurial Venturing, inpress.
Granovetter, Mark S. (1973) The strength of weak ties. American journal of sociology. pages
1360-1380. University of Chicago Press
Hellmann, T. (2000) Venture capitalists: the coaches of Silicon Valley. W. Miller, C.M. Lee,
M.G. Hanock, H. Rowen (Eds.), The Silicon Valley Edge: A Habitat for Innovation and
Entrepreneurship, Stanford Univ. Press, Stanford, CA, pp. 267–294
Hellmann, T. and Puri, M. (2002) Venture Capital and the Professionalization of Start-Up Firms:
Empirical Evidence. The Journal of Finance, 57: 169–197. doi:10.1111/1540-6261.00419
Higashide, H., & Birley, S. (2002) The consequences of conflict between the venture capitalist
and the entrepreneurial team in the United Kingdom from the perspective of the venture
capitalist. Journal of Business Venturing, 17(1), 59–81
Insight, G. (2007). Venture impact: The economic importance of venture capital backed
companies to the US economy. Washington DC: Global Insights and NVCA.
Jee, Charlotte (2016) Why You Should Avoid Venture Capitalists Techworld. N.p., Web
Kaplan S.N., Strömberg P. (2003) Financial Contracting Theory Meets the Real World: An
Empirical Analysis of Venture Capital Contracts. Review of Economic Studies, 70:2, p. 281.
Khanin, D., & Turel, O. (2013) Conflicts Between Venture Capitalists and CEOs Of their
Portfolio Companies. Journal of Small Business Strategy, 23(1), 31-54
Lahr, Henry and Andrea Mina (2016) Venture Capital Investments and the Technological
Performance of Portfolio Firms." Research Policy, vol. 45 pp. 303-318. EBSCOhost,doi:10.1016/j.respol.2015.10.001

17
Lerner J. (1995) Venture capitalists and the oversight of private firms. Journal of Finance, 50:
301-318
Dave McClure. 99 vc problems but a batch ain’t one: Why portfolio size matters for returns,
2015. URL https://500hats.com/99-vc-problems-but-a-batch-ain-t-one-why-portfolio-sizematters.
Mizruchi, Mark S. (1996) What Do Interlocks Do? An Analysis, Critique, and Assessment ofResearch on Interlocking Directories. Annual Review of Sociology.
Mullins, John. (2014) VC Funding Can Be Bad For Your Start-Up. Harvard Business Review.
N.p., Web. https://hbr.org/2014/08/vc-funding-can-be-bad-for-your-start-up
Pommet, Sophie. (2017) The Impact of the Quality of VC Financing and Monitoring on theSurvival of IPO Firms. Managerial Finance, vol. 43, no. 4, p. 440.
Robbie, Wright, and Ken Mike (1998) Venture capital and private equity: A review andsynthesis." Journal of Business Finance & Accounting 25.5‐6: 521-570.
T Reve, L.W Stern (1989) The relationship between interorganizational form, transaction
climate, and economic performance in vertical interfirm dyads. L Pellegrini, S.K Reddy(Eds.), Marketing channels: relationship and performance, Lexington Books, Lexington, MA.
Wang, Clement K., et al. (2003) Effects of Venture Capitalists’ Participation in Listed
Companies. Journal of Banking and Finance, vol. 27, pp. 2015-2034.
Woike, J. K., Hoffrage, U., & Petty, J. S. (2015). Picking profitable investments: the success ofequal weighting in simulated venture capitalist decision making. Journal of BusinessResearch, 68(8), 1705-1716. doi:10.1016/j.jbusres.2015.03.030.
Wu, Andy, Fujie Jin, and Lorin Hitt (2015) Social Is the New Financial: How Startups' Social
Media Activities Influence Funding Outcomes." Working Paper.
Xi, Yang and Wang Su-Sheng (2016) Do Venture Capital Firms Play as Supportive Mentors or
Free-Riders? Evidence from China. Journal of Commercial Biotechnology, vol. 22, no. 4, pp.
3-18