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This paper provides insights into the way in which angel investors evaluate the investment potential of new ventures. Previous research has identified a broad array of different investment criteria that are thought to directly influence whether an investment opportunity is perceived as attractive by angel investors. This study contributes to this stream of research by identifying what is referred to as the investment paradox which occurs when the basic and fundamental investment criteria associated with a new venture are positively evaluated, yet the venture is simultaneously evaluated as exhibiting relatively poor investment potential. A model in which the relationship between fundamental investment criteria and overall investment potential is moderated by fit-based technology development criteria is proposed as an explanation for this apparent paradox. This model is tested with a large sample of investment proposals evaluated by angel investors using multi-level modeling. The results support the proposed moderated model of angel investor evaluation criteria. These results have clear and important implications for entrepreneurs, angel investors, public policy, and future research focused on angel investors’ evaluations of investment opportunities.
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Venture Capital
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The investment paradox: why attractive new
ventures exhibit relatively poor investment
potential
Kevin C. Cox, Jason Lortie & Kimberly Gramm
To cite this article: Kevin C. Cox, Jason Lortie & Kimberly Gramm (2017) The investment paradox:
why attractive new ventures exhibit relatively poor investment potential, Venture Capital, 19:3,
163-181, DOI: 10.1080/13691066.2016.1247982
To link to this article: http://dx.doi.org/10.1080/13691066.2016.1247982
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VENTURE CAPITAL, 2017
VOL. 19, NO. 3, 163181
https://doi.org/10.1080/13691066.2016.1247982
The investment paradox: why attractive new ventures exhibit
relatively poor investment potential
Kevin C. Coxa, Jason Lortieb and Kimberly Grammc
aDepartment of Management, College of Business, Florida Atlantic University, Boca Raton, FL, USA; bSchool of
Business Administration, University of Mississippi, Oxford, MS, USA; cInnovation Hub at Research Park, Texas
Tech University, Lubbock, TX, USA
ABSTRACT
This paper provides insights into the way in which angel investors
evaluate the investment potential of new ventures. Previous research
has identied a broad array of dierent investment criteria that are
thought to directly inuence whether an investment opportunity
is perceived as attractive by angel investors. This study contributes
to this stream of research by identifying what is referred to as the
investment paradox which occurs when the basic and fundamental
investment criteria associated with a new venture are positively
evaluated, yet the venture is simultaneously evaluated as exhibiting
relatively poor investment potential. A model in which the relationship
between fundamental investment criteria and overall investment
potential is moderated by t-based technology development criteria
is proposed as an explanation for this apparent paradox. This model
is tested with a large sample of investment proposals evaluated by
angel investors using multi-level modeling. The results support the
proposed moderated model of angel investor evaluation criteria.
These results have clear and important implications for entrepreneurs,
angel investors, public policy, and future research focused on angel
investors’ evaluations of investment opportunities.
Introduction
Informal early stage investors – known as business angels – are private individuals who make
investments directly in unlisted companies in which they have no family connections (Mason
and Harrison 2000, 137). They play an essential role in the entrepreneurship process by
providing much needed early stage funding to new rms characterized by substantial growth
potential. In the USA in 2013, a total of 70,730 entrepreneurial ventures received angel
funding, with the total amount of funding raised exceeding $24.8 billion (Sohl 2014). The
provision of nance to these entrepreneurs creates economic benets in the form of new
job creation. In 2013, angel investments resulted in the creation of 290,020 new jobs in the
United States (4.1 jobs per investment).
© 2016 Informa UK Limited, trading as Taylor & Francis Group
KEYWORDS
New venture financing;
angel investors; opportunity
evaluation
ARTICLE HISTORY
Received 17 February 2015
Accepted 28 September 2016
CONTACT Kevin C. Cox Kcox24@fau.edu
164 K. C. COX ET AL.
Despite this profound impact that angel investors have on entrepreneurs, the growth of
startups, and economic activity in general, there is still much to be learned about the way
angels evaluate the investment potential of startups and eventually decide whether or not
to invest. Specically, we seek to shed light on what can be referred to as the investment
paradox which is clearly evident in the way angels evaluate new venture opportunities. The
investment paradox occurs when angels generally provide a positive evaluation of a startup,
yet simultaneously view the investment potential less favorably when compared to other
investment criteria. We seek to oer an explanation as to why this investment paradox occurs
among angel investors. Developing a thorough understanding of this investment paradox
has the potential to provide multiple contributions to research focused on angel investors
as well as informing entrepreneurs, investors, educators, and policy-makers.
The paper is organized as follows. The next section provides an overview of angel investor
research, highlighting the key factors and criteria that have been previously identied as
inuential in angel investors’ evaluations of investment potential as well as their eventual
hypotheses. Section four provides a description of the sample and research methodology.
Section 5 presents the complete analysis and results followed by a brief discussion. Finally,
the implications are discussed.
Overview of angel investment research
Angel investors operate markedly dierently when compared to both bank and venture capital
(VC) nancing. First, angel investors, unlike VCs and banks, are willing to invest at the earliest
stages of the entrepreneurial process (e.g. Haar, Starr, and MacMillan 1988; Mason and Harrison
1996b; Wiltbank and Boeker 2007). Empirical evidence has found that more than 75% of angel
nancing occurs during either the seed or startup-stage of the venture while less than 10%
occurs during later stages (Wiltbank and Boeker 2007). Angel investors, therefore, fulll an
important gap in venture funding between personal nances and family/friends nancing
and more traditional institutional investors (e.g. venture capital) and mainstream nancial
services providers (e.g. banks). “Technology angels” have been found to invest in even earlier
stages than their non-technology counterparts (Erikson and Sørheim 2005). Second, banks
and VCs generally invest dierent amounts compared to angels. In most cases, banks invest
less, and the amount is dependent upon collateral being available, while VCs generally invest
substantially larger amounts. Angel investor investments vary quite substantially, but the
most recent average investment in the United States by angels is estimated at approximately
$350,000 which is more than banks and substantially less than VCs (Sohl 2014). Third, angels
are investing their own money whereas banks and VCs are investing money raised from third
parties. One implication is that angels are able to make much faster investment decisions.
Finally, angel investors utilize dierent criteria when assessing the investment potential of
new ventures. Empirical evidence suggests that bankers place more emphasis on nancial
aspects of the proposal and give little consideration to the market or the entrepreneur, while
VC fund managers and angels emphasize both the market as well as nancial issues (Mason
and Stark 2004). However, there are dierences between the way VCs and angels evaluate
investment opportunities, with VCs placing greater emphasis on detailed business plans and
competitive advantage when compared to angels (Haar, Starr, and MacMillan 1988)
Prior to 1980, the informal capital market within which angels operate was largely
unknown to, or overlooked by, both academia and public policy-makers (Haines, Madill, and
VENTURE CAPITAL 165
Riding 2003). (Homan (1972) is an exception.) Wetzel’s (1983) seminal work spearheaded
an initial stream of angel investor research which attempted to more clearly dene the size
of the market and the characteristics of the investors. This early work spawned further
research on angels from around the globe which has resulted in a well-developed under-
standing of the market and the characteristics of the individual actors in the angel investor
market (Mason 2006). A more detailed review of the historical developments in research
focused on angel investors is provided by Mason and Harrison (1999), Mason (2006), Kelly
(2007), and Politis (2008).
More recently, a stream of research focused on angel investor activity has emerged. Much
of the existing research on angel investor activity has explicitly focused on how angel inves-
tors evaluate new venture opportunities and eventually make the decision whether or not
to invest in a new venture. This stream of research is particularly important because of its
widespread practical implications for entrepreneurs seeking investment from angels.
Procuring an investment from an angel investor has proven to be a challenging task for
entrepreneurs, with previous studies reporting that rejection rates are consistently high,
ranging from 75 to 95% globally (Argerich, Hormiga, and Valls-Pasola 2013). Even in instances
when the entrepreneur has an idea worthy of investment, the inability to attract funding
can be attributed to factors such as poor decision-making processes on the part of investors,
market ineciencies (Mason and Harrison 2002), and lack of information (Wetzel 1987).
However, rejection is most commonly based on the evaluation of the actual opportunity by
the angel investor (Mason and Harrison 1996a).
Some of the earliest studies focused on the way angel investors evaluate new venture
opportunities and eventually make investment decisions. They highlighted the importance
of both individual preferences (e.g. Homan 1972) and also the investor’s previous knowl-
edge and experience in the industry or market in which the entrepreneur is operating (e.g.
Shapero 1983; MacDonald 1991). When compared to venture capital investors, research has
consistently found that angels are more prone to make investment decisions based on an
array of non-nancial considerations as opposed to more explicitly basing decisions on the
expected return on investment (ROI; Van Onsabrugge and Robinson 2000). For example,
one reason that angels consistently cite for investing is the satisfaction they derive from
helping entrepreneurs and being involved with the business venture (Freear, Sohl, and Wetzel
1995; Mason and Harrison 2002). Building on this earlier research, more recent ndings
suggest that the t between the angel and the entrepreneur is an important factor in the
investment decision (Mason and Rogers 1997; Mason and Stark 2004; Paul, Whittam, and
Wyper 2007). Other research highlights the importance of the investment proposal, man-
agement team competency, and the future market potential of the rm (Haar, Starr, and
MacMillan 1988; Mason and Harrison 1996a; Haines, Madill, and Riding 2003).
The growth in the number of studies focused on angel investor decision criteria has
produced a comprehensive and extensive array of dierent factors that are thought to be
important to the evaluation of potential new ventures and which are therefore expected to
inuence the eventual decisions of angel investors. For example, Maxwell, Jerey, and
Moren’s (2011) extensive review of independent variables that have been previously shown
to inuence the decision-making process of angel investors identies 35 dierent criteria.
However, many of these results are inconsistent or even contradictory. For example, whereas
the ndings of Mason and Stark (2004) and Paul, Whittam, and Wyper (2007) suggest that
the investor-entrepreneur “t” is important to the investment decision process, Sudek (2006)
166 K. C. COX ET AL.
ndings suggest that some t-based criteria are not all that important to angels. Similarly,
some ndings suggest that the angel’s previous experience and knowledge of an industry
or market is an important factor in the investment decision (Shapero 1983) while other
ndings suggest that angels’ investment decisions are not heavily inuenced by previous
industry experience (Haar, Starr, and MacMillan 1988).
Comparing the ndings of studies that investigate the reason why angels choose to invest
in proposals (e.g. Landstrom 1998) with others that examine factors that inuence why
angels reject proposals (e.g. Mason and Harrison 1996a) reveal that the reasons that angels
reject opportunities are not the opposite of the reason that they choose to invest in oppor-
tunities (Feeney, Haines, and Riding 1999). Adding to the complexity, some studies suggest
that dierent criteria may be important at dierent stages of the investment process (e.g.
Brush, Edelman, and Manolova 2012). Recent ndings suggest that angels do not apply a
fully compensatory model but instead rely on heuristics (Maxwell, Jerey, and Moren 2011).
Finally, an emerging stream of research has examined the importance of other factors that
are thought to inuence the decision process of angels, notably perceived passion (Mitteness,
Sudek, and Cardon 2012) and impression management (Parhankangas and Ehrlich 2014).
In addition to the extensive array of dierent criteria that are purportedly important to
angels’ evaluations of potential investments and the various inconsistencies between studies
are numerous methodological challenges and shortcomings of previous research in this
area. First, many studies have relied on small sample sizes (e.g. Bachher and Guild 1996;
Mason and Harrison 1996a; Mason and Stark 2004; Paul, Whittam, and Wyper 2007) suggest-
ing that these ndings should be interpreted with caution as they may not be widely gen-
eralizable. Second, many previous studies rely on the recollections (using retrospective
questionnaires) of the angels, rather than on actual behavior which can be inuenced by a
variety of dierent biases, again suggesting the results must be interpreted with caution.
Third, many of the decision-making criteria constructs utilize only single item indicators
which may be too simplistic to fully capture the construct of interest. Fourth, some research
has attempted to analyze the investment criteria used for the entirety of the investment
process even though criteria may be dierent at dierent stages of the process (Landstrom
1998; Brush, Edelman, and Manolova 2012). Finally, the overwhelming majority of previous
empirical research may oversimplify angel investor evaluations by only looking at basic
relationships between the investment potential of a venture and explanatory constructs as
opposed to intervening moderating or mediating factors.
This study seeks to address many of these limitations of the previous research focused
on angel investment decision criteria. Specically, it develops and tests what we refer to as
the investment paradox. Then, in order to explain this apparent paradox, we oer a moderated
model of some criteria that inuence angel investors’ evaluation of new venture investment
potential.
Theoretical development and hypotheses
The investment paradox
The investment criteria that have previously been identied as inuential in angel investment
decisions (e.g. Maxwell, Jerey, and Moren 2011) can be included under one of the following
criteria: internal criteria, external criteria, t criteria, and technological criteria. Internal invest-
ment criteria include constructs associated with entrepreneur or team. Examples include
VENTURE CAPITAL 167
management team capability, experience, passion, and knowledge all of which have been
identied as important criteria to angels (e.g. Bachher and Guild 1996; Feeney, Haines, and
Riding 1999; Haines, Madill, and Riding 2003) and for establishing venture legitimacy during
the early stage of new ventures (Navy and Glynn 2011; Pollack, Rutherford, and Nagy 2012).
We focus specically on management capability as it represents one of the most fundamental
internal investment criteria.
External criteria include constructs related to the viability of the market associated with
the opportunity; examples include market size, growth potential, and competitive advantage
which have also been identied as important investment criteria (e.g. Haar, Starr, and
MacMillan 1988; Landstrom 1998). We focus explicitly on what can be broadly referred to as
market opportunity because this represents a fundamental external investment criterion.
Fit criteria include all constructs that represent how well a specic new venture oppor-
tunity is suited to a particular angel. Examples include entrepreneur t, industry t, and
business t that have all been identied as inuencing the investment decision (e.g. Shapero
1983; Paul, Whittam, and Wyper 2007). In terms of t, we focus specically on industry expe-
rience and educational background, as they both have an important role in determining the
investment potential of a new venture, as discussed in detail in the following section. The
complete model and all hypothesized relationships are depicted in Figures 1 and 2.
We argue that internal and external criteria represent basic and fundamental investment
criteria that must be evident in order for any new venture opportunity to warrant serious
consideration of investment. We also speculate that these criteria are viewed as fundamen-
tally important in the minds of entrepreneurs seeking investment to the extent that no
reasonable entrepreneur knowingly seeks investment without having established these
minimal and fundamental investment criteria, though the extent to which an investment
opportunity will exhibit these criteria will vary. However, a new venture which simply exhibits
the minimally acceptable investment criteria does not oer adequate investment potential.
We argue this is due to the considerably high level of risk and uncertainty associated with
the early stage ventures that angels typically invest in. In eect, investors may view the
potential investment as generally positive as it exhibits favorable internal and external cri-
teria, but not attractive for them personally.
To be clear, we do not expect that basic internal and external investment criteria will be
negatively related to the investment potential as evaluated by an angel investor. Instead,
we expect there to be a strong positive relationship, yet the evaluation of overall investment
potential will be signicantly lower than the fundamental investment criteria. This is the
Management Capability
H1
Investment Potential
H1
Market Opportunity
H2
Investment Potential
H2
Figure 1.Evidence of investment paradox.
168 K. C. COX ET AL.
investment paradox which occurs when angel investors give positive evaluations of the core
and fundamental attributes of business opportunity (including both internal and external
criteria), but simultaneously give the overall investment potential a relatively poor
evaluation.
Anecdotal evidence of this phenomenon is widely available and can also used to inform
the theorizing of this investment paradox. For example, it is common for the winners of
business plan and pitch competitions to receive almost perfect scores (e.g. istart.org), yet
very few of these rms achieve funding. We argue that exhibiting fundamental criteria that
contribute to high scores in competition are not insucient to warrant investment. Simply
put, angels will generally perceive the new venture as a good idea but will remain unwilling
to invest. As discussed previously, we focus on management capability as a fundamental
internal investment criterion and market opportunity as a fundamental external investment
criterion. The preceding arguments culminate in the following hypotheses which represent
the investment paradox.
H1: Management capability will be rated higher on average than the overall investment potential.
H2: Market opportunity will be rated higher on average than the overall investment potential.
H3: Management capability will be positively related to the overall investment potential.
H4: Market opportunity will be positively related to the overall investment potential.
Risk reduction and the moderating role of t
As an explanation for this counterintuitive phenomenon of the investment paradox, we
argue that the relationship between fundamental investment criteria and nal judgment
about the overall investment potential is heavily moderated by a variety of t criteria (e.g.
how well the specic investment opportunity is suited for the specic investor). As previous
Management Capability
Market Opportunity
Investment Potential
Industry Experience
Educational
Background
New
Technology
H3
H4
H5 H6
H7 H8 H9 H10
Figure 2.Explanation of investment paradox.
VENTURE CAPITAL 169
evidence has shown, t-based investment criteria are not fundamental criteria that must
characterize an opportunity in order for it to warrant investment potential (e.g. Haar, Starr,
and MacMillan 1988; Sudek 2006). However, t has been found to be an important aspect
of angel investors’ decision-making criteria (Mason and Rogers 1997; Mason and Stark 2004;
Paul, Whittam, and Wyper 2007). Therefore, because t criteria are not independently
required for a new venture opportunity to represent an attractive investment, but remain
important to angels, we conclude that this represents a moderated relationship. Specically,
t criteria moderate the relationship between basic internal and external investment criteria.
In fact, when only the t-based criteria are evident, the overall attractiveness of the invest-
ment is not heavily (i.e. directly) inuenced, as evident in the inconsistent ndings previously
discussed. As such, this explanation of the investment paradox also serves as a proposed
explanation for contradictory ndings associated with t-based investment criteria.
The rationale for this proposed moderated relationship revolves around the high level of
risk associated with investing in the early stage of new ventures. Seed and early stage rms
are characterized by very high levels of risk because of considerable uncertainty about their
future outcomes. Unlike investing in later stages, early stage investments are characterized
by uncertainty and idiosyncratic risk, rather than systematic risk (Korteweg and Sorensen
2010). The riskiness of these early stage investments is clearly evident in widespread variation
in investment returns (Wiltbank and Boeker 2007).
Previous research has established that angel investors engage in a variety of behaviors
aimed primarily as risk-mitigation (e.g. Erikson, Sorheim, and Reitan 2003; Kelly and Hay
2003; Lahti 2011). We posit that risk reduction strategies are also employed when angels
evaluate the investment potential of new venture opportunities. Further, opportunities that
are best suited for an angel, in terms of t, serve as one way in which angels can attempt to
mitigate risks. Specically, risks can be reduced when the industry that the new venture is
operating in is one that an angel investor has previous experience working in (i.e. the new
venture itself ‘ts’ with the angel). Risk is reduced when the angel possesses unique resources
(e.g. Alvarez and Busenitz 2001) derived from extensive experience and knowledge of the
specic industries. These resources can aid them both in their ability to evaluate the invest-
ment potential associated with a new venture, and also contribute to the growth and devel-
opment of the venture by providing unique knowledge and insight. As such, industry
experience within the same industry that the new venture is operating in can reduce the
risk associated with the investment opportunity which, when combined with fundamental
internal and external investment criteria, results in higher overall investment potential. This
is summarized in the following hypotheses.
H5: Angel industry experience in the industry the new venture is operating positively moderates
the relationship between management capability and investment potential.
H6: Angel industry experience in the industry the new venture is operating positively moderates
the relationship between market opportunity and investment potential.
Based on similar rationale, we also expect that the angel’s perceived ‘t’ with the entre-
preneur is a further factor that can serve to reduce the overall risk associated with the invest-
ment. A number of previous studies have suggested that the t between the entrepreneur
and the angel is an important factor that can inuence the investment decision (e.g. Mason
and Stark 2004; Paul, Whittam, and Wyper 2007). Specically, we focus on the educational
background of the entrepreneur as compared to the industry experience of the angel
170 K. C. COX ET AL.
investor (e.g. accounting degree – CPA). The educational background of the entrepreneur
refers to the focused area of study in higher education (e.g. engineering or computer science).
The industry experience of the investor serves as a proxy for their educational background.
These functional backgrounds can have implications for angels as they signal the knowledge
and capabilities of the entrepreneur which can be instrumental in determining whether or
not they will make a good t. Further, when angels possess similar knowledge and experi-
ence, they can more accurately determine the resources that the entrepreneur possesses
that are applicable to the new venture and thereby creating additional value. Educational
background also inuences communication style which can inuence how eective the new
venture opportunity is communicated to the angel investor. In summary, a situation in which
the educational background of the entrepreneur matches the experience of the angel is not
expected to directly inuence the investment potential of the new venture. However, based
on the preceding arguments, it is suggested that when combined with fundamental internal
and external investment criteria of management capability and market opportunity, the
match between the entrepreneur’s education and the angel’s experience can reduce the
overall risk associated with investing in the new venture, resulting in increased overall invest-
ment potential. Put simply, a match between the entrepreneur and angel moderates the
relationships between both management capability and market opportunity and funding
potential.
H7: Matching entrepreneur educational background and angel industry experience positively
moderates the relationship between management capability and investment potential.
H8: Matching entrepreneur educational background and angel industry experience positively
moderates the relationship between market opportunity and investment potential.
New technology and the moderating role of the risk/return ratio
The development of new technology, or other forms of breakthrough innovation, also has
an important role in inuencing the way that angels evaluate the investment potential of a
new venture. On the one hand, it can potentially result in a very attractive investment (e.g.
Shane and Cable 2002). But, if new technology is not accompanied by credible management
or sucient market opportunity this will reduce its investment potential. This relates back
to the riskiness associated with early stage investing that angels engage in. However, it is
not directly associated with risk mitigation. Instead, the development of new technology
directly inuences the risk-return ratio associated with the investment opportunity.
Specically, the development of a new technology can result in the potential for consid-
erably higher investment returns (Roure and Keeley 1990). First, the development of new
technology can enable rapid scalability. Perhaps the most tting example would be the
development of new software or licensing new technology to large established rms.
Second, in some cases, the development of new technology and associated intellectual
property can result in a competitive advantage via the resulting formation of barriers to
competitor entry.
Therefore, the development of new technology can increase potential returns without
drastically increasing the risk prole of the investment (Mason and Harrison 2004). Thus,
new technology can modify the risk-return ratio associated with the venture in favorable
and attractive manner. The result is that when the fundamental internal and external criteria
have been established, and are combined with the development of new technology, the
VENTURE CAPITAL 171
venture will be evaluated as having a greater investment potential. This proposed moderated
relationship is summarized in the following hypotheses.
H9: The development of new technology positively moderates the relationship between man-
agement capability and investment potential.
H10: The development of new technology positively moderates the relationship between market
opportunity and investment potential.
Research design and sampling procedure
We sought to gather data from what is referred to as the screening or selection stage (Riding,
Maddill, and Haines 2007; Maxwell, Jerey, and Moren 2011), which represents the initial
stage of the angel investment process. We specied this stage for two reasons. First, this
preliminary stage corresponds to the highest rejection rate (Riding, Duxbury, and Haines
1995). Second, this enables us to avoid confounding results from across dierent stages
which are characterized by dierent investment criteria (Landstrom 1998; Brush, Edelman,
and Manolova 2012). An annual business plan competition held annually at a university in
Southeastern region of the United States served as a suitable site for data collection.
Following other research in the area (e.g. Mason and Rogers 1997; Mason and Stark 2004),
business plans served as proxies for investment proposals and were evaluated as such.
Contestants in the competition consisted of both students and non-students with the larger
proportion of competitors being non-students. These contestants were competing for
$100,000 – $200,000 in cash and in-kind prizes which were awarded to the top six ventures.
The top ranking ventures received a larger proportion of cash and prizes.
We collected data from annual competitions held during 2012 through 2014. All business
plans were pre-screened in order to eliminate poorly executed or incomplete plans that
could not be properly evaluated. Statistical tests were performed in order to determine if
there were any statistical dierences across the years. Specically, we compared all control,
independent, and dependent variables across each of the three years using ANOVA analysis
to determine if there were any signicant dierences. No signicant dierences were iden-
tied between the three years, so the individual years were pooled to create a single database
of 241 total business plans.
The judges for this business plan competition comprised both active and inactive angel
investors as well as others with explicit knowledge and experience of the angel investment
process. Each business plan was randomly distributed to between 4 and 10 dierent “judges”
who were tasked with evaluating the plans on a number of criteria as well as assessing the
overall investment potential of the plan. Again, statistical analysis was performed on the
scores of the dierent judges from the dierent years and no signicant dierences were
evident. We also attempted to determine if there were any rating aects because of dierent
judges across the dierent plans, or, from having a dierent number of judges assigned to
each plan. Since inter-rater reliability could not be calculated due to the way plans were
judged by dierent judges and number of judges, we instead calculated the average amount
of within plan variance there was for each item across each year. We found that the average
within plan variance of judge ratings was consistently around 20% for the seven rating items
across all three years (minimum of 19.7% and maximum of 23.3%). Therefore, all of the
judges’ evaluations were combined resulting in 1359 ratings that were provided by 127
total judges.
172 K. C. COX ET AL.
Independent variables
Market opportunity
The market opportunity criterion was measured using three items. These items were used
to assess the established market potential, market advantage, and market risks associated
with the new venture proposal. Each item was measured using a 5-point Likert scale ranging
from “Excellent” to “Poor. Item wording can be found in the Appendix 1.
Management capability
The management capability criterion was also measured using three items. The individual
items were used to capture overall team assessment, financial acumen, and the pres-
entation of the proposal. Again, each item was measured using a 5-point Likert scale
ranging from “Excellent” to “Poor. Item wording can be found in the Appendix 1. It is
challenging to accurately capture and comprehensively assess the full range individual
management capabilities using content included in a business plan. However, each of
the business plans includes an entire section focused on the management team. This
section includes a brief biography, usually accompanied by additional information which
can provide insight into management ability. Further, in many instances, a complete
resume or another form of additional information about the founder(s) is included in the
Appendix 1.
Industry t
All judges provided background information about their previous business experience
in the form of a short biography. Two authors independently classied all judges’ experi-
ence based on their industry backgrounds. Judges ranged from having experience in one
industry up to ve industries. Judges could be classied in terms of 13 dierent industries.
Next, the industry experience of the judges was compared to each of the plans that they
judged, which resulted in the creation of the binary variable where “0” represented that
the judge’s background did not match the industry the rm was operating in and “1”
represented that the judge’s background did match with the industry of the venture being
judged.
Entrepreneur t
We also sought to measure whether or not the educational background of the lead entre-
preneur matched with the previous industry experience of the judge (e.g. degree in nance
– nancial advisor) which serves as a proxy for entrepreneur and angel t. To do so, we
compared the industry classications of the judges (see above) with the educational back-
ground of the lead entrepreneur. This again resulted in a binary variable with “0” representing
no t and “1” representing that the educational background of the entrepreneur did match
with the industry experience of the judge (i.e. angel investor).
Development of new technology
Upon submission, participants were required to record whether or not their venture gener-
ated new technology. The development of new technology was recorded as “0” if no new
technology was generated by the venture and “1” if new technology had been generated
by the business venture.
VENTURE CAPITAL 173
Control variables
Gender
Previous research suggests that gender can have an important role in predicting an array
of dierent entrepreneurial outcomes (e.g. Hisrich and Brush 1986). More specically, gender
has been found to be inuential in the angel investment process (Becker-Blease and Sohl
2007). Therefore, a dichotomous dummy variable was created in order to control for any
gender eects. The gender of the lead entrepreneur was coded as “0” for female and “1” for
male.
Money invested
The amount of prior progress that a new venture has made, or readiness of the rm, can
inuence evaluations of a ventures investment potential and eventual funding (Brush,
Edelman, and Manolova 2012). In order to control for the stage, or progression, of each
investment opportunity, we controlled for previous investment. New ventures that had not
been previously invested in (by owners or outside investment) were coded as “0” and those
that had been invested in were coded as “1”.
Start-up team
We also sought to control for teams vs. individual entrepreneurs because the team size could
have implications for the overall investment potential. Proposals that were submitted by an
individual entrepreneur were coded as “0” and those that were submitted by a team were
coded as “1”. Per the competition’s guidelines, no teams exceeded ve total team
members.
Dependent variable
Investment potential
The overall investment potential associated with each of the plans was measured by a single
item designed to capture whether or not the opportunity, as a whole, represented an attrac-
tive investment opportunity that the judge would seriously consider investing in. This item
was measured using a 5-point Likert scale ranging from “strongly disagree” to “strongly agree.
Analysis
The testing of the hypotheses proceeded as follows. First, a factor analysis of the seven judge
rating items was run in order to conrm the external and internal factors of market oppor-
tunity and management capability, respectively (three items each). In addition, the depend-
ent variable of investment potential was also included as its own single item indicator to
measure overall model t.
Once the CFA conrmed the factor structure, ANOVA analysis was used to test hypotheses
one and two. The ANOVA tested the hypotheses that the mean scores between market
opportunity, management capability, and investment potential were signicantly dierent,
while post hoc analysis using multiple comparison techniques provided signicance tests
for each potential construct pairing. Ninety-ve percent condence intervals for each con-
struct pairing were also calculated.
174 K. C. COX ET AL.
Finally, hierarchical linear modeling (HLM) was utilized to test hypotheses three through
10. HLM is an appropriate analysis when data are constructed or measured in a nested nature
such as employees in rms, students in schools or, as is the case with our analysis, multiple
judges within individual plans. Regression and other traditional OLS methods require that
observations are independent of each other. However, since the judges’ ratings are inherently
related to the individual plan, such an assumption cannot be made. HLM controls for this
grouping eect by rst estimating the eect that the group has on the DV and then esti-
mating the dierent eects of the predictors at the two dierent levels of analysis. Hypotheses
3 and 4 evaluate the direct eects of management capabilities and market opportunity while
Hypotheses 5–10 evaluate the interaction eects.
Results
The overall results of our analysis largely support the theory developed above. First, the CFA
conrmed that the two a priori factors were valid. As seen in Table 1, all items had standard-
ized loadings of at least 0.71. The market opportunity factor had a Cronbach’s Alpha of 0.82
while the management capabilities factor has a Cronbach’s Alpha of 0.83. Once the two
factors were constructed, means, standard deviations, and correlations were computed and
organized in Table 2.
Table 3 displays the results of the ANOVA and post hoc analyses using the Tukey HSD, LSD,
and Bonferroni tests. The post hoc analysis conrmed both Hypothesis 1 and Hypothesis 2,
that market opportunity and management capability would both be rated signicantly
higher than investment potential. The consistent results of all three tests lend credence to
these results. On average, market opportunity and management capability were rated 0.49
and 0.54 higher than investment potential on a ve-point scale, respectively. This translates
to investment potential being rated 10% lower than market opportunity and 11% lower
than management capability on average. These signicant results lend quantitative support
to the existence of the investment paradox.
Finally, the results of the HLM analysis are presented in Table 4. Prior to any hypothesis
testing, we rst checked the independent and control variables for any multicollinearity.
Variance ination factor scores were all under the suggested cuto of 10. In model 1, we
entered investment potential as the dependent variable alone in order to get a baseline for
future comparisons. Since HLM does not produce traditional R2 estimates, a base measure
of model variance is rst acquired via a basic estimation. It should be noted that the level 2
Table 1.Confirmatory factor analysis loadings.
Market opportunity
Established market potential 0.82
Distinctive competency 0.83
Foreseen risks 0.71
Cronbach’s alpha 0.82
Management capabilities
Team assessment 0.77
Financial acumen 0.80
Proposal presentation 0.79
Cronbach’s alpha 0.83
RMSEA 0.00
χ2166.06
DF 12.00
VENTURE CAPITAL 175
Table 2.Means, standard deviations, and correlations.
*Correlation is significant at the 0.05 level; **Correlation is significant at the 0.01 level
Mean STDV 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1 Gender 0.71 0.454
2 Money invested 0.306 0.461 0.006
3 Start-up team 0.414 0.493 .090** −0.017
4 Industry fit 0.213 0.41 0.016 0.001 0.036
5 New technology
developed
0.546 0.498 0.150** 0.061* 0.104** 0.028
6 Entrepreneur fit 0.299 0.458 0.123** −0.053* 0.156** 0.068* −0.012
7 Market opportunity 2.96 1.03 0.054* 0.071** 0.123** −0.04 0.162** −0.071**
8 Management
capabilities
3 1.06 0.033 0.101** 0.141** −0.03 0.135** −0.095** 0.785**
9Industry fit × Mkt
opportunity
0.61 1.27 0.018 0.027 0.061* 0.928** 0.065* 0.037 0.133** 0.109**
10 Technology × Mkt
opportunity
1.7 1.71 0.146** 0.068* 0.120** 0.02 0.904** −0.034 0.450** 0.358** 0.102**
11 Entrepreneur
fit × Mkt
opportunity
0.85 1.41 0.125** −0.023 0.173** 0.041 0.024 0.923** 0.138** 0.074** 0.058* 0.056*
12 Industry fit × MGMT
capabilities
0.63 1.3 0.016 0.034 0.057* 0.931** 0.066* 0.034 0.100** 0.136** 0.976** 0.089** 0.047
13 Technol-
ogy × MGMT
capabilities
1.71 1.73 0.141** 0.063* 0.124** 0.029 0.900** −0.041 0.385** 0.432** 0.096** 0.958** 0.035 0.106**
14 Entrepreneur
fit × MGMT
capabilities
0.85 1.42 0.112** −0.004 0.176** 0.045 0.019 0.917** 0.099** 0.126** 0.054* 0.038 0.966** 0.060* 0.049
15 Investment
potential
2.462 1.29 0.052 0.089** 0.148** −0.032 0.145** −0.061* 0.759** 0.736** 0.098** 0.372** 0.095** 0.088** 0.371** 0.096
176 K. C. COX ET AL.
intercept is signicant in this model (along with every other model) suggesting that the
grouping eect of the judge ratings is a signicant predictor of investment potential. This
basic nding shows that the plans are signicantly dierent from each other in terms of
investment potential, and lend credence to the use of HLM since the grouping eect is large.
In model 2, we entered the three plan level control variables while in model 3 we entered
the rst judge level variables of market opportunity and management capabilities. As it can
be seen in model 3, both market opportunity and management capabilities are positively
and signicantly related to investment potential while controlling for the grouping eects
of judge ratings within individual plans. These results hold in model 4 and model 5 when
other predictors are added. These results conrm both Hypothesis 3 and Hypothesis 4 and
provide more quantitative evidence for the existence of the investment paradox.
Table 3.Investors paradox ANOVA with post-hoc tests.
*Difference is significant at the 0.05 level.
Post hoc test DV IV
Mean
dierence
95% condence interval
Lower bound Upper bound
Tukey HSD Investment
potential
Market opportunity −0.493* −0.594 −0.391
Management capabilities −0.542* −0.644 −0.440
LSD Investment
potential
Market opportunity −0.493* −0.578 −0.408
Management capabilities −0.542* −0.627 −0.457
Bonferroni Investment
potential
Market opportunity −0.493* −0.597 −0.389
Management capabilities −0.542* −0.646 −0.438
Table 4.Hierarchical linear modeling results – DV of investment potential.
**Significant at the 0.05 level; ***Significant at the 0.01 level.
Hypothesis Model 1 Model 2 Model 3 Model 4 Model 5
Level 2 model
Intercept 2.34*** 2.04*** 2.39*** 2.21*** 2.01***
Entrepreneur gender 0.07 0.05 0.04 0.04
Investment 0.28** 0.01 0.01 0.03
Start-up team 0.42*** 0.08 0.07 0.08
New technology (NT ) 0.03 0.05
Level 1 model
Industry fit (IF) 0.18** 0.05
Entrepreneur fit (EF) 0.31** 0.09
Management capabilities (MC) H3 0.83*** 0.83*** 0.48***
Market opportunity (MO) H4 0.27*** 0.27*** 0.22***
MC × IF H5 0.54***
MO × IF H6 0.13**
MC × EF H7 0.46***
MO × EF H8 0.11**
Cross-level moderation model
MC × NT H9 0.23***
MO × NT H10 −0.12**
Level 1 variance 1.1957 1.1941 0.8919 0.8311 0.5985
Level 1 Pseudo R20.00 0.25 0.30 0.50
Level 2 variance 0.4559 0.4094 0.2216 0.2013 0.1716
Between level Pseudo R20.10 0.51 0.56 0.62
χ2782.24*** 722.34*** 612.14*** 610.96*** 584.98***
DF 240 237 237 236 237
VENTURE CAPITAL 177
By combining the results of Hypotheses one, two, three, and four, we show evidence for
the investment paradox. The results show that market opportunity and management capa-
bilities are signicant predictors of investment potential, yet, investors are still hesitant to
invest even after acknowledging that the proposal has quality.
In model 4, we entered the dichotomous predictor variables that would set up the mod-
erating variables tested in model 5. The level 2 predictor of new technology is not a signicant
predictor of investment potential while industry t and entrepreneur t both are. These
exploratory results suggest that the potential for new technology in a plan does not inuence
an angel’s investment potential rating.
In model 5, we entered the six interaction terms to test Hypotheses 5 through 10. We
found support for Hypothesis 5, that industry t signicantly and positively moderates an
angel’s investment potential rating based on management capability. Hypothesis 6 also
received support, suggesting that industry t also positively moderated the relationship
between market opportunity and investment potential. We found support for Hypothesis
7 that entrepreneur t positively moderates the relationship between management capa-
bility and investment potential. We also found support for Hypothesis 8, suggesting that
entrepreneur t also positively moderates the relationship between market opportunity
and investment potential. It should also be noted that both industry t and entrepreneur
t are no longer signicant with the presence of the interaction terms.
We also display in Model 5 the results of the cross-level moderating eects of new tech-
nology being present in the plan. We did not nd support for Hypothesis 9, that new tech-
nology would positively moderate the market opportunity and investment potential
relationship. To our surprise, the analysis showed that new technology has a negatively
signicant moderating eect on the market opportunity and investment potential relation-
ship. However, we did nd signicant support for Hypothesis 10, that new technology pos-
itively moderates the management capability and investment potential relationship.
Discussion
The empirical ndings provide considerable support for the existence of the hypothesized
investment paradox. There is also evidence that suggests that some of the investment criteria
that angels utilize (e.g. industry and entrepreneur t) can potentially be more accurately
modeled as moderators, rather than having direct eects on the overall evaluation of the
investment potential of a new venture. The proposed moderated model, therefore, provides
a sucient explanation of the investment paradox. Moderating eects may also provide an
explanation for some of the inconsistencies evident in previous research ndings, though
further investigation is required.
Only one hypothesized relationship did not receive empirical support. Indeed, the rela-
tionship was in the opposite direction of what was expected suggesting that when com-
bined, the market opportunity and the development of new technology negatively inuences
overall investment potential. We speculate that this may occur because although the devel-
opment of new technology increases the potential return on the investment, the presence
of a strong market opportunity simultaneously increases the perceived risk associated with
the investment, therefore negatively inuencing the risk/reward ratio. The combined market
opportunity and development of new technology could potentially increase the perceived
risk for a number of reasons. First, it could be that angels assume many competitors are also
178 K. C. COX ET AL.
working toward exploiting this market opportunity with similar (or better) technology.
Second, when considerable market opportunity exists and is coupled with potentially dis-
ruptive technology, there may be concern about the risk that large incumbent competitors
will swiftly enter the market by developing their own technology. Third, there may be a
concern whether a small startup rm can eectively develop the technology required to
satisfy the considerable market needs and defend it against larger competitors.
Conclusion
The ndings have important implications for future research on the way that angel investors
evaluate investment opportunities. In particular, the ndings suggest that future research
should more thoroughly consider investment criteria that have direct eects as well as those
that may have moderating eects. This will require the development of more sophisticated
and accurate models that can more precisely predict the investment potential of new ven-
tures seeking funding from angels.
The ndings also have important implications for practitioners. They suggest that entre-
preneurs seeking funding should do more than simply try to ensure that their rms exhibit
the fundamental investment criteria required for consideration by angels. Entrepreneurs
also need to consider other factors that inuence the evaluation of their rm’s investment
potential, notably, the industry-angel t, the entrepreneur angel-t, and the development
of new technology. This can enable them to partially determine which angels might be most
suitable for them to approach. Future research should also investigate other factors which
inuence entrepreneur-investor t and therefore inuence the assessment of a venture’s
investment potential. For example, in addition to industry experience and educational back-
ground, other personal and demographic factors may also inuence t.
The ndings provide angels with an explanation of how dierent factors (that they may
or may not be aware of) inuence their assessment of investment potential. Although these
ndings do not speak directly to the performance of angels’ eventual investments, the mod-
erating eects, or biases, toward certain industries and/or entrepreneurs, may have impor-
tant implications for investment returns. In addition, with the emergence of angel groups
(or syndicates), the once inecient market processes associated with angel investing (e.g.
Mason and Harrison 2002) are evolving. For example, entrepreneurs historically relied on an
introduction to an angel from someone in their network whereas now many angel groups
have online portals through which entrepreneurs can simply apply for funding consideration.
Developing a better understanding of the most eective and inuential criteria that can be
used to assess and evaluate potential investments can further enhance the eciency and
eectiveness of these application and assessment processes.
Finally, these ndings, although preliminary, have implications for pedagogy. They can
inform educators about how to best instruct entrepreneurship students in terms of seeking
funding from angel investors. Educators can instruct students on the important investment
criteria, as well as how to determine which angels are most suitable to approach given a
specic new venture.
Disclosure statement
No potential conict of interest was reported by the authors.
VENTURE CAPITAL 179
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Appendix 1
Measures
Judges were instructed to evaluate each of the following items for each investment opportunity. All
items were measured using a 5-point Likert scale ranging from “Excellent” to “Poor”
Market Opportunity (3-items)
Established market potential: There is a clear denition of the product/service oered and the market
VENTURE CAPITAL 181
need to be served, as well as a way to take advantage of that market need. The target customer and
market size are well dened and quantied. The market shows high potential.
Market advantage: The competing products/services and their marketers are analyzed well. The com-
pany provides something novel/unique/special that gives it a competitive advantage in its target
market. Its plan to develop and commercialize their product/service is clearly described and is credible.
Market risks: The business plan describes any major business or technology risks that are foreseen, as
well as contingency plans to overcome them. The milestones for measuring success at dierent stages
of implementing the business plan for the venture are clear and credible.
Management Capability (3-items)
Team assessment: The team can eectively develop this company and handle the challenges associated
with the venture. If the management team does not have any required skill or experience, a credible
plan has been provided to access it, for example, via an advisory board.
Financial acumen: The team has a solid understanding of the nancial requirements of the business.
The bases for revenues, expenses, capital expenditure projections (if applicable), as well as external
funding requirements, are clearly described and are credible.
Proposal presentation: The team presented their venture in a logical, persuasive manner, free of typo-
graphical and grammatical errors.
Investment Potential
Overall investment potential: The venture’s protability, risk and return on investment prole are attrac-
tive. The business represents a real investment opportunity complete with milestones at the dierent
stages of product/concept development in which you would consider investing.
... Despite these research efforts to uncover early-stage investment criteria, anecdotal evidence from the industry points to an investment paradox. This paradox arises when early-stage equity investors favourably evaluate a given investment opportunity based on key attributes related to the entrepreneur and the opportunity independently, but simultaneously rank this same investment opportunity as unfit for early-stage funding (Cox et al., 2017). Furthermore, an evolving perception has emerged in the literatureone that emphasizes the evaluation of an investment opportunity as 'an extremely complex task, highly dependent both on the experience and intuition of the specific investor' (Ferrati & Muffatto, 2021, pp. ...
... However, researchers have still assumed that investors use a well-defined set of so-called 'acceptance' and 'rejection' criteria (Carpentier & Suret, 2015;Croce et al., 2017;Mason et al., 2017) that they consider one-by-one. Cox et al. (2017) argued that many investment opportunities that receive almost perfect scores on these investment criteria are not able to secure early-stage funding. A so-called investment paradox occurs when early-stage equity investors favourably evaluate an investment opportunity based on key criteria, but still rate the investment potential as poor (Cox et al., 2017). ...
... Cox et al. (2017) argued that many investment opportunities that receive almost perfect scores on these investment criteria are not able to secure early-stage funding. A so-called investment paradox occurs when early-stage equity investors favourably evaluate an investment opportunity based on key criteria, but still rate the investment potential as poor (Cox et al., 2017). This paradox has sparked researchers' interest in the interaction effects between different investment criteria. ...
Article
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To date, much of the research on early-stage equity investing has revolved around the question of whether early-stage equity investors place more importance on the entrepreneur (jockey) or the business opportunity (horse). Yet research has failed to agree on the relative importance of these two aspects of investment opportunity. The purpose of this study is to clarify how early-stage equity investors use available information about the entrepreneur and the business opportunity to make investment decisions. We analysed empirical data from semi-structured interviews with experienced early-stage equity investors active in Europe. In the analysis, we followed the twin slate approach, which accounts for literature review in the analytical process. Our results suggest that investors make sense of the business opportunity as a whole by integrating information about the entrepreneur and the business opportunity. We identified four aspects of early-stage investor decision-making that led us to conclude that investors’ evaluation of investment opportunities is holistic in nature. The study offers a number of practical implications for investors and entrepreneurs and enriches ongoing discussions about early-stage investors’ investment criteria.
... More recently, Cox et al. (2017) suggest that an 'investment paradox' exists when fundamental criteria about the team, the product and the market are met but investors are still unwilling to invest in a new venture. They show that the relationship between the business angels' criteria and the overall investment potential of a new venture is moderated by the business angels' industry experience. ...
... Intangible assets in regard to communication are of crucial importance for the success of a business idea in this second stage of the process, especially because entrepreneurs at this early stage typically cannot refer to a convincing track record of achievements (Rasmussen & Sorheim, 2012;van Werven et al., 2019). However, the effect of the communication of the same business idea can vary depending on the fit between investors and entrepreneurs (Cox et al., 2017) and the cognitive scheme that govern the attention and information evaluation of the investors (Drnovsek et al., 2018). Hence, the decision-making process in which business ideas are evaluated is characterised by a large degree of subjectivity. ...
Article
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It is of key importance to identify the degree of novelty and probability of incorporation of business ideas in an early stage, so that targeted support of these different types of entrepreneurship is possible. Selection of business ideas for investments and support programs rely on quantitative and qualitative metrics. The qualitative assessment, however, is biased by subjective impressions and experiences of the decision-maker. Therefore, this paper examines the narrative of business idea descriptions to improve the identification of the degree of novelty and to enhance the estimation of the incorporation probability by advancing the objectivity of qualitative metrics. The paper aims to answer two questions: (1) Are there differences in topic prevalences in novel and non-novel business ideas?, and (2) Does the composition of topics related to a business idea influence its incorporation probability? Structural topic modelling and classification tree analysis are applied on business idea descriptions from a competition in Bremen, Germany, from 2003 until 2019. The results show that business idea descriptions are a rich source of information to identify novel and non-novel business ideas with higher incorporation prospects.
... Further, it is our hope that engaging and applying these processes might have positive implications that reverberate well beyond the initial startup phase of new ventures. For example, establishing both hygiene and motivational factors in MVPs may enhance perceived investment potential of an early-stage venture (Cox et al., 2017) by enabling entrepreneurs to rely on consumers positive reactions as opposed to mere hopefulness or optimism (e.g., Wales et al., 2019). In summary, we believe that by clarifying the path to MVPs that will have a positive response from the environment, entrepreneurship educators will be able to more effectively provide structure for their students as they attempt to find product-market fit with their startups. ...
Article
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Lean startup methodologies are believed to reduce the overall risk and cost for launching new businesses. Many of these methodologies provide processes and tools that aid new entrepreneurs in their attempts to make informed decisions before, during, and after the launch of their minimum viable product (MVP). Drawing on theories from the Knowledge Based View, Organizational Learning, Lean Entrepreneurship, and Herzberg's Two-Factor Theory of Hygiene and Motivating Factors, we propose a theoretical framework of incremental innovation and lean launch that is capable of increasing the probability of the MVP receiving a positive environmental response. Our framework models the phenomena of responses to MVPs within a specific market through knowledge of existing offerings and the ideas we introduce around satisfaction and dissatisfaction as two separate continuums of responses intended customers may have to MVPs. Additionally, we propose that the relationship between individual and organizational knowledge can be moderated by the individual’s level of embeddedness, and that the relationship between organizational knowledge and the environmental response to the MVP can be moderated by the organization’s capabilities and access to resources.
... Considering the year of publication, the first article dates back to 1984 (Tyebjee and Bruno, 1984) and the most recent one was published in 2017 (Cox, Lortie and Gramm, 2017). ...
Conference Paper
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What assessment criteria are most widely used by equity investors during their funding decisions? In the context of the so-called picking winner’s problem, which aspect do they consider most? Is it the jockey (entrepreneurial team), the horse (product/service), the race-track (market) or the odds (financials) to make the difference? Despite the investment evaluation funnel being very selective, about 35% of the venture-backed firms actually fail and, considering a conservative estimate, an additional 20% doesn’t provide the expected return on investment. The data therefore indicate that the investment process has large room for improvement. This paper is a systematic literature review of the research about the assessment criteria used by equity investors (venture capital and angel investors) during their investment decision making process. The research is designed around three research questions. RQ.1: what are the criteria used by equity investors to support their decision-making process in venture funding? RQ.2: what are the investment criteria that have been most discussed in the literature? RQ.3: which aspects of the company are mostly assessed by investors? After screening the abstract of 894 unique journal publications, 53 articles were selected for a detailed analysis. The criteria mentioned in every study were registered and 208 distinct drivers were identified. The criteria were classified into 35 specific categories, 11 generic classes and 4 main domains of analysis (respectively related to the venture, the investor, the risks factors and the environment). The high detail and granularity of the analysis is one of the added values of this work compared with previous literature. The authors propose a new approach to research, based on the use of large databases on ventures funding (e.g. Crunchbase). By analysing data on thousands of actual investments, researchers could introduce a radical change of perspective in this field of research.
... It is useful to understand the differences in the target audiences for funding. Bankers place more emphasis on financial aspects with less emphasis on the market or entrepreneur, whereas venture capitalists and angels emphasize both market and financial issues (Cox et al., 2017;Mason and Botelho, 2016). ...
Article
The evolution of innovative products has continued to move business and society into an ever more complex, and digital, world. Researchers have sought to understand how to best support innovation and how decisions are made regarding funding for entrepreneurs seeking to bring their products to market. Funding by investors can shape the direction of an innovation, especially for advances in information technology. The funding can be obtained from multiple sources, ranging from the more traditional angel and venture capitalists to newer, technology-enabled online structures such as crowd-sourced funding sites. This paper seeks to identify the factors that are important for information technology investment decisions, particularly considering the availability of newer funding methods. It starts by reviewing the literature on investment funding and decision making. Then a content analysis is performed, from which six dominant factors emerge: entrepreneur, product, market, proposal, management team, and financial considerations. Each of these factors has multiple dimensions, which are abstracted and categorized into a set of representative characteristics. Implications are provided for research and practice, and directions proposed for future work. Keywords: Information technology, entrepreneurship, technological investments, angel investor, funding, startup, entrepreneur, product, market, proposal, management team, financial, digital innovation, funding, crowd-sourced sites
... A similar argument is made by Capizzi (2015) who states that BAs with stringent "deal killer" criteria will generate higher returns, and that syndications and investor network membership will lead to more refined decision criteria. In this respect, Cox et al. (2017) suggest that decision criteria need to be divided between those that have a direct effect on the evaluation and those with moderating effects (e.g., industry and entrepreneurial fit). ...
Article
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As researchers we need to be relevant, not only to our peers, but also to external stakeholders. We need to make a societal impact. In this study we explore the extent and characteristics of the implications for external stakeholders identified in articles on Business Angels published in Venture Capital: An International Journal of Entrepreneurial Finance between 1999 and 2017. We identified 75 articles on Business Angels. The number of articles on Business Angels has declined over time. Many do not provide any implications for external stakeholders. When researchers provide implications for external stakeholders they are usually vague and in some cases fairly obvious to external stakeholders. We conclude that most of the implications provided will probably never have a large impact on external stakeholders. We suggest that there should be less focus on those scholars who do not have anything to say about policy and practice. Instead, scholars who possess the knowledge to write relevant and insightful implications should be encouraged to increase their contributions.
Article
Building on social-psychological insights into social perception and judgment and empirical findings from the entrepreneurship literature, we propose that early-stage equity investors look at two main dimensions to assess entrepreneurs seeking early-stage financing: competence and cooperativeness. In all, 84 angel investors and venture capitalists active in Europe participated in a conjoint experiment. The results show that investors prioritize entrepreneurs’ competence over their cooperativeness. Entrepreneurs’ competence is even more appealing to investors when combined with coachability. We find that entrepreneurs can compensate for a lack of experience by demonstrating solid market knowledge and appearing to be coachable. Furthermore, the results suggest that investors differ in their consideration of entrepreneurs’ cooperativeness, but not competence, when making investment decisions—a finding that is conditional on investors’ usual level of involvement in their portfolio ventures. We discuss these findings from a theoretical and practical perspective.
Chapter
Once an angel gets deal proposals, how does she deal with it, that’s what we are going to learn here. We will look at the different stages of screening, selection, and due diligence and analyze the criterion and critical aspects of this process in this chapter. Angel investment decision-making starts with a screening of the deals. Typical screening criteria includes source, sector, stage, problem statement, and founder. The investment decision framework of angel investor mostly contains three broad parameters—Market, Idea, and Entrepreneur. Due Diligence in angel investing is mostly informal in nature, including reference checks from third parties.
Article
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Venture capitalists and angel investors usually apply a set of assessment criteria to evaluate the key elements of entrepreneurial projects. However, since each investor considers different criteria, previous researchers who analysed investors’ decision making, ended up analysing a variety of divergent aspects. In this paper, a systematic literature review on the assessment criteria applied by equity investors was carried out. The purpose of this study was to identify and classify all the criteria considered by previous researchers to determine whether some aspects were investigated more extensively than others and to understand the reasons for this type of approach. After screening the abstracts of 894 journal publications, 53 articles were selected for a detailed analysis. In total, 208 unique criteria were identified and were subsequently classified into 35 specific categories, 11 generic classes and 4 main domains of analysis. The high level of detail and granularity of this work is one of its added values and can provide a knowledge base for future researchers who intend to apply new methodologies for the analysis of investors’ decision-making. Starting from the results obtained so far, a new agenda for future research is suggested to encourage a more data-driven approach leveraging data science techniques.
Article
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The objective of this paper is to provide knowledge about the determinants of success in the screening phase of the investment process and to demonstrate its relationship with success in obtaining capital from business angels (BA). This research sets out to achieve this objective by analyzing the impact that the evaluation of the business opportunity, the managing team and the presentation have on success in the screening phase. To do this, the research proposes four main hypotheses that are tested on 215 projects presented at a BA's network. The data for the analysis are extracted both from the BA and from the entrepreneurs. The results show that the evaluation of the presentation is the most important factor that influences success in the screening phase, followed by the evaluation of the business opportunity.
Book
The Life Cycle of Entrepreneurial Ventures discusses topical issues in entrepreneurship organized around the various stages of venture creation, development and performance. The book is arranged in several parts, dealing with the pre-start stage, followed by venture creation, financing ventures, venture development, and venture performance. Each part contains several chapters written by experts in the relevant field. Like its predecessors, there are contributions from several disciplines, including economics, strategy, business, industrial organization, economic geography, finance and sociology. The multi-disciplinary flavor of the book is complemented by its international evidence base, featuring results from a range of different countries. The volume will be essential reading for everyone interested in entrepreneurship because: • It contains digestible overviews of several topical issues in entrepreneurship, including nascent entrepreneurship; social (not-for-profit) entrepreneurship; formal, informal and developmental start-up capital; job creation; venture performance; and harvesting. This will help researchers and practitioners who want to cut through the information overload and distil the key points emerging from the latest academic thinking • It provides new results at the cutting edge of entrepreneurship research • It portrays entrepreneurship as a coherent entity by spanning the various stages of enterprise evolution
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We theorize about how the entrepreneurial identity, which we define as the constellation of claims around the founder, new venture, and market opportunity as to "who we are" and "what we do," serves as a touchstone for investor judgments about new venture plausibility. We propose that entrepreneurial identities are judged favorably when they are legitimately distinctive, and that such judgments are influenced by market context and are mediated by identity narratives that provide institutional primes and equivocal cues in investor sensemaking.
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This paper reports an empirical study of Canadian informal “angel” investors. A key contribution is the development of a portrait of the decision making of these angels as well as a framework which was successful in structuring this decision making. Angels are well educated and experienced as investors. They tend to hold other full time jobs. They invest in new growth-oriented businesses, usually at the earliest stages of business development. They report a shortage of investment-ready businesses in which the principals are willing to partner with experienced investor-mentors. Investors learn about opportunities mostly from business associates. Evaluation tends to be informal, although some investors have extensive sets of due diligence materials. The key dimensions of investable business opportunities are the market potential of the business, the capability of the principals to commercialize the service or product, and the opportunity for investors to make substantive non-financial contributions to the firm.
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This paper provides an analysis of the acceptance and rejection criteria of private investors using formal qualitative analysis. The findings indicate that private investors view the overall business opportunity and the principals of the company as key criteria in the decision-making process. Active and occasional investors differ somewhat in the emphases that they place on particular criteria. Perhaps the single most important finding, however, is that the reasons that prompt investors to reject opportunities are not simply the converse of reasons that prompt them to invest.