A Decision Making Model for Selecting Start-up Businesses in a Government Venture Capital Scheme

Management Decision
A decision making model for selecting start-up businesses in a government
venture capital scheme
Eric Afful-Dadzie, Anthony Afful-Dadzie,
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A decision making model for
selecting start-up businesses
in a government venture
capital scheme
Eric Afful-Dadzie
Faculty of Applied Informatics,
Tomas Bata University in Zlin, Zlin, Czech Republic, and
Anthony Afful-Dadzie
Business School, University of Ghana, Accra, Ghana
Abstract
Purpose – The purpose of this paper is to propose an intuitionistic fuzzy technique for order
preference by similarity to ideal solution (TOPSIS) multi-criteria decision making method for the
selection of start-up businesses in a government venture capital (GVC) scheme. Most GVC funded
start-ups fail or underperform compared to those funded by private VCs due to a number of reasons
including lack of transparency and unfairness in the selection process. By its design, the proposed
method is able to increase transparency and reduce the influence of bias in GVC start-up selection
processes. The proposed method also models uncertainty in the selection criteria using fuzzy set theory
that mirrors the natural human decision-making process.
Design/methodology/approach – The proposed method first presents a set of criteria relevant to
the selection of early stage but high-potential start-ups in a GVC financing scheme. These criteria are
then analyzed using the TOPSIS method in an intuitionistic fuzzy environment. The intuitionistic
fuzzy weighted averaging Operator is used to aggregate ratings of decision makers. A numerical
example of how the proposed method could be used in GVC start-up candidate selection in a highly
competitive GVC scheme is provided.
Findings – The methodology adopted increases fairness and transparency in the selection of start-up
businesses for fund support in a government-run VC scheme. The criteria set proposed is ideal
for selecting start-up businesses in a government controlled VC scheme. The decision-making
framework demonstrates how uncertainty in the selection criteria are efficiently modelled with the
TOPSIS method.
Practical implications – As GVC schemes increase around the world, and concerns about failure
and underperformance of GVC funded start-ups increase, the proposed method could help bring
formalism and ensure the selection of start-ups with high potential for success.
Originality/value – The framework designs relevant sets of criteria for a selection problem,
demonstrates the use of extended TOPSIS method in intuitionistic fuzzy sets and apply the proposed
method in an area that has not been considered before. Additionally, it demonstrates how intuitionistic
fuzzy TOPSIS could be carried out in a real decision-making application setting.
Keywords Decision making, Start-up businesses, Government venture capital (GVC),
Intuitionistic fuzzy TOPSIS (IFS)
Paper type Research paper
Management Decision
Vol. 54 No. 3, 2016
pp. 714-734
© Emerald Group Publishing Limited
0025-1747
DOI 10.1108/MD-06-2015-0226
Received 10 June 2015
Revised 7 October 2015
5 January 2016
Accepted 11 January 2016
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0025-1747.htm
This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S,
further by financial support of research project NPU I No. MSMT-7778/2014 by the Ministry of
Education of the Czech Republic and also by the European Regional Development Fund under
the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089. Further, this work was supported by Internal
Grant Agency of Tomas Bata University under the project No. IGA/FAI/2015/054.
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1. Introduction
Venture capital (VC) investment is proving to be the mainstay in the lives of start-up
businesses around the world, especially those in the “high-tech” industry. Evidence
from the USA, Europe, China, India, Canada and Israel, points to a gradual global
acceptance of VC support for early stage but high-potential businesses. In 2013, global
VC investment was estimated at US$48.5 billion (Ernst & Young, 2014). According to
Bertoni et al. (2011), Gompers and Lerner (2004), Chemmanur et al. (2011) and
Alperovych et al. (2015), there is enough evidence to show that the commercial success
rates of start-up businesses that receive support from VC far outweigh those that do
not receive any such supports. However, in recent times, due to very strict demands on
start-up businesses from Private Venture Capitalists (PVCs) and the apparent lack of
opportunities at securing financial support through traditional investment sources,
many governments around the world have joined the fray as far as VC investment is
concerned (Bertoni and Tykvová, 2015; Nkusu, 2011; Colombo et al., 2014). For instance,
in Europe, a total of 40 per cent of all VC investments in 2013 were reported to have
come from their governments. Similarly in the USA, the federal government’s Small
Business Innovation Research programme is the single largest investor of early stage
innovations (Ernst & Young, 2014; Audretsch, 2003; Lerner, 2000). Government
venture capital (GVC) is also quite popular in Brazil, Russia, India, China and South
Africa and in developing countries where private VC funding are hard to come by
compared to what exist in the USA and Europe (Ernst & Young, 2014). In spite of the
growing interests in GVCs, many studies report of a worrying trend of GVC supported
early stage businesses underperforming against their counterparts that obtain funding
from PVCs (Brander et al., 2008; Luukkonen et al., 2013; Grilli and Murtinu, 2014;
Alperovych et al., 2015; Bertoni and Tykvová, 2012). A number of reasons for the
underperformance has been offered. Some authors assert that the selection process
in GVC schemes lack the rigorousness demanded in PVC schemes (Christofidis and
Debande, 2001; Leleux and Surlemont, 2003). Many also cite the undue influence of
political and pressure groups (usually aligned with governments) as a major reason for
the poor performance of GVC funded start-up businesses (Knoesen, 2009; Nattrass and
Seekings, 2001; Iheduru, 2004). Others also argue that such underperformance is
exacerbated by the lack of models that explicitly feature important aspects of the
selection process such as uncertainties in some of the evaluation criteria (Zacharakis
and Shepherd, 2001; Muzyka et al., 1996; Zacharakis and Meyer, 2000). This paper
addresses the concerns of such authors and proposes a model for the evaluation and
selection of start-up businesses in a GVC scheme that incorporates uncertainties in the
selection criteria. To do this, the paper first contrasts GVCs with PVCs for a better
understanding of their differences and subsequently, the reasons why GVC funded
start-ups usually underperform against PVC funded start-ups.
1.1 PVCs vs GVCs
The main objective of a PVC firm is to generate enough returns for its investors and
maximize their value typically above the level of the public equity markets (Mulcahy,
2014). The fear of losing investors, guide PVC firms to avoid favouritism in the
selection of start-up businesses. In view of this, a candidate start-up technology
business must have high potential to succeed and demonstrate the ability to make
significant profits over a period of time. With this in mind, PVCs usually look out for
early entrepreneurs that can deliver impressive growth within specific time period,
typically not more than six years (Da Rin et al., 2011).
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To reduce the chances of failure, PVC funded start-up businesses typically undergo
lengthy and demanding screening processes (Landstrîm, 2007; Lerner, 2002). PVCs also
place greater emphasis on the experience of the management team and often demand
for a representation on the management board of the start-up firm so as to be able to
monitor and prevent wasteful spending that may derail the development and growth of
the business (Chemmanur et al., 2011; Lerner, 2002). PVCs are prevalent in the
information systems sector, and also in some specific health sectors such as
the pharmaceutical industry (Lerner, 2002).
GVCs on the other hand, mostly operate in sectors that normally lack VC financing
such as education, environment and health sectors (Lerner, 2002). They usually fund
start-ups that possess promising technology beneficial to society but which lack the
necessary funding to bring the technology to fruition. In this regard, technologies with
potential to spawn positive externalities, such as those with prospects of stimulating
growth in other sectors, have higher chances of attaining GVC funding (Lerner, 2002).
Since the main objective of a GVC investment is welfare maximization to the state,
GVCs demand rates of returns tend to be far lower than that of a typical PVC (Griliches,
1992). As a result, a GVC investment might not yield direct monetary profit to the state
and could still be considered a success. GVC investments are usually subject to
statutory terms and conditions in respect to the type of investments and the manner at
which the investment is carried out (Landstrîm, 2007). Most often however, such terms
and conditions are less stricter than those of PVCs.
Using data from the VC industry in Belgium, Alperovych et al. (2015) finds that
PVC-backed firms are more efficient than GVC-backed firms. More tellingly, they find
that GVC-backed firms are less efficient than non-VC-backed firms. PVC-backed
companies also mostly meet exiting deadlines and conditionalities than GVC-backed
companies (Cumming et al., 2014; Chemmanur et al., 2011; Luukkonen et al., 2013;
Bertoni and Tykvová, 2012; Brander et al., 2008). Grilli and Murtinu (2014) show in
their study that PVC funding leads to increase growth in new start-ups than GVC
funding. Lerner (2002) also finds that a prevalent characteristics among
underachieving start-up companies is that most are funded through research grants
from government agencies.
A number of factors could account for the gap in performance. Some of these are low
capital recovery rates and undefined exit paths for candidate start-up businesses in
GVC schemes (Biekpe, 2004). In addition, unlike PVCs, GVCs usually do not require a
position on the management team of the start-up company. The lack of involvement by
GVCs in the management team (and therefore lack of proper monitoring) of the start-up
company is believed by many as one of the main reasons why GVC funded start-ups
underperform compared to PVC funded start-ups (Chemmanur et al., 2011;
Cumming, 2007). Without proper monitoring, it is easy for a start-up firm to engage
in over spending or lose focus and venture into business programmes unrelated to the
original business idea. Furthermore, Christofidis and Debande (2001) observed that
most GVCs are run by inexperienced civil servants who are less motivated unlike their
counterpart fund managers at PVCs. Leleux and Surlemont (2003); Meyer and
Mathonet (2011) explain that the seeming lack of motivation of government staff at
GVCs, is because they do not directly share in returns that accrue to the GVCs they
manage. There are also the criticisms of an apparent lack of robust selection criteria
(Bertoni et al., 2011), lack of due diligence in the selection process (Baeyens et al., 2006),
poor programme design and implementation challenges (Lerner, 2009) in GVCs.
The award of GVC funds to start-ups, unlike in PVCs, is prone to biases and
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favouritism (which consequently could lead to failure) since selection could be influenced
by powerful interest groups aligned to governments and politicians who may seek to
direct the award of GVC funding in a manner that benefits themselves (Cumming, 2007;
Lerner, 2002) and their constituents. This is especially the case in developing countries
where selection of candidates for such capital financing schemes are sometimes clouded
by political, tribal and social affiliations (Nkusu, 2011). According to Pina-Stranger
and Lazega (2011) and Sorenson and Rogan (2014), these challenges could be avoided
or their impact mitigated through a transparent decision making process (devoid of
personal ties and affiliations) for selecting start-up businesses.
These observations have led to a renewed interests in research aimed at improving the
performance of GVCs (Munari andToschi, 2015). One of such interest is a mechanism for a
transparent and efficient decision making process for determining the commercial viability
and the eventual selection of a technology start-up business in a government-run VC.
Any such decision-making mechanism must be able to address the problems listed
above including that of bias and favouritism. More importantly, the mechanism must
place greater importance on the need for effective management team for the success of
the start-up.
1.2 Research gap
Some non-fuzzy decision-making models for evaluating and selecting start-ups in VC
financing schemes have been proposed. Woike et al. (2015) used computer simulation to
study the impact of different strategies on the financial performance of VCs. Riquelme
and Rickards (1992) proposed a self-explicated, hybrid conjoint model to aid the
selection of start-ups for financing in a VC scheme. These non-fuzzy methods rely on
historical data of past beneficiaries to arrive at a decision. This approach may not all
the time be appropriate for assessing and selecting early stage entrepreneurs that have
little or no past data and might lead to sub-optimal decisions. Since future values of
data needed for evaluation are uncertain at the time of selection, fuzzy models that have
the ability to explicitly consider uncertainty in the models might be appropriate.
The main objective of this paper is therefore to propose a fuzzy multi-criteria
decision making (MCDM) model for the selection of start-ups in GVCs that addresses
the obvious uncertainty problems in such decision problems. It is also hoped that the
proposed approach would help generate interest regarding research in decision models
for VC selection problems.
In our search of literature, only the works of Zhang (2012), and Aouni et al. (2014),
attempt to use fuzzy theory for selecting start-up businesses in a VC. Aouni et al.
(2014) used a fuzzy goal programming approach to model uncertainty. However, such
models cannot accommodate qualitative factors such as leadership experience and
product quality. Zhang (2012), considers fuzziness but only in the weights of the
evaluators and not in the values for the competing start-up candidates. Zhang (2012)
also does not explicitly model uncertainty but instead attempts to overcome it using
entropy technique to determine the weights. The model by Zhang (2012) is a
combination of an optimization and a multi-attribute model that seeks to select a
candidate based on maximizing risk-adjusted returns. In contrast, our proposed
approach uses a multi-attribute model to generate a composite index that however takes
qualitative factors as well as a “more is better” and a “less is better” criterion into
account. By considering uncertainties directly in the values for assessment, the proposed
intuitionistic fuzzy technique for order preference by similarity to ideal solution
(TOPSIS) framework is thus more efficient at modelling the natural thought processes of
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humans in decision making. The proposed decision framework also includes selection
criteria specially tailored to address challenges faced by GVCs such as bias and
favouritism, as well as ascertaining the effectiveness of the management team of the
start-ups. The proposed method in particular can be used to help address some of the
challenges encountered in the selection of start-up businesses especially in a government
high priority area such as in information systems/information communication
technology (IS/IT) sectors. This is because selecting the ideal start-up to support in IS/
IT areas can be very challenging and complex since most of the criteria involved are
subjective or hold uncertain data (Pina-Stranger and Lazega, 2011).
The rest of the paper is organized as follows. First, an elaborate selection criteria
that hinge on the attainment of the objectives of a GVC and the success of the start-up
business culled from literature is introduced. This is followed by a methodology
comprising of an introduction to classical fuzzy set theory and its extension into
intuitionistic fuzzy sets (IFS), especially as used in decision making. Next is
a systematic outline with definitions and formulas of intuitionistic fuzzy TOPSIS
method to help select potential candidates in a highly competitive but limited funding
situation in a GVC programme. Finally, a numerical example of how intuitionistic fuzzy
TOPSIS could be used to rank and select high-potential start-ups in a government
backed VC is illustrated.
2. GVC funded start-up business selection criteria
Several authors have researched into the main criteria used by venture capitalists to
evaluate start-up businesses. Table I summarizes major works on these criteria in the
literature, particularly those relevant to GVC schemes. The criteria under entrepreneur/
team personality, entrepreneur/team experience, and product or service potential, model
qualitative attributes whiles the criteria under financial characteristics, market
characteristics and social impact/contribution model quantitative criteria. As can
be perceived from the criteria, the relevant values needed for evaluation cannot be
determined in the present time but must be estimated based on the judgement of experts.
In classical decision analysis, possible outcomes with their probabilities of occurrence
would be considered in the final decision making. In the case where qualitative criteria
are present, such uncertainty can be modelled using fuzzy theory that is able to
accommodate both qualitative and quantitative criteria. The next section gives brief
introduction to fuzzy theory and its extension to intuitionistic fuzzy TOPSIS.
3. Methodology
3.1 Modelling subjectivity with IFS
According to Hisrich and Jankowicz (1990) and Mitchell et al. (2005), venture capitalists
use many subjective criteria and intuition in their decision making. In view of this,
research must focus on developing methods that model the intuition and the
subjectiveness in the selection process. This section introduces the fuzzy concept that is
generally used to model intuition and subjectivity in human decision making processes
such as that of start-up business selection. The notion of fuzzy set theory was proposed
by Zadeh (1965) as a mathematical construct to help deal with issues of uncertainty,
subjectivities, vagueness and imprecision in human judgments (Afful-Dadzie
et al., 2014). Since the conception of fuzzy set theory, it has successfully been applied
in many areas including situations that demand efficient modelling of human decisions
and judgments (Wang, 1999; Klir and Yuan, 1995). In addition, several extensions and
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