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FACTORS INFLUENCING VENTURE CAPITAL
INVESTMENT DECISIONS ON TECHNOLOGY
STARTUPS IN KENYA
BY
PETER KANAKE
UNITED STATES INTERNATIONAL UNIVERSITY-
AFRICA
SUMMER 2023
2
FACTORS INFLUENCING VENTURE CAPITAL
INVESTMENT DECISIONS ON TECHNOLOGY
STARTUPS IN KENYA
BY
PETER KANAKE
A Research Project Report Submitted to the Chandaria School of
Business in Partial Fulfillment of the Requirement for the Degree of
Masters in Business Administration (MBA)
UNITED STATES INTERNATIONAL UNIVERSITY-
AFRICA
SUMMER 2023
STUDENT'S
DECLARATION
I,
the
undersigned, declare
that
this
is
my original work
and has not
been submitted
to any
other
college, institution,
or
university
other
than
the
United States International
University
-
Africa
in Nairobi
for
academic credit.
Signed: Date: / f (^^^^
CI
02 3
Peter
Kanake (ID
No:
660496)
This
research project report
has
been presented
for
examination
with
my
approval
as the
appointed supervisor.
Signed
Dr.
Elizabeth
Kalunda
PhD.
signed:
Signed: Date:
I
^
Dean,
Chandaria
School of Business
ii
4
COPYRIGHT
All Rights Reserved
Copyright © Peter Kanake, 2023
5
ABSTRACT
The general objective of the study was to determine the factors influencing venture
capital investment decisions on technology startups in Kenya. The specific objectives of
this study were: to identify entrepreneur characteristics; competition from other funding
source; and how the venture capital’s natural entry point affects venture capitalists’
investment decisions in relation to technology startups in Kenya.
The research design adopted the descriptive research design. The unit of analysis were all
the fifty-two venture capital firms registered under the East Africa Venture Capital
Association which formed the study’s population. The respondents consisted of fund
principals and senior investment analysts from Venture Capital Firms. Stratified sampling
method was used to compute a sample size of 150 respondents. Data was collected using
structured questionnaires. Data analysis was both descriptively and inferentially and was
aided using Statistical Package for Social Sciences (SPSS) version 27.
The study showed that their existed a statistically significant linear association between
entrepreneur characteristics and venture capital investment decisions on technology
startups in Kenya (r = 0.839, p<.05). The regression analysis revealed that entrepreneur
characteristics explain 70.1% of the variance in venture capital investment decisions on
technology startups in Kenya. The study revealed further that entrepreneur experience,
entrepreneur education and management characteristics increases venture capital
investment decisions on technology startups in Kenya by 25.9%; 23.3% and 34.7%
respectively.
The study revealed that their existed a statistically significant linear association between
competition from other funding sources and venture capital investment decisions on
technology startups in Kenya (r = 0.712, p<.05). The regression analysis indicated that
competition from other funding sources explain 50.2% of the variance in venture capital
investment decisions on technology startups in Kenya. The study additionally revealed
that, captive versus independent sources of funds, reputation of venture capital firm and
experience of the venture capital firm increases venture capital investment decisions on
technology startups in Kenya by 11.9%; 19.8%; and 42.1% respectively.
6
The study indicated the existence of a statistically significant linear association between
venture capital’s natural entry point and venture capital investment decisions on
technology startups in Kenya (r = 0.662, p<.05). The regression analysis revealed that
venture capital’s natural entry point explains 43.3% of the variance in venture capital
investment decisions on technology startups in Kenya. The study also showed that, early-
stage, growth stage and late stage venture capital investment increases venture capital
investment decisions on technology startups in Kenya by 15.8%; 42%; and 3.6%
respectively.
The study concludes that the founder’s experience and networking skills were critical to a
start-up and improved their ability to secure funding. A more reputable venture capital
firm saw larger streams of deal flow than less reputable venture capital funds. The goal of
the growth stage was to achieve business-model fit, which was a repeatable, scalable,
profitable business model where the product created as much value for the company as
the customer, and a portion of the superior risk-adjusted return was due to the lower
failure rates among growth-stage companies.
The study recommends the managers of venture capital firms to invest in technological
startups that have entrepreneurs who have an excellent ability to process information.
They should also invest in startups that have different strategic approaches that give them
a competitive advantage, as well as those that are effective and efficient in allocating
finances and resources. This increases the chances of startups to evolve into successful
ventures.
7
ACKNOWLEDGEMENTS
I give praise to the God for giving me the gift of life and allowing me to continue my
education at the graduate level. I would like to thank my supervisor, Dr. Elizabeth
Kalunda PhD., for her invaluable advice throughout the process and for never failing to
express her satisfaction with the results.
I would also like to thank myself for putting in the effort throughout the graduate
program.
8
TABLE OF CONTENTS
STUDENT’S DECLARATION ii
COPYRIGHT iii
ABSTRACT iv
ACKNOWLEDGEMENTS vi
TABLE OF CONTENTS vii
LIST OF TABLES x
LIST OF FIGURES xii
LIST OF ABBREVIATIONS AND ACRONYMS xiii
CHAPTER ONE 1
1.0 INTRODUCTION 1
1.1 Background of the Study 1
1.2 Statement of the Problem 7
1.3 General Objective 8
1.4 Specific Objectives 8
1.5 Justification of Study 8
1.6 Scope of the Study 9
1.7 Definition of Terms 9
1.8 Chapter Summary 10
CHAPTER TWO 11
2.0 LITERATURE REVIEW 11
2.1 Introduction 11
2.2 Entrepreneur Characteristics 11
2.3 Competition from other Funding Sources 16
2.4 Venture Capital’s Natural Entry Point 22
2.5 Chapter Summary 28
CHAPTER THREE 30
3.0 RESEARCH METHODOLOGY 30
3.1 Introduction 30
9
3.2 Research Design 30
3.3 Population and Sampling Design 30
3.4 Data Collection Methods 33
3.5 Research Procedures 34
3.6 Data Analysis Methods 36
3.7 Chapter Summary 37
CHAPTER FOUR 39
4.0 RESULTS AND FINDINGS 39
4.1 Introduction 39
4.2 Demographic Data and Response Rate 39
4.3 Entrepreneur Characteristics 46
4.4 Competition from other Funding Sources 55
4.5 Venture Capital’s Natural Entry Point 63
4.6 Chapter Summary 72
CHAPTER FIVE 73
5.0 DISCUSSION, CONCLUSION AND RECOMMENDATIONS 73
5.1 Introduction 73
5.2 Summary 73
5.3 Discussions 75
5.4 Conclusions 84
5.5 Recommendations 86
REFERENCES 87
APPENDICES 117
APPENDIX I: COVER LETTER 117
APPENDIX II: DEBRIEFING FORM 118
APPENDIX III: INFORMED CONSENT FORM 119
APPENDIX IV: QUESTIONNAIRE 120
APPENDIX V: IRB PERMIT 128
10
APPENDIX IV: NACOSTI PERMIT 129
11
LIST OF TABLES
Table 3.1: Population Size 31
Table 3.2: Sample Size 33
Table 3.3: Reliability Outcome 35
Table 4.1: Descriptives for Venture Capital Investment Decision Preference 43
Table 4.2: Descriptives for Venture Capital Investment Decision Capacity 44
Table 4.3: Descriptives for Entrepreneur Experience 47
Table 4.4: Descriptives for Entrepreneur Education 48
Table 4.5: Descriptives for Management Characteristics 49
Table 4.6: Correlation for Entrepreneur Characteristics 50
Table 4.7: Normality Test for Entrepreneur Characteristics 51
Table 4.8: Linearity Test for Entrepreneur Characteristics 51
Table 4.9: Multicollinearity Test for Entrepreneur Characteristics 52
Table 4.10: Model Summary between Entrepreneur Characteristic and Venture Capital
Investment Decisions 52
Table 4.11: ANOVA between Entrepreneur Characteristic and Venture Capital
Investment Decisions 53
Table 4.12: Regression Coefficients between Entrepreneur Characteristics and Venture
Capital Investment Decisions 54
Table 4.13: Descriptives for Captive versus Independent Funds 55
Table 4.14: Descriptives for Reputation of the Venture Capital Firm 57
Table 4.15: Descriptives for Experience of the Venture Capital Firm 58
Table 4.16: Correlation for Competition from other Funding Sources 59
Table 4.17: Normality Test for Competition from other Funding Sources 59
Table 4.18: Linearity Test for Competition from other Funding Sources 60
Table 4.19: Multicollinearity Test for Competition from other Funding Sources 60
Table 4.20: Model Summary between Competition from other Funding Sources and
Venture Capital Investment Decisions 61
Table 4.21: ANOVA between Competition from other Funding Sources and Venture
Capital Investment Decisions 62
Table 4.22: Regression Coefficients between Competition from other Funding Sources
and Venture Capital Investment Decisions 62
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Table 4.23: Descriptives for Early-Stage Venture Capital Investment 64
Table 4.24: Descriptives for Growth Stage Venture Capital Investment 65
Table 4.25: Descriptives for Late-Stage Venture Capital Investment 67
Table 4.26: Correlation for Venture Capital’s Natural Entry Point 68
Table 4.27: Normality Test for Venture Capital’s Natural Entry Point 68
Table 4.28: Linearity Test for Venture Capital’s Natural Entry Point 69
Table 4.29: Multicollinearity Test for Venture Capital’s Natural Entry Point 69
Table 4.30: Model Summary between Venture Capital’s Natural Entry Point and Venture
Capital Investment Decisions. 70
Table 4.31: ANOVA between Venture Capital’s Natural Entry Point and Venture Capital
Investment Decisions 70
Table 4.32: Regression Coefficients between Venture Capital’s Natural Entry Point and
Venture Capital Investment Decisions 71
13
LIST OF FIGURES
Figure 4.1: Response Rate 39
Figure 4.2: Gender 40
Figure 4.3: Age 40
Figure 4.4: Position 41
Figure 4.5: Countries of Operation 41
Figure 4.6: Duration of Operation 42
Figure 4.7: Determinants of Venture Capital Investment Decision 46
Figure 4.8: Other Entrepreneur Characteristics 54
Figure 4.9: Competition Effect 63
Figure 4.10: Determinants of Venture Capital Natural Entry Point 72
14
LIST OF ABBREVIATIONS AND ACRONYMS
ANOVA: Analysis of Variance
CMA: Capital Markets Authority
EAVCA: East Africa Venture Capital Association
IPO: Initial Public Offering
IRB: Institutional Registration Board
NACOSTI: National Commission for Science, Technology and Innovation
SPSS: Statistical Package for Social Sciences
US: United States
USD: United States Dollar
USIU-A: United States International University - Africa
VC: Venture Capital
VIF: Variance Inflation Factor
15
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background of the Study
Venture Capital (VC) has been touted as the best alternative source of funding worldwide
as these firms are willing to take risks to provide funding for startups either as debt or
equity (Morawczynski, 2020; Lin et al., 2020). The European Venture Capital
Association defines venture capital as a subset of private equity investments made for the
launch, early development, or expansion of a startup enterprise (NVCA, 2007). Pierrakis
& Saridakis (2019) define venture capital as a process through which investors fund early
stage, risky ventures and thus such investments are usually associated with elevated risk
but have the potential for above average returns. According to Grilli et al. (2018), venture
capital is defined as equity or equity-linked investments in young privately held
companies, where the investor is a financial intermediary who is typically active as a
director, advisor, or even a manager of the firm.
A venture capital fund is managed by a venture capital company that invests the funds in
the startup enterprise to support them in four basic stages of development: Seed or
startup, early growth, business expansion and later stage activities (Hain et al., 2016).
Mishra & Zachary (2014) state that venture capital firms provide privately held startup
firms with equity, debt, or hybrid forms of financing, often in conjunction with
managerial expertise unlike commercial banks or insurance. This capital investment by
professional investors of long-term, unquoted, risk equity finance in new firms is the
primary reward as an eventual capital gain, supplemented by dividend yield (Liang et al.,
2019). Venture capital funding contracts come with sets of requirements for technological
startups such as representation on board of directors, involvement in recruitment of
certain positions and oversight on key strategic decisions (Hernandez et al., 2015).
There are venture capital firms that are more prone to invest in specific industries,
especially those that are linked to higher levels of innovation (Block et al., 2019).
According to Rosenbusch et al. (2013), the ability to sort out the most prospective
industries is a crucial ability of venture capital firms and leads to higher profitability of
their investments. Presently, venture capital firms are attracted to the technology space in
16
areas around agriculture, health care, education, retail, transport, and financial services
(Gompers et al., 2016). Venture capital firms are heterogeneous as financial institutions
and some of their characteristics may be linked to their investment policies, as well as to
the importance they attach to investment criteria (Archibald & Possani, 2019).
The technology startup ecosystem is recognized as a vital component of any economic
development (Cukier & Kon, 2018). Globally governments and private entities have
turned to these startups as a way of promoting economic development in areas where
large enterprises or foreign direct investments have failed to take off (Joshi &
Subrahmanya, 2015). Technology startups are driving significant growth in the global
economy (Mworia & Gugu, 2017). The traditional sources of funding, such as savings,
loans from family and friends, available for these startups are usually not enough to scale
the enterprise to compete on a global scale (Calvino et al., 2020). Technological startups
lack conventional financial security or collateral that most financial institutions require to
extend funding as they have considerable risk (Weru & Rotich, 2017). Startup founders
are in turn required to look for other avenues of funding (Achibane & Tlaty, 2018).
Financial institutions are normally the default go to organizations when businesses
require capital (Garg & Shivam, 2017).
However, technology startups are considered high risk as most of the solutions and
innovations they offer have not been tried and tested which signifies notable risk and
uncertainty for the banking institutions (Njubi, 2018). The conditions the banks place to
mitigate risk are usually difficult for the startups to attain meaning that they must source
for capital through alternative channels (McMullen & Warnick, 2016). These technology
startups have in turn looked to alternative funding sources such as venture capital away
from conventional sources that mostly included commercial banks (Marco et al., 2016).
Some entrepreneurs seeking innovative opportunities have risky business models that
require venture capital funding (Scarlata et al., 2016). Venture Capital firms tend to reject
majority of the business plans from startup entrepreneurs (Wang et al., 2016). When
venture capital funds reject most business plans from startups, they may become a
hindrance to spurring economic growth through innovation (Warnick et al., 2018).
17
There are distinct stages of venture capital funding that are categorized into a series of
funding steps denoted by letters with each corresponding with the development stage of
the technology startup (Feld & Mendelson, 2016). The pre-seed stage helps the startup get
off the ground (Gleasure, 2015). This takes place when the business is early in its journey,
mostly less than a year old (Lungeanu & Zajac, 2016). Funding often helps support the
initial market research and development (Archibald & Possani, 2019). Most startups are
at the prototype stage and have not fully developed their ideas or even know exactly who
they want to sell to (Wang & Schøtt, 2020). The stage that follows is the seed stage which
is the funding round that contains less than fifteen investors who will gain convertible
notes, equity, preferred stock option in exchange of their funding (Weiblen &
Chesbrough, 2015). This will be the first opportunity the technology startup will have to
employ staff outside the original founding members (De Bettignies & Duchene, 2015).
Angel investors and early-stage venture capital firms are the main backers at pre-seed and
seed funding (Shu et al., 2016).
Series A funding is where technology startups have a proven history and ability to scale
quickly and provide a serious return for investors (Stevenson et al., 2021). Some
technology startups will skip seed funding and go straight to Series A as a venture capital
firm will approach the company first (Welter et. al, 2017). In such instances the
entrepreneur will be asked to give away a large chunk of equity often bigger than twenty
percent (Warnick et al, 2018). During the Series A stage, startup valuation will be
calculated by its proof of concept, progress made with initial seed capital, quality of
executive team, market size and risk involved (Walthoff-Borm et al., 2018). Advantages
of Series A funding include ability to scale faster with a larger financial reserve and
recognition in the industry (Vismara, 2016). Technology startups looking to raise Series B
capital will already have fully launched products or services and will now be targeting a
market share in business to compete against larger more established competitors
(Stevenson et al., 2019). It is often considered the hardest round even though Series B
technology startups are considered less risky than those at Seed or Series A stage with
funding raised in this stage based on fact (Shankar & Shepherd, 2018).
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Technology startups that successfully raise Series B funding will normally invest in
business development, sales, advertising, and technology as well starting to eye possible
international expansion (McMullen & Warnick, 2016). During the Series B stage, a
startup’s valuation will be calculated by its performance in comparison to that of its
sector, revenue forecasts and assets such as intellectual property (Hallen & Pahnke,
2016). Startups that are at Series C stage of funding have proven to venture capital firms
that they will be a long-term success-with the original backers’ shares now having
increased in value (Gleasure, 2015).
Series C are considered a safe bet from an investor’s point of view (Fischer et al, 2020).
Businesses at the Series C stage will look for an even greater market share and to develop
even more products and services and may start preparing for acquisition-both of itself by
a larger corporate or to buy a smaller competitor (Feld & Mendelson, 2016). The final
stage for many startups before they seek an Initial Public Offering (IPO), a valuation of a
business at Series C is done based on hard data (Estrin et al, 2018). Use paragraphs. One
page paragraph is highly discouraged.
A study by Gantenbein et al. (2019) in Canada found that venture capitalists look at the
entrepreneur's characteristics such as personality and experience as a major criterion for
investment while United States (US) venture capitalist are more focused on financial
aspects and the investment's liquidity. A research study by Lee (2020) in Korea found that
the venture selection criteria fall into six categories that include managerial capabilities,
financing ability, venture capitals natural entry point, competition from other funding
sources and production capacity. The criteria that affected the venture capitalist decisions
more was managerial capabilities. Huijie (2018) found similarities (with which) in their
research study conducted in Japan, where venture capitalist decisions were more focused
on the venture capitals natural entry point. According to Grilli et al. (2018), European
venture capitalists placed greater value on the entire management team compared to skills
of a single entrepreneur. Joshi (2018) conducted a research study on Indian venture
capitalists and found that they considered entrepreneur personality that includes
entrepreneur integrity and ability to grow to be of high importance.
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However, financial returns, competition from other funding sources and the venture
capital’s natural entry point were given low priority (Panda & Dash, 2016). Srinivas &
Nagaraja (2013) later looked at the investment criteria used by venture capitalists in India
and found that entrepreneur’s honesty and integrity, long-run vision, competition from
other funding sources and the venture capitals natural entry point were essential
considerations in financing a technology startup. Urbano et al. (2021) recognized that
competition from other funding sources, entrepreneur integrity, familiarity, managerial
skills, and a balanced team were significant evaluation criteria for venture capitalists in
Thailand. Wang et al. (2018) concluded that the ability of the managerial team was a
critical criterion for investment evaluation in Hong Kong. A study by Jones and Mlambo
(2018) on South African venture capitalists’ evaluation on new startups found that
entrepreneur’s honesty and integrity, market acceptance and the venture capitals natural
entry point were considered as an important criterion. The study on South African venture
capitalists concluded that entrepreneur’s honesty and integrity; the venture capitals
natural entry point; and a high internal rate of return were the three most important
criteria.
According to a survey done by Africa Tech Venture Capital Report (2019), venture
capital firms increasingly showed interest to invest in East Africa and preferably put their
money in Kenya owing to a vibrant private sector and ease of doing business. The country
had the highest number of coding schools on the continent according to Briter Bridges
(2021), a sure sign of the level of investment and interest in the country’s technology
ecosystem. Kenya is also home to the mobile banking platform M-Pesa whose creation in
2007 by Safaricom revolutionized banking across Africa and brought financial inclusion
to millions by providing access to banking services through legacy mobile phones (Jacob,
2016). The platform’s success launched Kenya’s technology ecosystem into the spotlight,
with incubators, hubs and venture capital subsequently flowing to the country (Twum et
al., 2022). In 2019, Kenya launched a Digital Blueprint, which targets more than six
hundred million people in twenty-four countries across Africa and it lays out a framework
to transform the region into a sustainable digital ecosystem (Briter Bridges, 2021).
20
In 2021, Kenyan technology startups secured United States Dollar (USD).369.6 million
over sixty-seven disclosed funding rounds which amounts to seven percent of Africa’s
total funding in 2021, which was USD.4.76 billion and nine percent of the four-hundred-
ninety-three disclosed funding rounds (The Baobab Network, 2021). In comparison,
Nigerian startups secured over USD.1.8 billion over one-hundred-eighteen disclosed
funding rounds which is thirty-eight percent of the total amount and twenty-three of the
total African funding rounds (Magnitt, 2022). South African technology companies
closed over USD.1 billion over ninety-four funding rounds, which represents twenty-two
percent of the funding amount and nineteen percent of the total disclosed rounds in Africa
(Partech, 2020). Egypt secured USD.464.1 million in venture capital funding, over one
hundred & one funding rounds; ten percent of the African total amount and twenty
percent of the total disclosed funding rounds (Disrupt Africa, 2020).
Kenya’s startup ecosystem is one of the oldest and most established on the continent.
(Frey & Osborne, 2017). The Kenyan ecosystem is robust, with entrepreneurs building
solutions for their communities that they want to scale across the globe (Kaplinsky &
Morris, 2014). There are several training and funding opportunities available to these
entrepreneurs that are run by incubators and accelerators across the country (Gustafsson
et al., 2015). There are also several regular opportunities to engage entrepreneurs building
sector specific solutions or engage with public or private sector players to talk through
opportunities available to local entrepreneurs (Wonglimpiyarat, 2015). There are
opportunities for entrepreneurs to get involved in shaping policy and ensure that they are
startup friendly (Fourie, 2014). As a result of these opportunities, entrepreneurs in Kenya
enjoy more ease of doing business than in several other countries (Foster et al., 2018).
Kenya has been heavily impacted by the reduction of venture capital deal size, (The
Baobab Network, 2021) and has the fewest funded startups among the top four countries
in Africa (Partech, 2020). The country has the innovation and capability activity to keep
producing success stories year on year, with the potential for more investment into
technology startups at all stages of the lifecycle (Magnitt, 2022). This research paper will
provide the framework that will look at the factors influencing venture capital investment
decisions on technology startups in Kenya.
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1.2 Statement of the Problem
Technological innovation requires large investments with venture capital being a
prominent financial source for innovative technology startups (Block et al., 2019). The
US economy, for example, relies on entrepreneurs to spur economic growth through
innovation (Gompers et al., 2016). Lee et al., (2016) argued that society should encourage
risk-taking and promote entrepreneurship through maximizing upside gains while
minimizing losses associated with entrepreneurial initiatives. The high-growth and high
variance potential of entrepreneurial ventures could serve as a driver for economic growth
and a catalyst for new industry development (Hoenig & Henkel, 2015). However, startups
require funding to pursue innovative opportunities which have not been forthcoming (Hsu
et, 2012). A challenge regarding venture capital’s willingness to invest in startup
entrepreneurs includes the homogeneous decision-making approach in most venture
capital firms (Wood & Williams, 2014). Within the venture capital environment,
uncertainty avoidance is attributed to low tolerances for risk-taking within investment
activities of formal institutions (Moritz et al., 2016). According to a report by Grant
Thornton and Assocham (2016), startups in India faced funding difficulties in the initial
stages. With major leaps in technology, investors have raised the bar when it comes to
funding technology startups.
Despite studies in this field, the researcher noted that there was a gap in literature that
looked at the factors influencing venture capital investment decisions on technology
startups in Kenya. Notably, the studies reviewed do not reflect these factors that continue
to limit more Kenyan technology startups from accessing venture capital thus creating a
knowledge gap (Gugu & Mworia, 2016). These gaps include the entrepreneur
characteristics that venture capital funds consider when evaluating Kenyan technology
startups for investment, effect of competition from other funding sources on the
evaluation for funding by the venture capital and how the venture capital’s natural entry
point affects decision-making process of evaluating the technology start-up to fund. It is
for this reason that the current study specifically sought to fill this gap by investigating
the factors influencing venture capital investment decisions on technology startups in
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Kenya which include entrepreneur characteristics, competition from other funding
sources and venture capital’s natural entry point.
1.3 General Objective
The purpose of the study was to determine the factors influencing venture capital
investment decisions on technology startups in Kenya.
1.4 Specific Objectives
The specific objectives of this study were:
1.4.1 To determine the effect of entrepreneur characteristics on technology start-ups
investment decisions by venture capitalists.
1.4.2 To determine the effect of competition from other funding sources on technology
start-ups investment decisions by venture capitalists.
1.4.3 To determine the effect of venture capital’s natural entry point on technology start-
ups investment decisions by venture capitalists.
1.5 Justification of Study
Several factors have been cited in theoretical models and empirical studies enumerating a
range of factors affecting venture capital investment decisions when evaluating
technology startups. The following are the constituents who stand to benefit from the
study which seeks to provide viable solutions to startup owners, investors and add to the
body of knowledge in this field.
1.5.1 Venture Capitalists
The Kenyan technology start up ecosystem has shown tremendous growth in recent years
with venture capitalists from different parts of the world setting up shop in the country.
This paper gives them a better appreciation of how Kenyan technology startup
entrepreneurs approach funding decisions hence guiding their decisions on which
organizations to fund and why.
1.5.2 Entrepreneurs
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The study was aimed at educating the entrepreneur further on the criteria that venture
capitalists use when evaluating a potential venture. The research may help the
entrepreneurs understand what factors make a business attractive to venture equity fund
and hence model and structure their business accordingly and what factors hinder them
from accessing venture equity funds and hence improve on the same. This makes it easier
for the technology startups to access capital which is hard to obtain from the mainstream
sources which mostly are never adequate. In general, the study gives the entrepreneurs
more insights and understanding on how venture capitalists operate and hence demystify
the alternative source of funding.
1.5.3 Government and Policymakers
The government needs to help create a better regulatory environment that minimizes risk
and encourages more investors to bring in funding. There is a bill in the senate, Startup
Bill 2020 that aims among others to streamline the funding environment by venture
capitalists. This government and policymakers may use this paper to better understand
what the venture capitalists expect before releasing the funds for investments.
1.5.4 Researchers/Academics
Researchers and academics may benefit from this study by having a locally researched
paper looking at the factors influencing venture capital investment decisions on
technology startups in Kenya to further their work and add to their existing body of work.
1.6 Scope of the Study
The study focused on factors influencing venture capital investment decisions on
technology startups in Kenya. The venture capital funds in the study are those registered
by the Capital Markets Authority (CMA) and under the East Africa Venture Capital
Association (EAVCA). The target population of this study comprised of fund principals
and senior analysts in the fifty-two venture capital firms registered under the East Africa
Venture Capital Association that operate in Kenya. The geographic focus was Kenya.
The methodology for data collection was both primary and secondary within a six-month
period between 2022 and June 2023.
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1.7 Definition of Terms
1.7.1 Entrepreneur Characteristics
The abilities of an enterpriser to identify new business opportunities and to take initiatives
by utilizing them in competitive environmental situations (Velichová, 2013).
1.7.2 Competition
The process of economic interaction, interconnection and rivalry between the market
enterprises which set the goal of providing better conditions for selling their products and
satisfying the consumers’ needs (Knott, 2018).
1.7.3 Natural Entry Point
This refers to the price point, which is suitable for investing in or buying a security.
(Breuer & Pinkwart, 2018).
1.7.4 Venture Capital
Equity or Equity-linked investments in young privately held companies, where the
investor is a financial intermediary who is typically active as a director, advisor, or even a
manager of the firm (Grilli et al., 2018).
1.7.5 Technology Startup
A human institution designed to create a new product or service under conditions of
extreme uncertainty using technology for problem solving so that goods and services can
be invented, developed, produced, and used (Cavallo et al., 2021).
1.7.5 Investment Decision
This relates to the decision made by the investors or the top-level management with
respect to the amount of funds to be deployed in the investment opportunities (Drover et
al., 2017).
1.8 Chapter Summary
Chapter one covered the background of the study, statement of the problem, purpose of
the study and definition of terms. Chapter two covers review of literature on factors that
influence the decisions and how they were used by venture capital funds to evaluate
25
Kenyan technology startups for investment. Chapter three covers the research
methodology used for this study whereas chapter four focuses on the results and the
findings of this study. Finally, chapter five focuses on discussions, conclusions and
recommendations made during the study.
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CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Introduction
This chapter covers existing research literature on the factors influencing venture capital
investment decisions on technology startups highlighting the entrepreneur characteristics,
effect of competition from other funding sources and venture capital’s natural entry point
affects the decision-making process of evaluating the technology startup to fund.
2.2 Entrepreneur Characteristics
This section examines the role of entrepreneur characteristics in venture capital decisions.
It elaborates on the entrepreneur’s experience, entrepreneurs’ education, and the
management characteristics.
2.2.1 Entrepreneur Experience and Venture Capitalists Investment Decisions
Margolis (2014) argued for intensive study of habitual entrepreneurs in the United States
who have the experience of generating multiple businesses. He stated that habitual
entrepreneurs have had the opportunity to learn how to efficiently and swiftly overcome
the stumbling blocks they encountered in their first efforts. Thus, they accumulated
entrepreneurial skills from their experiences. By studying habitual entrepreneurs, Wood
& Williams (2014) were able to uncover and codify their skills and techniques and gain a
deeper understanding of the process of business creation. This view was echoed by many
other researchers such as Falco & Haywood, (2016); Gindling & Newhouse, (2014);
Fritsch et al. (2015). Gupta et al. (2014) in India argued that not only were there
theoretical reasons, but there were also policy reasons to study habitual entrepreneurs. A
better understanding of how habitual entrepreneurs differed from novice ones greatly
helped design policies to foster entrepreneurship. The importance of experience was
proven by a review study done by Wood & Williams (2014), who is considered it as very
important by venture capitalists.
In a research study in Ghana, Essel et al. (2019) interpreted the positive association
between experience and early-stage technology startup as experienced entrepreneurs were
better at evaluating opportunities. However, this interpretation requires a silent, but
27
important, assumption that the desired scale of venturing is homogeneous across all
entrepreneurs such as all entrepreneurs want to start and operate the largest new
businesses possible. Empirical evidence of entrepreneurial preferences is generally
inconsistent with this assumption of homogeneity and demonstrates that scale preferences
of entrepreneurs vary in predictable ways, suggesting that inferring the quality of
entrepreneurial judgment solely from new venture outcomes is problematic. The
importance of experience was proven by review study done by Bienkowska et al. (2016),
who list them at first place in decision criteria related to entrepreneur used by venture
capitalists.
In research that directly compared entrepreneur experience to new venture startups in
Kenya, Shimoli et al. (2020), found new entrepreneurs with industry experience were
relatively more pessimistic than those with prior business experience. However, given
their survey data they could not define the extent that experience improves forecasting
performance, only that experience is associated with relatively more optimism or
pessimism. Using a smaller sample, found no association between industry experience
and forecast bias in entrepreneurs starting new businesses. Data constraints in this domain
also limited understanding in various ways. First, experience is typically measured using
dichotomous variables that obscure the relation between experience and forecast
performance. For example, it is unclear if experience results in an immediate, linear, or
monotonic but diminishing improvement in entrepreneur forecast performance. Second,
these studies did not control for potential selection biases, which given the high
proportion of attrition in new venture panel samples may significantly influence study
inferences. Third, research in this domain has not argued or tested for heterogeneity of the
impact of experience on entrepreneurial expectations across venture type. For example,
should experience equally benefit entrepreneurs who start firms in different industries?
Finally, aside from the empirical implications, given their alternative focus, these studies
devote limited or no theoretical attention to the experience-judgment relation. De
Bettignies & Duchene (2015) supported the studies that listed industrial experience as
considered by venture capitalists to be more important than others.
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There are several proposals about how the experience should be classified, and, in many
authors, there is overlap in the content (Hallen & Pahnke, 2016). In some cases, the
experience is defined vaguely and ambiguously. There are researchers when describing
experience, use a term such as high credibility of the entrepreneur or management team.
From this statement, it is difficult to identify what specific experience is there and it
indicates that there are many types of experience behind this term. Kuratko & Morris
(2018) concluded in their study that entrepreneurs with venture-backed founding
experience tended to raise more venture capital at an early round of financing.
2.2.2 Entrepreneur Education and Venture Capitalists Investment Decisions
Research studies in several European countries indicated the importance attributed to the
international scalability of a venture’s business and its current profitability depended on
the level of education of a decision maker (Muscio & Vallanti, 2014; Ramaciotti & Rizzo,
2015; Sauermann & Roach, 2016). The studies also found that the international expansion
for new ventures is an important step to exploit growth opportunities and with that,
realize performance advantages and increase profitability. The research results suggested
that a higher level of education increased the awareness of decision makers regarding
international scalability as an indicator of the potential of a venture’s business idea.
Furthermore, the relative importance of profitability decreased with an increased level of
education. This result indicated that entrepreneurs with a higher level of education
seemed to emphasize the future potential of the startup instead of its current financial
situation. Besides, entrepreneurs with a higher level of education seemed to prefer
innovation-centered business models. The conclusion therefore from the studies was that
a higher level of education was significantly and positively connected with the funding
decisions made by venture capital funds.
According to a study in Kenya by Piper et al. (2018), a high level of academic
achievement such as a doctoral degree among entrepreneurs had a positive effect on the
evaluation of startups in technology related industries by venture funds. From a cognitive
perspective, entrepreneurs with a high level of academic ability can quickly recognize and
identify complex phenomena and have an excellent ability to process information.
Entrepreneurs with a high level of academic achievement also had an innovative tendency
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and were well-formed in cooperative relationships with others. Entrepreneurs with a high
level of education tended to form external social networks and develop social capital.
Education received from prestigious universities was considered high social capital by
venture capitalists, as the socially embedded network links of founders constitute an
important resource base. Here, the level of education typically refers to the attained level
of formal education as an indicator of an individual’s cognitive abilities. These results
added to prior research investigating the consequences of different levels of education,
specifically in the venture fundraising context. However, the study was not without
limitations, some of which relate to the conjoint approach applied. The research problem
was analyzed using the regression model. The target population of the study was startups
in the country across various industries.
Ahlers et al. (2015) assessed the link between the academic achievements of
entrepreneurs to venture capital decisions on startups in Singapore. A startup founded by
a scholar had a high survival rate and was likely to hire new employees to create more
jobs, apply for a patent, and conclude a contract. The study further stated that a high level
of academic achievement such as a doctoral degree among entrepreneurs had a positive
effect on the evaluation of technology startups for funding by venture capital funds. From
a cognitive perspective, individuals with a high level of academic ability could quickly
recognize and identify complex phenomena and have an excellent ability to process
information. Individuals with a high level of academic achievement also had an
innovative tendency and were well-formed in cooperative relationships with others.
Entrepreneurs with a high level of education tended to form external social networks and
develop social capital. Education received from international institutions of higher
learning was considered high social capital by itself, as the socially embedded network
links of founders constitute an important resource base. The conclusion drawn from the
study was that entrepreneurs with a natural science background tended to focus more on
the product. This could be understood as an indicator of initial market success, a
competitive advantage, effective management, and, eventually, firm survival.
2.2.3 Management Characteristics and Venture Capitalists Investment Decisions
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A study in the United States by (Chen et al., 2015; Dlouhá & Burandt, 2015) looked at the
relationship between business founders’ self-perceived competencies and venture capital
funding decisions. It identified areas associated with successful business founders: human
and conceptual competencies, the ability to recognize opportunities, technical-functional
competencies, and political competencies. The study also provided evidence that
founders’ entrepreneurial and managerial competencies directly related to the ability of
the firm to raise venture funds. They focused on ambiguity-tolerance, deal making, stress
management, oral and written communication, or human relations. These competencies
focused on fixed behaviors and inflexible traits and have been criticized about several
conceptual issues. Intention and motivational factors that affect behavior indicate an
individual’s effort to put these behaviors into practice. The conclusion from the study was
that technology startups should encourage robust managerial and entrepreneurial
competencies by adapting curricula and extra-curricular activities accordingly.
Other research studies in Europe combined the concepts of entrepreneurial characteristics
and managerial characteristics to create the concept of entrepreneurial-managerial
competencies (Adams et al., 2017; Adomßent et al., 2014; Ajamieh, 2016). This concept
included the managerial competencies that are vital for driving venture capital decisions
on technology startups. The studies argued that entrepreneurial competencies identified
were higher compared to standard level ability that was promoted through education and
encompassed the necessary skills, knowledge, and abilities to perform an innovative role
successfully. The researchers continued to argue that competencies to create the concept
of entrepreneurial-managerial competencies included the managerial competencies that
were vital for performing entrepreneurial activities successfully. The studies concluded
that managerial competencies were crucial to the success of technology startups in raising
venture capital funding.
A study in South Africa, (Fleisch, 2017) discovered that entrepreneurial business venture
performance was positively related to the innovative component of the entrepreneur’s
management characteristics. South Africa has experienced significant political, social,
and economic change over the past twenty years. As such, embracing an emerging
enterprise culture in the technology startup industry was considered a potential solution to
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some of management challenges entrepreneurs faced in running their startups. The study
indicated that startups, as they were smaller in size, were more vulnerable because of their
limited access to capital, debt capacity, market share, technology acquisition, among
others. The study concluded by arguing for the relevance of managerial competencies and
self-efficacy on venture capital decision making.
In Kenya, Gachiri (2015) highlighted that the entrepreneurial manager tended to create
new value through identifying new opportunities, attracting the venture capital funding
needed to pursue those opportunities, and building an organization to manage those
resources during the entrepreneurial process. The study further supported his claims by
indicating that an entrepreneurial manager takes up any opportunity for promising
business disregarding the level and nature of resources he is currently controlling.
Educational institutions had a significant impact on the development of entrepreneurial
attitudes. Through entrepreneurial education, entrepreneurs’ managerial competencies
increase innovative start-up intentions among entrepreneurs directly or indirectly via the
mediating roles of entrepreneurial self-efficacy and attitude.
2.3 Competition from other Funding Sources
This section examines the effect of competition from other funding sources on venture
capital funding decisions. It elaborates on captive versus independent funds, reputation of
the venture capital firm, and the experience of the venture capital firm.
2.3.1 Captive versus Independent Funds and Venture Capitalists Investment
Decisions
A research study in the United Kingdom by Biney (2018) highlighted significant
differences across the investment specialization patterns of several types of venture
capitalists. In comparison with other investor types, independent venture capital funds
tended to select older and larger companies in their expansion stages. This pattern of
investment specialization was stable over time. Evidence from this study suggested that
European independent venture capital firms abstained from the riskiest investments.
Further the study posited that alternative sources of financing for entrepreneurs such as
individual investors or angels, corporations, and strategic alliances were increasingly
32
considered as viable options. The results of this and other studies suggest that outcomes
such as timing, magnitude, and riskiness of returns of choosing such alternatives will vary
widely and will depend in great part on the strategies of the venture capital firms
involved.
Ekanem et al. (2019) in Ghana argued that captive venture capital firms’ investments are
an important element of parent companies’ open innovation strategies and, in addition to,
or even in substitution of, financial objectives, they were driven by the wish to open a
technology window on the development of promising new technologies by
entrepreneurial ventures. In accordance with this view, captive venture capitalists were
particularly attracted by companies operating in industries with high technological
ferment. They were also more active in industries with weak intellectual property
protection in which other mechanisms to obtain access to promising modern technologies
such as licenses are ineffective. The findings revealed that captive venture funds
specialized in internet-based technology startups. They generally abstained from investing
in biotechnology and pharmaceutical startups. The former industry is characterized by a
weak appropriability regime and high technological turbulence in the observation period.
Previous studies, based on North American data, also indicated that captive venture
capital funds were less likely to invest in early-stage startups than independent venture
capital funds (Ahlers et al., 2015 on Canada; Katila, Allison et al., 2015 and Anglin et al.,
2014 on the US). These studies however did not find any evidence that captive venture
funds were more likely to syndicate than average investors. Conversely, captive venture
funds adopted a more global investment strategy than the other investor types and are
more prone to select technology startups located far away from their home base. Hence,
the studies confirmed the view that venture capital funds often used captive venture
capital to access foreign technology startups and enter geographically distant markets.
A research study by Bertoni et al. (2019) in Italy documented that bank affiliated venture
capital firms employed more passive strategies than other venture capital types and were
more inclined to invest in older and larger startups that, being in a later stage of
development, are closer to an initial public offering. Accordingly, bank affiliated venture
33
capital firms were more likely to exit through an initial public offering than other venture
capital types. They also specialized in investments of shorter durations. In addition, they
more frequently employed syndication as a means of reducing investment risk. The study
further argued that the main objective of bank affiliated venture capital funds was to
support the establishment of profitable bank relationships with technology startups rather
than to realize large capital gains. In accordance with this view, bank affiliated venture
capital funds compared to independent venture capital funds and captive venture capital
funds, were more likely to invest locally, where they could exploit their superior ability to
gather soft information on the technology startups.
The study by Du et al. (2015) in Korea found that government venture capital funds were
specialized in startups that were not attractive to other investor types. Because of the
information asymmetries surrounding young, small high-tech startups and their high risks
of failure, these companies found it difficult to attract private funding, especially at the
seed stage. The study showed that these were precisely the categories in which
government venture capital funds were specialized. The duration of the investments of
government venture capital funds was also longer than for all other investor types. The
study concluded that investment strategies and specific policy-related objectives of
government venture capital funds differed from those of other investor types, which
explained why they rarely took part in syndicated investments and are forced to invest on
a stand-alone basis.
2.3.2 Reputation of the Venture Capital Firm and Venture Capitalists Investment
Decisions
Chang et al. (2014) in China argued that the market would be efficient in recognizing the
value high-reputation venture capitalists provide; it would value their involvement more
highly and venture capital reputation would have a positive relationship with operating
performance. Their research further showed that markets tended to be less efficient in the
short-term, especially when uncertainty and complexity are high and responded to
signaling behaviors and certifications that may be decoupled from actions and quality.
The pattern of findings where venture capital reputation had a positive relationship with
initial market valuation but had no relationship with subsequent operating performance
34
would suggest that the value of venture capital reputation was in its ability to reduce
investors’ perceived uncertainty based on assumed benefits that may be rationalized
myths. A finding that venture capital reputation was only positively associated with
operating performance suggested that the relationship may yield substantive operating
benefits that investors do not fully discern at the time. The study also focused on three
characteristics that were likely to affect a venture capitalist’s ability to provide
substantive value to its portfolio firms and that could also affect investors’ perceived
uncertainty about the firm: the length of the venture capital fund’s involvement with the
technology start-up, its geographic proximity to the start-up and the extent of its
experience with the start-up’s industry.
The study by Paul & Criado (2020) showed that all else being equal, a high-reputation
venture capital firm that specialized in the focal firm’s industry would be able to provide
more substantive value to a start-up than a less specialized high-reputation venture capital
firm, which would have less ability to make these substantive contributions and whose
involvement will be valued less by investors. Accordingly, the greater the venture
capital’s specialization in the focal firm’s industry, the greater the influence of venture
capitalists’ reputation on its decision to invest in a specific technology startup. It can
therefore be argued that reputation depends on many aspects of the venture capital firm
and is the aggregate culmination of many small procedures, conduct, and performance
levels which the venture capital firm maintains. By demonstrating that venture capitalists
are perceived to add more value than other venture capital firms, it was found that
reputation and performance go hand in hand when making decisions to invest in
technology startups.
As explored by Ng et al. (2016) in Vietnam, a more reputable venture capital firm saw a
larger stream of deal flow than did a less reputable venture capital fund. Potentially more
important, more reputable venture capitalists negotiated more attractive terms with
technology startups in terms of price, options, and protections, reflecting the greater
desire of entrepreneurs to affiliate with a better-known venture capitalist. Strong venture
capital reputation yielded important benefits for startups, including advice from more
experienced venture capital partners; better access to professional management talent,
35
credit, and reputable investment banks; implicit venture capital guarantees on technology
startups; a greater likelihood of larger rounds of venture capital funding; greater venture
capital staying power in later funding rounds; and a greater likelihood of a successful
initial public offering or trade sales at higher takeover premium.
2.3.3 Experience of the Venture Capital Firm and Venture Capitalists Investment
Decisions
A study from Van Stel & Van der Zwan (2020), found that venture capital funds with and
without a business background showed differences in their decision-making on
technology startups. Their study linked experience with a performance measure for
decision-making. Using conjoint analysis, they found that inexperienced investors
increased their reliability and performance in decision-making with experience. However,
they did not specifically analyze the effect of experience on the importance of various
decision criteria. The study also shed light on this question by evaluating the effect of
investors’ experience on the weight of the decision criteria used. They found evidence
that experience had an impact on decision-making. In their study of fifty-one venture
capitalists from Germany and Austria, they found that the experience of the decision-
making firm had a statistically significant influence on the evaluation of the educational
background, leadership experience, and management skill of the technology startup. They
derived their results from cognitive research and argued that an individual’s cognitive
structures, also referred to as schemata, could explain the different importance assigned to
the decision criteria. Despite the study on the effect of experience, no other studies
elaborated on this relationship or investigated others, such as the effect of entrepreneurial
background on decision-making. This was particularly interesting, since many venture
capitalists were former entrepreneurs themselves.
Tamvada (2015) found that venture capital funds with more specific human capital
including law and finance experience exhibited a lower probability of failure in decision
making but found no evidence for a positive relationship with the probability of the
technology startup going public. The study also found that startups funded by more
experienced venture capitalists were more likely to go public. However, the study focused
36
on decision making from the perspective of experienced venture capital funds. In contrast
to most prior research, the impact of experience differences between venture capital funds
on company growth from the company’s perspective was studied. Particularly
experienced venture capitalists were expected to be the type of firms that could produce
better financial results from their investment decisions on technology startups. The
experience that venture capital firms accumulated across time would alter their
investment behavior. Venture capital firms were likely to learn through prior investments
and develop routines based on past experiences. The routines that would become part of a
venture capital fund’s decision-making process were those that previously produced
favorable outcomes. The application of routines would increase their efficiency and hence
the likelihood of a desirable outcome on their decisions. Hence, it was expected that
experienced venture capitalists would be better at selecting the most promising
technology startups and offering value-adding services compared to their less experienced
counterparts.
Fitzenberger & Schulze (2014) in their study suggested that experienced venture capital
firms selected high potential startups and provided more valuable services. They showed
that experienced venture capitalists were normally easier to raise funds from. The study
also confirmed that experience in due diligence, advisory services, monitoring, as well as
well-planned exit strategies were particularly important. The problem of asymmetric
information was more severe in young venture capital firms than mature firms, and
venture capitalists would be better at reducing the cost of information asymmetry as it
grew older and accumulated more experiences. However, another group of scholars
challenged whether venture funds could enhance decisions on funding technology
startups (McCormack et al., 2014; Sharchilev et al., 2018; Chan & Park, 2015; Bruton et
al., 2018). The valuable finance and value-added services enabled those experienced
venture capital funds to require a higher return from startups.
According to Li et al. (2018), a venture capital firm could better grasp the nuances of the
investment decision at hand based on its experience with due diligence and valuation to
deflate some of the buoyant optimism about the startup by focusing on the critical
technological and social developments that could affect the venture's viability and
37
competitive position. These arguments suggest that greater experience can help the
venture capitalist firm both identify and make a more informed decision on investment
opportunities such as those originating in new markets. Broader experience enabled a
firm to add new variations in its decision making and enhance its ability to acquire and
assimilate new and more diverse information. Operating in multiple market domains
increased the likelihood that incoming information will be connected to what is already
known by the organization. It also enabled the firm to observe and learn from a larger set
of players, thereby choosing competitive moves and business models from one setting to
the next. Such broad oversight helps the firm to scan a wider range of potential ideas,
thereby something it has experienced already.
According to Pan & Yang (2019), familiarity with different strategic approaches and
competitive contexts can assure the venture capital firm that it can discern threats or
opportunities in a timely manner and make sound investment decisions. Some researchers
argue that venture capital firms with broader investment experience can be better
equipped to identify and manage opportunities within technology startups. Given such an
advantage, venture capital firms with broader experience will seek to explore
opportunities in technology startups as not doing so may increase the opportunity costs of
their alternative actions.
2.4 Venture Capital’s Natural Entry Point
This section examines the role the venture capital’s natural entry point on venture
capitalists’ investment decisions. It elaborates on early-stage venture capital investment,
growth stage venture capital investment, and late-stage venture capital investment.
2.4.1 Early-Stage Venture Capital Investment and Venture Capitalists Investment
Decisions
Given the significant challenges and opportunities associated with early-stage venture
capital, the volume of research on this topic is increasing, whether measured in terms of
published research articles, publication outlets, or support provided by private donors or
policy. The empirical research was mostly conducted during the 1980s, four decades after
the first venture capital firm was established in the United States (Hayter et al., 2018).
38
The pioneers in early-stage venture capital research focused on fundamental questions,
such as what role venture capitalists played and the value, they added in technology
startups. For instance, the researchers discussed the model of venture pioneers in early-
stage venture capital research focused primarily on the US. However, the diffusion of
American style venture capital practices to other nations was followed by a stream of
international venture capital research describing the European and Asian context. These
studies typically addressed the cross-country differences in early-stage venture capital
activities and the role of the decisions venture capital funds made on early-stage startups
in Europe and Asia. Investing in early-stage companies is incredibly hard, especially
when no data is available to support the decision process. Venture capitalists often rely on
gut feeling or heuristics to reach a decision, which is biased and potentially harmful.
Because of the minor advancements and remaining biases in previous studies, Eesley et
al. (2014) executed a conjoint study to mitigate these shortcomings by asking key
questions such as: What were the key factors for European venture capitalists in
evaluating potential investments; Did venture capitalists throughout Europe consistently
apply these factors; Was there any clustering of venture capitalists based on their decision
criteria? In addition to answering these questions, they argued that the methodology used
to assess decision-making was a key question, as researchers should not present venture
capitalists with a list of criteria via Likert-scale ratings. Answering the first question, the
scholars hypothesized that the management team was the most important decision criteria
for venture capital firms, followed by a competitive market position and the management
team’s ability to execute the business plan. Of minor importance were the decision
criteria associated with the deal itself like the fit with the fund. Regarding the second
question and in line with previous research, they did not find significant differences in the
criteria across European countries. Despite their findings, the criteria evaluated consist of
decision criteria from previous research and therefore remained, in some parts, vague and
general. With the democratization of infrastructure services and the proliferation of low-
cost technology stacks, over the last few decades, it has been easy to transform an idea
into a proper startup.
39
Next to the methodical implications and suggesting that researchers focus on the stage,
Mueller and Murmann (2016) also investigated the relative importance of venture capital
funds decision criteria for early-stage ventures. They concluded that the industry-related
competence of the team and educational capabilities were the most important decision
criteria based on a conjoint experiment with forty-seven Australian venture capital firms.
These respondents attached less importance to competitive rivalry, lead time, and entry
timing. They made another attempt to compare decision criteria across different investors.
Compared to earlier studies, they also included debt investors. The authors compared
verbal protocols from ten investors, of which three were banks, another three were
venture capital funds, and four were business angels. They concluded that bankers
assigned the biggest importance to financial aspects such profit or collateral and only a
very small weight to the entrepreneur or market characteristics. In contrast, venture
capital funds assigned the biggest importance to market issues, financial issues, the
entrepreneur, and the strategy, whereas business angels focused mainly on the
entrepreneur, as they invest in the people. Their verbal protocol analysis indicated that
that the entrepreneur is not the primary determinant at the early stage for venture capital
funds.
Gornall and Strebulaev (2020) in their research on investing in new startups concluded
that venture capitalists served a valuable intermediary function by creating more
rewarding financial outcomes for their investee ventures and ultimately for themselves.
Although there has been much discussion about how venture capitalists should invest,
how to implement various contracting technologies and where they should attempt to add
value, many questions remain unanswered. The study has been asking: “Do early-stage
technology startup impact a venture capital’s investment decisions?” A large amount of
previous literature exists on the topic, with a variety of different works that have tried in
the past to identify specific variables that could explain, to different extents, the
likelihood of a venture capital fund to invest in an early-stage technology startup.
2.4.2 Growth Stage Venture Capital Investment and Venture Capitalists Investment
Decisions
40
A study by Armanios et al. (2017) analyzed venture capital finding success factors, the
relationship between the market volatility and venture capitalist investment decision.
Study findings showed that the decision to invest in the growth stage by the venture
capital firm results in positive financial performance. Venture capitalists in this stage
invest only in business since it has a proven record of success, because of this the return
on loss drops significantly contributing to positive performance in the industry. The study
recommended that a venture capitalist must identify the best stage for an investment
decision that will promote and enhance return on their funds. Critical analysis of venture
capital decision success factors, the relationship between market volatility and investment
by the venture capitalist’s success was adequately dealt with in this study. Nevertheless,
parameters like agency cost, fund size and investment duration were ignored in the study
despite influencing venture capital decision making.
In the study by Colombelli et al. (2016) examining venture capital in Italy, they found out
that growth financing happens five to eight years after incorporation though may extend
up to fifteen years. If a decision to invest is done past fifteen years from the date the firm
was started, then further studies must be done since the venture capitalists do not want to
plough their finances in earlier stages or in firms that are not willing to get venture capital
fund financing in their beginning years. Despite the study looking at investment decision
at growth stage, the exact effect on the performance of venture capital firms seems
inadequately covered. It only highlighted unwillingness by venture capitalists to invest in
this stage but lacked a precise and conclusive argument on their reluctance. It only shows
the maximum period for growth stage depicting the duration beyond which it warrants
scrutiny.
Research by Schøtt & Sedaghat (2014) sought to evaluate pros and cons of investment
decisions in the growth stage financing on startups among the venture capital community
in Germany. During this phase, all funding information about the performance of the firm
which could either be negative or positive was received by the investor and hence focused
attention. The study revealed that during the second phase of the startup there was no
evidence that growth stage financing affected the investment performance. The study
recommended that to enhance decision making the venture capital fund must be more
41
disciplined when using growth stage financing to adopt negative net present value
projects. They also found that venture capital firms believe the short investment duration
compared to early stage is likely to bring high return. Additionally, the investment was
still not certain to be profitable even though it had made progress. However, the venture
capitalist invested more if the possibility of termination of the investment reduced, and
the marginal revenue returns rose to the best level.
Cheraghi et al. (2014) sought to explain the role of cash restrictions over the venture
capital funding decision in the growth stage. Analyzing a sample of venture capital
backed firms that were able to gain more than one financial round of funds, the second
round gave the investor a higher bargaining power to get more returns. A twist from the
norm, the cash restriction suggested that the growth stage strengthens the venture
capitalist bargaining position leading to improved financial performance. Growth-stage
venture capital may come later in a company’s lifecycle with the advent of longer holding
periods and larger late-stage rounds of funding. Startups in the growth stage - five to 10
years old and in their first to fourth round of funding - receive investments of $6.8 million
on average. This is over three times the investment of an average early-stage startup,
which is under five years old and in its first or second round of funding. The reason is that
early-stage startups are more of a risk for investors compared to growth-stage ones for
which there is more information available to judge their track record. Another factor
affecting deal size is the size of the stake venture capital funds acquired in return for
investment. A 1% increase in stake in a startup increases venture capital funding by 0.7%,
the researchers found.
According to a study by Jackson (2015) in Kenya, while early-stage venture capital
companies may be pioneers of new industries with evolving business models, growth-
stage companies have established business models and greater traction in the marketplace,
which may support venture capital investment decisions. These characteristics could
potentially reduce the risk relative to earlier-stage investments. As the study figures
showed, the median net internal rate of return of growth-stage venture capital firms had
been competitive with early-stage venture capital while exhibiting a more attractive risk
profile.
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According to Colombelli et al. (2016), a portion of the superior risk-adjusted return was
probably due to the lower failure rates among growth-stage companies. While earlier
rounds of funding exhibited asymmetrically high returns, they were further out on the risk
spectrum, with higher failure rates than growth stage rounds. The faster pace of
distributions enabled allocators to better manage their liquidity budget and support
liabilities and spending needs. In terms of capacity, growth-stage companies sought to
scale while remaining private may present the opportunity for venture capital funds to put
more capital to work, often during the final injections of capital before the initial public
offering. In addition, the shorter-duration J-curve may be attractive to institutions
building out their private equity program (Schøtt & Sedaghat, 2014).
2.4.3 Late-Stage Venture Capital Investment and Venture Capitalists Investment
Decisions
A study by Barbi & Mattioli (2019) revealed that most of the venture capital financing
decisions were made in the late-stage round of financing. The financing market is mainly
distinguished by a short life span period of investments. The study also showed that late-
stage financing had a great influence on funding decisions since the venture capital fund
did not need to provide additional financing round at this stage. The main finding from
the study showed that it was effective to allocate more funding in the late stage of an
investment since it increases the chances for successful ventures. At this stage the
possibility of termination of a project decreased and hence the marginal return on the
business went up and hence the venture capital fund invested greatly. The study also
showed the implication of decisions in staging investments as skewing the effective
dispensation of financial resources geared towards the later stages which will lead to
better performance of venture capital funds.
Agarwal et al. (2020) in their study showed that startups may or may not be profitable in
the late stage, but with a higher chance of higher returns than in any other developmental
stages of a business and have positive cash flows. If profitable the positive cash flow
raises the return on the investments made by the venture capitalists. At this stage, a
venture capital firm should decide on its exit especially if firms see that the return will be
43
low. Since the firm is near exit, most venture capitalists in this stage take a short span of
period in terms of years to exit and get their return. The study focused on the investment
period as one of factors that influence the decisions of venture capital funds. Their
research study revealed that there were negative consequences of using late-stage
financing. The argument was that venture capital funds in this stage faced a termination
dilemma. A venture fund may decide to terminate the financing in a startup that its
performance is struggling. The venture capital firm not only avoids injecting more capital
after poor results, but also let go of the opinion of possibility of performance
reconsideration and hence the result of financial success.
Past research focused mainly on investigating the decision-making behavior of venture
capitalists regarding early-stage ventures rather than later-stage ventures. Therefore, little
was known about the decision-making behavior of venture capital funds investing in
later-stage ventures (Rossi & Vismara, 2018). While many studies have been published in
this field, they mainly focused on the case of early-stage companies. These studies
suffered from various methodological, theoretical, and data limitations; and did not focus
on later-stage ventures. However, as later-stage ventures have a significant relevance for
developed economies and are associated with employment growth and innovation, this
was an important research gap that several studies addressed (Rossi et al., 2019, Signori
& Vismara, 2018; Vismara, 2016, 2018; Walthoff-Borm, 2018a; Block et al., 2017). A
study done by Wang et al. (2019) found that venture capitalists choose not to invest late
in the business. This was because of the prices of equity shares being deemed not to be in
line with the total industry expectation due to the risk of uncertainty and the opportunity
for growth. If venture capitalists’ risk taking decreased, future returns would be expected
to decrease due to the lower returns from later stage firms than early-stage companies on
average.
Nunes et al. (2014) conducted a questionnaire of twenty Portuguese venture capitalists to
identify the importance they assigned to various decision criteria when evaluating late-
stage ventures. They found that late-stage investors assign less importance to the
company’s ability to create a new market for the product or service and financial
characteristics compared to early-stage investors. However, most previous studies did not
44
specifically investigate the decision behavior in the case of later-stage ventures, as they
only split the sample of investors into two investor types depending on their stage
preference. These results, however, were rather preliminary and no conclusions could be
drawn if other decision criteria were used for ventures in later stages of development.
Whether the decision criteria identified in previous research are transferable to the context
of later-stage ventures is therefore questionable. Here, different costly signals might exist
for later-stage ventures than for early-stage ventures, due to different information
available and different goals, risks and needs of later-stage ventures.
2.5 Chapter Summary
This chapter has focused on the study’s literature. It has provided the empirical literature
for factors influencing venture capital investment decisions on technology startups in
Kenya. It has examined the role of entrepreneur characteristics, effect of competition
from other funding sources, and the venture capital’s natural entry point. The next section
explores the research methodology that was used. The fourth chapter presents the study
results and findings, and chapter five explores the discussion, conclusion, and
recommendations of the study in detail.
45
CHAPTER THREE
3.0 RESEARCH METHODOLOGY
3.1 Introduction
This chapter discusses the research design for this study, population and sampling design,
data collection methods to be used, research procedures and finally data analysis method.
Under population, the population for the study was specified and its size stated. In
sampling design, sampling frame, sampling technique and sample size were specified.
3.2 Research Design
A research design is the procedure for collecting, analyzing, interpreting, and reporting
data in research studies. It is the plan for connecting conceptual research problems with
pertinent (and achievable) empirical research (Creswell, Plano & Clark, 2018). The base
of classification relies on the purpose of the research area as each design serves a
different end purpose (Blumberg, Cooper, and Schindler, 2014). For instance, the purpose
of a descriptive study is to provide a picture of a situation, person or event or show how
things are related to each other and as it naturally occurs (Valtakoski, 2019). However,
descriptive studies cannot explain why an event has occurred and is suitable for a new or
unexplored research area (Rosenzweig et al., 2016).
The research design that was adopted for this study was the descriptive research design.
According to Gall & Borg (2007), descriptive research design is more concerned with
what rather than how or why something has happened. Therefore, observation and survey
tools are used to gather data. Under descriptive research, the data may be collected
qualitatively, but it is often analyzed quantitatively, using frequencies, percentages,
averages, or other statistical analyses to determine relationships.
3.3 Population and Sampling Design
3.3.1 Population
Creswell (2014) defines a research population as a group of individuals or entities with
some common characteristic that the researcher plans to study with the aim of
generalizing the findings about the target population. The unit of analysis was the fifty-
two venture capital firms registered under the East Africa Venture Capital Association
46
which formed the study’s population. The respondents were fund principals and senior
analysts in venture capital firms. The fund principals and senior analysts were selected
because it was believed that they drove the funding decisions on which technology
startups to invest in. Hence, they gave their different opinions on the factors that
influenced venture capital decisions. The EAVCA was the representative organization for
private equity and venture capital funds in East Africa, with a growing membership of
private capital investors and advisors looking at the region for growth (East Africa
Venture Capital Association, 2018).
Table 3.1: Population Size
Venture Funds Population Categories Population
Firm Respondents
Private Equity Firms 10 Fund Principal
Senior Analysts
20
40
Venture and Seed Capital Fund 30 Fund Principal
Senior Analysts
30
50
Impact Investments 12 Fund Principal
Senior Analysts
40
60
Total 52 240
Source: East Africa Venture Capital Association (2022)
3.3.2 Sampling Design
A sample design is the framework, or road map, which serves as the basis for the
selection of a survey sample and affects many other important aspects of a survey as well.
In a broad context, survey researchers are interested in obtaining some type of
information through a survey for some population, or universe, of interest (Lavrakas,
2008). Kabir (2016) defines sample design as the plans and methods to be followed in
selecting sample from the target population and the estimation technique formula for
computing the sample statistics. These statistics are the estimates used to infer the
population parameters. The sampling design includes sampling frame, sampling
techniques and sample size determination.
47
3.3.2.1 Sampling Frame
Sampling refers to the process of obtaining representative data or observations from a
group or from a larger population. The sampling frame can be defined as a list of all the
elements in the population from which the sample is obtained (Fowler Jr, 2013). A
sampling frame usually includes a numerical identifier for each individual element plus
additional information to identify the characteristics of the individuals to help in analysis
and allow division into more frames for further in-depth analysis. In this study, the
sampling frame was obtained from the East Africa Venture Capital Association. The list
consisted of fund principals and senior investment analysts at Venture Capital Firms in
July 2022.
3.3.2.2 Sampling Technique
Sampling technique is the tactic that is used by the researcher to ensure that different
kinds of groups that are either heterogeneous or homogeneous are well represented in the
final selection of the sample to be studied (Cooper & Schindler, 2014). This study
deployed stratified sampling method in ensuring that all the levels of fund principals and
senior analysts were well represented in the selection of the respondents for the study.
Stratified sampling is the ideal method in ensuring that there is no bias in selection of the
respondents (Lewis-Beck, Bryman, & Liao, 2004), therefore, this was used in picking
fund principals and senior analysts that took part in the study.
3.3.2.3 Sample Size
Sample size refers to a smaller unit that forms a larger population of the study (Cooper &
Schindler, 2014). In determining the sample size, the researcher is guided by the level of
confidence that they need to have in the data, the kind of analysis to be conducted, the
accuracy and the total population of the study. To establish the sample size at a
confidence level of 95%, this study used the Yamane’s formula. From the Yamane’s
formula, the sample size of the study was determined, and based on the study population
of 240 respondents, the sample size based on the formula was 150 and was
proportionately distributed as provided in Table 3.2.
48
Where: 𝑛 = sample size
𝑁 = study population
𝑒 = alpha level, 0.05
The estimated sample size is thus:
Table 3.2: Sample Size
Venture Funds Population Categories Population Sample
Private Equity Firms Fund Principal
Senior Analysts
20
40
13
25
Venture and Seed Capital Fund Fund Principal
Senior Analysts
30
50
19
30
Impact Investments Fund Principal
Senior Analysts
40
60
25
38
Total 240 150
3.4 Data Collection Methods
According to Kabir (2016) data collection is the process of gathering and measuring
information on variables of interest, in an established systematic fashion that enables one
49
to answer stated research questions, evaluate hypotheses, and evaluate outcomes. The
goal for all data collection is to capture quality evidence that then translates to rich data
analysis and allows the building of a convincing and credible answer to questions that
have been posed. Regardless of the field of study or preference for defining data
(quantitative, qualitative), accurate data collection is essential to maintaining the integrity
of research. For the study being conducted, the data was collected using structured
questionnaires administered to both the fund principals and senior analysts of the venture
capital firms identified in the target population. The construct was measured using the
five-point Likert scales ranging from SD=Strongly Disagree to SA=Strongly Agree
(Junita et al., 2018; Sullivan et al., 2013). The main advantage of the Likert Scale is that it
uses a universal method of collecting data, which means it is easy to understand them.
Working with quantitative data, it is easy to draw conclusions, reports, results, and graphs
from the responses. Furthermore, because the Likert Scale questions use a scale,
respondents are not forced to express an either-or opinion, allowing them to be neutral.
The questions covered in the questionnaire addressed the issues around the venture capital
investment decisions on technology startups in Kenya; entrepreneurial characteristics,
competition from other funding sources and lastly, to determine how the venture capital’s
natural entry point plays a role in the decision-making process of evaluating the
technology startup to fund. For the analysis, the questionnaires had a fixed set of
responses which were encoded in them the requisite measures and thus were readily
compared and computed. The questionnaires had both closed-ended and open-ended
responses.
3.5 Research Procedures
The research procedure started with an approval from the supervisor. An Institutional
Registration Board (IRB) letter was then obtained from the United States International
University –Africa (USIU-A) which was later followed by the application for a permit
from the National Commission for Science, Technology, and Innovation (NACOSTI)
which came in handy in conducting this research. After identification of the issues to be
researched on, the research proposal was developed which went through the research
design strategy process of developing a data collection design, determining sample size
and the recruitment plans for the research assistants who conducted data collection. The
50
enumerators (research assistants - data clerks) were then recruited, who were trained on
the research tool to minimize the possible data collection errors which could have
emanated from the researchers themselves or the respondents.
A pilot test was conducted on the questionnaires before actual administration. A pilot
study can be defined as a small study to test research protocols, data collection
instruments, sample recruitment strategies, and other research techniques in preparation
for a larger study (Cooper & Schindler, 2014). To achieve this, the study used ten (10)
respondents to determine the reliability and validity of the questionnaires. The researcher
utilized the Cronbach alpha test to examine the reliability and consistency of the research
questionnaire. Salda (2012) states that Cronbach’s alpha is a coefficient that is commonly
used to test the reliability and consistency of structured questionnaires. According to
Kumar (2014), the threshold for the Cronbach alpha is set at ≥ 0.7, and thus, the study
adopted the same measure for a reliable and consistent instrument, and Table 3.3
indicates that the questionnaire was reliable.
Table 3.3: Reliability Outcome
Items Alph
a
Outcom
e
Entrepreneur Characteristics
Effect of Competition from Other Funding Sources
Venture Capital’s Natural Entry Point
Venture Capital Investment Decisions on Technology
Startups
Overall
12
12
12
10
46
.722
.720
.835
.860
.933
Reliable
Reliable
Reliable
Reliable
Reliable
The structured questionnaires were administered to the respondents who were fund
principals and senior investment analysts of the venture capital firms identified within the
target population. The questionnaires were administered through face-to-face interviews
by the enumerators to fund principals and senior investment analysts as they executed
their day-to-day activities after politely requesting them to participate and they also
ensured they explained the purpose of the study and assured the respondents of response
51
confidentiality. What followed the collection of data was insight development and
interpretation of the data that was collected from the respondents. A comprehensive
research report followed all the data which had been carefully considered and interpreted.
This report was used to make relevant decisions pertaining to the study.
3.6 Data Analysis Methods
The data analysis methods involve establishing a relationship between the entrepreneur
characteristics, effect of competition from other funding sources, and the venture capital’s
natural entry point on venture capital investment decisions. The data analysis was in the
form of descriptive and inferential statistics from which the research findings were
obtained. The data analysis was conducted with the aid of Statistical Package for Social
Sciences (SPSS) software version 27. Tables and figures were used to present the data
collected for ease of understanding and analysis. Tables were used to summarize
responses for further analysis and facilitate comparison. These were then used to generate
quantitative reports through tabulations, percentages, and measures of central tendency.
Bouncken et al. (2015) note that the use of percentages is important for two reasons; first
they simplify data by reducing all the numbers to range between 0 and 100. Second, they
translate the data into standard form with a base of one hundred for relative comparisons
(Calabrò et al., 2019). These provide simple summaries about the samples and the
measures. The output after analysis was displayed through frequency tables, graphic
presentations, and inferential statistics outputs.
Prior to conducting linear regression analysis, the researcher carried out a diagnostic test
to ensure that the data was normally distributed and did not contain multicollinearity
symptoms. This included a Normality test, Correlations analysis and Multicollinearity
test. Linear regression model was used to estimate the relationship between the dependent
variable (venture capitalists) and the independent variables (entrepreneur characteristics,
competition from other funding sources and venture capital’s natural entry point). It was
used to predict the value of the estimate effect that each independent variable had on the
dependent variable. The one-way analysis of variance (ANOVA) was used in the study to
determine whether there existed any statistically significant differences between the
means of the independent study variables. The regression coefficients were used to
52
measure the average functional relationship between the study variables. In this study, it
was used to measure the degree of dependence of venture capitalists on the independent
variables. The regression model was as follows:
The linear regression model that was used for entrepreneur characteristics was as follows:
γ = 𝑎 + β1χ1 + 𝑒
Where:
𝑎 = Constant (Point at which Line Crosses Y Axis)
χ1 = entrepreneur characteristics,
β1 = Slope (regression coefficient) for variable χ1
𝑒 = error (or residual) value
The linear regression model that was used for competition from other funding sources
was as follows:
γ = 𝑎 + β2χ2 + 𝑒
Where:
𝑎 = Constant (Point at which Line Crosses Y Axis)
χ1 = competition from other funding sources,
β2= Slope (regression coefficient) for variable χ2
𝑒 = error (or residual) value
The linear regression model that was used for venture capital’s natural entry point was as
follows:
γ = 𝑎 + β3χ3 + 𝑒
Where:
𝑎 = Constant (Point at which Line Crosses Y Axis)
χ3 = venture capital’s natural entry point,
β3 = Slope (regression coefficient) for variable χ3
𝑒 = error (or residual) value
3.7 Chapter Summary
53
This chapter highlighted the research design where the population represented fund
principals and senior investment analysts working in registered venture capital funds in
Kenya. The Stratified random sampling technique was used in the identification of the
sample. Structured questionnaires were utilized for data collection and administered to the
fund principles and senior analysts of the venture capital firms. Factor analysis
determined the relationship between the independent variables and the dependent
variable. The Data analysis was conducted using regression analysis with the aid of SPSS
data analysis tool and Microsoft Excel and visually represented in both graphical and
tabular formats. Chapter four looked at the result and findings which explain the data
collected.
54
CHAPTER FOUR
4.0 RESULTS AND FINDINGS
4.1 Introduction
This chapter covers the results and findings of the factors influencing venture capital
investment decisions on technology startups in Kenya. The chapter is directed by the
entrepreneur characteristics, effect of competition from other funding sources and venture
capital’s natural entry point affects the decision-making process of evaluating the
technology startup to fund.
4.2 Demographic Data and Response Rate
This section of the study chapter presents the response rate and provides the outcome of
the demographic data which covers the obtained outcome of the respondents’ gender,
their age, and position they held in their firm. It also provides the number of countries in
which Venture Capital operated in, and the duration they have been in operation.
4.2.1 Response Rate
Figure 4.1 shows a response rate of 74.7% which was deemed sufficient. Salda (2012)
opines that a study that attains a response rate of above 51% of its population is adequate
for analysis, and this study surpassed this threshold. It denotes that the study obtained 112
responses from the targeted 150 responses.
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%
Obtained Responses
Unobtained Responses
74.70%
25.30%
55
Figure 4.1: Response Rate
4.2.2 Gender
The study respondents were asked to indicate their gender and Figure 4.2 presents the
attained outcome. It shows that 56% of the study respondents were male, and 44% were
female. This denotes that majority of the employees in the Venture Capital Firms were
male.
0% 10% 20% 30% 40% 50% 60%
Male
Female
56%
44%
Figure 4.2: Gender
4.2.3 Age
The respondents were requested to indicate their age group, and Figure 4.3 shows that
46% of the respondents were aged between 40-49 years, 40% were between 30-39 years,
and 14% were between 20-29 years. This shows that most of the employees in Venture
Capital Firms were above the age of 20 years.
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
40-49 Years
30-39 Years
20-29 Years
46%
40%
14%
Figure 4.3: Age
56
4.2.4 Position
The study respondents were asked to indicate their position in their respective firms and
Figure 4.4 presents the attained outcome. It shows that 62% of the study respondents were
senior analysts, and 38% were fund principles. This denotes that all the targeted positions
of employees in the Venture Capital Firms were captured in the study.
0% 10% 20% 30% 40% 50% 60% 70%
Senior Analyst
Fund Principal
62%
38%
Figure 4.4: Position
4.2.5 Countries of Operation
The respondents were requested to indicate the number of countries that their Venture
Capital Firm operated in, and Figure 4.5 shows that 50% of the respondents stated that
their firm operated in 1-5 countries, 26% operated in 6-10 countries, 12% were in
operation between 11-15 countries, 7% operated in more than 21 countries, while 5%
operated in 16-20 countries.
0% 10% 20% 30% 40% 50% 60%
1-5 Countries
6-10 Countries
11-15 Countries
21 Countries and Above
16-20 Countries
50%
26%
12%
7%
5%
Figure 4.5: Countries of Operation
4.2.6 Duration of Operation
57
The study respondents were asked to indicate the duration that their respective firms had
been in operation and Figure 4.6 presents the attained outcome. It shows that 30% of the
firms had been in operation for 1-3 years and 4-6 years respectively, 14% had been in
operation for 10-12 years, 12% had been in operation for over 13 years, 8% had been in
operation for less than a year, and 6% had been in operation for 7-9 years. This denotes
that most of the Venture Capital Firms had been in operation for more than 4 years.
0% 5% 10% 15% 20% 25% 30% 35%
1-3 Years
4-6 Years
10-12 Years
Above 13 Years
Less than 1 Year
7-9 Years
30%
30%
14%
12%
8%
6%
Figure 4.6: Duration of Operation
4.2.7 Venture Capital Investment Decisions on Technology Startups
4.2.7.1 Descriptives for Venture Capital Investment Decision Preference
Table 4.1 shows that venture capital filled the void between sources of funds for
innovation and traditional sources of capital available to technology start-ups as shown by
67.9% of the respondents who agreed, 27.7% strongly agreed, and 4.5% were neutral
(mean=4.23; standard deviation=0.520). Venture capital investors were most interested in
a business that offered them an opportunity for a significant return as shown by 59.8% of
the respondents who agreed, 17.9% were neutral, 16.1% strongly agreed, and 6.3%
disagreed (mean=3.86; standard deviation=0.758). Venture capitalists preferred to invest
in technology startups with mature products and actual financial performance as shown
by 53.6% of the respondents who agreed, 20.5% disagreed, 14.3% strongly agreed, and
11.6% were neutral (mean=3.62; standard deviation=0.970).
Venture capitalists relied on the information they gathered about entrepreneurs to predict
whether a startup would be successful as shown by 47.3% of the respondents who agreed,
58
24.1% were neutral, 22.3% strongly agreed, 4.5% strongly disagreed, and 1.8% disagreed
(mean=3.81; standard deviation=0.954). A venture capitalist with a strategy to diversify
across industries selected different investments to a venture capitalist that wanted to
create synergistic value between portfolio companies as shown by 54.5% of the
respondents who agreed, 33% strongly agreed, and 12.5% were neutral (mean=4.21;
standard deviation=0.646).
Table 4.1: Descriptives for Venture Capital Investment Decision Preference
SD D N A SA
N M SD
% % % % %
Venture capital fills the void
between sources of funds for
innovation and traditional
sources of capital available to
technology start-ups
0 0 4.5 67.
9
27.
7
11
2
4.2
3
.520
Venture capital investors are
most interested in a business
that offers them an opportunity
for a significant return
0 6.3 17.
9
59.
8
16.
1
11
2
3.8
6
.758
Venture capitalists prefer to
invest in technology startups
with mature products and
actual financial performance
0 20.
5
11.
6
53.
6
14.
3
11
2
3.6
2
.970
Venture capitalists rely on the
information they gather about
entrepreneurs to predict
whether a startup will be
successful
4.5 1.8 24.
1
47.
3
22.
3
11
2
3.8
1
.954
A venture capitalist with a
strategy to diversify across
industries may select different
investments to a venture
0 0 12.
5
54.
5
33 11
2
4.2
1
.646
59
capitalist that wants to create
synergistic value between
portfolio companies
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
4.2.7.2 Descriptives for Venture Capital Investment Decision Capacity
Table 4.2 shows that a disruptive invention encouraged a fresh round of growth
opportunity for venture capital funds as shown by 57.1% of the respondents who agreed,
34.4% strongly agreed, and 4.5% were neutral (mean=4.34; standard deviation=0.562).
Venture capitalist’s ability to select and finance successful startups positioned them
within the profession, enabling them to build their reputation as shown by 52.7% of the
respondents who agreed, and 47.3% strongly agreed (mean=4.47; standard
deviation=0.502).
Table 4.2: Descriptives for Venture Capital Investment Decision Capacity
SD D N A SA
N M SD
% % % % %
A disruptive invention will
encourage a fresh round of
growth opportunity for venture
capital funds
0 0 4.5 57.
1
38.
4
11
2
4.3
4
.562
Venture capitalist’s ability to
select and finance successful
startups positions them within
the profession, enabling them
to build their reputation
0 0 0 52.
7
47.
3
11
2
4.4
7
.502
Deciding to invest in a start-
up’s involves the development
of a collaborative relationship
between the venture capital
firm and the business founder
0 0 0 43.
8
56.
3
11
2
4.5
6
.498
The evaluation of the start-up’s
value provides a basis for
0 0 8.9 56.
3
34.
8
11
2
4.2
6
.611
60
negotiation on how to
distribute the capital in line
with the amount of equity the
venture capital fund puts into
the entrepreneur’s company
Venture capitalists invest in
startups with commercially
viable know-how
0 6.3 0 64.
3
29.
5
11
2
4.1
7
.721
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
Table 4.2 also shows that deciding to invest in a start-up’s involved the development of a
collaborative relationship between the venture capital firm and the business founder as
shown by 56.3% of the respondents who strongly agreed, and 43.8% agreed (mean=4.56;
standard deviation=0.498). The evaluation of the start-up’s value provided a basis for
negotiation on how to distribute the capital in line with the amount of equity the venture
capital fund puts into the entrepreneur’s company as shown by 56.3% of the respondents
who agreed, 34.8% strongly agreed, and 8.9% were neutral (mean=4.26; standard
deviation=0.611). Venture capitalists invested in startups with commercially viable know-
how as shown by 64.3% of the respondents who agreed, 29.5% strongly agreed, and 6.3%
disagreed (mean=4.17; standard deviation=0.721).
4.2.8 Determinants of Venture Capital Investment Decision
The study respondents were asked to indicate what else determines venture capital
investment decisions in technology startups in Kenya and Figure 4.7 presents the
outcome. It shows that 21% stated management capability, 18% market size and trend,
while 17% cited market opportunities. It also indicates that 11% stated government laws
and policies, 9% cited resources availability, 8% indicated market experience, while 7%
stated founder's track record, 5% cited company vision, and 4% stated community value.
61
0% 5% 10% 15% 20% 25%
Management Capability
Market Size and Trend
Market Opportunities
Government Laws and Policies
Resources Availability
Market Experience
Founder's Track Record
Company Vision
Community Value
21%
18%
17%
11%
9%
8%
7%
5%
4%
Figure 4.7: Determinants of Venture Capital Investment Decision
4.3 Entrepreneur Characteristics
The study sought to identify the entrepreneur characteristics considered when evaluating
Kenyan technology start-ups investment decisions investment decisions by venture
capitalists. This section of the study chapter presents the outcome of the descriptive
analysis, the diagnostic tests, and the inferential analysis for entrepreneur characteristics.
4.3.1 Descriptives for Entrepreneur Experience
Table 4.3 shows that the criteria that was most important to the venture capitalist was the
entrepreneur's experience as shown by 60.7% of the respondents who agreed, 13.4%
strongly agreed, 13.4% were neutral, and 12.5% disagreed (mean=3.75; standard
deviation=0.844). A founder’s experience and networking skills were critical to a start-up
and improved their ability to secure funding as shown by 92% of the respondents who
agreed, and 8% were neutral (mean=4.31; standard deviation=0.616). Investors were
likely to place greater emphasis on the attributes of the founders experience relative to
other aspects of the business in uncertain environments as shown by 83% of the
respondents who agreed, 8.9% were neutral, and 8% disagreed (mean=3.87; standard
deviation=0.717). Entrepreneurs experienced in running startups were better at evaluating
opportunities thus were likely to be funded as shown by 73.2% of the respondents who
agreed, 20.5% disagreed, and 6.3% were neutral (mean=3.88; standard deviation=1.113).
Table 4.3: Descriptives for Entrepreneur Experience
62
SD D N A SA N M SD
% % % % %
The criteria most important to
the venture capitalist is the
entrepreneur's experience
0 12.
5
13.
4
60.
7
13.
4
11
2
3.7
5
.844
A founder’s experience and
networking skills are critical to
a start-up and improves their
ability to secure funding
0 0 8 52.
7
39.
3
11
2
4.3
1
.616
Investors are likely to place
greater emphasis on the
attributes of the founders
experience relative to other
aspects of the business in
uncertain environments
0 8 8.9 71.
4
11.
6
11
2
3.8
7
.717
Entrepreneurs experienced in
running startups are better at
evaluating opportunities thus
are likely to be funded
0 20.
5
6.3 37.
5
35.
7
11
2
3.8
8
1.113
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
4.3.2 Descriptives for Entrepreneur Education
Table 4.4 shows that decision makers with a higher level of education seemed to prefer
innovation-centered business models as shown by 40.2% of the respondents who agreed
25.9% strongly agreed, 20.5% were neutral, and 10.7% disagreed while 2.7% strongly
disagreed (mean=3.76; standard deviation=1.042). The entrepreneurial education as a
function of entrepreneurial competence was the combined capacity to identify and pursue
opportunities, and to obtain and coordinate resources as shown by 95.5% of the
respondents who agreed, and 4.5% were neutral (mean=4.29; standard deviation=0.544).
Entrepreneurs with a high level of academic achievement did not have an innovative
tendency thus were unlikely to be funded as shown by 37.5% of the respondents who
disagreed, 33.9% agreed, and 28.6% were neutral (mean=2.96; standard
63
deviation=0.848). Entrepreneurs with an academic background in science were more
focused on the product thus were likely to be funded as shown by 40.2% of the
respondents who agreed, 39.3% were neutral, and 20.6% disagreed (mean=3.33; standard
deviation=1.008).
Table 4.4: Descriptives for Entrepreneur Education
SD D N A SA
N M SD
% % % % %
Decision makers with a higher
level of education seem to
prefer innovation-centered
business models
2.7 10.
7
20.5 40.2 25.9 11
2
3.76 1.04
2
The entrepreneurial education
as a function of entrepreneurial
competence is the combined
capacity to identify and pursue
opportunities, and to obtain
and coordinate resources
0 0 4.5 62.5 33 11
2
4.29 .544
Entrepreneurs with a high level
of academic achievement also
have an innovative tendency
thus are likely to be funded
0 37.
5
28.6 33.9 0 11
2
2.96 .848
Entrepreneurs with an
academic background in
science are more focused on
the product thus are likely to
be funded
1.8 18.
8
39.3 25 15.2 11
2
3.33 1.00
8
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
4.3.3 Descriptives for Management Characteristics
Table 4.5 shows that superior performance of entrepreneurs resulted from their ability to
learn on the job which boosted their business skills as shown by 56.3% of the respondents
64
who agreed, 36.6% strongly agreed and 7.1% were neutral (mean=4.29; standard
deviation=0.595).
Table 4.5: Descriptives for Management Characteristics
SD D N A SA
N M SD
% % % % %
Superior performance of
entrepreneurs may result from
their ability to learn on the job
which boosts their business
skills
0 0 7.1 56.
3
36.
6
11
2
4.2
9
.595
The quality of management
and work commitment are the
criteria that receive the highest
weight in the assessment of
proposals
0 16.
1
15.
2
39.
3
29.
5
11
2
3.8
2
1.033
An important source of a
venture capital firm’s interest
in the management
characteristics of entrepreneurs
is that both parties agree to
form a joint company financed
by the fund’s money and
managed by the entrepreneur
4.5 12.
5
24.
1
44.
6
14.
3
11
2
3.5
2
1.031
Managerial competencies are
vital for performing
entrepreneurial activities
successfully thus are likely to
be funded
0 8 1.8 33.
9
56.
3
11
2
4.3
8
.872
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
65
The table also shows that the quality of management and work commitment were the
criteria that received the highest weight in the assessment of proposals as shown by
68.8% of the respondents who agreed, 16.1% disagreed, and 15.2% were neutral
(mean=3.82; standard deviation=1.033). An important source of a venture capital firm’s
interest in the management characteristics of entrepreneurs was that both parties agree to
form a joint company financed by the fund’s money and managed by the entrepreneur as
shown by 58.9% of the respondents who agreed, 24.1% were neutral, and 17% disagreed
(mean=3.52; standard deviation=1.031). Managerial competencies were vital for
performing entrepreneurial activities successfully thus were likely to be funded as shown
by 90.2% of the respondents who agreed, 8% disagreed, and 1.8% were neutral
(mean=4.38; standard deviation=0.872).
4.3.4 Correlations Analysis
To determine whether there was a statistically linear association between entrepreneur
characteristics and venture capital investment decisions, correlation analysis was
conducted. The findings in Table 4.6 shows the existence of a statistically significant
linear association between entrepreneur characteristics and venture capital investment
decisions on technology startups in Kenya (r = 0.839, p<.05).
Table 4.6: Correlation for Entrepreneur Characteristics
Investment Decision
Investment Decision 1
Entrepreneur Characteristics
N
.839**
.000
112
** Correlation is significant at the 0.01 level (2-tailed)
4.3.5 Linear Regression Diagnostic Tests
Linear regression diagnostic tests were carried out to examine the study data. These were
conducted to ascertain that the data was normally distributed, and that a linear
relationship existed between the study variables, and that the data did not contain
66
multicollinearity symptoms. The tests included normality test, linearity, and
multicollinearity test.
4.3.5.1 Normality Test
The Shapiro-Wilk Test was used in this study to ascertain the distribution of the study
data. Table 4.7 shows that the obtained study data for entrepreneur characteristics was
normally distributed as indicated by the Shapiro-Wilk Sig. value of 0.343 which is greater
than 0.05.
Table 4.7: Normality Test for Entrepreneur Characteristics
Kolmogorov-Smirnov Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Entrepreneur Characteristics .198 112 .200* .902 112 .343
4.3.5.2 Linearity Test
To evaluate the assumption of the linear regression analysis, the linearity test for
entrepreneur characteristics and venture capital investment decisions on technology
startups in Kenya was conducted. The findings in Table 4.8 shows the existence of a
linear association between entrepreneur characteristics and venture capital investment
decisions on technology startups in Kenya (sig value for deviation from linearity 0.000
<0.05).
Table 4.8: Linearity Test for Entrepreneur Characteristics
Sum of
Square
s
df Mean
Squar
e
F Sig.
VC Investment
* Entrepreneur
Characteristics
Between
Groups
Within Groups
Total
(Combined
)
Linearity
Deviation
from
Linearity
15.147
11.721
3.426
1.512
16.659
11
1
10
10
0
11
1
1.377
11.72
1
.343
.015
91.063
775.11
4
22.658
.00
0
.00
0
.00
0
67
4.3.5.3 Multicollinearity Test
To evaluate the assumption of the linear regression analysis, the multicollinearity test for
entrepreneur characteristics and venture capital investment decisions on technology
startups in Kenya was conducted. The multicollinearity statistics of the variables are
shown in Table 4.9, and there were no indications of multicollinearity between the
research variables because the entrepreneur characteristics Variance Inflation Factor
(VIF), which runs from 1 to 10, was 2.312.
Table 4.9: Multicollinearity Test for Entrepreneur Characteristics
Model
Collinearity Statistics
Tolerance VIF
1 (Constant)
Entrepreneur Characteristics .433 2.312
a. Dependent Variable: Investment Decision
4.3.6 Linear Regression Analysis Test.
The linear regression analysis presents the outcome of the linear regression model
summary for entrepreneur characteristics and venture capital investment decisions on
technology startups in Kenya, as well as the Analysis of Variance (ANOVA), and the
regression coefficient findings.
4.3.6.1 Model Summary
Table 4.10 presents the obtained outcome of the model summary between entrepreneur
characteristic and venture capital investment decisions on technology startups in Kenya.
The outcome indicates that entrepreneur characteristic explain 70.1% of the variance in
venture capital investment decisions on technology startups in Kenya (adjusted R² =
.701).
Table 4.10: Model Summary between Entrepreneur Characteristic and Venture
Capital Investment Decisions
68
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .839 .704 .701 .21188
a. Predictors: (Constant), Entrepreneur Characteristics
4.3.6.2 Regression ANOVA
Table 4.11 presents the obtained outcome of the ANOVA between entrepreneur
characteristic and venture capital investment decisions on technology startups in Kenya. It
was used to determine whether there was a statistically linear association between
entrepreneur characteristic and venture capital investment decisions. The outcome shows
the existence of a statistically significant linear association between entrepreneur
characteristic and venture capital investment decisions on technology startups in Kenya
(F (1,111) = 261.078, p<.05).
Table 4.11: ANOVA between Entrepreneur Characteristic and Venture Capital
Investment Decisions
Model Sum of Squares df Mean Square F Sig.
1 Regression
Residual
Total
11.721
4.938
16.659
1
110
111
11.721
.045
261.078 .000
a. Predictors: (Constant), Entrepreneur Characteristics
b. Dependent Variable: Investment Decisions
4.3.6.3 Regression Coefficients
Table 4.12 presents the obtained outcome of the regression coefficients between
entrepreneur characteristic factors (entrepreneur experience, entrepreneur education, and
management characteristics) and venture capital investment decisions on technology
startups in Kenya. The outcome shows that entrepreneur experience statistically influence
the venture capital investment decisions on technology startups in Kenya (β = 0.259, t-stat
= 7.658, p<.05), in that a single unit change in entrepreneur experience increases venture
capital investment decisions on technology startups in Kenya by 25.9%. Entrepreneur
education statistically influence the venture capital investment decisions on technology
startups in Kenya (β = 0.233, t-stat = 5.639, p<.05), in that a single unit change in
69
entrepreneur education increases venture capital investment decisions on technology
startups in Kenya by 23.3%. Management characteristics statistically influence the
venture capital investment decisions on technology startups in Kenya (β = 0.347, t-stat =
10.014, p<.05), in that a single unit change in management characteristics increases
venture capital investment decisions on technology startups in Kenya by 34.7%.
Table 4.12: Regression Coefficients between Entrepreneur Characteristics and
Venture Capital Investment Decisions
Model
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
1 (Constant)
Entrepreneur Experience
Entrepreneur Education
Management Characteristics
.901
.259
.233
.347
.197
.034
.041
.035
.442
.327
.517
4.565
7.658
5.639
10.014
.000
.000
.000
.000
a. Dependent Variable: Investment Decisions
4.3.7 Other Entrepreneur Characteristics
The study respondents were asked to state other entrepreneur characteristics that
influenced venture capital decisions on technology startups in Kenya and Figure 4.8
presents the outcome. It shows that 19% stated great decision-making skills, 16% stated
innovative ideas, 13% indicated great communication skills, 12% stated business
experience, 11% spoke of strong networks, 9% indicated talent attraction and retention,
8% stated having innovative strategies, 7% stated social impact, 3% spoke of strong
vision, and 2% stated having passion.
70
0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%
Great Decision-Making Skills
Innovative Ideas
Great Communication Skills
Business Experience
Strong Networks
Talent Attraction and Retention
Innovative Strategies
Social Impact
Strong Vision
Passion
19%
16%
13%
12%
11%
9%
8%
7%
3%
2%
Figure 4.8: Other Entrepreneur Characteristics
4.4 Competition from other Funding Sources
The study sought to establish effect of competition from other funding sources on
technology start-ups technology start-ups investment decisions by venture capitalists in
Kenya. This area of the study chapter presents the outcome of the descriptive analysis, the
diagnostic tests, and the inferential analysis for competition from other funding sources.
4.4.1 Descriptives for Captive versus Independent Funds
Table 4.13 shows that alternative sources of financing for entrepreneurs were increasingly
viable options for technology start-ups when sourcing for venture capital funds as shown
by 74.1% of the respondents who agreed, 8% strongly agreed, 10.7% disagreed, and 7.1%
were neutral (mean=3.79; standard deviation=0.737). Independent venture capital funds
tended to select older and larger companies in their expansion stages for funding
consideration as shown by 48.2% of the respondents who agreed, 34.9% disagreed, and
17% were neutral (mean=3.09; standard deviation=0.982). Captive venture capitalists
were attracted by companies operating in industries with high technological focus when
evaluating technology startups for funding as shown by 65.1% of the respondents who
agreed, 32.1% were neutral, and 2.7% disagreed (mean=3.96; standard deviation=0.874).
Bank affiliated venture capital firms employed more passive strategies than other venture
capital types and were more inclined to invest in late-stage startups as shown by 78.5% of
the respondents who agreed, 14.3% disagreed, and 7.1% were neutral (mean=4.02;
standard deviation=1.230).
71
Table 4.13: Descriptives for Captive versus Independent Funds
SD D N A SA
N M SD
% % % % %
Alternative sources of
financing for entrepreneurs are
increasingly viable options for
technology start-ups when
sourcing for venture capital
funds
0 10.
7
7.1 74.
1
8 11
2
3.7
9
.737
Independent venture capital
funds tend to select older and
larger companies in their
expansion stages for funding
consideration
4.5 30.
4
17 48.
2
0 11
2
3.0
9
.982
Captive venture capitalists are
attracted by companies
operating in industries with
high technological focus when
evaluating technology startups
for funding
0 2.7 32.
1
32.
1
33 11
2
3.9
6
.874
Bank affiliated venture capital
firms employ more passive
strategies than other venture
capital types and are more
inclined to invest in late-stage
startups
8 6.3 7.1 33 45.
5
11
2
4.0
2
1.230
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
4.4.2 Descriptives for Reputation of the Venture Capital Firm
Table 4.14 shows that a high-reputation venture capital firm that specialized in the focal
firm’s industry provided more substantive value to a start-up than a less specialized high-
reputation venture capital firm as shown by 42% of the respondents who agreed, 28.6 %
72
strongly agreed 15.2% were neutral, and 12.5% disagreed while 1.8 strongly disagreed
(mean=3.83; standard deviation=1.039). The primary way for a venture capital firm to
win funding the technology start-up over its competitors is to have a better reputation as
shown by 72.3% of the respondents who agreed, 17.9% disagreed, and 9.8% were neutral
(mean=3.82; standard deviation=1.033). Reputation depends on many aspects of the firm
and is the aggregate culmination of many small procedures, conduct, and performance
levels which the venture capital firm maintains as shown by 93.7% of the respondents
who agreed, and 6.3% were neutral (mean=4.35; standard deviation=0.596). A more
reputable venture capital firm sees a larger stream of deal flow than does a less reputable
venture capital fund as shown by 82.1% of the respondents who agreed, 16.1% were
neutral, and 1.8% disagreed (mean=4.02; standard deviation=0.671).
Table 4.14: Descriptives for Reputation of the Venture Capital Firm
SD D N A SA
N M SD
% % % % %
A high-reputation venture
capital firm that specializes in
the focal firm’s industry can
provide more substantive value
to a start-up than a less
specialized high-reputation
venture capital firm
1.8 12.
5
15.
2
42 28.
6
11
2
3.8
3
1.039
The primary way for a venture
capital firm to win funding the
technology start-up over its
competitors is to have a better
reputation
0 17.
9
9.8 44.
6
27.
7
11
2
3.8
2
1.033
Reputation depends on many
aspects of the firm and is the
aggregate culmination of many
small procedures, conduct, and
performance levels which the
venture capital firm maintains
0 0 6.3 52.
7
41.
1
11
2
4.3
5
.596
73
A more reputable venture
capital firm sees a larger
stream of deal flow than does a
less reputable venture capital
fund
0 1.8 16.
1
60.
7
21.
4
11
2
4.0
2
.671
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
4.4.3 Descriptives for Experience of the Venture Capital Firm
Table 4.15 shows that experienced venture capital firms selected high potential
entrepreneurial firms and provided more valuable services as shown by 50.9% of the
respondents who agreed, 30.4% strongly agreed, 14.3% were neutral, and 4.5% disagreed
(mean=4.07; standard deviation=0.791). Companies funded by more experienced venture
capitalists were more likely to go public as shown by 61.7% of the respondents who
agreed, 19.6% disagreed, and 18.8% were neutral (mean=3.72; standard
deviation=1.100). Venture capital firms were likely to learn through prior investments
and develop routines based on past experiences as shown by 97.3% of the respondents
who agreed, and 2.7% disagreed (mean=4.46; standard deviation=0.643). A venture
capital firm can better grasp the nuances of the investment at hand based on its experience
as shown by 91% of the respondents who agreed, 4.5% were neutral, and another 4.5%
disagreed (mean=4.20; standard deviation=0.721).
Table 4.15: Descriptives for Experience of the Venture Capital Firm
SD D N A SA
N M SD
% % % % %
Experienced venture capital
firms select high potential
entrepreneurial firms and
provide more valuable services
0 4.5 14.
3
50.
9
30.
4
11
2
4.0
7
.791
Companies funded by more
experienced venture capitalists
are more likely to go public
0 19.
6
18.
8
31.
3
30.
4
11
2
3.7
2
1.100
Venture capital firms are likely
to learn through prior
0 2.7 0 45.
5
51.
8
11
2
4.4
6
.643
74
investments and develop
routines based on past
experiences
A venture capital firm can
better grasp the nuances of the
investment at hand based on its
experience
0 4.5 4.5 58 33 11
2
4.2
0
.721
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
4.4.4 Correlations Analysis
To determine whether there was a statistically linear association between competition
from other funding sources and venture capital investment decisions, correlation analysis
was conducted. The findings in Table 4.16 shows the existence of a statistically
significant linear association between competition from other funding sources and venture
capital investment decisions on technology startups in Kenya (r = 0.712, p<.05).
Table 4.16: Correlation for Competition from other Funding Sources
Investment Decision
Investment Decision 1
Funding Sources Competition
N
.712**
.000
112
** Correlation is significant at the 0.01 level (2-tailed)
4.4.5 Linear Regression Diagnostic Tests
Linear regression diagnostic tests were carried out to examine the study data. These were
conducted to ascertain that the data was normally distributed, and that a linear
relationship existed between the study variables, and that the data did not contain
multicollinearity symptoms. The tests included normality test, correlation analysis, and
multicollinearity test.
4.4.5.1 Normality Test
75
The Shapiro-Wilk Test was used in this study to ascertain the distribution of the study
data. Table 4.17 shows that the obtained study data for competition from other funding
sources was normally distributed as indicated by the Shapiro-Wilk Sig. value of 0.203
which is greater than 0.05.
Table 4.17: Normality Test for Competition from other Funding Sources
Kolmogorov-Smirnov Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Funding Sources Competition .230 112 .200* .873 112 .203
4.4.5.2 Linearity Test
To evaluate the assumption of the linear regression analysis, the linearity test for
competition from other funding sources and venture capital investment decisions on
technology startups in Kenya was conducted. The findings in Table 4.18 shows the
existence of a linear association between competition of funding from other sources and
venture capital investment decisions on technology startups in Kenya (sig value for
deviation from linearity 0.000 <0.05).
Table 4.18: Linearity Test for Competition from other Funding Sources
Sum of
Square
s
df Mean
Squar
e
F Sig.
VC Investment
* Competition
from other
Funding
Sources
Between
Groups
Within Groups
Total
(Combined
)
Linearity
Deviation
from
Linearity
14.479
8.441
6.038
2.180
16.659
13
1
12
98
11
1
1.114
8.441
.503
.022
50.061
379.39
4
22.617
.00
0
.00
0
.00
0
4.4.5.3 Multicollinearity Test
76
To evaluate the assumption of the linear regression analysis, the multicollinearity test for
competition from other funding sources and venture capital investment decisions on
technology startups in Kenya was conducted. The multicollinearity statistics of the
variables are shown in Table 4.19, and there were no indications of multicollinearity
between the research variables because the competition from other funding sources VIF,
which runs from 1 to 10, was 2.430.
Table 4.19: Multicollinearity Test for Competition from other Funding Sources
Model
Collinearity Statistics
Tolerance VIF
1 (Constant)
Funding Sources Competition .412 2.430
a. Dependent Variable: Investment Decision
4.4.6 Linear Regression Analysis Test
This bit of the chapter focuses on linear regression analysis. It presents the outcome of the
linear regression model summary for competition from other funding sources and venture
capital investment decisions on technology startups in Kenya, as well as the ANOVA,
and the regression coefficient findings.
4.4.6.1 Model Summary
Table 4.20 presents the obtained outcome of the model summary between competition
from other funding source and venture capital investment decisions on technology
startups in Kenya. The outcome indicates that competition from other funding sources
explain 50.2% of the variance in venture capital investment decisions on technology
startups in Kenya (adjusted R² = .502).
Table 4.20: Model Summary between Competition from other Funding Sources and
Venture Capital Investment Decisions
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .712 .507 .502 .27334
a. Predictors: (Constant), Funding Sources Competition
77
4.4.6.2 Regression ANOVA
Table 4.21 presents the obtained outcome of the ANOVA between competition from
other funding sources and venture capital investment decisions on technology startups in
Kenya. It was used to determine whether there was a statistically linear association
between competition from other funding sources and venture capital investment
decisions. The outcome shows the existence of a statistically significant linear association
between competition from other funding sources and venture capital investment decisions
on technology startups in Kenya (F (1,111) = 112.976, p<.05).
Table 4.21: ANOVA between Competition from other Funding Sources and Venture
Capital Investment Decisions
Model Sum of Squares df Mean Square F Sig.
1 Regression
Residual
Total
8.441
8.218
16.659
1
110
111
8.441
.075
112.976 .000
a. Predictors: (Constant), Funding Sources Competition
b. Dependent Variable: Investment Decisions
4.4.6.3 Regression Coefficients
Table 4.22 presents the obtained outcome of the regression coefficients between
competition from other funding source factors (captive vs. independent funds, reputation
of venture capital firm, and experience of venture capital firm) and venture capital
investment decisions on technology startups in Kenya. The outcome shows that captive
vs. independent sources of funds statistically influence the venture capital investment
decisions on technology startups in Kenya (β = 0.119, t (112) = 2.338, p<.05), in that a
single unit change in captive vs. independent sources of funds increases venture capital
investment decisions on technology startups in Kenya by 11.9%. Reputation of venture
capital firm statistically influence the venture capital investment decisions on technology
startups in Kenya (β = 0.198, t (112) = 4.101, p<.05), in that a single unit change in the
reputation of venture capital firm increases venture capital investment decisions on
78
technology startups in Kenya by 19.8%. Experience of venture capital firm statistically
influence the venture capital investment decisions on technology startups in Kenya (β =
0.421, t (112) = 7.854, p<.05), in that a single unit change in the experience of venture
capital firm increases venture capital investment decisions on technology startups in
Kenya by 42.1%.
Table 4.22: Regression Coefficients between Competition from other Funding
Sources and Venture Capital Investment Decisions
Model
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
1 (Constant)
Captive vs. Independent Funds
Reputation of Venture Firm
Experience of Venture Firm
1.188
.119
.198
.421
.272
.051
.048
.054
.172
.263
.577
4.362
2.338
4.101
7.854
.000
.021
.000
.000
a. Dependent Variable: Investment Decisions
4.4.7 Competition Effect
The study respondents were asked to state how increased competition had affected
venture capital decisions on technology startups in Kenya and Figure 4.9 presents the
outcome. It shows that 26% noted an increase in knowledge and competency, 22% stated
improved performance, 19% experienced an increase in credibility, 15% observed better
investor deals, 11% realized an increase in the capital base of the firm, and 7% stated an
increase in regulation changes.
0% 5% 10% 15% 20% 25% 30%
Increased Knowledge and Competency
Improved Performance
Increased Credibility
Better Investor Deals
Increased Capita Base
Regulation Changes
26%
22%
19%
15%
11%
7%
79
Figure 4.9: Competition Effect
4.5 Venture Capital’s Natural Entry Point
The study sought to determine how the venture capital’s natural entry point affects
venture capitalists’ investment decisions. This bit of the study chapter presents the
outcome of the descriptive analysis, the diagnostic tests, and the inferential analysis for
venture capital’s natural entry point.
4.5.1 Descriptives for Early-Stage Venture Capital Investment
Table 4.23 shows that early-stage investments involved commitments of funds to firms
with little more than a business plan or an initial prototype and some market studies as
shown by 56.3% of the respondents who agreed, 17.9% strongly agreed, 15.2% were
neutral, and 10.7% disagreed (mean=3.81; standard deviation=0.855). Early-stage venture
capitalist actions impacted a venture capital’s prospects for profitably exiting a venture as
shown by 84% of the respondents who agreed, and 16% were neutral (mean=4.14;
standard deviation=0.669). Venture capital funds in early-stage investment focused
heavily on equity investments where they could receive board seats and influential
positions as shown by 67.9% of the respondents who agreed, 27.7% were neutral, and
4.5% disagreed (mean=3.92; standard deviation=0.861). Early-stage venture capitalist
actions impacted a venture capital’s prospects for profitably exiting a venture as shown
by 85.7% of the respondents who agreed, and 14.3% were neutral (mean=4.09; standard
deviation=0.609).
Table 4.23: Descriptives for Early-Stage Venture Capital Investment
SD D N A SA
N M SD
% % % % %
Early-stage investments
involve commitments of funds
to firms with little more than a
business plan or an initial
prototype and some market
studies
0 10.
7
15.
2
56.
3
17.
9
11
2
3.8
1
.855
80
Early-stage venture capitalist
actions impact a venture
capital’s prospects for
profitably exiting a venture
0 0 16.
1
53.
6
30.
4
11
2
4.1
4
.669
Venture capital funds in early-
stage investment focus heavily
on equity investments where
they can receive board seats
and influential positions
0 4.5 27.
7
39.
3
28.
6
11
2
3.9
2
.861
Early-stage venture capitalist
actions impact a venture
capital’s prospects for
profitably exiting a venture
0 0 14.
3
62.
5
23.
2
11
2
4.0
9
.609
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
4.5.2 Descriptives for Growth Stage Venture Capital Investment
Table 4.24 shows that venture capitalists in the growth stage invested only in a start-up
with a proven record of success as shown by 44.6% of the respondents who agreed, 3.6%
strongly agreed, 27.7% were neutral, and 19.6% disagreed while 4.5% strongly disagreed
(mean=3.23; standard deviation=0.958). Growth-stage companies seeking to scale while
remaining private may present the opportunity for venture capital funds to put more
capital to work as shown by 74.1% of the respondents who agreed, 24.1% were neutral,
and 1.8% disagreed (mean=3.89; standard deviation=0.689).
The table also shows that the goal of the growth stage was to achieve business-model fit,
which was a repeatable, scalable, profitable business model where the product created as
much value for the company as the customer as shown by 95.6% of the respondents who
agreed, and 4.5% were neutral (mean=4.36; standard deviation=0.567). A portion of the
superior risk-adjusted return was due to the lower failure rates among growth-stage
companies as shown by 91.1% of the respondents who agreed, 7.1% were neutral, and
1.8% disagreed (mean=4.18; standard deviation=0.633).
81
Table 4.24: Descriptives for Growth Stage Venture Capital Investment
SD D N A SA
N M SD
% % % % %
Venture capitalists in the
growth stage invest only in a
start-up with a proven record
of success
4.5 19.
6
27.
7
44.
6
3.6 11
2
3.2
3
.958
Growth-stage companies
seeking to scale while
remaining private may
present the opportunity for
venture capital funds to put
more capital to work
0 1.8 24.
1
57.
1
17 11
2
3.8
9
.689
The goal of the growth stage
is to achieve business-model
fit, which is a repeatable,
scalable, profitable business
model where the product
creates as much value for the
company as the customer
0 0 4.5 55.
4
40.
2
11
2
4.3
6
.567
A portion of the superior risk-
adjusted return is due to the
lower failure rates among
growth-stage companies
0 1.8 7.1 62.
5
28.
6
11
2
4.1
8
.633
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
4.5.3 Descriptives for Late-Stage Venture Capital Investment
Table 4.25 shows that late-stage companies have established business models and greater
traction in the marketplace, which may support more attractive revenue growth rates as
shown by 52.7% of the respondents who agreed, 18.8% strongly agreed, 24.1% were
neutral, and 4.5% disagreed (mean=3.86; standard deviation=0.769). Late-stage venture
82
capital funds target startups with high revenue growth rates and demonstrated viability by
virtue of user-adoption or sales, with a strong shot at an IPO as shown by 61.6% of the
respondents who agreed, 38.4% disagree, and 32.1% were neutral (mean=3.91; standard
deviation=0.823). Late-stage venture capital investments usually have less risk than early-
stage venture capital investments as shown by 69.6% of the respondents who agreed,
18.8% disagreed, and 11.6% were neutral (mean=3.72; standard deviation=1.006). Late-
stage investments targets have already established their market presence, their key
developmental goals include achieving market share targets and profitability goals to
make it possible for venture capitalists to successfully exit the investment as shown by
68.8% of the respondents who agreed, 18.8% were neutral, and 12.5% disagreed
(mean=3.73; standard deviation=0.890).
Table 4.25: Descriptives for Late-Stage Venture Capital Investment
S
D
D N A SA
N M SD
% % % % %
Late-stage companies may have
established business models
and greater traction in the
marketplace, which may
support more attractive revenue
growth rates
0 4.5 24.
1
52.7 18.8 11
2
3.86 .769
Late-stage venture capital funds
target startups with high
revenue growth rates and
demonstrated viability by virtue
of user-adoption or sales, with a
strong shot at an IPO
0 0 38.
4
32.1 29.5 11
2
3.91 .823
Late-stage venture capital
investments usually have less
risk than early-stage venture
capital investments
0 18.
8
11.
6
48.2 21.4 11
2
3.72 1.006
Late-stage investments targets 0 12. 18. 51.8 17 11 3.73 .890
83
have already established their
market presence
5 8 2
SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree
4.5.4 Correlations Analysis
To determine whether there was a statistically linear association between venture capital’s
natural entry point and venture capital investment decisions, correlation analysis was
conducted. The findings in Table 4.26 shows the existence of a statistically significant
linear association between venture capital’s natural entry point and venture capital
investment decisions on technology startups in Kenya (r = 0.662, p<.05).
Table 4.26: Correlation for Venture Capital’s Natural Entry Point
Investment Decision
Investment Decision 1
Natural Entry Point
N
.662**
.000
112
** Correlation is significant at the 0.01 level (2-tailed)
4.5.5 Linear Regression Diagnostic Tests
Linear regression diagnostic tests were carried out to examine the study data. These were
conducted to ascertain that the data was normally distributed, and that a linear
relationship existed between the study variables, and that the data did not contain
multicollinearity symptoms. The tests included normality test, correlation analysis, and
multicollinearity test.
4.5.5.1 Normality Test
The Shapiro-Wilk Test was used in this study to ascertain the distribution of the study
data. Table 4.27 shows that the obtained study data for venture capital’s natural entry
point was normally distributed as indicated by the Shapiro-Wilk Sig. value of 0.251
which is greater than 0.05.
84
Table 4.27: Normality Test for Venture Capital’s Natural Entry Point
Kolmogorov-Smirnov Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Natural Entry Point .204 112 .200* .899 112 .251
4.5.5.2 Linearity Test
To evaluate the assumption of the linear regression analysis, the linearity test for venture
capital’s natural entry point and venture capital investment decisions on technology
startups in Kenya was conducted. The findings in Table 4.28 shows the existence of a
linear association between venture capital’s natural entry point and venture capital
investment decisions on technology startups in Kenya (sig value for deviation from
linearity 0.000 <0.05).
Table 4.28: Linearity Test for Venture Capital’s Natural Entry Point
Sum of
Square
s
df Mean
Squar
e
F Sig.
VC Investment
* VC’s Natural
Entry Point
Between
Groups
Within Groups
Total
(Combined
)
Linearity
Deviation
from
Linearity
13.070
7.297
5.773
3.590
16.659
11
1
10
10
0
11
1
1.188
7.297
.577
.036
33.100
203.28
0
16.082
.00
0
.00
0
.00
0
4.5.5.3 Multicollinearity Test
To evaluate the assumption of the linear regression analysis, the multicollinearity test for
venture capital’s natural entry point and venture capital investment decisions on
technology startups in Kenya was conducted. The multicollinearity statistics of the
variables are shown in Table 4.29, and there were no indications of multicollinearity
85
between the research variables because the venture capital’s natural entry point VIF,
which runs from 1 to 10, was 1.792.
Table 4.29: Multicollinearity Test for Venture Capital’s Natural Entry Point
Model
Collinearity Statistics
Tolerance VIF
1 (Constant)
Natural Entry Point .558 1.792
a. Dependent Variable: Investment Decision
4.5.6 Linear Regression Analysis Test
The linear regression analysis in this section presents the outcome of the linear regression
model summary for venture capital’s natural entry point and venture capital investment
decisions on technology startups in Kenya, as well as the ANOVA, and the regression
coefficient findings.
4.5.6.1 Model Summary
Table 4.30 presents the obtained outcome of the model summary between venture
capital’s natural entry point and venture capital investment decisions on technology
startups in Kenya. The outcome indicates that venture capital’s natural entry point factors
(early-stage venture capital investment, growth stage venture capital investment, and late-
stage venture capital investment) explain 43.3% of the variance in venture capital
investment decisions on technology startups in Kenya (adjusted R² = .433).
Table 4.30: Model Summary between Venture Capital’s Natural Entry Point and
Venture Capital Investment Decisions.
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .662 .438 .433 .29174
a. Predictors: (Constant), Natural Entry Point
4.5.6.2 Regression ANOVA
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Table 4.31 presents the obtained outcome of the ANOVA between venture capital’s
natural entry point factors (early-stage venture capital investment, growth stage venture
capital investment, and late-stage venture capital investment) and venture capital
investment decisions on technology startups in Kenya. It was used to determine whether
there was a statistically linear association between venture capital’s natural entry point
factors (early-stage venture capital investment, growth stage venture capital investment,
and late-stage venture capital investment) and venture capital investment decisions. The
outcome shows the existence of a statistically significant linear association between
venture capital’s natural entry point factors (early-stage venture capital investment,
growth stage venture capital investment, and late-stage venture capital investment) and
venture capital investment decisions on technology startups in Kenya (F (1,111) =
85.731, p<.05).
Table 4.31: ANOVA between Venture Capital’s Natural Entry Point and Venture
Capital Investment Decisions
Model Sum of Squares df Mean Square F Sig.
1 Regression
Residual
Total
7.297
9.362
16.659
1
110
111
7.297
.085
85.731 .000
a. Predictors: (Constant), Natural Entry Point
b. Dependent Variable: Investment Decisions
4.5.6.3 Regression Coefficients
Table 4.32 presents the obtained outcome of the regression coefficients between venture
capital’s natural entry point factors (early-stage venture capital investment, growth stage
venture capital investment, and late-stage venture capital investment) and venture capital
investment decisions on technology startups in Kenya. The outcome shows that early-
stage venture capital investment statistically influences the venture capital investment
decisions on technology startups in Kenya (β = 0.158, t (112) = 2.081, p<.05), in that a
single unit change in early-stage venture capital investment increases venture capital
investment decisions on technology startups in Kenya by 15.8%. Growth stage venture
capital investment statistically influences the venture capital investment decisions on
technology startups in Kenya (β = 0.420, t (112) = 5.704, p<.05), in that a single unit
87
change in growth stage venture capital investment increases venture capital investment
decisions on technology startups in Kenya by 42%. Late-stage venture capital investment
did not statistically influence the venture capital investment decisions on technology
startups in Kenya (β = 0.036, t (112) = 0.724, p>.05), in that a single unit change in late-
stage venture capital investment increases venture capital investment decisions on
technology startups in Kenya by 3.6%.
Table 4.32: Regression Coefficients between Venture Capital’s Natural Entry Point
and Venture Capital Investment Decisions
Model
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
1 (Constant)
Early-Stage Venture Capital
Growth Stage Venture Capital
Late-Stage Venture Capital
1.739
.158
.420
.036
.225
.076
.074
.049
.223
.511
.066
7.722
2.081
5.704
.724
.000
.040
.000
.471
a. Dependent Variable: Investment Decisions
4.5.6 Determinants of Venture Capital Natural Entry Point
The study respondents were asked to indicate what else determines a venture capital
investment’s natural entry point in technology startups in Kenya and Figure 4.10 presents
the outcome. It shows that 22% stated growth opportunity, 18% cited innovation and
scalability, 16% indicated revenue and cash flow, 13% stated entrepreneur's experience,
11% business networks, while 9% cited past performance, 7% stated shareholding, and
4% indicated business future.
88
0% 5% 10% 15% 20% 25%
Growth Opportunity
Innovation and Scalability
Revenue and Cash Flow
Entrepreneur's Experience
Business Networks
Past Performance
Shareholding
Business Future
22%
18%
16%
13%
11%
9%
7%
4%
Figure 4.10: Determinants of Venture Capital Natural Entry Point
4.6 Chapter Summary
This chapter has covered the results and findings of venture capital investment decisions
on technology startups in Kenya. The chapter was guided by the specific objectives that
sought to identify the entrepreneur characteristics, effect of competition from other
funding sources, and the venture capital’s natural entry point that affect decision-making
process of evaluating the technology startup to fund. The next chapter is the discussion,
conclusion, and recommendations.
CHAPTER FIVE
5.0 DISCUSSION, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
This chapter covers the discussion, conclusion, and recommendations for venture capital
investment decisions on technology startups in Kenya. The chapter is steered by the
entrepreneur characteristics, effect of competition from other funding sources and venture
capital’s natural entry point affects the decision-making process of evaluating the
technology startup to fund.
89
5.2 Summary
The general objective of the study was to determine the factors influencing venture
capital investment decisions on technology startups in Kenya. The specific objectives of
this study were: to identify the entrepreneur characteristics considered when evaluating
Kenyan technology start-ups investment decisions by venture capitalists, to establish
effect of competition from other funding sources on technology start-ups investment
decisions by venture capitalists in Kenya, and to determine how the venture capital’s
natural entry point affects venture capitalists’ investment decisions.
The research design that was adopted for this study was the descriptive research design.
The unit of analysis was the fifty-two venture capital firms registered under the East
Africa Venture Capital Association which formed the study’s population. The sampling
frame was obtained from the East Africa Venture Capital Association, and it was a list
that consisted of fund principals and senior investment analysts at Venture Capital Firms.
This study deployed stratified sampling method. The Yamane’s formula was used to
compute a sample size of 150 respondents. Data in this study was collected using
structured questionnaires that were closed-ended. They were tested for reliability before
being administered to the study sample. The data analysis was in the form of descriptive
and inferential statistics. Tables and figures were used to present the data collected for
ease of understanding and analysis.
The study showed that their existed a statistically significant linear association between
entrepreneur characteristics and venture capital investment decisions on technology
startups in Kenya (r = 0.839, p<.05). The regression analysis revealed that entrepreneur
characteristics explain 70.1% of the variance in venture capital investment decisions on
technology startups in Kenya. The ANOVA indicated the existence of a statistically
significant linear association between entrepreneur characteristics and venture capital
investment decisions on technology startups in Kenya (F (1,111) = 261.078, p<.05),
where a single unit change in entrepreneur experience increases venture capital
investment decisions on technology startups in Kenya by 25.9%; in entrepreneur
education increases venture capital investment decisions on technology startups in Kenya
by 23.3%; and in management characteristics increases venture capital investment
decisions on technology startups in Kenya by 34.7%.
90
The study revealed that their existed a statistically significant linear association between
competition from other funding sources and venture capital investment decisions on
technology startups in Kenya (r = 0.712, p<.05). Regression analysis indicated that
competition from other funding sources explain 50.2% of the variance in venture capital
investment decisions on technology startups in Kenya. The ANOVA analysis showed the
existence of a statistically significant linear association between competition from other
funding sources and venture capital investment decisions on technology startups in Kenya
(F (1,111) = 112.976, p<.05), where a single unit change in captive vs. independent
sources of funds increases venture capital investment decisions on technology startups in
Kenya by 11.9%; in the reputation of venture capital firm increases venture capital
investment decisions on technology startups in Kenya by 19.8%; and in the experience of
venture capital firm increases venture capital investment decisions on technology startups
in Kenya by 42.1%.
The study indicated the existence of a statistically significant linear association between
venture capital’s natural entry point and venture capital investment decisions on
technology startups in Kenya (r = 0.662, p<.05). Regression analysis revealed that
venture capital’s natural entry point explains 43.3% of the variance in venture capital
investment decisions on technology startups in Kenya. The ANOVA analysis showed the
existence of a statistically significant linear association between venture capital’s natural
entry point and venture capital investment decisions on technology startups in Kenya (F
(1,111) = 85.731, p<.05), where a single unit change in early-stage venture capital
investment increases venture capital investment decisions on technology startups in
Kenya by 15.8%; in growth stage venture capital investment increases venture capital
investment decisions on technology startups in Kenya by 42%; and in late-stage venture
capital investment increases venture capital investment decisions on technology startups
in Kenya by 3.6%.
5.3 Discussions
5.3.1 Entrepreneur Characteristics and Venture Capitalists Investment Decisions
The criteria that was most important to the venture capitalist was the entrepreneur's
experience. These results concur with Margolis (2014) who argued for intensive study of
habitual entrepreneurs in the US who have the experience of generating multiple
91
businesses. He stated that habitual entrepreneurs have had the opportunity to learn how to
efficiently and swiftly overcome the stumbling blocks they encountered in their first
efforts. Thus, they accumulated entrepreneurial skills from their experiences.
A founder’s experience and networking skills were critical to a start-up and improved
their ability to secure funding. This outcome is in tandem with Wood & Williams (2014),
who by studying habitual entrepreneurs, were able to uncover and codify their skills and
techniques and gain a deeper understanding of the process of business creation. A better
understanding of how habitual entrepreneurs differed from novice ones greatly helped
design policies to foster entrepreneurship.
Investors were likely to place greater emphasis on the attributes of the founders’
experience relative to other aspects of the business in uncertain environments. The
outcome concurred with Shimoli et al. (2020), who found new entrepreneurs with
industry experience were relatively more pessimistic and some evidence that serial
entrepreneurs are relatively more optimistic than those that have not started a business
previously. However, given their survey data they could not define the extent that
experience improves forecasting performance, only that experience is associated with
relatively more optimism or pessimism.
Entrepreneurs experienced in running startups were better at evaluating opportunities thus
were likely to be funded. The results agree with Essel et al. (2019) who interpreted the
positive association between experience and early venture scale or growth to be consistent
with more experienced entrepreneurs being better at evaluating opportunities. However,
this interpretation requires a silent, but important, assumption that the desired scale of
venturing is homogeneous across all entrepreneurs such as all entrepreneurs want to start
and operate the largest new businesses possible.
Decision makers with a higher level of education seemed to prefer innovation-centered
business models. These results agree with various authors (Muscio & Vallanti, 2014;
Ramaciotti & Rizzo, 2015; Sauermann & Roach, 2016) whose study results indicated that
entrepreneurs with a higher level of education seemed to emphasize the future potential of
92
the startup instead of its current financial situation. Besides, entrepreneurs with a higher
level of education seemed to prefer innovation-centered business models. The conclusion
therefore from the studies was that a higher level of education leads to higher awareness
and receptiveness for innovation.
Entrepreneurial education as a function of entrepreneurial competence was the combined
capacity to identify and pursue opportunities, and to obtain and coordinate resources.
These results agree with Chen et al. (2015) and Dlouhá and Burandt (2015) whose studies
provided evidence that founders’ entrepreneurial and managerial competencies directly
related to the performance of the firm. They focused on ambiguity-tolerance, deal
making, stress management, oral and written communication, or human relations. These
competencies focused on fixed behaviors and inflexible traits and have been criticized
about several conceptual issues.
Entrepreneurs with a high level of academic achievement did not have an innovative
tendency thus were unlikely to be funded. This outcome disagrees with Piper et al. (2018)
whose study showed that a high level of academic achievement such as a doctoral degree
among entrepreneurs had a positive effect on the evaluation of ventures in technology
related industries. From a cognitive perspective, entrepreneurs with a high level of
academic ability can quickly recognize and identify complex phenomena and have an
excellent ability to process information. Entrepreneurs with a high level of academic
achievement also had an innovative tendency and were well-formed in cooperative
relationships with others.
Entrepreneurs with an academic background in science were more focused on the product
thus were likely to be funded. The outcome agrees with Ahlers et al. (2015) who assessed
the link between the academic achievements of entrepreneurs to the financial
performance of the entrepreneurial firms, and their study concluded that entrepreneurs
with a natural science background tended to focus more on the product. This could be
understood as an indicator of initial market success, a competitive advantage, effective
management, and, eventually, firm survival.
93
Superior performance of entrepreneurs resulted from their ability to learn on the job
which boosted their business skills. The results concur with Chen et al. (2015) and
Dlouhá and Burandt (2015) who looked at the relationship between business founders’
self-perceived competencies and venture performance and identified areas associated with
successful business founders: human and conceptual competencies, the ability to
recognize opportunities, technical-functional competencies, and political competencies.
The quality of management and work commitment were the criteria that received the
highest weight in the assessment of proposals. The outcome agrees with Essel et al.
(2019) whose empirical evidence of entrepreneurial preferences is generally inconsistent
with this assumption of homogeneity and demonstrates that scale preferences of
entrepreneurs vary in predictable ways, suggesting that inferring the quality of
entrepreneurial judgment solely from new venture outcomes is problematic.
An important source of a venture capital firm’s interest in the management characteristics
of entrepreneurs was that both parties agree to form a joint company financed by the
fund’s money and managed by the entrepreneur. This result agrees with Chen et al.
(2015); and Dlouhá and Burandt (2015) whose studies provided evidence that founders’
entrepreneurial and managerial competencies directly related to the performance of the
firm. They focused on ambiguity-tolerance, deal making, stress management, oral and
written communication, or human relations. These competencies focused on fixed
behaviors and inflexible traits and have been criticized about several conceptual issues.
Managerial competencies were vital for performing entrepreneurial activities
successfully, thus were likely to be funded. This outcome agrees with (Adams et al.,
2017; Adomßent et al., 2014; Ajamieh, 2016) who note that managerial competencies are
vital for performing entrepreneurial activities successfully. The studies argued that
entrepreneurial competencies identified were higher compared to standard level ability
that was promoted through education and encompassed the necessary skills, knowledge,
and abilities to perform an innovative role successfully. The studies concluded that
managerial competencies were crucial to the success of new businesses founded by
entrepreneurs.
94
5.3.2 Competition from other Funding Sources and Venture Capitalists Investment
Decisions
Alternative sources of financing for entrepreneurs were increasingly viable options for
technology start-ups when sourcing for venture capital funds. This obtained outcome
agrees with Biney (2018) who notes that, alternative sources of financing for
entrepreneurs such as individual investors or angels, corporations, and strategic alliances
were increasingly considered as viable options. The results of this and other studies
suggest that outcomes such as timing, magnitude, and riskiness of returns of choosing
such alternatives will vary widely and will depend in great part on the strategies of the
venture capital firms involved.
Independent venture capital funds tended to select older and larger companies in their
expansion stages for funding consideration. The results concur with Biney (2018) who
highlighted significant differences across the investment specialization patterns of several
types of venture capitalists. In comparison with other investor types, independent venture
capital funds tended to select older and larger companies in their expansion stages. This
pattern of investment specialization was stable over time. Evidence from this study
suggested that European independent venture capital firms abstained from the riskiest
investments.
Captive venture capitalists were attracted by companies operating in industries with high
technological focus when evaluating technology startups for funding. The results agree
with Ekanem et al. (2019) who observed that captive venture capitalists were particularly
attracted by companies operating in industries with high technological ferment. They
were also more active in industries with weak intellectual property protection in which
other mechanisms to obtain access to promising modern technologies such as licenses are
ineffective. The findings revealed that captive venture funds specialized in the internet
and abstained from investing in biotechnology and pharmaceuticals.
Bank affiliated venture capital firms employed more passive strategies than other venture
capital types and were more inclined to invest in late-stage startups. This outcome agrees
with the research study by Bertoni et al. (2019) who documented that, bank affiliated
95
venture capital firms employed more passive strategies than other venture capital types
and were more inclined to invest in older and larger companies that, being in a later stage
of development, are closer to an initial public offering. Accordingly, bank affiliated
venture capital firms were more likely to exit through an initial public offering than other
investor types and were specialized in investments of shorter durations.
A high-reputation venture capital firm that specialized in the focal firm’s industry
provided more substantive value to a start-up than a less specialized high-reputation
venture capital firm. These results are in tandem with those of Paul and Criado (2020)
who showed that, all else being equal, a high-reputation venture capital firm that
specialized in the focal firm’s industry would be able to provide more substantive value to
a start-up than a less specialized high-reputation venture capital firm, which would have
less ability to make these substantive contributions and whose involvement will be valued
less by investors.
The primary way for a venture capital firm to win funding from the technology start-up
over its competitors is to have a better reputation. According to Ng et al. (2016) in
Vietnam, a more reputable venture capital firm saw a larger stream of deal flow than did a
less reputable venture capital fund. Potentially more important, more reputable venture
capitalists negotiated more attractive terms with entrepreneurs in terms of price, options,
and protections, reflecting the greater desire of entrepreneurs to affiliate with a better-
known venture capitalist. Strong venture capital reputation yielded important benefits for
portfolio firms, including advice from more experienced venture capital partners, better
access to professional management talent, credit, and reputable investment banks.
Reputation depends on many aspects of the firm and is the aggregate culmination of many
small procedures, conduct, and performance levels which the venture capital firm
maintains. These results concur with Paul and Criado (2020) who opine that it can be
argued that reputation depends on many aspects of the firm and is the aggregate
culmination of many small procedures, conduct, and performance levels which the
venture capital firm maintains. By demonstrating that venture capitalists are perceived to
96
add more value than other venture capital firms, it was found that reputation and
performance go hand in hand.
A more reputable venture capital firm sees a larger stream of deal flow than does a less
reputable venture capital fund. According to Ng et al. (2016) in Vietnam, a more
reputable venture capital firm saw a larger stream of deal flow than did a less reputable
venture capital fund. Potentially more important, more reputable venture capitalists
negotiated more attractive terms with entrepreneurs in terms of price, options, and
protections, reflecting the greater desire of entrepreneurs to affiliate with a better-known
venture capitalist. Strong venture capital reputation yielded important benefits for
portfolio firms, including advice from more experienced venture capital partners, better
access to professional management talent, credit, and reputable investment banks.
Experienced venture capital firms selected high potential entrepreneurial firms and
provided more valuable services. This result concurs with Fitzenberger & Schulze (2014)
who in their study suggested that experienced venture capital firms selected high potential
entrepreneurial firms and provided more valuable services. They showed that experienced
venture capitalists were normally easier to raise funds. The study also confirmed that
experience in due diligence, advisory services, monitoring, as well as well-planned exit
strategies were particularly important.
Companies funded by more experienced venture capitalists were more likely to go public.
These results are in tandem with Tamvada (2015) who found that the portfolio companies
of venture capital funds with more specific human capital including law and finance
experience exhibited a lower probability of failure but found no evidence for a positive
relationship with the probability of going public. The study also found that companies
funded by more experienced venture capitalists were more likely to go public. However,
the study focused on performance from the perspective of venture capital funds.
Venture capital firms were likely to learn through prior investments and develop routines
based on past experiences. This study’s outcome is in tandem with observations made by
Tamvada (2015) that venture capital firms were likely to learn through prior investments
and develop routines based on past experiences. The routines that would become part of a
97
venture capital fund’s repertoire were those that previously produced favorable outcomes.
The application of routines would increase their efficiency and hence the likelihood of a
desirable outcome.
A venture capital firm can better grasp the nuances of the investment at hand based on its
experience. According to Li et al. (2018), a venture capital firm could better grasp the
nuances of the investment at hand based on its experience with due diligence and
valuation to deflate some of the buoyant optimism about the venture by focusing on the
critical junctions or technological and social developments that could affect the venture's
viability and competitive position.
5.3.3 Venture Capital’s Natural Entry Point and Venture Capitalists Investment
Decisions
Early-stage investments involved commitments of funds to firms with little more than a
business plan or an initial prototype and some market studies. The results concur with
Mueller and Murmann (2016) who investigated the relative importance of venture capital
funds decision criteria for early-stage ventures. They concluded that the industry-related
competence of the team and educational capabilities were the most important decision
criteria based on a conjoint experiment with forty-seven Australian venture capital firms.
These respondents attached less importance to competitive rivalry, lead time, and entry
timing. They made another attempt to compare decision criteria across different investors.
Compared to earlier studies, they also included debt investors.
Early-stage venture capitalist actions impacted a venture capital’s prospects for profitably
exiting a venture. This outcome agrees with Gornall and Strebulaev (2020) who in their
research on investing in new startups concluded that venture capitalists served a valuable
intermediary function by creating more rewarding financial outcomes for their investee
ventures and ultimately for themselves. The study has been asking: “Do early-stage
venture capitalist actions impact a venture capital’s prospects for profitably exiting a
venture?” A large amount of previous literature exists on the topic, with a variety of
different works that have tried in the past to identify specific variables that could explain,
to different extents, the likelihood of a company succeeding.
98
Venture capital funds in early-stage investment focused heavily on equity investments
where they could receive board seats and influential positions. The results are in tandem
with Gornall and Strebulaev (2020) who in their research on investing in new startups
concluded that venture capitalists served a valuable intermediary function by creating
more rewarding financial outcomes for their investee ventures and ultimately for
themselves. Although there has been much discussion about how venture capitalists
should invest, how to implement various contracting technologies and where they should
attempt to add value, many questions remain unanswered.
Early-stage venture capitalist actions impacted a venture capital’s prospects for profitably
exiting a venture. This outcome agrees with Gornall and Strebulaev (2020) who in their
research on investing in new startups concluded that venture capitalists served a valuable
intermediary function by creating more rewarding financial outcomes for their investee
ventures and ultimately for themselves. The study has been asking: “Do early-stage
venture capitalist actions impact a venture capital’s prospects for profitably exiting a
venture?” A large amount of previous literature exists on the topic, with a variety of
different works that have tried in the past to identify specific variables that could explain,
to different extents, the likelihood of a company succeeding.
Venture capitalists in the growth stage invested only in a start-up with a proven record of
success. These outcomes are in tandem with Armanios et al. (2017) who analyzed the
venture capital finding success factors, the relationship between the market volatility and
venture capitalist investment. They note that venture capitalists in this stage invest only in
business since it has a proven record of success, because of this the return on loss drops
significantly contributing to positive performance in the industry. The study
recommended that a venture capitalist must identify the best stage for staging that will
promote and enhance return on their investment.
Growth-stage companies seeking to scale while remaining private may present the
opportunity for venture capital funds to put more capital to work. These results agree with
Schøtt & Sedaghat (2014) who opine that, in terms of capacity, growth-stage companies
sought to scale while remaining private may present the opportunity for venture capital
99
funds to put more capital to work, often during the final injections of capital before the
initial public offering. In addition, the shorter-duration J-curve may be attractive to
institutions building out their private equity program.
The goal of the growth stage was to achieve business-model fit, which was a repeatable,
scalable, profitable business model where the product created as much value for the
company as the customer. These results agree with Schøtt & Sedaghat (2014) who
recommended that to enhance performance the financing management must be more
disciplined when using stage financing to adopt negative net present value projects. They
also found that venture capital firms believe the short investment duration compared to
early stage is likely to bring high return to the investor. Additionally, the investment was
still not certain to be profitable even though it had made progress.
A portion of the superior risk-adjusted return was due to the lower failure rates among
growth-stage companies. These results concur with Colombelli et al. (2016) who
observed that a portion of the superior risk-adjusted return was probably due to the lower
failure rates among growth-stage companies. While earlier rounds of funding exhibited
asymmetrically high returns, they were further out on the risk spectrum, with higher
failure rates than growth stage rounds. The faster pace of distributions enabled allocators
to better manage their liquidity budget and support liabilities and spending needs.
Late-stage companies have established business models and greater traction in the
marketplace, which may support more attractive revenue growth rates. According to a
study by Jackson (2015) in Kenya, while early-stage venture capital companies may be
pioneers of new industries with evolving business models, growth-stage companies have
established business models and greater traction in the marketplace, which may support
more attractive revenue growth rates. These characteristics could potentially reduce the
risk relative to earlier-stage investments.
Late-stage venture capital funds target startups with high revenue growth rates and
demonstrated viability by virtue of user-adoption or sales, with a strong shot at an IPO.
These results agree with Schøtt & Sedaghat (2014) who opine that, in terms of capacity,
100
growth-stage companies sought to scale while remaining private may present the
opportunity for venture capital funds to put more capital to work, often during the final
injections of capital before the initial public offering. In addition, the shorter-duration J-
curve may be attractive to institutions building out their private equity program.
Late-stage venture capital investments usually have less risk than early-stage venture
capital investments. The study outcome is in tandem with that of Barbi & Mattioli (2019)
which revealed that it was effective to allocate more finances and resources in the late
stage of an investment since it increases the chances for successful ventures. At this stage
the possibility of termination of a project decreased and hence the marginal return on the
business went up and hence the investor invested greatly.
Late-stage investments targets have already established their market presence, their key
developmental goals include achieving market share targets and profitability goals to
make it possible for venture capitalists to successfully exit the investment. This outcome
disagrees with Wang et al. (2019) who found that venture capitalists choose not to invest
late in the business since the prices of equity shares were deemed not to be in line of the
total industry expectation due the risk of uncertainty and the opportunity for growth. If
venture capitalists’ risk taking decreased, future returns would be expected to decrease
due to the lower returns from later stage firms than early-stage companies on average.
5.4 Conclusions
5.4.1 Entrepreneur Characteristics and Venture Capitalists Investment Decisions
The study concludes that the founder’s experience and networking skills were critical to a
start-up and improved their ability to secure funding. Entrepreneurs experienced in
running startups were better at evaluating opportunities thus were likely to be funded, and
decision makers with a higher level of education seemed to prefer innovation-centered
business models. The entrepreneurial education as a function of entrepreneurial
competence was the combined capacity to identify and pursue opportunities, and to obtain
and coordinate resources, and superior performance of entrepreneurs resulted from their
ability to learn on the job which boosted their business skills. Managerial competencies
101
were vital for performing entrepreneurial activities successfully thus were likely to be
funded.
5.4.2 Competition from other Funding Sources and Venture Capitalists Investment
Decisions
The study concludes that captive venture capitalists were attracted by companies
operating in industries with high technological focus when evaluating technology startups
for funding, and bank affiliated venture capital firms employed more passive strategies
than other venture capital types and were more inclined to invest in late-stage startups.
Reputation depended on many aspects of the firm and was the aggregate culmination of
many small procedures, conduct, and performance levels which the venture capital firm
maintained. A more reputable venture capital firm saw larger streams of deal flow than
less reputable venture capital funds. Venture capital firms were likely to learn through
prior investments and develop routines based on past experiences and could better grasp
the nuances of the investment at hand based on this experience.
5.4.3 Venture Capital’s Natural Entry Point and Venture Capitalists Investment
Decisions
The study concludes that early-stage venture capitalist actions impacted a venture
capital’s prospects for profitably exiting a venture. The goal of the growth stage was to
achieve business-model fit, which was a repeatable, scalable, profitable business model
where the product created as much value for the company as the customer. A portion of
the superior risk-adjusted return was due to the lower failure rates among growth-stage
companies, while late-stage companies had established business models and greater
traction in the marketplace, which supported more attractive revenue growth rates. The
late-stage venture capital funds targeted startups with high revenue growth rates and
demonstrated viability by virtue of user-adoption or sales, with a strong shot at an IPO.
5.5 Recommendations
5.5.1 Recommendations for Improvement
5.5.1.1 Entrepreneur Characteristics and Venture Capitalists Investment Decisions
102
The study recommends the managers of venture capital firms to invest in technological
startups that have entrepreneurs with a high level of academic ability who have the
capacity to quickly recognize and identify complex phenomena and have an excellent
ability to process information. Such entrepreneurs are well-formed and can cooperate
with others.
5.5.1.2 Competition from other Funding Sources and Venture Capitalists Investment
Decisions
The study recommends the managers of venture capital firms to invest in technological
startups that have different strategic approaches that give them a competitive advantage.
This would ensure the venture capital firms have the capacity to discern threats or
opportunities in a timely manner and relay appropriate advice or source relevant expertise
from their networks.
5.5.1.3 Venture Capital’s Natural Entry Point and Venture Capitalists Investment
Decisions
The study recommends the managers of venture capital firms to invest in technological
startups that are effective and efficient in allocating finances and resources. This increases
the chances of startups to evolve into successful ventures.
5.5.2 Recommendations for Further Studies
This study focused on determining the factors influencing venture capital investment
decisions on technology startups in Kenya. It examined the effect of entrepreneur
characteristics, competition from other funding sources, and venture capital’s natural
entry point and how they influence venture capital investment decisions on technology
startups in Kenya. The outcome of the study can be applied to the startups that were
investigated. Therefore, there is need for more research to be conducted on other sectors
as well as the need to examine other factors that influence investment decisions in
technology startups that have not been addressed by this study.
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APPENDICES
APPENDIX I: COVER LETTER
United States International University,
P.O. Box 14634 – 00800,
Nairobi - Kenya.
29th July 2022
Dear Respondent,
RE: REQUEST TO PARTICIPATE IN A RESEARCH STUDY.
I am a student at United States International University – Africa (USIU-A), pursuing a
master’s degree, and as part of my degree requirement, I am supposed to conduct a study
on factors influencing venture capital investment decisions on technology startups in
Kenya.
My study focuses on venture capital investment decisions and has selected employees of
venture capital firms as the target respondents. Therefore, you are allowed to take part in
this study, which aims to provide answers to the above-mentioned topic. Please note that
this research is for academic purposes and that your responses will be treated with
confidentiality since you are not required to provide your name, and contacts.
Should you require any further clarification, or do wish to access the results, do not
hesitate to contact me through USIU-A. Thank you for taking the time to complete the
questionnaire.
Yours sincerely,
Peter Kanake
135
APPENDIX II: DEBRIEFING FORM
Dear Participant,
In this study you have been requested to participate by answering the questions in the
questionnaire. You have been told that the general objective of the study is to examine the
factors influencing venture capital investment decisions on technology startups in Kenya.
You are reminded that your original consent document includes the following
information: your right to withdraw from the study at any time without any implications
from me.
If you have any concerns about your participation or the information you have provided
concerning this disclosure, please discuss this with me. I will be happy to provide all the
answers to questions you have about this study.
If your concerns are such that you would like to have your data withdrawn, and the data is
identifiable, I will do so.
If you have questions about your participation in the study, please contact me by email or
my project supervisor at ekalunda@usiu.ac.ke.
If you have questions about your rights as a research participant, you may contact the
USIU-A Institutional Review Board office telephone 254730116127, or email
irb@usiu.ac.ke.
Please again accept my appreciation for your participation in this study.
Name _____________________________ Sign ___________________
136
APPENDIX III: INFORMED CONSENT FORM
Title of Study: Examine the factors influencing venture capital investment decisions on
technology startups in Kenya.
Name of Researcher: Peter Kanake.
This research aims at examining how various factors like entrepreneur characteristics,
competition from other funding sources, and venture capital’s natural entry point
influence venture capital investment decisions on technology startups in Kenya. By
signing below, you are consenting to the following:
a. I have received a copy for participation, read it carefully and understood the
information it contains.
b. I was given sufficient time to consider my decision to participate and come to an
agreement to participate
c. I understand that my involvement is voluntary and my and I am free to withdraw
myself from participating without being questioned.
d. I understand that all the personal details like name and employer address will be
treated with high confidentiality and be accessed only by the researcher.
e. The explanations about the research are well understood and I was able to ask
questions and being answered to my satisfaction.
f. I understand that the research is approved by the United States International
University – Africa’s Institutional Review Board (IRB) and the National
Commission of Science, Technology, and Innovation (NACOSTI).
Participant: __________________ Signature: _____________ Date ____________
Researcher: __________________ Signature: _____________ Date ____________
137
APPENDIX IV: QUESTIONNAIRE
This questionnaire aims to provide answers to the factors influencing venture capital
investment decisions on technology startups in Kenya. The information that you will give
will be treated with the greatest confidentiality and be used for academic purposes. Fill in
your responses in the spaces provided in each of the questionnaire items. The information
provided will be treated as confidential.
SECTION A: DEMOGRAPHIC DATA
Please tick (√) ONE appropriate box below
1. Gender Male ( )
Female ( )
2. What is your age? Less than 20 ( )
20-29 ( )
30-39 ( )
40-49 ( )
50-59 ( )
60 and above ( )
3. What position do you hold in the Venture Capital Firm?
Fund Principal ( )
Senior Analyst ( )
4. How many countries does your Venture Capital operate in?
_________________________________________________________________
5. How long has the Venture Capital been in operation
Less than one year ( )
1-3 Years ( )
4-6 Years ( )
7-9 Years ( )
10-12 Years ( )
138
Above 13 Years ( )
Section B: Entrepreneur Characteristics
6. Please indicate your level of agreement on entrepreneur characteristics according
to your experience by (√) ticking the relevant based on the following scale: SD-
Strongly Disagree, D-Disagree, N-Neutral, A-Agree, and SA-Strongly Agree.
No: SD D N A S
A
B1 The criteria most important to the venture capitalist is
the entrepreneur's experience
B2 A founder’s experience and networking skills are
critical to a start-up and improves their ability to secure
funding
B3 Investors are likely to place greater emphasis on the
attributes of the founders experience relative to other
aspects of the business in uncertain environments
B4 Entrepreneurs experienced in running startups are better
at evaluating opportunities thus are likely to be funded
B5 Decision makers with a higher level of education seem
to prefer innovation-centered business models
B6 The entrepreneurial education as a function of
entrepreneurial competence is the combined capacity to
identify and pursue opportunities, and to obtain and
coordinate resources
B7 Entrepreneurs with a high level of academic
achievement also have an innovative tendency thus are
likely to be funded
B8 Entrepreneurs with an academic background in science
are more focused on the product thus are likely to be
funded
B9 Superior performance of entrepreneurs may result from
139
their ability to learn on the job which boosts their
business skills
B1
0
The quality of management and work commitment are
the criteria that receive the highest weight in the
assessment of proposals
B1
1
An important source of a venture capital firm’s interest
in the management characteristics of entrepreneurs is
that both parties agree to form a joint company financed
by the fund’s money and managed by the entrepreneur
B1
2
Managerial competencies are vital for performing
entrepreneurial activities successfully thus are likely to
be funded
7. In your opinion, which other entrepreneur characteristics influence venture capital
decisions on technology startups in Kenya?
________________________________________________________________________
__________________________________________________________________
____________________________________________________________
Section C: Effect of Competition from Other Funding Sources
8. Please indicate your level of agreement on the effect of competition from other
funding sources according to your experience by (√) ticking the relevant based on
the following scale: SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, and
SA-Strongly Agree.
No: SD D N A S
A
C1 Alternative sources of financing for entrepreneurs are
increasingly viable options for technology start-ups
when sourcing for venture capital funds
C2 Independent venture capital funds tend to select older
and larger companies in their expansion stages for
140
funding consideration
C3 Captive venture capitalists are attracted by companies
operating in industries with high technological focus
when evaluating technology startups for funding
C4 Bank affiliated venture capital firms employ more
passive strategies than other venture capital types and
are more inclined to invest in late-stage startups
C5 A high-reputation venture capital firm that specializes
in the focal firm’s industry can provide more
substantive value to a start-up than a less specialized
high-reputation venture capital firm
C6 The primary way for a venture capital firm to win
funding the technology start-up over its competitors is
to have a better reputation
C7 Reputation depends on many aspects of the firm and is
the aggregate culmination of many small procedures,
conduct, and performance levels which the venture
capital firm maintains
C8 A more reputable venture capital firm sees a larger
stream of deal flow than does a less reputable venture
capital fund
C9 Experienced venture capital firms select high potential
entrepreneurial firms and provide more valuable
services
C1
0
Companies funded by more experienced venture
capitalists are more likely to go public
C1
1
Venture capital firms are likely to learn through prior
investments and develop routines based on past
experiences
C1
2
A venture capital firm can better grasp the nuances of
the investment at hand based on its experience
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9. In your opinion, what other effect of competition from other funding sources is
there on the factors influencing venture capital investment decisions on
technology start-ups in Kenya?
________________________________________________________________________
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Section D: Venture Capital’s Natural Entry Point
10. Please indicate your level of agreement on the venture capital’s natural entry point
according to your experience by (√) ticking the relevant based on the following
scale: SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, and SA-Strongly
Agree.
No: SD D N A S
A
D1 Early-stage investments involve commitments of funds
to firms with little more than a business plan or an
initial prototype and some market studies
D2 Early-stage venture capitalist actions impact a venture
capital’s prospects for profitably exiting a venture
D3 Venture capital funds in early-stage investment focus
heavily on equity investments where they can receive
board seats and influential positions
D4 Early-stage venture capitalist actions impact a venture
capital’s prospects for profitably exiting a venture
D5 Venture capitalists in the growth stage invest only in a
start-up with a proven record of success
D6 Growth-stage companies seeking to scale while
remaining private may present the opportunity for
venture capital funds to put more capital to work
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D7 The goal of the growth stage is to achieve business-
model fit, which is a repeatable, scalable, profitable
business model where the product creates as much
value for the company as the customer
D8 A portion of the superior risk-adjusted return is due to
the lower failure rates among growth-stage companies
D9 Late-stage companies may have established business
models and greater traction in the marketplace, which
may support more attractive revenue growth rates
D1
0
Late-stage venture capital funds target startups with
high revenue growth rates and demonstrated viability
by virtue of user-adoption or sales, with a strong shot at
an IPO
D1
1
Late-stage venture capital investments usually have less
risk than early-stage venture capital investments
D1
2
Late-stage investments targets have already established
their market presence, their key developmental goals
include achieving market share targets and profitability
goals in order to make it possible for venture capitalists
to successfully exit the investment
11. In your opinion, what else determines how the venture capital’s natural entry point
affects venture capitalists’ investment decisions?
________________________________________________________________________
__________________________________________________________________
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Section E: Venture Capital Investment Decisions on Technology Startups
12. Please indicate your level of agreement on the venture capital investment
decisions on technology start-ups according to your experience by (√) ticking the
relevant based on the following scale: SD-Strongly Disagree, D-Disagree, N-
Neutral, A-Agree, and SA-Strongly Agree.
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No
:
SD D N A S
A
E1 Venture capital fills the void between sources of funds
for innovation and traditional sources of capital
available to technology start-ups
E2 Venture capital investors are most interested in a
business that offers them an opportunity for a
significant return
E3 Venture capitalists prefer to invest in technology
startups with mature products and actual financial
performance
E4 Venture capitalists rely on the information they gather
about entrepreneurs to predict whether a startup will be
successful
E5 A venture capitalist with a strategy to diversify across
industries may select different investments to a venture
capitalist that wants to create synergistic value between
portfolio companies
E6 A disruptive invention will encourage a fresh round of
growth opportunity for venture capital funds
E7 Venture capitalist’s ability to select and finance
successful startups positions them within the profession,
enabling them to build their reputation
E8 Deciding to invest in a start-up’s involves the
development of a collaborative relationship between the
venture capital firm and the business founder
E9 The evaluation of the start-up’s value provides a basis
for negotiation on how to distribute the capital in line
with the amount of equity the venture capital fund puts
into the entrepreneur’s company
E1 Venture capitalists invest in startups with commercially
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0 viable know-how
13. In your opinion, what else determines venture capital investment decisions on
technology start-ups?
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THANK YOU FOR TAKING YOUR TIME TO COMPLETE THE
QUESTIONNAIRE
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APPENDIX V: IRB PERMIT
146
APPENDIX IV: NACOSTI PERMIT
