indicators of success in cybersecurity startups: …...indicators of success in cybersecurity...
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INDICATORS OF SUCCESS IN CYBERSECURITY STARTUPS: TOWARDS A COMPETITIVE INDICATORS AND WARNING
ANALYTIC MODEL
ANDREW P. COFFEY
A Thesis
Submitted to the Faculty of Mercyhurst University
In Partial Fulfillment of the Requirements for
The Degree of
MASTER OF SCIENCE IN
APPLIED INTELLIGENCE
RIDGE COLLEGE OF INTELLIGENCE STUDIES AND APPLIED SCIENCES MERCYHURST UNIVERSITY
ERIE, PENNSYLVANIA MARCH 2016
RIDGE COLLEGE OF INTELLIGENCE STUDIES AND APPLIED SCIENCES MERCYHURST UNIVERSITY
ERIE, PENNSYLVANIA
INDICATORS OF SUCCESS IN CYBERSECURITY STARTUPS: TOWARDS THE DEVELOPMENT OF A COMPETITIVE INDICATORS AND
WARNING ANALYTIC MODEL
A Thesis Submitted to the Faculty of Mercyhurst University
In Partial Fulfillment of the Requirements for The Degree of
MASTER OF SCIENCE
IN APPLIED INTELLIGENCE
Submitted By:
ANDREW P. COFFEY
Certificate of Approval: ___________________________________ Shelly Freyn, M.B.A. Assistant Professor The Ridge College of Intelligence Studies and Applied Sciences ___________________________________ Dawn M. Wozneak, Ph.D. Assistant Professor The Ridge College of Intelligence Studies and Applied Sciences ___________________________________ David J. Dausey, Ph.D. Provost and Vice President for Academic Affairs Mercyhurst University
March 2016
iii
Copyright © 2016 by Andrew P. Coffey All rights reserved.
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DEDICATION
To all the individuals who have been victimized by a cyber-attack (myself included), maybe
somebody can fix this problem.
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ACKNOWLEDGEMENTS
My parents, Mary and Dave Coffey, and the rest of my family, who have given me all the
opportunities and tools to succeed, and for their continued support and encouragement.
Professor Shelly Freyn, who ignited my interest in Competitive Intelligence and has always
acted a mentor, resource, and advisor – as well as an excellent educator – throughout my
career at Mercyhurst University.
Professor Dawn Wozneak, who has always made time to provide assistance, feedback, and
encouragement throughout my career at Mercyhurst University.
The faculty and staff of the Ridge College of Intelligence Studies and Applied Sciences, for
all their lessons, guidance, and support that will benefit me throughout my career.
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ABSTRACT OF THE THESIS
Indicators of Success in Cybersecurity Startups:
Towards the Development of a Competitive Indicators and Warning Analytic Model
A Critical Examination
By
Andrew P. Coffey
Master of Science in Applied Intelligence
Mercyhurst University, 2016
Assistant Professor Shelly Freyn, Chair
This study analyzes the literature on entrepreneurial efforts and factors contributing to
the success of new companies in order to determine which factors are indicative of likely
success of cybersecurity startups. After determining which factors most directly contribute to
success, the researcher developed a model that can be applied to new cybersecurity startups
in order to estimate the likelihood of the new startup’s success. This research and resulting
model will benefit competitive and market intelligence professionals, potential investors, and
companies seeking to acquire a startup firm.
This thesis will review existing literature covering topics of entrepreneurial efforts,
success factors, and the field of intelligence. Following the literature review, the study
translates the structured analytic technique Indicators and Warning analysis (a national
security and military intelligence method) into a competitive intelligence methodology.
Expansion on the reviewed literature and development of the model will result in a synthesis
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of market and business research on startup successes with intelligence and analytic
methodologies that improve an analyst’s forecasting accuracy.
Through a comparative case study of three cybersecurity startups, this thesis will
examine several success factors based on the startups’ funding, firm, and founder attributes
in order to determine which factors are more indicative of success. The study analyzes one
successful case (Palo Alto Networks) and two unsuccessful cases (ISC8, Inc. and Cryptine
Networks) to assess the success factors. The study then tests the developed Competitive
Indicators and Warning model on the three cases to determine the model’s validity, then
analyzes Area 1 Security, a nascent cybersecurity startup, to demonstrated the model’s
estimative capabilities.
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TABLE OF CONTENTS
Page
TABLE OF CONTENTS...................................................................................... viii
LIST OF TABLES................................................................................................ xi
LIST OF FIGURES.............................................................................................. xii
LIST OF ABBREVIATIONS............................................................................... xiii
CHAPTER 1: INTRODUCTION......................................................................... 1
Introduction to the Problem...................................................................... 1
Background of the Problem ..................................................................... 2
Statement of the Problem.......................................................................... 4
Purpose of the Study................................................................................. 4
Research Questions................................................................................... 5
Definitions of Terms................................................................................. 5
Nature of the Study................................................................................... 7
Relevance and Significance of the Study.................................................. 8
Assumptions and Limitations.................................................................... 9
Organization of the Study......................................................................... 10
CHAPTER 2: LITERATURE REVIEW.............................................................. 12
Introduction to the Literature Review....................................................... 12
Entrepreneurial Theory Framework.......................................................... 12
Entrepreneurship....................................................................................... 12
Defining Success....................................................................................... 16
Success Factors......................................................................................... 19
Intelligence Theory and Methodological Frameworks............................. 27
Intelligence................................................................................................ 27
Indicators and Warning Analysis.............................................................. 29
Redefining Indicators and Warning Analysis........................................... 32
Chapter 2 Summary.................................................................................. 36
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CHAPTER 3: METHODOLOGY........................................................................ 38
Introduction............................................................................................... 38
Research Design........................................................................................ 38
Selection of Cases..................................................................................... 40
Data Collection......................................................................................... 42
Data Analysis Procedures......................................................................... 43
Data Analysis............................................................................................ 49
Limitations of the Research Design.......................................................... 50
Credibility................................................................................................. 51
Transferability........................................................................................... 52
Ethical Issues............................................................................................. 52
Chapter 3 Summary.................................................................................. 52
CHAPTER 4: FINDINGS..................................................................................... 54
Introduction............................................................................................... 54
Palo Alto Networks, Inc. .......................................................................... 55
ISC8, Inc. .................................................................................................. 59
Cryptine Networks.................................................................................... 64
Comparison of Cases................................................................................ 68
Testing the Model..................................................................................... 70
Chapter 4 Summary.................................................................................. 81
CHAPTER 5: CONCLUSION.............................................................................. 83
Introduction............................................................................................... 83
Summary of the Study............................................................................... 83
Discussion of the Findings........................................................................ 85
Implications for Practice........................................................................... 88
Recommendations for Further Research................................................... 90
Conclusions............................................................................................... 94
REFERENCES...................................................................................................... 96
APPENDICES...................................................................................................... 116
x
Appendix A............................................................................................... 116
Appendix B................................................................................................ 117
Appendix C................................................................................................ 118
Appendix D............................................................................................... 119
Appendix E................................................................................................ 121
Appendix F................................................................................................ 123
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LIST OF TABLES
Page
Table 1 Terms modified in translation of DOD definition of I&W to Competitive I&W...........................................................................
35
Table 2 The startups to be analyzed through a comparative case study...... 42
Table 3 Success Factors regarding Funding attributes................................ 45
Table 4 Success Factors regarding Firm attributes..................................... 46
Table 5 Success Factors regarding Founder attributes................................ 47
Table 6 Palo Alto Networks’ Indicators of Success – Funding Attributes. 56
Table 7 Palo Alto Networks’ Indicators of Success – Firm Attributes....... 57
Table 8 Palo Alto Networks’ Indicators of Success – Founder Attributes. 59
Table 9 ISC8’s Indicators of Success – Funding Attributes....................... 61
Table 10 ISC8’s Indicators of Success – Firm Attributes............................. 62
Table 11 ISC8’s Indicators of Success – Founder Attributes....................... 64
Table 12 Cryptine Networks’ Indicators of Success – Funding Attributes... 65
Table 13 Cryptine Networks’ Indicators of Success – Firm Attributes........ 67
Table 14 Cryptine Networks’ Indicators of Success – Founder Attributes... 68
Table 15 Comparison of notable Indicators of Success................................ 70
Table 16 Area 1 Security’s Indicators of Success – Funding Attributes....... 77
Table 17 Area 1 Security’s Indicators of Success – Firm Attributes............ 77
Table 18 Area 1 Security’s Indicators of Success – Founder Attributes....... 79
Table 19 Sample representation of a macroenvironmental analysis using STEEP............................................................................................
92
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LIST OF FIGURES
Page
Figure 1 Cybersecurity financing history: 2010-2014.................................. 4
Figure 2 Decision stairway indicating the probability of war resulting from A’s actions over time.............................................................
31
Figure 3 Decision stairway depicting Actor B’s possible responses to Actor A’s decisions........................................................................
31
Figure 4 Competitive Indicators and Warning template used to examine startup firms....................................................................................
48
Figure 5 Example of a decision stairway representing the amount of points over a period of time............................................................
50
Figure 6 Competitive Indicators and Warning Analysis of Palo Alto Networks.........................................................................................
73
Figure 7 Competitive Indicators and Warning Analysis of ISC8................. 74
Figure 8 Competitive Indicators and Warning Analysis of Cryptine Networks.........................................................................................
75
Figure 9 Competitive Indicators and Warning Analysis of Area 1 Security 80
Figure 10 Decision Stairway of Area 1 Security’s Indicators of Success...... 81
Figure 11 Proposed model framework for inclusion of macroenvironmental and industrial analysis with Competitive I&W analysis................
91
Figure 12 Sample Porter’s five forces analysis of the cybersecurity industry 92
xiii
LIST OF ABBREVIATIONS
BA Bachelor of Arts
BS Bachelor of Science
DOD US Department of Defense
I&W Indicators and Warning analysis
MIT Massachusetts Institute of Technology
MS Master of Science
NSA National Security Agency
NYSE New York Stock Exchange
PANW Palo Alto Networks’ ticker symbol on the NYSE
SCIP Strategic and Competitive Intelligence Professionals
STEEP Social, Technological, Economic, Ecological, and Political/Legal – five dimensions of macroenvironmental analysis
US United States
VC Venture Capitalists
1
INTRODUCTION
Introduction to the Problem
The cybersecurity industry is experiencing rapid growth prompting entrepreneurs
to found new companies and earn their share of the market. With numerous startups
opening annually, competitive and market intelligence professionals can be overwhelmed
trying to estimate which firms have the potential to be successful. The Kauffman Index
(2015), which measures startup activity in the United States, determined that startup
activity has increased year-over-year from 2014 to 2015, the largest year-over-year
increase of the past 20 years and the first increase since 2010. In the same time frame, the
US Census Bureau’s Business Dynamics Statistics (2014) depict that 677,708
establishments were created in 2013, an increase of just under two percent from the
thirty-year low in 2009. While increasing startup activity and number of new
establishments help drive the US economy, estimates of failure rates suggest that
approximately 90% of these new ventures will likely fail (Carroll, 2014; Graham, 2013).
According to the US Small Business Administration (SBA), nearly 4 million technology
startups exist in the country (Small Business Profiles, 2015); looking specifically at failed
technology startups from 2010 through 2013, 69% of failed companies raised less than $5
million (the median funding raised was $1.3 million) and 71% of companies survived for
less than two years after their last round of funding (CB Insights, 2014a).
Gartner estimated that the cybersecurity market will reach $76.9 billion in 2015
and grow to $101 billion by 2018 (Gartner, 2014; Morgan, 2015). MarketsandMarkets
estimate that by 2020, the cybersecurity market will exceed $170 billion at a Combined
Annual Growth Rate (CAGR) of 9.8% from 2015 (MarketsandMarkets, 2015). Finding
2
success in a growing market requires increasing product differentiation and innovation
(Puspa & Kühl, 2006), a common strategy in such an environment and one that has been
shown to positively affect organizational performance (Antoncic, 2006). One critical
element of successful diversification that can be directly applied to cybersecurity
companies is keeping innovations aligned with demand (Downey, Greenberg, & Kapur,
2003). Determining which cybersecurity startups are likely to be successful depends
heavily on whether their products and services will be valuable to customers, i.e. offering
solutions that will truly change the technology security game (Lemos, 2015), or simply
become another available option on the market.
Background of the Problem
Between November 27 and December 15, 2013, peak holiday shopping season,
Ukrainian hackers victimized Target Corporation in the largest retail attack in US history
(Riley, Elgin, Lawrence, & Matlack, 2014). According to Brian Krebs (2014), who
originally broke the story of Target’s investigation into the breach, hackers stole 40
million credit and debit card numbers and 70 million records, which included the name,
address, email address, and phone number of Target shoppers. Earlier that year, Target
spent $1.6 million installing a malware detection tool manufactured by FireEye, a
cybersecurity and malware protection company; FireEye’s software detected the hacker’s
malware and alerted Target’s Minneapolis-based security team. The alerts occurred
before the hackers started transferring stolen data onto their own servers, but went
ignored by Target’s security team.
The hackers were neither one step ahead of the security team, nor did they use
highly advanced tools. According to Jim Walter, director of threat intelligence operations
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at McAfee, “the malware utilized [was] absolutely unsophisticated and uninteresting”
(Riley et al., 2014, p. 2). Target could have prevented the attack that impacted roughly 1
in 3 American consumers; however, their failure resulted in hundreds of millions of
dollars in costs to include restitution, lawsuits, and security upgrades – costs that could,
according to analyst estimates, run into the billions (Riley et al., 2014).
In today’s increasingly virtual world, cybercrime threatens companies across all
industries. According to the Ponemon Institute, over the course of 12 months from 2013
to 2014, 110 million Americans (about half of the country’s adults) fell victim to hackers
who exposed their personal information (CNNMoney, 2014). In 2014 alone, the Federal
Bureau of Investigation (FBI) alerted over 3,000 US companies that they had been
victims of cyber-attacks (PwC, 2015). Hackers have utilized attacks encoded on
sophisticated malicious software (malware) in order to steal individuals’ private
information and compromise the integrity of organizations, such as the aforementioned
attack on Target, and other major organizations, notably Sony and the US Office of
Personnel Management (OPM).
In response to these attacks, cybersecurity-specific firms have flooded the market
with products designed to protect networks and safeguard data. Since 2010, more than
$7.3 billion has been invested in 1,208 cybersecurity startups (CB Insights, 2014a).
Figure 1 (below) shows the annual growth of both the value of investments in
cybersecurity startups and the total number of investment deals, both of which have more
than doubled over the past five years. With so many companies and products on the
market (and more starting up every year), potential investors, acquiring companies, and
customers face significant difficulties determining where to spend their money.
4
Statement of the Problem
Competitive and market intelligence professionals – those not directly involved
with venture capitalism – lack a methodology designed to analyze and estimate the
likelihood of success during the formative years of a new startup. In order to accurately
estimate the likelihood of success of a startup, an analyst must assess numerous factors
regarding the startup – factors that are frequently unstructured or difficult to
conceptualize – in addition to economic and market conditions, forthcoming political
regulations, and future technological advances. The objective model that will be refined
and tested through this study will also help to reduce the analyst’s personal biases,
decreasing the likelihood of an uninformed and inaccurate judgment.
Purpose of the Study
The purpose of this study is to analyze cybersecurity startups in order to
determine factors that can be attributed to the success or failure of the companies through
$941 $936
$1,311
$1,764
$2,394
133
155
213
258269
0
50
100
150
200
250
300
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
2010 2011 2012 2013 2014
Number of Investmen
t Deals
US Dollars, M
illions
Cybersecurity Financing History: 2010‐2014
Investments Deals
Figure 1. Value and number of investments in cybersecurity startups from 2010 to 2014. Data obtained from CB Insights (2015).
5
a comparative case study. The study will expand on research conducted on
entrepreneurship and intelligence theories, success, and success factors. Additionally, this
study seeks to develop and test a model, based on Indicators and Warning analysis, a
military intelligence technique, which can be applied to new startup firms (in this case,
cybersecurity startups) in order to estimate their likelihood of success. This model can be
another tool utilized by competitive intelligence professionals, or it can be used by
potential investors, acquiring companies, and customers as a method to increase their
confidence in determining where to invest their time and money.
Research Questions
Much of the research on startups focuses on success ex post facto without any
attempt to develop or apply an ex ante model to analyze new startups. This study seeks to
answer two primary research questions. First, what factors have increased survival rates
and influenced growth of US cybersecurity startups? Second, after determining those
factors, what are the fundamental themes of success factors that must be included in a
competitive intelligence analytic model in order to improve estimates of success of a new
startup? These two questions will be answered through the collection and analysis of
literature, a qualitative case study of cybersecurity startups, and the application of
Indicators and Warning (I&W) analysis, a structured analytic technique.
Definition of Terms
This thesis uses the following terms and their associated definitions:
Startup. According to Schumpeter (1942) “the function of entrepreneurs is to
reform or revolutionize the pattern of production by exploiting an invention or, more
generally, an untried technological possibility for producing a new commodity or
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producing and old one in a new way, by opening up a new source of supply of materials
or a new outlet for products, by reorganizing an industry and so on” (p. 132). Therefore,
we can define a startup as an organization founded as a result of such entrepreneurial
activity.
Success. While there is no agreed-upon definition of success in terms of
entrepreneurship, a concept which will be discussed in more detail in the literature review
following this chapter, the definition this study will use is derived from the CB Insights
(2014a) statistics presented in the Introduction to the Problem. CB Insights (2014a) have
shown that 97% of technology startups that failed between 2010 and 2013, failed within
five years of their final round of funding. We can say, therefore, that a successful startup
is one that survives for more than five years after its last round of funding (in this
definition, funding refers to venture capitalist investments through specific funding
rounds and does not include funds raised through initial public offerings – IPOs).
Implications and nuance of this definition will be discussed in the Literature Review.
Success factors. Rockart (1979) defines success factors as “the limited number of
areas in which results, if they are satisfactory, will ensure successful competitive
performance for the organization” (p. 85). In other words, success factors are the areas
that a business must get right, and should therefore receive persistent and calculated
attention from leaders.
Failure. Shepherd (2003) considers a business venture to be a failure “when a fall
in revenues and/or a rise in expenses are of such a magnitude that the firm becomes
insolvent and is unable to attract new debt or equity funding; consequently, it cannot
continue to operate under the current ownership and management” (p. 318).
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Cybersecurity. According to the US Department of Homeland Security’s (DHS)
National Initiative for Cybersecurity Careers and Studies (NICCS) and adopted from the
National Institute of Standards and Technology (NIST), cybersecurity is “the activity or
process, ability or capability, or state whereby information and communications systems
and the information contained therein are protected from and/or defended against
damage, unauthorized use or modification, or exploitation” (DHS, 2009).
Nature of the Study
This study will use a comparative case study to determine which factors have
directly contributed to the success or failure of cybersecurity startups. The case study will
examine one successful cybersecurity company, Palo Alto Networks, and two
unsuccessful cybersecurity companies, ISC8, Inc. and Cryptine Networks. Palo Alto
Networks, a publicly traded cybersecurity company (NYSE: PANW), was founded in
2005, and recorded $928 million in revenue for the year ending July 31, 2015 – a 134%
increase from 2013 and a 55% from 2014. Palo Alto Networks held their initial public
offering (IPO) on July 20, 2012. ISC8, Inc., a cybersecurity company that targeted
attackers after infiltration of a system, started their cybersecurity business in January
2012 and declared Chapter 11 Bankruptcy less than four years later on September 14,
2015. Cryptine Networks was founded in 2001 as a security consulting firm, but shifted
focus to cybersecurity software development. The shift in focus fractured the team of
founders and drove away potential investors, ultimately resulting in its closure in 2006.
After extensively reviewing entrepreneurial literature, most research is based on
quantitative methodologies; therefore, this study will utilize the qualitative methodology
of comparative case studies to develop an I&W model for use in competitive and market
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analysis. The model does incorporate quantitative measures through the accounting of
scores for each of the indicators. While there is significant research into the success
factors of existing startups, there is a lack of research, and resulting models and
methodologies, regarding assessment of startups and potential for success during their
formative early years.
Relevance and Significance of the Study
In the first half of 2015 there were 604 cyber-attacks – approximately 23 attacks
each week (Hackmageddon, 2015). Due to the frequency of these cyber-attacks, many
companies are exploring and evaluating ways to protect the information stored on their
networks. In response to this demand, the cybersecurity market has ballooned, growing at
a rate of nearly 10% annually (MarketsandMarkets, 2015), with more than 500 firms
offering various products and services on the market (Cybersecurity Ventures, 2015). As
some companies seek to purchase security products, other companies look to purchase
cybersecurity firms, and venture capitalists look to invest their money in promising
startups. The cybersecurity market is providing an essential product and filling a need
that will likely persist through the coming years. However, despite predicted growth of
the market through 2020, some investors are becoming wary of a potential bubble
forming in the cybersecurity market that may decrease valuations and drive down overall
funding. This study, therefore, seeks to determine which factors are most indicative of
entrepreneurial success and develop a model that can be used to estimate the likelihood of
success of new cybersecurity startups.
This study will add to the body of knowledge in the intelligence and analytic
fields in three ways. First, this study will provide some clarity as to which factors
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contribute to a cybersecurity startup that is likely to experience success in the
aforementioned crowded cybersecurity market. Second, there is an absence of
entrepreneurial and business literature examining market opportunities through I&W
analysis, primarily due to the lack of national security or military intelligence
methodologies that can be used in a competitive intelligence setting. This study will
convert a predominantly military analytic technique that is used to identify and warn of
potential threats into a competitive and market analytic technique that can be used to
identify and notify practitioners of potential threats and opportunities in the market. The
primary challenge in converting any methodology from military intelligence to
competitive intelligence is the translation of terminology and targets, a concept that will
be addressed, with the focus of translation on I&W analysis, in the second chapter.
Finally, the developed I&W analytic model can be generalized and applied to the broader
field of technology startups, of which there are currently 4 million in existence.
Assumptions and Limitations
This thesis contains an assumption and several limitations. The assumption is that
the examined unsuccessful companies failed due to their own mismanagement, lack of
abilities, or unfavorable market conditions, and not due to any illegal or fraudulent
activities or significant factors outside their control.
The first limitation is that the researcher only has access to open source
information and does not have any access to private, insider information on any of the
studied cases. This thesis is also limited in that it does not assess all firms whose
offerings include cybersecurity solutions; firms such as IBM or Intel offer cybersecurity
products, but the product is not its primary endeavor, nor can companies such as these be
10
considered startups. Additionally, while some of the literature on entrepreneurial efforts
includes teams or divisions that were started inside a larger company, this study will not
include such startup programs. Due to the proprietary information involved in developing
products that are distinct within the market, much of the creative, thought, and
development processes of these startups are unavailable to the public. Finally, due to the
limited number of cases analyzed, the overall validity will need to be corroborated
through further research.
Organization of the Study
This thesis is organized into five chapters. The first chapter introduces and
provides background to the problem that the study addresses and the research questions
that the study seeks to answer. The first chapter also includes the purpose and nature,
significance to the body of knowledge in the intelligence and analytics field, and the
assumptions and limitations of the study. The second chapter is a review of the literature
which covers previous research conducted on startups and entrepreneurial efforts,
success, and success factors. The literature review will also examine the practice of
intelligence and the analytic methodology Indicators and Warning analysis. The second
chapter concludes with a revised definition of I&W analysis that is applicable to the use
of the methodology in competitive intelligence. The third chapter outlines the
methodology used to collect, measure, and analyze the data. This chapter will also
include a brief discussion of methodological limitations and their impact on the validity
of this study. The fourth chapter studies the three selected cases (Palo Alto Networks,
ISC8, Inc., and Cryptine Networks) in order to determine key success factors. Area 1
Security, a recently-founded cybersecurity startup, will be analyzed through the
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Competitive Indicators and Warning model. The fifth chapter concludes the thesis with a
summarization of the study, a discussion of the results and implications, and
recommendations for future research.
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LITERATURE REVIEW
Introduction
This review and synthesis of existing literature will cover entrepreneurial and
intelligence theories. The entrepreneurial theory and framework includes the act and
psychology of entrepreneurship, various definitions of success, and an examination of
factors that increase the probabilities of success. The intelligence theory and framework
section focuses on the definition and purpose of the intelligence profession, the military
intelligence methodology of Indicators and Warning (I&W) analysis, and the translation
to Competitive I&W.
In this study, the reviewed concepts will be tied together through the development
of a competitive intelligence methodology, based on I&W analysis, which can be used to
estimate the likelihood of success of a new cybersecurity startup. This review will also
examine three gaps in the literature: the absence of an agreed-upon definition of success,
the dearth of competitive intelligence methodologies derived from the national security or
military intelligence disciplines, and the lack of an available and user-friendly tool to
estimate the likelihood of startup success for use by competitive intelligence
professionals. While remedying the first gap is beyond the scope of this study, the final
two gaps will be addressed through the remainder of this thesis.
Entrepreneurial Theory and Framework
Entrepreneurship
Sharma and Chrisman’s (1999) review of entrepreneurship included one of the
earliest usages of the term – Richard Cantillion’s 1734 definition of entrepreneurship as
13
self-employment with an uncertain return. Entrepreneurship has been defined and
developed in a variety of way since Cantillion first used the term nearly three centuries
ago; as such, the lack of a commonly agreed upon definition has impaired the theoretical
development of the concept (Gartner, 1990; Low & MacMillan, 1988; McMullan &
Long, 1990; Shane & Venkataraman, 2000; Venkataraman, 1997).
Schumpeter (1934), one of the more popular theorists of entrepreneurship, posited
that an entrepreneur was an individual who carries out new combinations and creates
discontinuity (or sharp characteristic differences) in the market. These new combinations
could be the introduction of a new good (either completely unique or new quality),
introducing of a new method of production, opening of a new market, use of a new
source of raw materials or components, or the reorganization of an industry. Instead of
viewing the entrepreneur as a force of discontinuity, Kirzner (1979) suggested that
entrepreneurs actually stabilize the market by responding to the tensions resulting from
untapped opportunities. Schumpeter (1942) viewed entrepreneurship as an act reserved
for brilliant and daring innovators who “reform or revolutionize the pattern of production
by exploiting an invention or, more generally, and untried technological possibility” (p.
132). While Kirzner (1979) believed that entrepreneurship existed even when a market
participant discovered a way of doing things that was a little different, but utilized
available opportunities. Both theorists supported the notion that entrepreneurs drive up
the level of economic well-being through the creation of profits. While Schumpeter’s
(1934, 1942) theory focused on exploiting opportunities, Kirzner (1979) was more
concerned with the discovery of market opportunities. Reynolds et al. (2005) fused these
two views by indicating that entrepreneurship can be thought of as the discovery of
14
opportunities and subsequent creation of new economic activity – or exploitation of the
opportunity – frequently done so through the foundation of a new organization.
Expanding on the concept of opportunities central to entrepreneurship, Stevenson,
Roberts, Grousbeck, and Bhidé (1999) termed entrepreneurship as “the process of
creating value by bringing together a unique package of resources to exploit an
opportunity” (as cited by Brizek & Khan, 2008, p. 227); essentially, entrepreneurs create
value where there previously was none. Shane and Venkataraman (2000) submitted that
without the discovery, assessment, and exploitation of opportunities, the entrepreneurial
process and the creation of new goods, methods, markets, and organizations would not
exist. As entrepreneurship does not necessarily require the creation of a new firm and can
be initiated by more than a single actor, the act of entrepreneurship may actually begin
with opportunities (Murphy, 2009; Murphy & Coombes, 2008). While the identification
of opportunities depends on both the entrepreneur and the context of the opportunity
(Dew, Velamuri, & Venkataraman, 2004; Shane, 2000; Venkataraman & Harting, 2005),
Eckhardt and Shane (2003) defined entrepreneurial opportunities as “situations in which
new goods, services, raw materials, markets, and organizing methods can be introduced
through the formation of new means, ends, or means-ends relationships” (p. 336).
Economic profit is produced through entrepreneurship by individuals who are able to
detect and exploit these market opportunities (Vranceanu, 2014).
Cuervo, Ribiero, and Roig (2007) developed three basic ideas that explain the
presence of entrepreneurial activity: individual, economic, and institutional function. The
individual view described entrepreneurship as a human attribute that includes need-based
traits such as the need for achievement (McClelland, 1961), for autonomy, dominance,
15
high-energy, and persistence (Neider, 1987), or self-efficacy (Chen, Green, & Crick,
1989). Bull and Willard (1991) theorized that entrepreneurship will occur under four
conditions, three of which are individualistic. The entrepreneur must have task-related
motivation (a vision that motivates the entrepreneur to act), expertise of both present and
future needs, and expectation of gain (economic and/or psychological) for themselves.
Entrepreneurship also revolves around the acceptance of risk, which Caruana, Morris,
and Vella (1998) suggested is essential in the entrepreneurial creation of wealth. As
Cantillion noted in 1734, entrepreneurs must be willing to face uncertainty (Kihlstrom &
Laffont, 1979). Building upon this idea, Miller (1983) suggested that entrepreneurial
behavior is defined by the management of risk, innovation, and proactivity. Herron and
Robinson (1993) developed a model that examined the impact of factors such as
personality traits and motivations on entrepreneurial behavior; however, they concluded
that entrepreneurship is most affected by the structure of the external environment in
which the act was commenced and the entrepreneur’s strategy.
The second aspect of entrepreneurial activity is the economic view, specifically
the environmental factors that result in entrepreneurship. Bull and Willard’s (1991) fourth
condition of entrepreneurship is a supportive environment, or settings that provide
comfort and support for a new activity or minimize discomfort from a previous initiative.
Additionally, the dynamics of technological change (Tushman & Anderson, 1986) and
the size and structure of the market (Acs & Audretsch, 1990) influence entrepreneurial
activity. The third factor of entrepreneurial activity involves the functions of institutions,
cultures, and societal values. Sharma and Chrisman (1999) agreed with this view of
16
entrepreneurship, defining the phenomenon as “acts of organizational creation, renewal,
or innovation” (p. 17).
From a resource-based point of view, entrepreneurship can be said to occur as a
result of varying beliefs regarding the value of resources (Alvarez & Busenitz, 2001).
Several other authors found that innovative produces, processes, and organizations occur
through the combination of resources in new ways (Alterowitz & Zonderman, 1988;
Jennings & Young, 1990; Kanter, 1986; Schollhammer, 1982). Beyond the resource
view, Damanpour (1991) and Drucker (1985) suggested that entrepreneurship is based
solely on the innovation, development, and implementation of new ideas, behaviors, or
goods. While these authors provided a broad range of theories and definitions of
entrepreneurship, from opportunities to individual traits to external environments, two
overarching themes in theoretical development are innovation – the development of a
new, value-creating product, service, or method – and discontinuity – a sharp difference
in characteristics of organizations, activities, or processes. Entrepreneurs must take
advantage of opportunities, employ their traits and experiences, and do so in a supportive
environment in order to successfully innovate and create discontinuity.
Defining Success
The literature regarding success in business, and entrepreneurial success in
particular, is vast and lacking consensus (Fielden, Davidson, & Makin, 2000; Headd,
2003; Maidique 1985; Maidique & Zirger, 1985; Mansfield & Wagner, 1975; Stuart &
Abetti, 1987; Walker & Brown, 2004). Stuart and Abetti (1987) provided four
dimensions of success: (1) subjective versus objective, (2) bimodal, multimodal versus
continuous, (3) financial versus nonfinancial, and (4) meeting versus not meeting
17
expectations. Examining subjectivity in determining success, Cooper (1979) studied raw
judgment of the perceived success of a product. Maidique and Zirger (1984), on the other
hand, assessed product success or lack thereof based solely on the cash flows generated
by the product. This study utilizes statistical objectivity to define success. According to
data obtained from CB Insights (2014a), 97% of technology startups that failed between
2010 and 2013, failed within five years of their final round of funding; therefore, a
successful startup is one that survives for more than five years after its last round of
funding. This is the definition of success that this study will use. Funding rounds
(typically termed Series A, Series B, Series C, and so on) refer to the efforts by
entrepreneurs to raise money through investments from venture capitalists, business
angels, and general investors (Gil, 2011).
Bimodal models of success, or dichotomous classifications as either successful or
unsuccessful, and multimodal studies, such as Litvak and Maule’s (1980) three-point
scale (unsuccessful, marginal success, or successful) or Maidique and Zirger’s (1984)
seven-level scale based on returns, evolved into continuous measures of success, mainly
using figures such as sales growth, cash flows over time, or return on investments or
equities (Meyer & Roberts, 1986; Weiss, 1981).
Walker and Brown (2004) explored financial and nonfinancial criteria
determining success. Traditional measures of success center on either employee numbers
or financial performance, such as profit, turnover, or return on investment. Hall and
Fulshaw (1993) stated that “the most obvious measure of success are profitability and
growth” (p. 229), which Greenbank (1999) termed profit maximization. Financial
performance criteria are ‘hard’ measures of success, meaning they can be easily
18
administered and applied to nearly all organizations, thus increasing their popularity
(Barkham, Gudgin, Hart, & Hanvey, 1996; Gibb & Davies, 1992). Additionally, Marlow
and Strange (1994) stated, somewhat obviously, that businesses must be financially
sound in order to continue to exist. Headd (2003) examined business closings and posits
that while some entrepreneurs seek to continue and grow their business, others might
want to close and sell their business to make a profit before they start experiencing losses.
Not all small business owners want or need to grow their businesses. In situations
where financial gain is not their primary or only motivation, success must be measured
using other, nonfinancial criteria. Some small businesses even avoid hiring employees,
despite links to success and the potential detriment to profits, because growth and job
creation are not the entrepreneur’s primary motivations or goals (Gray, 1998). Fielden,
Davidson, and Makin (2000) found that while 88% of small business owners listed
money as a motivator of entrepreneurial activity, 71% of surveyed individuals recorded
that job satisfaction, greater independence, new opportunities and challenges, and the
ability to pursue their own interests were very important to them. Wheelock and Baines
(1998) suggested that these subjective benefits, or what they call psychological income,
are primarily enjoyed by individuals who have not been very successful financially, but
are satisfied with other types of rewards. These nonfinancial and subjective measure of
success, such as autonomy, job satisfaction, and work-life balance can be difficult to
define (Buttner & Moore, 1997; Green & Cohen, 1995) and, therefore, are difficult to
measure.
The final dimension, meeting or not meeting expectations, can be either an
objective or subjective measure. Maidique and Zirger (1985) assessed success to be the
19
achievement of a desired, planned, or attempted end goal. This goal could be financial,
but financial success is far from the only important goal, and it could also be any of the
other subjective measures. While ‘hard’ measures of success are more easily used to
compare organizational performances and are better tools for benchmarking, the
subjective ‘soft’ measures are not useless, with many commonalities occurring amongst
small business owners.
While these measures can be used for general business success, Stuart and Abetti
(1987) also quantified initial success through objective and subjective measures.
Objectively, initial success could be determined with the same financial measures
discussed earlier. Mansfield and Wagner (1975) defined success very strictly through
these objective measures, stating that a venture or product (what they call a
commercialized R&D project) can be considered successful if the “project will yield a
rate of return in excess of what was available from other (non-R&D) investment
alternatives” (p. 180). Subjectively, initial success can be measured through combinations
of achieving expectations, survival, ability to attract investors, and societal contributions.
While initial success confirms that the entrepreneur’s early efforts will be rewarded and
is essential to develop a customer base and maintain a steady flow of capital, it in no way
guarantees long-term success.
Success Factors
Watson, Hogarth-Scott, and Wilson (1998) developed a theoretical and analytical
framework for examining success factors in entrepreneurship consisting of internal and
external factors. The success factors related to the internal environment can be broken
down into four categories: funding attributes, firm attributes, founder attributes, and
20
market attributes. While the market has been deemed an important factor in the success
of new ventures (Chandler & Hanks, 1994; Holmes, Hunt, & Stone, 2010; Littunen &
Niittykangas, 2010; Miettinen & Littunen, 2013; Porter, 1985; Stuart & Abetti, 1987),
assessment of market attributes and their relationship with entrepreneurial success is a
massive undertaking and beyond the scope of this study, but it is a topic that should be
considered for future research. This study is limited to cybersecurity startups entering the
same (US) market. The market impact, therefore, on any one firm’s success should
equally impact any other firms’ success; as such, the market’s impact on any individual
startup will be disregarded for the purposes of this study.
Funding attributes. According to Miettinen and Littunen (2013), “sufficient
finance when starting up a new firm” is a key factor in determining success (p. 475).
Duchesneau and Gartner (1990) suggested that greater equity share is a strong indicator
of greater probabilities of success, where equity share is the amount of capital provided
by the entrepreneur(s) related to the total assets. This serves as a proxy for the founder’s
commitment, an important aspect that lenders value. Duchesneau and Gartner’s study,
however, was performed on startups in the food and beverage industry, which may rely
on equity share differently than startups in other industries. Cooper, Gimeno-Gascon, and
Woo (1994) found that greater financial capital not only allows for the undertaking of
more ambitious strategies, potential changes in direction, and increased capability to deal
with the demands of growth, but it also likely reflects better training and more robust and
detailed planning. In terms of loans, the average amount among the successful firms was
greater than the average loan amount of the total number of firms (Miettinen & Littunen,
2013). Multiple studies agreed that greater funds in the startup phase is strongly
21
associated with increased probability of success (Becchetti & Trovato 2002; Cooper et
al., 1994; Lussier & Halabi, 2010). In one study, a 75,000 Euro (about $85,762 – $94,335
when controlled for inflation) seed capital had a very significant positive correlation to
the success and growth of European startup businesses (Groenewegen & de Langen,
2012). In another, Lasch, Le Roy, and Yami (2007) suggested that capital greater than the
75,000 Euro threshold results in greater growth. However, according to data from CB
Insights (2014b), technology startups have averaged just over $1 million in seed capital
since the beginning of 2011.
In addition to seed capital, venture capitalists and business angels can have a
profound effect on a new venture’s success. Particularly in highly competitive industries,
startups that have been financed by larger and more active investors are likely to
dominate their competitors in terms of capital, growth, and market shares (Inderst &
Mueller, 2009). Despite this potential, Bygrave, Hay, Ng, and Reynolds (2003) estimated
that venture capitalists and business angels invest in less than 0.5% of nascent ventures.
Such a small percentage is most likely due to investors seeking either established,
profitable enterprises, or larger-scale ventures with high potential (Bhide, 2000). CB
Insights (2014a) analyzed failed technology companies from 2010 through 2013 and
found that 69% of the failed companies raised less than $5 million. The average funding
of these companies was $11.3 million, but the median (a more applicable figure) was
only $1.3 million. Finally, 13% of these technology startups raised more than $20 million
and still failed, indicating that while financing may have a strong correlation to success, it
is by no means a guarantee.
22
Firm attributes. The level of innovation of a startup’s product or service has a
very significant positive correlation to organization growth (Groenewegen & de Langen,
2012). Stuart and Abetti (1987) included unique innovations as factor contributing to
initial success, suggesting that innovations that increase probability of success are unique
products, processes, systems, or services that are “specialized or customized, beyond the
state-of-the-art, based on proprietary and patentable technology or on complex systems
engineering, and R&D requirements are high” (p. 219). Equally as important is the value
of this innovation to the customer. Greater benefits for less costs, such as improved
functionality, efficiency, or savings, will increase the value. According to Abetti (2000),
this is the degree in which the innovation offers a unique advantage compared to other
existing possibilities. In other words, innovative products increase a company’s
competitive advantage, thereby increase probability of survival and growth
An organization’s business plan and competitive strategy are integral and must be
thorough and robust in order to grow and succeed (Salomo, Weise, & Gemünden, 2007).
Stuart and Abetti (1987) suggested that a highly aggressive strategy, one that is clear and
proactive, first to the market, and includes strong efforts in R&D, sales, and marketing,
will increase a company’s likelihood of success. Watson, Hogarth-Scott, and Wilson
(1998) suggested that the underlying business concept would increase or decrease the
likelihood of success. Additionally, despite some anecdotal success within simplistic and
loosely-controlled organizations, it has been shown that a company’s business plan, at
least in the early stages, should allow for the entrepreneur to exert a fairly tight control of
the organization to increase the probability of success (Stuart & Abetti, 1987).
23
Firms that have been founded by teams (multiple founders) can take advantage of
a wider array of skills and resources, increased access to capital, and more sophisticated
systems of support and organization – all factors that increase the probability of growth
and success (Cooper et al., 1994; Friar & Meyer, 2003; Lafuente & Rabetino, 2011;
Littunen, 2000; Littunen & Niittykangas, 2010; Lussier & Halabi, 2010; Schutjens &
Wever, 2000). While teams do significantly contribute to high growth, those that break
up under hostile conditions or due to non-amicable relationships severely threaten the
survival of the business (Thurston, 1986).
Founder attributes. The skills and experiences of the entrepreneur(s) themselves
compose this family of attributes. Unger, Rauch, Frese, and Rosenbusch (2011) defined
human capital as “skills and knowledge that individuals acquire through investments in
schooling, on-the-job training, and other types of experience” (p. 343); a definition
derived from Becker’s (1964) Human Capital. This study will use the term ‘human
capital’ to refer to the founder’s attributes – i.e., the entrepreneur’s individual skills and
knowledge. Becker’s (1964) theory of human capital suggested that individuals expect
compensation for their human capital, and therefore invest in their human capital in order
to maximize their economic compensation.
Cassar (2006) tied this theory to entrepreneurship by positing that, of the
individuals engaged in entrepreneurial activity, those who have invested more in their
human capital are more likely to strive for higher growth and profits in their endeavors,
primarily because they want to receive greater compensation for their greater human
capital investments. Ray (1993) and Stanworth and Gray (1991) suggested that successful
entrepreneurship is related to an infinite variety of complex and interrelated personal
24
attributes, in which the probability of success increases when an individual’s positive
attributes outweigh their negative attributes. Entrepreneurs can increase their positive
attributes through investments in their own human capital, including education and
knowledge, work experience, skills, and competencies (Reuber & Fisher, 1994; Unger et
al., 2011). If the compensation does not equal the investments, entrepreneurs with high
human capital are likely to shut down their firms and seek more lucrative employment
opportunities (Gimeno et al., 1997). Therefore, according to human capital theory, an
entrepreneur who invests highly in their own human capital increases their likelihood of
entrepreneurial success. Some researchers (Miettinen & Littunen, 2013; Storey &
Wynarczyk, 1996) found that, due to a weak correlation with success, the founder’s
attributes are not a unique determinant of performance; however, many authors
(Barringer & Jones, 2004; Bonet, Amengot, & Martín, 2011; Bosma, Van Praag, Thurik,
& de Wit, 2004; Cooper et al., 1994; Haber & Reichel, 2005; Van der Sluis, Van Praag,
& Vijverberg, 2005) agreed that there is a strong positive relationship between the
entrepreneur’s attributes (or human capital) and success.
Human capital and entrepreneurial success. Unger et al. (2011) developed four
applications of human capital that should increase entrepreneurial success. First, greater
human capital should increase the capability of an individual to perform the
entrepreneurial task of discovering and exploiting opportunities (Shane & Venkataraman,
2000). This use of human capital harkens back to Schumpeter’s (1934, 1942) and
Kirzner’s (1979) theories on entrepreneurship. Second, according to Baum, Locke, and
Smith (2001) and Frese, et al. (2007), human capital is correlated with planning and
venture strategy, two organizational abilities that positively impact success. Brush,
25
Greene, and Hart (2001) asserted that knowledge is useful in the acquisition of other
necessary resources, such as financial and physical capital, and can even compensate for
a lack of financial capital, which has been noted as the most important factor in the
success of a new firm (Chandler & Hanks, 1998). Finally, human capital in necessary for
further learning and to acquire new knowledge and skills (Ackerman & Humphreys,
1990; Hunter, 1986). Entrepreneurs who harness all four applications of human capital
should be able to operate their organizations more effectively and efficiently than an
owner with lower human capital. Stuart and Abetti’s (1987) research found that the
entrepreneur (or entrepreneurial team) is integral to the success of new ventures.
Additionally, entrepreneurs who are driven by opportunity, exhibit persistence and
persevere, are dynamic and enthusiastic, and possess high quality networking and
communication skills are more likely to increase the probability of success.
Previous entrepreneurial experience. While authors agree that the entrepreneur’s
human capital is related to success, the magnitude of that relationship varies across
situations and studies and, therefore, remains unknown. Studies such as Duchesneau and
Gartner (1990) and Van der Sluis, Van Praag, and Van Witteloostuijn (2007) showed a
moderate to high relationship, whereas Davidsson and Honig (2003) and Gimeno, Folta,
Cooper, and Woo (1997) reported a low correlation. Personal business experience,
particularly resulting from founding or owning a business, has shown to be a critical
success factor for small firms (Coleman, 2007; Harada, 2003; Yusuf, 1995). In their
evaluation of a firm’s potential, investors have traditionally attached great importance to
the entrepreneur’s experiences (Stuart & Abetti, 1990). However, this attribute is not
agreed upon unanimously; Bosma et al. (2004) and Van Praag (2003) argued that
26
previous entrepreneurial experience does not have an influence on the performance of
small businesses. Blumberg and Letterie (2008) noted that banks do not value this type of
experience during the loan application phase.
Relevant social networks and previous managerial experience. Groenewegen
and de Langen (2012) demonstrated the importance of a relevant social network to an
entrepreneur, a factor that is positively correlated to business growth. Prior business
experience tends to increase the size of these social networks, which are therefore more
robust and developed than the social network of a beginner, who likely has fewer skills to
diversify their network team (Mosey & Wright, 2007; Zolin, Kuckertz, & Kautonen,
2011). Additionally, prior business and managerial experience and knowledge increase an
entrepreneur’s alertness, helping them find and take advantage of market opportunities
that would likely remained hidden from novices (Shane, 2000; Venkataraman, 1997;
Westhead, Ucbasaran, & Wright, 2005). According to Watson, Hogarth-Scott, and
Wilson (1998), previous managerial experience can improve the founder’s ability to
overcome common business problems. Venture capitalists often use management skills
and experience as evaluation and selection criteria in identifying promising investment
targets (Zacharakis & Meyer, 2000).
Previous industrial experience. Reuber and Fischer (1999) reported that
industrial knowledge and experience increase the probability of success. Multiple studies
have shown the positive effect of industrial experience on a new firm’s performance
(Blumberg & Letterie, 2008; Bosma et al., 2004; Cooper et al., 1994; Harada, 2003;
Lussier & Halabi, 2010; Van Praag, 2003). Previous work experience in the same
industry as the entrepreneurial effort is typically associated with skillsets, knowledge, and
27
abilities that would benefit a small business, particularly an in-depth understanding of
markets, customers, and technologies (Reuber & Fischer, 1999). Venture capitalists
greatly value and are more likely to finance entrepreneurs that are compatible with the
venture, meaning that the new endeavor aligns with the entrepreneur’s previous
experience, knowledge base, and skills (Stuart & Abetti, 1987).
Level of education. Educated entrepreneurs are perceived to have a greater
capability to adapt to and develop knowledge of a constantly changing environment
(Haber & Reichel, 2005). Additionally, entrepreneurs with higher levels of education are
more likely to prosper in their business ventures than those with less education (Bosma et
al., 2004; Cassar, 2006; Lussier & Halabi, 2010; Lussier & Pfeifer, 2001). Both Almus
(2002) and Van der Sluis et al. (2005) showed a positive relationship between the
entrepreneur’s education level and growth of their business. Almus (2002) and Cooper et
al. (1994) suggested that founders with a university degree or higher have a better chance
of rapidly growing their business. The level of formal education is correlated with, and
can therefore be used as a proxy for, an entrepreneur’s drive, energy, motivation, and
dedication to the business – attributes that lead to better business performance (Cooper et
al., 1994; Kim, Aldrich, & Keister, 2006; West & Noel, 2009). Watson, Hogarth-Scott,
and Wilson (1998) suggested additional founder attributes that could be considered,
including the founder’s motivation to start a business, specifically examining “push” and
“pull” motivations. Push motivations refer to necessity, in order to make a living, and a
pull motivation is the desire for independence, to use creative skills or work in an area
that they enjoy. Their findings suggested that startups formed due to “pull” motivations
were more likely to succeed.
28
Intelligence Theory and Methodological Frameworks
Intelligence
Between the National Security Act of 1947, the Intelligence Reform and
Terrorism Prevention Act of 2004, the seventeen US intelligence agencies, and the US
Department of Defense, there does not exist an agreed-upon, appropriate definition of
intelligence. As the official governmental definitions do not suffice to adequately define
intelligence, multiple experts have undertaken the task. Lowenthal (2002) defined
intelligence as “the process by which specific types of information important to national
security are requested, collected, analyzed, and provided to policymakers; the products of
that process; the safeguarding of these processes and this information by
counterintelligence activities; and the carrying out of operations as requested by lawful
authorities” (p. 8). Warner (2002) examined nearly 20 varying definitions of intelligence
and arrived at intelligence as a “secret, state activity to understand or influence foreign
entities” (p. 21). Clark (2003) stated that, “intelligence is about reducing uncertainty by
obtaining information that the opponent in a conflict wishes to deny you” and “can be
thought of as the complex process of understanding meaning in available information” (p.
13). These definitions improve on what the government offers, but they do not apply to
the competitive intelligence discipline.
The Strategic and Competitive Intelligence Professionals association (SCIP,
2015) defined competitive intelligence as “a necessary, ethical business discipline for
decision making based on understanding the competitive environment” (p. 1). While
SCIP’s definition depicted intelligence as a professional discipline with an external focus
and a clear customer, it fails to acknowledge the intelligence process. Fleisher and
29
Bensoussan (2003) defined competitive intelligence as a “value-added product resulting
from the collection, evaluation, analysis, integration, and interpretation of all available
information that pertains to one or more aspects of an executive’s needs, and that is
immediately or potentially significant to decision making” (p. 6). This definition, while
focused specifically on competitive intelligence and ignoring the customer of the process,
adequately acknowledges both the intelligence product and process, the customer, and the
necessary actionable nature of intelligence.
In order to resolve the discrepancies amongst the many definitions and to develop
a comprehensive definition of intelligence, Wheaton and Beerbower (2006) posited the
definition of intelligence as “a process, focused externally and using information from all
available sources, that is designed to reduce the level of uncertainty for a decision maker”
(p. 329). Wheaton and Beerbower (2006) ensured that their definition of intelligence is
applicable to national security, law enforcement, and competitive intelligence
professionals. This study will use Wheaton and Beerbower’s definition of intelligence
based on the definition’s disciplinary flexibility. The definition incorporates the focus of
intelligence, includes open and confidential sources of information, and acknowledges
the process and the customer of intelligence.
Indicators and Warning Analysis
Indicators and Warning analysis is a military intelligence methodology in which a
warning analyst detects and reports information on foreign or adversarial activities that
could threaten the United States, its interests, or their allies (Joint Military Intelligence
College, 2001). Growing into prominence after World War II, I&W analysis developed
as a response to the fear that America’s adversaries might once again engage in
30
devastating military actions without any conventional warning, such as a declaration of
war (Grabo, 2002). Warnings could be based on almost any development, or indicator, in
any part of the world, and should, therefore, be the responsibility of the entire
Intelligence Community. There is an important distinction between indications and
indicators: according to the Department of Defense (2015), indications are any piece of
information that reflects a potential enemy’s intent to select a particular course of action,
and are therefore inherently ambiguous or uncertain. An indicator, on the other hand, is
“a known or theoretical step which the adversary should or may take in preparation for
hostilities” (Grabo, 2002, p. 3).
The warning analyst is broad in scope and focuses on strategic-level intelligence
analysis. Davis (1969) discussed the warning analyst’s role as one who seeks and
assesses information that could indicate preparation for a military offensive. Specifically,
the analyst focuses on strategic threats, actual or potential, to the US. The warning
analyst must always consider the worst-case scenario or the unlikely, outside
possibilities. Swanson, Astrich, and Robinson (2012) suggested that preventing surprise
requires creative and diverse predictive estimates, stemming from intensive and rigorous
collection and analysis activities. To facilitate identification of potential warnings,
analysts develop a list of indicators, both supporting and refuting their judgment, in order
to provide “a rough estimate as to how ready a country may be to launch an attack”
(Davis, 1969, p. 39). These indicator lists are frequently developed by warning analysts,
current intelligence analysts, and regional analysts in advance of any hostilities in order
to identify anomalies as early as possible. Grabo (2002) discussed three sources of
knowledge that are used to develop the indicator lists: 1) logic or longtime historical
31
precedent, 2) specific knowledge of the military doctrine or practices of the nations
concerned, and 3) lessons learned from previous behaviors during recent wars or
international crises. Regardless of the amount of analysis, development, and knowledge
that is used to compile the indicator list, no list can possibly be exhaustive or include
every action in every contingency (Grabo, 2002).
While Indicators and Warning analysis can help identify strategic threats,
communicating these threats to a decision maker in a concise, logical manner completes
the action of warning. According to Belden (1977), the results of I&W analysis, the
adversary’s probability of war, can be visualized with a decision stairway. He described
the basic process of warning analysis as a cycle that begins when an adversary’s actions
(indicators to the warning analyst) are analyzed. This analysis, once conveyed to a
decision maker, leads to a decision and then an action. The conveyance of the warning is
an important step, as Grabo (2002) stated, “warning does not exist until it has been
Figure 2. Decision stairway indicating the probability of war resulting from A's actions over time.
32
conveyed to the policymaker, and that he must know that he has been warned. Warning
that exists only in the mind of the analyst is useless” (p. 14). The analysis-decision-action
process creates a number of indicators to the adversary (assuming they are conducting
their own intelligence), who then would engage in their own analysis-decision-action
process, completing the first round of the cycle and beginning a new cycle (Belden,
1977). The decision stairway (as seen in Figure 2), depicts an actor (A) and how their
decisions have increased their probability of war (y-axis) over time (x-axis). This model
can also be used to depict a second actor (B) and their potential responses to Actor A.
Figure 3 represents three different response options for Actor B: pre-emptive action,
defense action, or surprise. The short, vertical dashed line indicates a situation in which
Actor B is monitoring Actor A’s decisions, and decides to preemptively attack Actor A.
The most common situation depicts Actor B’s solid stairway, a situation in which Actor
B tracks Actor A’s actions but does not act pre-emptively. Therefore, Actor B is in a
Figure 3. Decision stairway depicting Actor B’s possible responses to Actor A’s decisions.
33
strong defensive position when Actor A attacks. Surprise, noted by the dashed line that
extends horizontally then turns completely vertical, is a situation in which Actor B is not
tracking any of Actor A’s decisions, and is therefore completely surprised by Actor A’s
actions. (Belden, 1977). This analytic methodology is used primarily in the national
security and military intelligence fields, but in order to utilize I&W as a competitive
intelligence technique, some modifications must be made.
Redefining Indicators and Warning Analysis
In order for Indicators and Warning analysis to be used as a competitive
intelligence methodology, its definition must be translated to one that incorporates the
terms and targets used by competitive intelligence professionals. According to the US
Department of Defense (2010/2015), warning intelligence is “an activity intended to
detect and report time-sensitive intelligence information on foreign developments that
forewarn of hostile actions or intentions against US entities, partners, or interests” (p.
259). This definition will serve as a base for I&W analysis as used in this study, however,
the definition needs to be refined and translated to be used in a competitive environment.
For the purposes of this study, Competitive Indicators and Warning analysis will be
defined as: activities intended to prevent surprises through the detection and reporting of
time-sensitive intelligence information on competitor or market developments that
forewarn of potential threats or opportunities that will likely affect an organization's
strategy, competitive advantage, or revenue. The process resulting in this translated
definition follows (definitional changes are noted in italics; a table of the modified
aspects, Table 1, can be found at the end of this section).
34
The first area that needs to be refined is the intention of the activities. One of
Herring’s (1996) five basic types of intelligence, preventing surprise by providing early
warning, should be stated at the beginning of the definition. This changes the definition
to: activities intended to prevent surprises through the detection and reporting of time-
sensitive intelligence information on foreign developments that forewarn of hostile
actions or intentions against US entities, partners, or interests.
Next, the focus of the activities must be changed because this methodology, as a
competitive analysis, is not designed to examine foreign activities that may lead to hostile
actions (these developments, if they are of interest to the company, would be reflected in
a STEEP analysis); therefore, “foreign” developments can be translated to “competitor or
market” developments. Porter (2008) defined competitors as established industry rivals
competing for profits. He also summarized markets as industries bounded by the
similarities between products and services and the geographic scope of the competitors.
This translation changes the definition to: activities intended to prevent surprises through
the detection and reporting of time-sensitive intelligence information on competitor or
market developments that forewarn of hostile actions or intentions against US entities,
partners, or interests.
The third part of this definition that needs to be translated is the “hostile actions or
intentions.” While I&W is traditionally concerned with military hostilities, companies
utilize early warnings to prevent surprises and detect threats and opportunities. Therefore,
“hostile actions or intentions” will be replaced with “potential threats or opportunities.”
Jackson and Dutton (1988) surveyed managers to determine how threats and
opportunities are identified. Their findings indicated that threats and opportunities are not
35
necessarily opposites; the two concepts are similar in terms of urgency, difficulty, and the
stakes associated with acting. However, threats and opportunities have characteristics that
clearly distinguish the two. Threats are clearly negative and cause feelings of under-
qualification and lack of control in decision makers. Threats also usually involve the
likelihood of loss with minimal possibility of gain. Opportunities, on the other hand, are
clearly positive, resulting in confidence in individual or organizational qualifications, the
autonomy and freedom to act, and the likely successful resolution of the issue.
Opportunities usually have a high likelihood of gain with minimal possibility of loss.
After this change, the competitive I&W definition is: activities intended to prevent
surprises through the detection and reporting of time-sensitive intelligence information
on competitor or market developments that forewarn of potential threats or opportunities
that could affect US entities, partners, or interests.
Finally, the object of the definition, “US entities, partners, or interests” must be
changed from a military focus, defending the United States, to a competitive focus,
preventing surprises to the company. Here, we can substitute “an organization’s strategy,
competitive advantage, or revenues” as the potential target of the indicated developments.
Porter (1996) discussed strategy as the core duty of an organization's leadership,
consisting of "defining and communicating the company's unique position, making
tradeoffs, and forging fit among activities" (p. 77). Leaders must make difficult strategic
decisions to develop and maintain competitive advantage despite pressures to
compromise vision or emulate rivals. An organization’s competitive advantage is
developed and maintained through what Barney (1991) termed a “value creating
strategy,” one which is not being utilized by any current or potential competitors (p. 102).
36
Table 1. Terms modified in translation of DOD definition of I&W to Competitive I&W
Therefore, the final definition of Competitive I&W is: activities intended to prevent
surprises through the detection and reporting of time-sensitive intelligence information
on competitor or market developments that forewarn of potential threats or opportunities
that will likely affect an organization's strategy, competitive advantage, or revenue.
Chapter 2 Summary
This review highlighted three key gaps in the entrepreneurial and intelligence
literature. The first gap identified is the lack of a definition of success, particularly in
terms of entrepreneurial activity. Despite the previously mentioned difficulties in coming
to an agreement on the success of startups, a definition of success should be present in
order to evaluate these new businesses. This thesis will use an objective statistical
definition, derived from CB Insights (2014a): a successful startup is one that survives for
more than five years after its last round of funding.
DOD Terms Competitive Terms
- Prevent surprises
Foreign developments Competitive and market developments
Hostile actions or intentions Potential threats or opportunities
Against Will likely affect
United States An organization’s
Entities Strategy
Partners Competitive advantage
Interests Revenue
37
Second, there have been very few attempts to convert military and national
security intelligence, a field that has historically been the focus of intelligence
professionals, to techniques that are usable in the competitive intelligence sphere. In
abstaining from taking advantage of these historical intelligence methodologies,
competitive intelligence practitioners are ignoring centuries of experience. Finally,
although there are many analytic tools and techniques for evaluating competitors,
industries, and general environments, there is an opportunity to combine tools to create a
synergistic effect and minimize some the individual methodological weaknesses.
This study seeks to fill the final two gaps with the conversion and development of
Indicators and Warnings Analysis (a traditionally national security and military
intelligence methodology) to a competitive intelligence methodology – Competitive
Indicators and Warning Analysis – that can be used by competitive intelligence
professionals, in conjunction with traditional CI tools, to estimate the likelihood of
success of a new cybersecurity startup. The next chapter will discuss the cases that were
selected and how the data was collected and analyzed, which will lead to the
development of the Competitive I&W model.
38
METHODOLOGY
Introduction
This study sought to develop an analytic model that can be used to aid in
estimating the likelihood of success of new startups; this thesis will test the model on
cybersecurity startups. This chapter will discuss this model which incorporates several
success factors as indicators that increase a startup’s likelihood of success. These success
factors, as identified in the literature review, are funding attributes (consisting of the
startup’s seed capital, the founders’ equity share, and the presence of investor financing),
firm attributes (including the startup’s business plan, the innovativeness of the product,
and whether the startup was founded by a team), and founder attributes (which assesses
the founder’s personal human capital, including previous entrepreneurial, industrial, and
management experience, educational background, and the relevancy of their social
network). This chapter will identify which cases will be examined and how each case was
selected, how the data was collected and analyzed, and the limitations, credibility, and
transferability of the study.
Using a qualitative case study, this study seeks to identify the specific success
factors that contributed to Palo Alto Networks’ success and, alternatively, contributed to
ISC8’s eventual bankruptcy and Cryptine Networks’ failure. Determination of these
success factors will signify greater importance of several success factors over the rest of
the factors, which, when applied to the model, will increase the model’s robustness.
Research Design
To determine which factors are indicative of success, this chapter assesses three
cybersecurity companies, Palo Alto Networks, ISC8, Inc., and Cryptine Networks. Palo
39
Alto Networks was well-funded from its inception, was founded by a team that developed
an innovative product, and was established by experienced and educated founders –
attributes that resulted in the startup’s success. ISC8, despite being well-funded, was
unable to develop the innovative product that they had promised, leading to a dissolution
of the founding team and, ultimately, bankruptcy. Cryptine Networks was founded by
individuals with no previous entrepreneurial experience, leading to several complications
which fractured the founding team and drove away potential investors. The results of
these three cases will be presented in Chapter Four. Following the case study, the
developed Competitive Indicators and Warning model will be applied to a nascent
startup, Area 1 Security, to demonstrate its use as an estimative and predictive tool.
This study utilizes a qualitative comparative case study to analyze cybersecurity
startups. Many studies (Stuart & Abetti, 1987; Watson, Hogarth-Scott, & Wilson, 1998;
Walker & Brown, 2004; Unger, Rauch, Frese, & Rosenbusch, 2011; Groenewegen & de
Langen, 2012; Miettinen & Littunen, 2013) have utilized quantitative methodologies to
determine entrepreneurial success factors (or indicators of success), but a quantitative
methodology would not be appropriate in using these factors to assess current businesses.
According to King, Keohane, and Verba (1994), qualitative studies can approach a small
amount of cases with intensive and in-depth analysis and, therefore, tend to unearth a
large amount of information. Using this theory, a qualitative methodology can be used
effectively to conduct deeper analyses and derive a large amount of information on
individual firms and processes. Because of the qualitative design, this study can also
discuss attributes that may not be easily expressed quantitatively.
40
The key focus of this study is examining the effect that entrepreneurial success
factors have on startups; specifically, the success of cybersecurity startups. Through a
review of the literature, several factors were identified that increase the likelihood of
success in a startup. This thesis will compare three cases to determine if any of the
identified factors are more indicative of success and then develop a model (based on
Indicators and Warning analysis) to estimate the likelihood of startup success. The model
will be tested against the three cases to determine the validity of the model (whether or
not the identified success factors hold true) and will be used to analyze a nascent
cybersecurity startup to demonstrate its estimative capabilities.
Selection of Cases
In order to objectively analyze the entrepreneurial success factors and develop a
valid model, this study sought three startup cybersecurity companies, one successful
startup and two unsuccessful startups. As defined, a successful startup is one that survives
for more than five years after its last round of funding (CB Insights, 2014); therefore, the
unsuccessful startups are those which did not survive five years past their last round of
investor funding. Two unsuccessful cases were necessary due to the lack of information
on individual failed startups. While ISC8 was founded within a larger organization, it
began to operate as a startup and was divested. Cryptine Networks was founded and
operated as a startup, but there is a lack of information regarding the company’s finances.
The two unsuccessful cases are both necessary and will complement each other to form a
complete picture of an unsuccessful cybersecurity startup. The cases were identified
through internet searches related to lists of successful and unsuccessful startups (such as
CB Insights’ Startup Failure Post-Mortems and lists from Inc. and Business Insider),
41
databases such as CrunchBase (a database of startups, investors, timelines, and startup
news), articles on cybersecurity (from sources such as Information Week’s Dark Reading
and Fortune Magazine), and articles and blogs on startups and venture capitalists (from
sources such as VentureBeat and Brad Feld’s FeldThoughts blog).
Based on the criteria for success, Palo Alto Networks was chosen as the
successful case and ISC8 and Cryptine Networks were chosen as the unsuccessful cases,
as found in table 2. Palo Alto Networks fits this study’s definition of a successful startup;
their last funding round took place in November, 2008, and the company has thrived
since then – the company’s revenue has increased since 2011 by an average annual
growth rate of 69% ($928 million recorded revenue for the year ending July 31, 2015)
(Palo Alto Networks, 2015) and the stock price (NYSE: PANW) has nearly quadrupled
since their initial public offering (IPO) on July, 20, 2012 (trading at $190.08 as of
December 9, 2015) (Google Finance, 2015).
ISC8, founded in November 2011, was chosen as an unsuccessful case because it
did not survive for five years following its last funding round in November, 2013. In
September, 2014, less than a year after their final round of funding, ISC8 filed for
Chapter 11 bankruptcy protection (Turnaround Letter, 2014). Despite receiving more
than $70 million of invested funds, ISC8 was never able to deliver on their product and
sold all of their assets for approximately $8 million in January, 2015 (PR Newswire,
2015).
Cryptine Networks was selected because the startup failed before ever holding a
funding round (Fife, 2006). Cryptine was founded in 2001 as a cybersecurity consulting
firm, but began developing cybersecurity software in late 2004. Cryptine sold their
42
consulting assets in 2005, which provided an undisclosed amount of seed capital, but
splintered the team of founders (PR Newswire, 2005). The split resulted in complications
redistributing equity which stalled the promising software production and drove away
potential investors.
Table 2. The startups to be analyzed through a comparative case study.
Startup Firm Palo Alto Networks ISC8 Cryptine Networks
Last Funding Round
November, 2008 ($10 million, total funding: $65 million)
November, 2013 ($5.14 million, total funding: $70 million)
November, 2005. (Sale of Consulting Services for undisclosed amount)
Status Public Company (IPO July, 2012)
Bankrupt (Filed September, 2014)
Closed (June, 2006)
Data Collection
Data collection focused on three main attribute families: the funding that the
startup received (not including funds raised from an initial public offering), the startup as
an organization, and the entrepreneur(s) who founded the startup. The data collection
time periods vary based on the cases and individuals involved. A majority of the data
collection focuses on early funding and the founders before they founded the startup. The
collection period ends in 2013 for Palo Alto Networks (five years after their last round of
funding), in early 2015 for ISC8 (at which time the company sold their assets), and in
mid-2006 for Cryptine Networks (when the startup closed).
Information collection on funding occurred through open sources including:
databases on startups (such as CrunchBase);
industry sources (such as VentureBeat and Fortune Magazine);
news articles (from sites including PR Newswire and MarketWired)
state bankruptcy court proceedings;
43
Securities and Exchange Commission (SEC) financial filings; and
Google services (such as Scholar and Finance).
Information on the startup as a firm came from the following open sources:
databases on startups (such as CrunchBase and PrivCo);
industry and news articles (from sources such as Business Insider, PR Newswire, VentureBeat, and Fortune Magazine); and
state bankruptcy court proceedings.
Information on the founders came from the following open sources:
databases on startups (such as CrunchBase and PrivCo);
the professional social media site LinkedIn; and
industry articles and interviews (such as VentureBeat, Andrew Fife’s Entrepreneurship Blog, and local news interviews with founders).
In order to increase validity and reliability of the data, sources with high
reliability were preferred and corroborating sources were used when possible. Norman’s
(2001) credibility criteria was used to determine the reliability of sources. Of the 15
criteria that signify credibility, the strongest criteria include whether the content can be
corroborated with other sources, the source was recommended by a subject-matter expert,
the author is reputable and is associated with a reputable organization, the content is
perceived as being accurate, a peer or editor reviewed the content, and the publisher or
web host is reputable. Norman’s full list of credibility criteria can be found in Appendix
A.
Data Analysis Procedures
The data was collected and analyzed according to the Success Factors on
Funding, Firm, and Founder attributes, as presented in the tables below. In order to be
indicative of success, evidence of the success factor (as listed in the ‘Indicators of
Success’ column in the tables below) must be present through the open sources as listed
44
in the previous section. In the ‘Indicators of Success’ column in the tables below, the
final bullet point of a row is the evidence that the researcher sought in order to affirm or
deny the presence of that success factor; earlier bullet points in the same row briefly
describe why the given success factor is an indicator of success. Several of the indicators
are subjective or difficult to collect information on, therefore, the methods used to
measure or evaluate the more complex indicators will be explained prior to the
corresponding table.
Funding Attributes
Both Seed Capital and Investor Financing were identified simply and objectively.
Financing for startups is well-documented in databases such as CrunchBase and PrivCo,
and it also frequently published in news articles from sources such as Fortune Magazine,
Business Insider, and Venture Beat. Seed Capital, provided by the founder(s) or
individual financiers, is typically reported separately from an official funding round in
which venture capitalists invest in the startup. Equity Share, or the amount of capital
provided by a founder related to the startup’s total assets prior to investor financing, was
a much more difficult indicator to identify. This may be due to the founder not wanting to
disclose their contributions to the startup’s capital, privacy regarding the startup’s total
assets (and therefore, potentially, the startup’s worth), or simply a lack of reporting on the
factor. These factors, and a brief description can be found in Table 3. Evidence of this
indicator may be identified through databases reporting on startups, news outlets, or
interviews with the startup’s founder(s).
45
Table 3. Success Factors regarding Funding attributes and the reasons and evidence required to indicate success.
Success Factors Indicators of Success
Fu
nd
ing
Seed capital
Seed capital greater than $1 million to start the business increases the likelihood of success
Evidence of seed capital either up to or greater than $1 million
Equity share
The amount of capital provided by the entrepreneur(s) related to the total assets
Equity share serves as a proxy; greater equity share means greater founder commitment
Evidence of equity share greater than 50 percent
Investor financing
Funding from venture capitalists (VC) or business angels (BA) in designated rounds
Startups financed by larger investors are likely to dominate their competitors
Evidence of VC or BA financing
Firm Attributes
Evidence of a team of founders and, if present, the fracture of the team, is simple
to objectively identify through news reports, social media sites such as LinkedIn, and
databases on startups and their founders such as CrunchBase. While a hostile break-up
may be more difficult to identify, there are likely interviews or news releases describing
tensions or relationships that were less than amicable. Additionally, a startup’s product
can be measured as innovative or not based on third-party evaluations from the same
industry or organizations that recognize and award innovation, such as the International
Consumer Electronics Show (CES), the Economist, or Pipeline Magazine.
The organization of a startup’s business plan can be more difficult to identify or
subjectively measure. In some instances, there is no information regarding the startup’s
strategic plans due to privacy concerns or simply a lack of reporting on the plan.
According to Gil (2011), one of the main uses for funds from the first round of investor
financing (Series A) is determining a business plan. Therefore, this study used the
46
presence of a second funding round (Series B) to represent increased investor confidence
in the startup and the business plan. These factors, and a brief description, can be found
in Table 4.
Table 4. Success Factors regarding the Firm attributes and the reasons and evidence required to indicate success.
Founder Attributes
Previous entrepreneurial experience, industrial work experience, and management
experience can all be objectively identified from sources such as LinkedIn’s
“Experience” section, news articles about the startup and founder(s), or interviews with
the founder(s). These attributes, which are summarized in Table 5, reflect the founder’s
ability to operate a firm in its infancy, knowledge of the industry actors and products, and
ability to effectively use employees. The founder’s highest level of education attained is
also usually represented on social media sites such as LinkedIn or Facebook, or published
as background or biographical information in a news article or interview. The relevancy
of a founder’s social network can be difficult to identify and subjective to measure, but
Success Factors Indicators of Success
Fir
m
Organized business plan Aggressive, robust, and proactive strategies
increase the likelihood of success Evidence of an organized business plan
Innovative product
Unique products, processes, systems, or services increase probability of success
Innovations must be of value to the customer Evidence of third-party evaluation of product
Team founding
Access to wide array of skills and resources Increases likelihood of success as long as the
entrepreneurial team remains intact Evidence of several founders influencing the
startup
Fracture of the founding team
While a team founding increases the likelihood of success, a team that fractures threatens the survival of the startup
Evidence that the broke apart under unfavorable circumstances
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can be determined based on the ways in which a founder can leverage their social
network. This study identified connections between founders and key individuals (outside
the organization) who helped the startup in any of the other indicators, such as investor
financing or industrial work experience. If such a connection was present, such as
previous employment with the same employer or a degree from the same university, and
the connection was used to improve the standing of the startup, then the social network
was deemed to be relevant.
Table 5. Success Factors regarding the Founder’s attributes and the reasons and evidence required to indicate success.
Pilot Study
After conducting a preliminary examination of the success factors, previous
entrepreneurial experience and the fracture of the founding team (and therefore the team
founding) were identified as having a greater impact on success than the other indicators
and are, therefore, worth two points in the Competitive I&W model. These results will be
Success Factors Indicators of Success
Fou
nd
er
Previous entrepreneurial experience
Investors traditionally assign high importance to individuals who have previous entrepreneurial or business ownership experiences
Evidence of a founder’s previous entrepreneurial ventures
Previous industrial work experience
Associated with applicable skillsets and knowledge of the markets, customers, and technologies
Evidence of previous jobs held by a founder in the same industry
Previous management experience
Increases an entrepreneur’s ability to identify and take advantage of market opportunities
Evidence of previously held managerial positions
Education Increases adaptability, is a proxy for energy,
motivation, and dedication to the business Evidence of a Bachelor’s degree or higher
Relevant social network Increases access to relevant skills and resources Evidence of founders using their social network to
increase likelihood of success
48
discussed in more detail in the next chapter. Additionally, due to the emphasis in the
reviewed literature on investor financing, it was weighted double as well. As can be seen
in figure 4 (and Appendix B), three indicators (investor financing, team founding, and
previous entrepreneurial experience) are worth two points, and one indicator (founding
team fracture) reduces the score by two points.
Firm:
Indicators Yes/No Date Score
Funding Attributes (5 possible points )Seed Capital
Up to $1 million (1) 0$1 million or greater (1) 0
Equity Share50% or more (1) 0
Investor Financing (2) 0Firm Attributes (4 possible points )
Organized business plan (1) 0Innovative product (1) 0Team founding (2) 0
If team fractures (-2) 0Founder Attributes (6 possible points )
Previous entrepreneurial experience (2) 0Previous industrial work experience (1) 0Previous management experience (1) 0Education
Bachelor's degree or above (1) 0Relevent social network (1) 0
Funding 0Firm 0Founder 0
Total Score 0 /15
Indications & Warning Analysis - Startups
Figure 4. Competitive Indicators and Warning template used to examine startup firms
49
Data Analysis
The first variables analyzed in each case study were those related to the
company’s funding attributes. When evidence was found of one of the indicators of
success, it was recorded along with the month and year that the indicator occurred (not
necessarily the date that evidence of the indicator is found). The variables for firm
attributes and founder attributes were analyzed using the same process. Once evidence of
an indicator of success was collected, its presence and the date were recorded. If no
evidence was collected, the indicator was recorded as negative. Alternatively, if evidence
that is contrary to an indicator of success is collected, then it was also be recorded as a
negative. After determining whether each indicator existed or not for each case, a table
was compiled to compare all cases and determine which success factors were of greater
importance to the startup’s success or failure. The complete table can be found in
Appendix C and will be discussed in greater detail in the Findings chapter.
The developed model was completed in the same manner as the case studies, but
is accompanied by a scoring system. The Competitive Indicators and Warning analysis
has a scoring system that is based out of a total of 15 possible points, where most
indicators are worth a single point; however, due to the emphasis on investor financing in
the reviewed literature, its presence is worth two points. After conducting the
Competitive I&W analysis, a decision stairway was built to visualize the likelihood of
success of the analyzed startup, an example of which can be seen in figure 5.
In this model, the more points a startup scores, the more likely it is that the startup
will be successful. However, scoring a perfect 15 out of 15 is not a guarantee of success;
a perfect score only indicates that the startup has achieved all of the factors that increase
50
the probability of success. Additionally, a low-scoring startup will not necessarily be a
failure; the low-scoring startup could increase their score over time and potentially
survive for the five years post-funding required to be successful.
Figure 5. Example of a decision stairway representing the amount of points over a period of time.
Limitations of the Research Design
The research design is limited in several ways. First, only public cybersecurity
companies were considered in the selection of the successful case. Not all successful
companies are public, and not all public companies are successful; however, the
prevalence of information available on public companies is beneficial in this type of
exploratory research. This methodology can become complex when analyzing some
organizations due to the lack of transparency and information available on various
success factors, particularly the founder’s equity shares and the startup’s business plan –
51
thus the need for two unsuccessful cases. Additionally, because data was collected from
different time periods, the startups could have been impacted by different market
conditions.
This research design does not cover all the factors that may influence a startup’s
success. As mentioned in the previous chapter, the inclusion of market attributes would
be too great of an undertaking for this study, but is an area that is worthy of future
research. Additionally, this study is limited because only three cases are analyzed. The
findings, while deep and thorough, are not as broad or generalizable without an
assessment of the model using several case studies or examining startups across a number
of different industries. Finally, there is a degree of subjectivity (and therefore bias) in the
determination of several of the indicators and the measurements of these indicators.
Credibility
The findings of this study are credible due to the sources used. Scholarly journals
and peer-reviewed literature were used to identify the success factors and develop a
model of Indicators and Warning analysis appropriate for competitive intelligence.
Reliable and corroborating sources were used in the collection of information and
evidence of indicators of success for the studied cases. Credibility is somewhat weakened
because, as mentioned in the previous section, there is a degree of subjectivity in this
study. Individual researchers may interpret or analyze the information differently than in
this study; however, this slight decrease in credibility is not significant enough to degrade
the credibility of the findings as a whole. This study has not only begun the process of
developing indicators of success, but also the methods of evaluating these criteria. As this
52
development is in its exploratory stage, this is an area that would benefit greatly from
additional application of case studies and future research.
Transferability
Despite focusing only on the cybersecurity industry, the concepts behind the
framework of this model are broad enough that they are applicable to startups in
industries other than cybersecurity. The model may need to be slightly altered or added to
in order to be used successfully, but it is designed to be transferable to startups in other
industries. If the model is used to compare startups across different industries, market and
industrial attributes should be incorporated to increase the robustness and validity of the
model for these purposes – a subject that would benefit from future research.
Ethical Issues
There are no ethical concerns throughout this study. The author worked alone and
did not use any participants to collect data or conduct research. The author does not have
any conflicts of interest or relationships, whether professional, commercial, or financial,
with any of the companies analyzed in this study.
Chapter 3 Summary
This study will analyze Palo Alto Networks, ISC8, Inc., and Cryptine Networks’
success factors as identified in the previous chapter and developed in this chapter. Data
will be collected and analyzed, as discussed in the previous sections of this chapter, to
determine which success factors are more indicative of success and assess each
company’s score in order to demonstrate the validity of the developed model.
While the study is limited in several ways, the findings are credible and
transferable to entrepreneurial activity in other industries. There were no ethical concerns
53
throughout this study. The results of these three cases will be presented in the next
chapter, followed by a test of the developed model on the same three cases, and the
application of the Competitive Indicators and Warning model to a nascent startup, Area 1
Security, so as to demonstrate its validity use as an estimative and predictive tool.
54
FINDINGS
Introduction
As stated in the first chapter, this study seeks to identify factors that increase the
likelihood of startup success, then test these factors with the proposed Competitive
Indicators and Warning analytic model. This chapter presents and compares the findings
for the successful case (Palo Alto Networks) and the two unsuccessful cases (ISC8, Inc.,
and Cryptine Networks), in order to determine which factors are more indicative of
success. As laid out in the methodology chapter, the funding, firm, and founder attributes
of each case were examined to determine which indicators of success were present and
which were lacking. The results are compiled in a table at the end of each section, as well
as a column regarding the complexity of the factor (based on the subjectivity of
measurement or difficulty finding open source information).
The findings of the case studies are followed by a test of the developed
Competitive Indicators and Warning (I&W) model on Palo Alto Networks, ISC8, and
Cryptine Networks (in order to validate the model), and finally the analysis of Area 1
Security. Area 1 Security is a nascent cybersecurity startup that has recently held its
second round of investor financing. The funding, firm, and founder attributes were
collected and presented in the same manner as the case studies. Area 1 Security’s
indicators were then assessed through the Competitive I&W model and visualized
through a decision stairway to demonstrate the model’s estimative capabilities.
Palo Alto Networks, Inc.
Palo Alto Networks was selected as the successful case for this study because it
not only survived, but thrived for at least five years after its final round of funding. The
55
startup was founded in January 2005 by Nir Zuk, Dave Stevens, Yuming Mao, and Rajiv
Batra, and held their first round of investor financing a year later. In November 2008,
Palo Alto Networks held their final round of investor financing (Series C) and has
survived for more than seven years since that round (CrunchBase, 2016a). On July 9,
2012, Palo Alto Networks held its initial public offering (IPO) of 6.2 million shares
(NYSE: PANW) (PANW, 2012). As of January 2016, PANW stock was being traded at
$146.56 per share, a nearly 180% increase from its post-IPO price of $53.13 per share
(Google Finance, 2016). Evidence and dates regarding Palo Alto Networks’ indicators of
success across funding attributes, firm attributes, and founder attributes can be found in
table 6, table 7, and table 8, respectively.
Funding Attributes
Nir Zuk first developed his idea for an advanced, traffic-focused firewall after
noticing deficiencies in the firewall sold by Juniper Technologies (which had acquired
NetScreen Technologies, which had acquired Zuk’s first startup, OneSecure). Juniper
Networks ignored his funding request to improve the firewall, so Zuk left the company
and approached contacts of his at Greylock Partners and Sequoia Capital to request
funding for his concept. In May 2005, both Greylock and Sequoia added funds to Zuk’s
own capital, totaling $250,000 of seed capital (PrivCo, 2014). Zuk has stated in an
interview that he only owns about 5-10% of the company because he preferred to highly
compensate his employees and engineers early in the company’s existence, for which he
sacrificed his equity (Mitra, 2010). While equity share was determined to be a complex
indicator in the last chapter, Zuk’s statement regarding his equity in the company made
the indicator easily identifiable.
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Shortly after the acquisition of seed capital, Palo Alto Networks held a Series A
funding round, during which Greylock Partners and Sequoia Capital invested a combined
$10 million. Investors (including Lehman Brothers, Icon Ventures, and Northgate Capital
in addition to Greylock and Sequoia) financed Palo Alto Networks to the tune of
approximately $65 million over the course of four funding rounds, ending in November
2008 (CrunchBase, 2016a).
Table 6. Evidence and Date of Palo Alto Networks’ Indicators of Success – Funding Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Seed Capital up to $1M No Yes – May 2005 Seed Capital Greater than $1M No No Equity Share Greater than 50% Yes No Investor Financing No Yes – January 2006
Firm Attributes
As stated in the previous chapter, the presence of a second funding round (Series
B) served as a proxy measurement for an organized business plan for this study. Palo
Alto Networks held their Series B funding in June 2007 (PrivCo, 2014). Additionally,
Zuk has also stated that they did not devise a go-to-market strategy until April 2007,
shortly after they completed a beta version of their product (Mitra, 2010). Palo Alto
Networks’ advanced next-generation firewall technology has been certified by the
International Computer Security Association (ICSA, an independent testing division of
Verizon), which stated that the product utilized several innovative technologies in
providing a better firewall (ICSA Labs, 2014). Palo Alto Networks was founded by a
team consisting of Nir Zuk, Dave Stevens, Yuming Mao, and Rajiv Batra (PrivCo, 2014).
57
Dave Stevens left the company after three years in order to pursue another opportunity,
but there was no evidence that the split was anything less than amicable (Stevens, n.d.).
Table 7. Evidence and Date of Palo Alto Networks’ Indicators of Success – Firm Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Organized Business Plan Yes Yes – August 2007 Innovative Product No Yes – August 2007 Team Founding No Yes – February 2005 Founding Team Fracture No No
Founder Attributes
Nir Zuk founded OneSecure, a network security company that developed an
intrusion prevention system, in early 2000. OneSecure was acquired by Netscreen
Technologies in 2002, where Zuk was retained and held the managerial position of Chief
Technical Officer (CTO). Juniper Networks acquired NetScreen Technologies in April
2004 where Zuk was again retained, albeit only for 11 months, as the Chief Security
Technologist. Noting deficiencies in Juniper Networks’ product, Zuk began formulating
the product that would eventually be sold by Palo Alto Networks. Industry-wise, Zuk
held computer security jobs while completing his mandatory service in the Israeli military
from 1990 to 1994 (Mitra, 2010; CrunchBase, n.d.).
Of the other founders, Dave Stevens had significant previous management
experience, holding several Vice President (VP) and executive-level positions primarily
in the telecommunications industry (Stevens, n.d.). Yuming Mao had both previous
industrial work experience and management experience as the Chief Architect of
NetScreen Technologies (Mao, n.d.). Rajiv Batra held a managerial position and two
58
Vice President positions in the telecommunications and software industries prior to
founding Palo Alto Networks (Batra, n.d.).
Zuk graduated from Tel Aviv University in 1995 with a Bachelor of Science (BS)
in Mathematics (CrunchBase, n.d.). Stevens earned both a BS and Master of Science
(MS) in Electrical Engineering from the University of Virginia and received an education
in public board governance from Stanford University’s law school (Stevens, n.d.). Mao
earned an MS in Computer Science from Stanford University in 1995, and Batra received
a Bachelor of Technology in Electrical Engineering and Computer Science from the
Indian Institute of Technology, as well as an unnamed later degree from University of
Wisconsin (Mao, n.d.; Batra, n.d.).
While the relevancy of a founder’s social network can be difficult to determine, it
was made slightly easier in this case study thanks to a statement Zuk made in an
interview. Zuk responded to a question regarding raising money to start Palo Alto
Networks with: “Of course I had connections and background, and I was dealing with
top-tier VCs [Venture Capitalists]. When I look at VCs, I prefer to work with ones who
have been entrepreneurs” (Mitra, 2010, p. 4). Not only was Zuk able to leverage these
connections to raise capital for his new startup, but he was also astute enough to connect
with the VCs who had previous entrepreneurial experience – which Zuk could tap into
and increase his own likelihood of success. Additionally, connecting with Yuming Mao
at NetScreen Technologies, where their employment dates overlapped, could be
considered an indication of a relevant social network but it is fairly inferential, whereas
Zuk has outright stated that his connections with VCs helped fund Palo Alto Networks.
59
Table 8. Evidence and Date of Palo Alto Networks’ Indicators of Success – Founder Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Previous Entrepreneurial Experience
No Yes – January 2000
Previous Industrial Work Experience
No Yes – January 1990
Previous Management Experience
No Yes – February 2002
Bachelor’s Degree or Above No Yes – May 1995
Relevant Social Network Yes Yes – March 2005
Conclusion
The only indicators of success that Palo Alto Networks lacked were the startup
capital greater than $1 million and an equity share of at least 50%. However, neither of
these deficiencies seemed to interfere with Palo Alto Networks’ growth and success.
Despite raising less than $1 million in seed capital, the startup received nearly $10
million in investor financing eight months later. Zuk stated that he did not own much of
the company because he preferred to reinvest that money in his employees and engineers
early on.
ISC8, Inc.
Irvine Sensors Corporation was founded in 1974 by John C. Carson. Initially,
Irvine Sensors developed optical technologies used in space surveillance, a development
from Carson’s time working with missile detection systems. The company conducted
research and development into high-density electronics and began producing similar
products for use in space and military applications, such as miniature sensors and
cameras, space-based navigational equipment, miniature gyroscopes and accelerometers.
In the mid-2000s, Irvine Sensors also became involved with internet security. In 2010,
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Irvine Sensors was rebranded ISC8, Inc., with a renewed focus on anti-malware and
cybersecurity products (Werner, 2013).
In 2013, ISC8 divested their space and military ventures to the newly-reformed
Irvine Sensors Corporation, reestablished by Carson so he could continue his work. ISC8,
Inc. became a pure cybersecurity company and began operating as a startup (4-Traders,
2013). While it did not begin as a pure startup, once ISC8 began separating from Irvine
Sensors, ISC8 took on the qualities and requirements of a startup, including the necessary
factors to increase their likelihood of success. In September 2014, ISC8, Inc. filed for
Chapter 11 protection with the US Bankruptcy Court in California, and sold all of its
assets four months later for less than $8 million. Evidence and dates regarding ISC8’s
indicators of success across funding attributes, firm attributes, and founder attributes can
be found in table 9, table 10, and table 11, respectively.
Funding Attributes
In December 2010, Costa Brava Partnership III LP and The Griffin Fund invested
a combined $11.4 million dedicated to developing Irvine Sensors’ cybersecurity products
(PE Hub Network, 2010). This investment served as seed capital (greater than $1 million)
when ISC8 became a cybersecurity-focused startup. Three years later, ISC8, Inc. raised
an addition $5.14 million from investors in their first and only funding round
(CrunchBase, 2016b).
While John Carson founded Irvine Sensors Corporation in 1974, there was no
information regarding the equity percentage he controlled in 2010 (when Irvine Sensors
rebranded and began producing cybersecurity products). Additionally, once ISC8 spun
off its space and military ventures, Carson resigned from all positions and relinquished all
61
control in order to reform Irvine Sensors and continue his space and military applications,
thus leaving him with no equity share of ISC8 (4-Traders, 2013). There is also no
evidence that the two other individuals integral in the development of cybersecurity
products and “founding” of ISC8, Bill Joll and Joy Randels, had any percentage of equity
share in the company (PR Newswire, 2011).
Table 9. Evidence and Date of ISC8’s Indicators of Success – Funding Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Seed Capital up to $1M No No, seed capital greater than $1M Seed Capital Greater than $1M No Yes – December 2010 Equity Share Greater than 50% Yes No Investor Financing No Yes – November 2013
Firm Attributes
As mentioned previously, John Carson founded Irvine Sensors Corporation in
1974. This, however, cannot be considered the same foundation as ISC8, Inc. because
they are two completely different companies with very different personnel. The founding
date for ISC8 is November 2011, when Irvine Sensors was rebranded as ISC8 and began
to shift their focus to cybersecurity. The rebranding, and, therefore, founding of ISC8 was
led by Carson, Bill Joll, and Joy Randels (PR Newswire, 2011). This founding team
fractured in March 2013 when Carson left ISC8 to resume his work on space and military
applications with the divested and reformed Irvine Sensors Corp (4-Traders, 2013). This
split makes it clear that Carson was not interested in ISC8’s direction and wanted to
revert back to his initial research and development. Additionally, Bill Joll resigned from
his positions in 2013 as ISC8 brought in a new President and CEO (Marketwired, 2014).
62
In 2013, ISC8 won the Pipeline Magazine Innovation Award in the Innovation in
Security and Assurance category. ISC8’s products included a detection system to identify
advanced persistent threats (APTs), big data security analytics, and increased user control
systems (Marketwired, 2013). However, ISC8 was unable to get their game-changing
product to markets, indicating a poorly organized business plan (Wilson, 2015).
Additionally, ISC8 did not receive investor financing beyond their Series A funding
round, which, as mentioned in the methodology chapter, shows that the startup was
unable to develop a sound go-to-market strategy.
Table 10. Evidence and Date of ISC8’s Indicators of Success – Firm Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Organized Business Plan Yes No Innovative Product No Yes – May 2013 Team Founding No Yes – November 2011 Founding Team Fracture No Yes – March 2013
Founder Attributes
John Carson was the only ISC8 founder with any previous entrepreneurial
experience, but that was through his founding of Irvine Sensors Corporation, which was
essentially the same company and spun off ISC8 in 2013 (Werner, 2013). However, when
Carson resigned from all positions with ISC8 after it was officially separated from Irvine
Sensors, the startup team lost the entrepreneurial experience that could have guided the
startup towards success. Joy Randels had studies entrepreneurship at both Harvard
Business School and Massachusetts Institute of Technology’s (MIT) Sloan School of
Management, but evidence of her studies are undated and there is no evidence that she
used these studies to found any companies before ISC8 (Randels, n.d.).
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Both Randels and ISC8’s other key founder, Bill Joll, had previous management
experience before starting on ISC8. Joll was the Vice President (VP), General Manager,
and eventually President of telecommunications company Nortel Networks from 1982-
2000 and held several other VP, President, and Chief Executive positions in technology
industries (Joll, n.d.). Randels has held several VP positions and Board memberships with
technology companies before moving on to ISC8 (Randels, n.d.). While both Joll and
Randels worked primarily in high-tech fields including telecommunications, software,
and cloud computing, Randel’s experience at WiMetrics from 2004-2009 was the only
position between the two founders in the cybersecurity industry.
Both Joll and Randels had earned at least a bachelor’s degree. Joll earned both his
bachelor’s degree and master’s degree in electrical engineering in 1979 and 1981,
respectively, from the University of Manitoba (Bloomberg, 2016a). Randels, as
mentioned earlier, studied entrepreneurship at both Harvard Business School and MIT’s
Sloan School of Management, but she also studied information technology at Emory
University’s Goizueta Business School (Randels, n.d.). As noted in the previous chapter,
evaluating the relevancy of one’s social network can be a complicated task, however,
evidence of this particular factor was found to contribute to ISC8. From 2009 to 2010,
Joll (President) and Randels (Executive VP, Global Sales and Marketing) worked
together at Velocitude (acquired by Akamai Technologies in June 2010) (Joll, n.d.;
Randels, n.d.). After leaving Akamai Technologies following the merger, Joll went on to
become President and CEO of ISC8, Inc., where he brought in Joy Randels as Executive
VP to help rebrand the company and to “bring this amazing new technology to the
forefront of the information security market” (PR Newswire, 2011).
64
Table 11. Evidence and Date of ISC8’s Indicators of Success – Founder Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Previous Entrepreneurial Experience
No No
Previous Industrial Work Experience
No Yes – January 2004
Previous Management Experience
No Yes – June 1982
Bachelor’s Degree or Above No Yes – May 1979 Relevant Social Network Yes Yes – October 2009
Conclusion
In September 2014, ISC8, Inc. began the process of filing for Chapter 11
bankruptcy and in January 2015, filed for the auction of its assets (worth $70 million) for
approximately $8 million (PR Newswire, 2014; US Bankr. C.D. Ca., 2015). None of
ISC8’s founders had any previous entrepreneurial experience, and the founding team
fractured in 2013. ISC8 was also lacking the 50% equity share and organized business
plan indicators.
Cryptine Networks
Cryptine Networks was founded in 2002 by Nate Underwood and Andrew Fife as
a security consulting firm. After developing cybersecurity software products in 2004 and
divesting their consulting services in 2005, Cryptine Networks encountered several
obstacles stemming from their poorly organized 2002 incorporation. The startup’s
inability to resolve these issues drove away investors which stalled production and, in
2006, Cryptine shut down operations. Evidence and dates regarding Cryptine Networks’
indicators of success across funding attributes, firm attributes, and founder attributes can
be found in table 12, table 13, and table 14, respectively.
65
Funding Attributes
Cryptine Network’s consulting services business unit was acquired by Helio
Solutions, Inc., in November 2005 for an undisclosed amount (PR Newswire, 2005).
According to Fife (2006), the sale “amounted to a small seed investment and allowed the
reaming team members to focus exclusively on software development” (para. 3). This is
evidence of seed capital up to $1 million because of Fife’s explicit statement; however,
because the amount of the acquisition (and therefore seed capital) was undisclosed, it
cannot be definitively used as evidence for seed capital greater than $1 million.
In addition to providing seed capital, the Helio acquisition also split up the
founders. After the sale, founders on both sides of the split worked towards a
capitalization model in which the departed founders retained a small percentage of equity
with the understanding that any investors would dilute the equity and drive that
percentage down (Fife, 2006). As this is the only information regarding Cryptine
Networks’ equity, there is no evidence of equity share greater than 50% by any one
founder. This equity distribution plan backfired; none of the potential investors wanted to
become involved in the complex legal efforts to reduce a founder’s equity. The poorly
formed equity distribution plan completely drove away all six of the interested investors,
resulting in a lack of investor financing for Cryptine Networks (Fife, 2006).
Table 12. Evidence and Date of Cryptine Networks’ Indicators of Success – Funding Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Seed Capital up to $1M No Yes – November 2005 Seed Capital Greater than $1M No No Equity Share Greater than 50% Yes No Investor Financing No No
66
Firm Attributes
As mentioned previously, Cryptine Networks hastily incorporated in 2002,
without any outside or legal consultations. This incorporation created a completely
internal and inexperienced Board of Directors, which became deadlocked and was unable
to resolve the equity redistribution problem. This deadlock and lack of experience
resulted in the company’s inability to prepare for such problems, inability to identify and
pursue all available options, and eventually close down the startup. According to Fife
(2006), an outside board member would have provided objectivity and an experience
corporate attorney would have likely been able to guide the company through these
troubles and possibly even prevent failure. The poorly organized incorporation and lack
of foresight are evidence of an unorganized business plan.
While Cryptine Networks had shown some success with the beta version of their
product (the startup had assembled 12 beta test partners and had received their first
purchase order), their offering was not innovative. Their product amounted to a firewall,
virtual private network (VPN), intrusion detection, and host hardening (Bloomberg,
2016b). Because this is the only third-party mention of Cryptine’s products, it is not
evidence of an innovative product because the source did not discuss innovativeness.
Nate Underwood and Andrew Fife are the only two named founders of Cryptine
Networks (Underwood, n.d.; Fife, n.d.), however, Fife (2016) mentions a team several
times but does not clarify if this team was composed of additional founders or simply
other employees. Even if only the two named founders are considered, this is sufficient
evidence for a team founding indicator. As mentioned previously, the acquisition of
Cryptine’s security consulting services by Helio Solutions in November 2005 fractured
67
the founding team. Underwood’s position was within the acquired business unit and,
following the acquisition, he left to start another company (PR Newswire, 2005;
Underwood, n.d.). Meanwhile, Fife and the software development team remained with
Cryptine Networks to continue work on their cybersecurity product (Fife, n.d.). The
disputes and disagreements regarding the post-split equity redistribution is evidence that
the team did not separate on good terms.
Table 13. Evidence and Date of Cryptine Networks’ Indicators of Success – Firm Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Organized Business Plan Yes No Innovative Product No No Team Founding No Yes – January 2002 Founding Team Fracture No Yes – November 2005
Founder Attributes
Neither Underwood nor Fife possessed any previous entrepreneurial experience
prior to founding Cryptine Networks, although both have since gone on to be active in
entrepreneurial communities (Underwood, n.d.; Fife, n.d.). Underwood had previously
worked in the cybersecurity industry as a network security engineer in August 1999 for
Liquid Audio. He also held security consultant and security project lead positions before
founding Cryptine. As a security project lead with Conxion, Underwood earned previous
management experience with a global security provider (Underwood, n.d.).
Both founders have earned at least a bachelor’s degree. Underwood received an
undated bachelor’s degree in Italian cultural studies from the University of California,
Santa Barbara (Underwood, n.d.). Fife has earned two master’s degrees, one in
international relations (2001) from University of St. Andrews, and one in politics of the
68
world economy (2002) from the London School of Economics and Political Science
(Fife, n.d.). There is no evidence that either founder leveraged their social network to
improve the startup’s likelihood of success, despite opportunities to do so, and therefore
it cannot be indicated that either found had a relevant social network.
Table 14. Evidence and Date of Cryptine Networks’ Indicators of Success – Founder Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Previous Entrepreneurial Experience
No No
Previous Industrial Work Experience
No Yes – August 1999
Previous Management Experience
No Yes – April 2001
Bachelor’s Degree or Above No Yes – May 2001 Relevant Social Network Yes No
Conclusion
While there was a dearth of information on Cryptine Networks, it is clear that the
company’s lack of investor financing, fracture of the founding team, and lack of previous
entrepreneurial experience resulted in a challenging and difficult road to success, despite
the founders’ previous industrial and management experience. When combined with a
poorly organized business plan, non-differentiated product, and inability to leverage their
social network, it is no surprise that Cryptine Networks failed.
Comparison of Cases
When comparing the three cases by the success factors, several indicators of
success become evident. Three indicators were absent in both of the unsuccessful cases:
organized business plan, founding team fracture, and previous entrepreneurial experience,
as seen in Table 15 below (the complete table comparing all indicators of success across
69
the three cases can be found in Appendix C). While equity share greater than 50% was
also missing in both unsuccessful cases, it was not identified in the successful case either.
Equity share, therefore, may either be a victim of its own complexity or lack of
information, or it may not be indicative of increased likelihood of success.
Of the three indicators that were missing in the unsuccessful cases, organized
business plan was considered a complex indicator in the previous chapter due to its
subjectivity and general lack of information. While the organization of a startup can be
inferred from interviews with founders or the presence of numerous funding rounds
(which were used as evaluation tools in this study), rarely – if ever – is the organizational
and strategic plan stated outright.
Both the founding team fracture and previous entrepreneurial experience were
easily and objectively identified through news and industry articles as well as the
professional social media site LinkedIn, reinforcing the non-complex determination
assigned in the methodology chapter. Both of these indicators were present in the
successful case and absent in both the unsuccessful cases. While also being non-complex
indicators, it is evident that the founding team fracture and previous entrepreneurial
experience increased the likelihood of startup success more than the other evaluated
factors. Additionally, because the fracture of the founding team is of greater importance,
then the team founding must be of equal importance to a startup’s likelihood of success.
Because of these results, both previous entrepreneurial experience and a team founding
(because it is equivalent to the fracture) are weighted double in the Competitive
Indicators and Warning analysis model. The fracture of the founding team is weighted as
a negative two points, which would therefore reduce the overall score.
70
Table 15. Comparison of notable Indicators of Success between Palo Alto Networks, ISC8, and Cryptine Networks.
Indicators of Success
Evidence and Date of Indicator
Palo Alto
Networks ISC8, Inc.
Cryptine
Networks F
un
din
g
Equity Share Greater
than 50% No No No
Investor Financing Yes
January 2006
Yes
Nov. 2013 No
Fir
m
Organized Business
Plan
Yes
August 2007 No No
Team Founding Yes
February 2005
Yes
Nov. 2011
Yes
January 2002
Founding Team
Fracture No
Yes
March 2013
Yes
Nov. 2005
Fou
nd
er Previous
Entrepreneurial
Experience
Yes
January 2000 No No
Testing the Model
The developed Competitive Indicators and Warning model was tested on each of
the three cases – Palo Alto Networks, ISC8, Inc., and Cryptine Networks – in order to
validate the accuracy of the model. Area 1 Security, a recently formed cybersecurity
startup, was also analyzed through the Competitive I&W model to demonstrate the
model’s estimative capabilities.
As noted in the methodology chapter and the previous section, investor financing,
team founding, and previous entrepreneurial experience are all weighted two points in the
model, with the fracture of the founding team reducing the score by two points. The
fracture of the founding team (and therefore the team founding) and previous
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entrepreneurial experience were determined to be more valuable to a startup’s success
through the case study, which is reflected in the model through an indicator that is
weighted heavier than the other factors. Investor financing, despite being present in the
unsuccessful case ISC8, Inc., was weighted equally as heavy due to the importance
imparted on the indicator through the reviewed literature (Bhide, 2000; Bygrave et al.,
2003; CB Insights, 2014a; Inderst & Mueller, 2009).
As was described in the previous chapter, the model presents the indicators of
success, organized by attribute family, in the left column followed by the number of
points corresponding to evidence of that indicator. The ‘Yes/No’ column is used to
represent whether evidence of that particular indicator has been found, with the month
and year of the indicator in the next column to the right, the ‘Date’ column. It must be
noted that the date of the indicator should be the date on which the indicator occurred or
was reported, not necessarily the date the indicator was found by the researcher or
analyst.
In the rightmost ‘Score’ column, the point total corresponding to that indicator is
noted. Most indicators receive one point, except for the fracture of the founding team,
which earns a negative two points, and investor financing, team founding, and previous
entrepreneurial experience, which receive two points each as described earlier. Finally,
the scores are totaled (and can be broken down by attribute family), and the sum is given
at the bottom of the model out of a total possible 15 points. As mentioned in the previous
chapter, the more points a startup scores, the more likely it is that the startup will be
successful. However, scoring a perfect 15 out of 15 is not a guarantee of success; a
perfect score only indicates that the startup has achieved all of the factors that increase
72
the probability of success. Additionally, a low-scoring startup will not necessarily be a
failure; the low-scoring startup could increase their score over time and potentially
survive for the five years post-funding required to be successful.
Following the assessment of a startup through Competitive I&W analysis, the
startups were rated into one of three groups. Startups that scored between zero and five
points were placed into the high risk of failure/low chance of success group. Firms that
scored between six and 10 points were grouped into the moderate risk of failure or
success category. Startups in this category should be monitored closely as any additional
factors or new information could move these firms into a more distinct scoring group.
Finally, startups that scored between 11 and 15 were placed in the high chance of
success/low risk of failure category. This rating system and the point cutoffs for each
group were developed through this study and based on the cases discussed. Since this is
exploratory research, assessments of additional cases would be necessary to strengthen
and validate this rating scale. From there, benchmark grades for success or failure could
be established and defended.
Competitive I&W Analysis of Palo Alto Networks
As can be seen in figure 6, Palo Alto Networks earned 13 out of a possible 15
points towards success. Despite the lack of seed capital greater than $1 million and an
equity share percentage of less than 50%, Palo Alto Networks is a successful
cybersecurity startup. If this analysis was conducted in the early days of the company, an
analyst could have reasonably estimated from the results of the model that Palo Alto
73
Networks has a high chance of success, and therefore a low risk of failure. The model and
correlating decision stairway can be found in Appendix D.
Competitive I&W Analysis of ISC8, Inc.
As can be seen in figure 7, ISC8 earned nine out of a possible 15 points. In
addition to lacking the key indicator of previous entrepreneurial experience, ISC8 also
went through a fracture of the founding team and did not have an organized business
Figure 6. Competitive Indicators and Warning Analysis of Palo Alto Networks
Firm:
Indicators Yes/No Date Score
Funding Attributes (5 possible points )Seed Capital
Up to $1 million (1) yes May-05 1$1 million or greater (1) no 0
Equity Share50% or more (1) no 0
Investor Financing (2) yes Jan-06 2Firm Attributes (4 possible points )
Organized business plan (1) yes Aug-07 1Innovative product (1) yes Aug-07 1Team founding (2) yes Feb-05 2
If team fractures (-2) no 0Founder Attributes (6 possible points )
Previous entrepreneurial experience (2) yes Jan-00 2Previous industrial work experience (1) yes Jan-90 1Previous management experience (1) yes Feb-04 1Education
Bachelor's degree or above (1) yes May-95 1Relevent social network (1) yes Mar-05 1
Funding 3Firm 4Founder 6
Total Score 13 /15
Indications & Warning Analysis - Startups
Palo Alto Networks
74
plan, while also lacking information regarding equity share percentage. Scoring a nine
out of 15 is a middle ground in this model that would place this firm in the moderate risk
of failure or success category, warranting continued monitoring. The model and
correlating decision stairway can be found in Appendix E.
Competitive I&W Analysis of Cryptine Networks
As can be seen in figure 8, Cryptine Networks only scored four out of a possible
15 points. In addition to lacking previous entrepreneurial experience and going through a
Figure 7. Competitive Indicators and Warning Analysis of ISC8, Inc.
Firm:
Indicators Yes/No Date Score
Funding Attributes (5 possible points )Seed Capital
Up to $1 million (1) yes Dec-10 1$1 million or greater (1) yes Dec-10 1
Equity Share50% or more (1) no 0
Investor Financing (2) yes Nov-13 2Firm Attributes (4 possible points )
Organized business plan (1) no 0Innovative product (1) yes May-13 1Team founding (2) yes Nov-11 2
If team fractures (-2) yes Mar-13 -2Founder Attributes (6 possible points )
Previous entrepreneurial experience (2) no 0Previous industrial work experience (1) yes Jan-06 1Previous management experience (1) yes Jun-82 1Education
Bachelor's degree or above (1) yes May-61 1Relevent social network (1) yes Oct-09 1
Funding 4Firm 1Founder 4
Total Score 9 /15
Indications & Warning Analysis - Startups
ISC8, Inc.
75
founding team fracture, the startup received no investor financing, had neither an
organized business plan nor an innovative product, and the founders did not leverage a
relevant social network. Again, there was no information regarding equity share. If this
analysis was conducted in Cryptine Networks’ infancy, the analyst or researcher would
place the startup in the high risk of failure/low chance of success category. While the
Firm:
Indicators Yes/No Date Score
Funding Attributes (5 possible points )Seed Capital
Up to $1 million (1) yes Nov-05 1$1 million or greater (1) no 0
Equity Share50% or more (1) no 0
Investor Financing (2) no - 0Firm Attributes (4 possible points )
Organized business plan (1) no - 0Innovative product (1) no - 0Team founding (2) yes Jan-02 2
If team fractures (-2) yes Nov-05 -2Founder Attributes (6 possible points )
Previous entrepreneurial experience (2) no - 0Previous industrial work experience (1) yes Aug-99 1Previous management experience (1) yes Apr-01 1Education
Bachelor's degree or above (1) yes May-01 1Relevent social network (1) no 0
Funding 1Firm 0Founder 3
Total Score 4 /15
Indications & Warning Analysis - Startups
Cryptine Networks
Figure 8. Competitive Indicators and Warning Analysis of Cryptine Networks
76
unsuccessful ISC8 was in the middle of the spectrum, Cryptine Networks is clearly at the
low end of the point scale. The model’s decision stairway can be found in Appendix F.
Competitive I&W Analysis of Area 1 Security
Area 1 Security was selected as the case on which to test the Competitive
Indicators and Warning model because it is a recently-founded cybersecurity startup that
is still in the early stages of investor financing. Additionally, the startup has been
receiving attention from entrepreneurship- and technology-focused news sources, which
has increased the amount of open-source information available on the company.
Area 1 Security was founded in either late 2013 or early 2014 (sources differ, as
the startup operated in a secretive, non-publicized manner known as stealth mode). The
startup was founded by three public founders, Oren Falkowitz, Blake Darché, and Phil
Syme, and two founders who prefer to remain anonymous. Evidence and dates regarding
Area 1 Security’s indicators of success across funding attributes, firm attributes, and
founder attributes can be found in table 16, table 17, and table 18, respectively.
Funding attributes. In May 2014, Area 1 Security came out of stealth mode with
the announcement that they had secured $2.5 million in seed capital from several
investors, including Ted Schlein, Ray Rothrock, and several VC firms including Kleiner
Perkins Caufield & Byers, Allegis Capital, and Cowboy Ventures, among others (Reilly,
2014; CrunchBase, 2016c). The startup also held a successful Series A funding in
December 2014 and Series B funding in October 2015, where they raised an additional
$23 million of funds from investors such as Icon Ventures, First Round, and Data
Collective, in addition to the previous seed round investors (Marketwired, 2015;
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CrunchBase, 2015). There was no evidence that any of the founders had equity shares
greater than 50%.
Table 16. Evidence and Date of Area 1 Security’s Indicators of Success – Funding Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Seed Capital up to $1M No Yes – May 2014 Seed Capital Greater than $1M No Yes – May 2014 Equity Share Greater than 50% Yes No Investor Financing No Yes – December 2014
Firm attributes. As mentioned previously, Area 1 Security was founded in either
late 2013 or early 2014 (sources differ) by Oren Falkowitz, Blake Darché, and Phil Syme,
as well as two additional anonymous co-founders (Hackett, 2014; Greenfield, 2014). As
of January 2016, the founding team is still operating cohesively with no evidence of a
pending fracture. The team has successfully held two investor funding rounds, with the
most recent (Series B) held in October of 2015 (CrunchBase, 2015). The presence of a
second funding round serves as evidence of a startup’s organized business plan.
Table 17. Evidence and Date of Area 1 Security’s Indicators of Success – Firm Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Organized Business Plan Yes Yes – October 2015 Innovative Product No Yes – January 2014 Team Founding No Yes – January 2014 Founding Team Fracture No No
Founder Attributes. Of the three known founders, Oren Falkowitz is the only
one with previous entrepreneurial experience. In July 2012, Falkowitz co-founded Sqrrl,
a big data analytics and cybersecurity firm (Falkowitz, n.d.; Alspach, 2012). All three of
78
the known founders have previous industrial work experience primarily with the National
Security Agency (NSA) (Hackett, 2014). Falkowitz worked as an NSA analyst from 2006
until 2012 (Falkowitz, n.d.). Blake Darché worked as a computer network exploitation
analyst for the NSA from 2006 until 2010 (Darché, n.d.). Several sources (Reilly, 2014;
Hackett, 2014; Bort, 2014) group Phil Syme in with Falkowitz and Darché as a former
NSA analyst, but Syme’s LinkedIn profile has no mention of the NSA and claims that he
worked as a chief engineer for Next Century Corporation from 2003 until 2014, which is
experience in the same industry (Syme, n.d.). Marketwired (2015) claims that the two
anonymous co-founders worked in security at MIT and data analytics at Disney, which
demonstrates that the entire team of founders had previous industrial work experience.
Falkowitz and Darché both have previous management experience, with Falkowitz’s
coming from 2010 to 2012 as the Director of Technology and Data Science Programs
with the US Cyber Command, and Darché’s occurring between 2012 and 2014 as
CrowdStrike’s Principal Consultant and Facility Security Officer (Falkowitz, n.d.;
Darché, n.d.).
Two of the three of the known co-founders have earned at least a Bachelor’s
degree. Syme earned his Bachelor of Science (BS) in Mathematics and Computer Science
from Carneigie Mellon University in 1995 (Syme, n.d.). Darché earned a BS in
Information Technology from Rochester Institute of Technology in 2005 and his MS in
Security Informatics from Johns Hopkins University in 2009 (Darché, n.d.). While there
is no evidence of Falkowitz earning a degree, he does follow two George Washington
University groups on LinkedIn, one of which is the official group of students, alumni,
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faculty and staff of GWU’s Elliot School of International Affairs – a group that requires
approval to join (Falkowitz, n.d.; Elliot School, n.d.).
The Area 1 Security team leveraged their social network by approaching Ted
Schlein and his investing firm, Kleiner Perkins Caufield & Byers, to raise seed and
subsequent capital. Schlein had previously invested in the security firm Synack, which
was founded by two former NSA analysts (Hill & Fox-Brewster, 2014). Schlein has
publicly stated his support for and confidence in cybersecurity firms founded by NSA
alumni, saying that, “NSA engineers are highly talented and smart people with deep
experience with how to battle bad forces” (Hill, 2014). Through their NSA background,
Area 1 Security founders were able to secure early funding and therefore leverage a
relevant social network.
Table 18. Evidence and Date of Area 1 Security’s Indicators of Success – Founder Attributes
Indicators of Success Complex
Indicator? Evidence and Date of Indicator
Previous Entrepreneurial Experience
No Yes – July 2012
Previous Industrial Work Experience
No Yes – February 2006
Previous Management Experience
No Yes – August 2010
Bachelor’s Degree or Above No Yes – May 1995 Relevant Social Network Yes Yes – December 2014
Conclusion. The only indicator of success missing from Area 1 Security’s
founding is the equity share greater than 50%, which, as discussed in the earlier case
comparison, may either be too difficult to find evidence of, or may not be indicative of
success at all. Even without the evidence of equity share, there was evidence of Area 1
Security possessing all of the other 11 positive indicators of success, with the only other
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‘no’ being attributed to the founding team fracture, which had not occurred as of January
2016. The completed analysis of Area 1 Security through the Competitive Indicators and
Warning model can be seen in figure 9, below, where the startup scores 14 out of a
possible 15 points. Figure 10 shows the decision stairway, a chronological and visual
representation of Area 1 Security’s attainment of each indicator of success. The
Competitive Indicators and Warning analysis indicates that Area 1 Security is in the high
chance of success/low risk of failure category; however, this estimate cannot be
confirmed for five years after their last funding round.
Figure 9. Competitive Indicators and Warning analysis of Area 1 Security
Firm:
Indicators Yes/No Date Score
Funding Attributes (5 possible points )Seed Capital
Up to $1 million (1) yes May-14 1$1 million or greater (1) yes May-14 1
Equity Share50% or more (1) no 0
Investor Financing (2) yes Dec-14 2Firm Attributes (4 possible points )
Organized business plan (1) yes Oct-15 1Innovative product (1) yes Jan-14 1Team founding (2) yes Jan-14 2
If team fractures (-2) no 0Founder Attributes (6 possible points )
Previous entrepreneurial experience (2) yes Jul-12 2Previous industrial work experience (1) yes Feb-06 1Previous management experience (1) yes Aug-10 1Education
Bachelor's degree or above (1) yes May-95 1Relevent social network (1) yes Dec-14 1
Funding 4Firm 4Founder 6
Total Score 14 /15
Indicators & Warning Analysis - Startups
Area 1 Security
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Figure 10. Decision stairway reflecting Area 1 Security’s attainment of indicators of success.
Chapter 4 Summary
Of the three cases examined (Palo Alto Networks, ISC8, Inc., and Cryptine
Networks), only Palo Alto Networks assembled enough success factors to move on from
investor funding rounds and survive for five years, and in doing so become a successful
cybersecurity startup (as defined by this study). ISC8 assembled a significant number of
success factors, while Cryptine Networks faltered in that regard; however, both were
missing the key indicators of success by lacking previous entrepreneurial experience and
going through a fracture of the founding team. In addition to these two factors identified
through the case study, investor financing was deemed to be a key indicator of success
due to the emphasis in the reviewed literature. Thus, each of these three factors are worth
two points in the developed Competitive Indicators and Warning model, while all other
indicators are worth one point.
After analyzing the three cases with the Competitive Indicators and Warning
analysis, the model can be considered to be valid. The scores that Palo Alto Networks
and Cryptine Networks earned would have resulted in accurate estimations on their
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chances of success or risks of failure, while ISC8’s score prompts further monitoring of
the startup. The developed scoring and rating system, which was exploratory in nature,
warrants further research into the interpretation and categorization of the results of
Competitive I&W. To demonstrate the model’s estimative capability, Area 1 Security (a
nascent cybersecurity startup) was analyzed and its score (14 out of 15 possible points)
indicates that the startup is likely to be successful; however, this will be impossible to
determine until at least 2020.
The findings from this chapter will be discussed further in the concluding chapter,
as well as a discussion of their implications to practitioners. Additionally,
recommendations for future research, will be made in the next chapter, as well as a
summary of and conclusion to the study as a whole.
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CONCLUSION
Introduction
This study was conducted to determine whether funding, firm, or founder
attributes increased the likelihood of a startup’s success. The findings were then applied
to the development of the Competitive Indicators and Warning (I&W) model. The
Competitive I&W model was designed to be used by competitive and market intelligence
professionals as a tool to objectively estimate the likelihood of startup companies, which
have become a disruptive force in many industries. This chapter will fully review and
summarize the study followed by a discussion of the findings presented in the previous
chapter. This chapter will also discuss the implications of these findings on the practice
of competitive intelligence, as well as make recommendations for further research, as
identified throughout this thesis.
Summary of the Study
This study was conducted to examine factors that are indicative of success in
startups and develop an analytic model that can be used to estimate a startup’s likelihood
of success. Despite the difficulty and subjectivity in examining numerous complicated
factors and variables, competitive intelligence professionals lack an objective
methodology to analyze and estimate the likelihood of success of a new startup firm
during its formative years. This study transformed Indicators and Warning analysis, a
predominately national security and military intelligence methodology, into a
methodology that can be used in a competitive intelligence setting to estimate the
likelihood of a startup’s success.
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After a thorough review of the literature regarding success and entrepreneurship,
several factors were identified that influenced entrepreneurial success. In order to test
these factors and determine if any factors were more indicative of success than the others,
this thesis utilized a qualitative case study through the examination of one successful
cybersecurity startup, Palo Alto Networks, and two unsuccessful cybersecurity startups,
ISC8, Inc. and Cryptine Networks. The cybersecurity industry was chosen due to its rapid
market growth and the severity and breadth of recent cyber-attacks, targeting over 3,000
US companies and approximately 110 million Americans. In response to these attacks,
cybersecurity-specific startups have flooded the market; since 2010, more than $7.3
billion has been invested in 1,208 cybersecurity startups.
Using information from the open sources discussed in the methodology chapter,
data on the three cases’ funding, firm, and founder attributes was collected and analyzed.
The result of a comparison of the cases (found in Appendix C) suggests that the absence
of previous entrepreneurial experience and the fracture of the team founding were
indicators that both negatively influenced the likelihood of success of the two
unsuccessful cases, but positively influenced the successful case. The successful
company’s founder had previous entrepreneurial experience and the founding team did
not fracture; therefore, the weighting of these indicators was doubled in the Competitive
I&W model. Additionally, investor financing was also awarded a double weighting due
to its emphasis in the reviewed literature.
In the Results chapter, the Competitive I&W model was tested on the three cases
to prove its validity and accuracy (Appendices D, E, and F), then used to analyze a
nascent cybersecurity startup, Area 1 Security, to demonstrate its capability as an
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estimative analytic methodology. After data collection on Area 1 Security’s funding,
firm, and founder attributes and analysis of these factors through the Competitive I&W
model, the nascent startup scored 14 points out of a possible 15 points and included the
presence of investor financing, team founding, and previous entrepreneurial experience
(the three weighted indicators), without experiencing a fracture of the founding team as
of January 2016. Area 1 Security, therefore, has a high chance of success and low risk of
failure.
Discussion of the Findings
The findings presented in the previous chapter imply that a founder’s previous
entrepreneurial experience, a team founding, and the fracture of the founding team (if
evident) are more indicative of startup success than the other examined factors. When
comparing these indicative factors with the literature from which they were derived, the
reasons for their importance become evident.
In support of several studies (Cooper et al., 1994; Friar & Meyer, 2003; Lafuente
& Rabetino, 2011; Littunen, 2000; Littunen & Niittykangas, 2010; Lussier & Halabi,
2010; Schutjens & Wever, 2000), teams of founders have the opportunity to take
advantage of a wide array skills and resources, attributes that may not be present in a
single founder. Additionally, the team founding means that more individuals have vested
interests in the success of the startup, which frequently leads to greater access to capital
(both financial and human) and more sophisticated support systems. While teams of
founders increase the probability of startup success, the findings of this study indicate
that the fracture of founding teams due to non-amicable conditions, may not only
86
severely threaten the survival of the business, as Thurston (1986) suggested, but also may
be indicative of failure.
Despite Bosma et al. (2004) and Van Praag’s (2003) denial that previous
entrepreneurial experience positively influences small business success, the importance
of previous entrepreneurial experience to startup success was a clear result of this
comparative case study. Through this type of experience, founders develop a better
understanding of how to organize and recruit investors and talent to a new company.
While the founder is lacking this understanding and experience during their first
entrepreneurial venture, it will likely benefit them in future endeavors. The indicator’s
presence in this study’s findings supports Coleman (2007), Harada (2003), and Yusuf’s
(1995) conclusions that entrepreneurial experience is a critical success factor to startups.
Additionally, Stuart and Abetti (1990) asserted that this previous experience was highly
valued by financial investors, but it was not valued by banks during the loan application
process (Blumberg & Letterie, 2008).
Because evaluating startups is a primary focus of venture capitalist firms, the
presence of investor financing was also determined to be worth weighting. Investor
financing was present in the successful case, as well as one of the two unsuccessful cases.
Despite the results of the case study not supporting this indicator, Inderst and Mueller
(2009) asserted that startups that have been financed by large investors are more likely to
dominate their competition, particularly in highly competitive industries like the
cybersecurity industry. Additionally, because only about a half of a percent of startups
are financed, competition for financing can be fierce (Bygrave et al. 2003), thereby
demonstrating the potential success of those ventures that earn investor financing.
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While Duchesneau and Gartner (1990) claimed that when the founder contributes
and controls a greater equity share, the startup’s likelihood of success increases, the
findings of this case study did not support their claim. However, these findings do not
necessarily mean that greater equity share does not increase the likelihood of success.
Because this indicator was missing from all three cases (and the new startup Area 1
Security), it may just be difficult to find due to privacy concerns of the founder or a lack
of reporting on the part of news sources and industries. While Nir Zuk, the founder of
Palo Alto Networks, specifically stated that he only controlled about 5-10% of the
company (significantly less than the sought after 50% equity share), he noted that this
was because he preferred to invest his equity into the company, its employees, and its
resources. So despite not possessing a significant equity share, Zuk utilized his shares to
improve his company. As this indicator was neither supported nor refuted through this
study, it should be considered as a topic for further research.
The development of the Competitive I&W model, while including the previously
mentioned success factors, was guided by the goal of intelligence products and activities:
to reduce uncertainty in a decision maker (Wheaton & Beerbower, 2006). This study and
the developed Competitive Indicators and Warning analytic model successfully complete
this objective by determining identifiable factors that are indicative of startup success and
organizing those factors into a manageable, scored model that can assist analysts in
categorizing new startup ventures into distinct groups based on their risk of failure or
chance of success. Additionally, this study and the developed Competitive I&W
methodology builds upon the analytic methodologies common the national security and
military intelligence professions, demonstrating that the competitive intelligence
88
discipline can be expanded and improved, to the benefit of competitive intelligence
professionals, through the conversion and development of methodologies used in other
fields of intelligence, such as military or national security.
The definitional translation of I&W analysis started as a US Department of
Defense (2010/2015) method, define as “an activity intended to detect and report time-
sensitive intelligence information on foreign developments that forewarn of hostile
actions or intentions against US entities, partners, or interests” (p. 259). After a review of
business-focused literature from authors such as Herring (1996), Jackson and Dutton
(1988), Porter (1996, 2008), and Barney (1991), key terms in the DOD definition were
converted to language that can be used in competitive intelligence, resulting in the
definition of Competitive I&W developed through this study: activities intended to
prevent surprises through the detection and reporting of time-sensitive intelligence
information on competitor or market developments that forewarn of potential threats or
opportunities that will likely affect an organization's strategy, competitive advantage, or
revenue.
Implications for Practice
This study has two major implications for practitioners, not only competitive
intelligence analysts and consultants assessing startups in their own industries, but also
for potential investors and companies that may be interested in acquiring a new startup.
While the study identified four factors (previous entrepreneurial experience, team
founding, fracture of the founding team, and investor financing) that were indicative of
the analyzed startups’ chances of success or risks of failure, these cannot be generalized
across industries. It is tempting to argue that a competitive intelligence practitioner could
89
focus their analysis on these four factors, but without further research on these factors,
their generalizability cannot be determined.
The developed analytic methodology, Competitive Indicators and Warning can be
used by practitioners to objectively analyze and compare startups. The tool can be used to
organize information collection activities and identify intelligence gaps because it lists all
the necessary information in one location and can easily be modified to include potential
and actual information sources. Because the model requires that evidence of each factor
be recorded and dated, any factors lacking evidence are prominent and should be
emphasized in future collection efforts. The Competitive I&W model can also be used to
support and defend a practitioner’s analysis by clearly categorizing a startup based on
chances of success or risks of failure and, as mentioned earlier, can be easily modified to
include sources. The scoring system and source lists can be used to support the analysis
and serve as an auditing tool to show how the analysis or decisions were founded. When
analyzed through Competitive I&W, analysts should be able to identify blind spots, such
as unexpected potential new entrants or competitors, and reduce surprises in the market.
Second, the results of this study demonstrate the importance of the four identified
factors to future studies and the potential for the developed Competitive I&W model to
be generalizable to startups beyond the cybersecurity industry. This study did not use any
factors that were specific to the cybersecurity industry itself. However, the model could
be easily expanded to include industry-specific factors, thus increasing the level and
detail of the analysis. Additionally, macroenvironmental factors could be incorporated in
order to improve the robustness of analysis when comparing startups across different
90
industries. Any expansion or alteration of the model would require an alteration of the
categories resulting from the final scores.
Recommendations for Further Research
This study uncovered several topics that would benefit from further research.
Because an organization’s environment is divided into three distinct levels (internal,
industry, and macroenvironment), the impact of market and industrial factors could be
very significant and influential on the success or failure of a startup venture. Utilizing an
analytic framework such as STEEP1 to assess the macroenvironment may help to reveal
potential indicators that are broad in scope or outside the founder’s control (Fleisher &
Bensoussan, 2003). Macroenvironmental analysis can be complex, due to varying
interpretations, misperceptions, inaccuracy, and uncertainty can mar the results of
macroenvironmental analysis (Diffenbach, 1983). To determine factors that are industry
specific and may impact a startup, Porter’s (2008) five forces2 analysis can be used to
assess industrial factors. In volatile industries with multiple competitors, demanding
customers, and powerful suppliers, companies must improve their strategy and become
more entrepreneurial in order to survive, let alone thrive (Birkinshaw, Hood, & Young,
2005). While this analysis is intended to estimate how attractive an industry is by
examining its profitability, the analysis would also reveal factors that may increase or
decrease the chance of success of a startup in that industry. These additional
methodologies increase the thoroughness of analysis and the likelihood of producing an
accurate estimate. When combined with macroenvironmental and industrial analyses,
1 STEEP is an acronym which stands for Social, Technological, Economic, Ecological, and Political/Legal – the five dimensions to analyze the macroenvironment. 2 Porter’s five forces are composed of the threat of new entrants, bargaining power of suppliers, bargaining power of buyers, threat of substitute products or services, and the rivalry amongst existing competitors.
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Competitive I&W would become a more robust, powerful, and all-encompassing analytic
model.
A proposed framework for the inclusion of STEEP and Porter’s Five Forces
analysis can be found in Figure 11. Macroenvironmental factors can be represented
through STEEP analysis as in Table 19 and industrial factors can be depicted through
Porter’s Five Forces analysis as in Figure 12. Studies that research methods of combining
macroenvironmental and industrial analysis with Competitive I&W will have to develop
scoring or evaluation systems to determine the impact of individual factors and the
overall favorability of the macroenvironment and industry into which the startup is
entering. While the proposed model uses a high, moderate, or low impact or threat level,
greater development and refinement of the evaluation system will increase the accuracy
of analysis and provide greater value to decision makers.
Figure 11. Proposed model framework for inclusion of macroenvironmental and industrial analysis with Competitive Indicators and Warning analysis.
92
Table 19. Sample representation of a macroenvironmental analysis using STEEP.
STEEP Categories Primary Macroenvironmental Factors Impact
Social
Number of American internet users expected
to increase to 87% by 2020 Moderate
Increasing fear of cybercrime likely to
motivate investments in security products High
Technological
200 billion ‘smart’ devices will require
advanced cybersecurity solutions High
Shortage of talent in security fields will lead to
recruiting competition Moderate
Figure 12. Sample Porter’s five forces analysis of the cybersecurity industry.
The indicators of success used in the Competitive I&W model could be refined
through further research. Specifically, the four factors identified through this study
(previous entrepreneurial experience, team founding, fracture of the founding team, and
93
investor financing) should be the focus of future quantitative and qualitative research on
the subject of startup success. Another particular indicator is equity share; because equity
share was missing in all three cases, it warrants the question of whether or not equity
share is indicative of success. Equity share greater than 50% was a proxy for founder
commitment, but because of the lack of information on this factor, future research should
determine if there is a proxy that is more easily identifiable than equity share. The next
indicator deserving of further research is the educational indicator. Education was used as
a proxy for knowledge development capabilities, adaptability, and several other qualities
such as motivation and dedication. This study used a founder’s bachelor’s degree as
evidence of education, but the founders of Cryptine Networks, one of the unsuccessful
cases, had earned degrees in Italian cultural studies and international relations, which
raises the question of whether degrees in subjects radically different from the startup’s
industry may hinder the likelihood of success. Future studies could research and analyze
any correlations between specific degrees and entrepreneurial success.
The system for measuring and evaluating the indicators developed in this was
exploratory in nature; techniques for indicator evaluation could be improved on through
further research. Additionally, the scoring and categorical ranking system were developed
through this study. Well-defined categories and benchmarks or cutoff scores for risks of
failure or chances of success could likely be determined and specified through further
research utilizing more case studies. Because this was a qualitative study, the key
indicators would likely benefit by the addition of quantitative statistical testing. This type
of research could identify to what magnitude these key factors are more indicative of
success than the other indicators, thus refining the scoring system. Because quantitative
94
studies can evaluate a larger sample size than qualitative studies, the key indicators can
be analyzed in hundreds of companies across a multitude of industries. While a
quantitative study would help refine the scoring system, the Competitive I&W model,
which was developed to be generalizable beyond the cybersecurity industry, should be
qualitatively and quantitatively tested across additional industries and environments in
order to validate the model and increase its robustness.
Conclusions
This study qualitatively identified four success factors that were more indicative
of success in cybersecurity startups than several other examined indicators. The
implications of these findings for practitioners and recommendations for future research
to improve on these finding were discussed in this final chapter. Through the study, the
Competitive Indicators and Warning analytic model was converted from a primarily
military intelligence methodology (Indicators and Warning analysis) to a competitive
intelligence methodology, validated through analysis of the three studied cases, and its
estimative capabilities were demonstrated through the analysis of a nascent cybersecurity
startup. The Competitive I&W assists intelligence professionals analyze startups by
organizing success factors into funding, firm, and founder attribute families while
providing a structured, tested model for more objective analysis.
This study adds to the body of knowledge and literature on competitive
intelligence and analytic methodologies, with particular focus of the scarce research area
of competitive intelligence with relation to entrepreneurship. Additionally, this study has
demonstrated significant opportunities to continue to expand competitive intelligence
literature and develop methodological tools based on the techniques used in the more
95
established intelligence disciplines of national security and military intelligence. The
cybersecurity market is experiencing rapid growth and entrepreneurs are attempting to
earn their share of the increasing venture capital finances. As disruptive technologies
continue to advance, the markets for related products and services will experience rapid
shifts, resulting in more startups designed to meet the newest customer need. Without a
valid, objective, analytic methodology such as Competitive I&W, competitive
intelligence professionals, investors, and companies may be blind to the opportunities and
threats that these startups present.
96
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APPENDICES
Appendix A
Norman’s (2001) Trust Scale and Web Site Evaluation Worksheet
Score: Trust Scale:
Criteria Tips Value Enter Y or N 0
60.9-46.75 High
Content can be
corroborated?
Check some of the s i te's
facts
5.17
46.74-35.06 Moderate
Recommended by subject
matter expert?
Doctor, biologis t, country
expert
4.94
35.05-7.47 Low
Author i s reputable? Google for opinions , ask
others
4.64
7.46-0 Not Credible
You perceive s i te as
accurate?
Check with other
sources ; check
affi l ia tions 4.56
Information was reviewed
by an editor or peers?
Science journa ls ,
newspapers
4.52
Author i s associated with
a reputable org?
Google for opinions , ask
others .
4.42
Publ i sher i s reputable? Google for opinions , ask
others .4.02
Authors and sources
identi fied?
Trustworthy sources
want to be known 3.78
You perceive s i te as
current?
Last update?3.78
Severa l other Web s i tes
l ink to this one?
Si tes only l ink to other
s i tes they trust3.68
Recommended by a
genera l i s t?
Librarian, researcher3.65
Recommended by an
independent subject
guide?
A travel journa l may
suggest s i tes
3.56
Domain includes a
trademark name?
Trademark owners
protect their marks 3.45
Site's bias in clear? Bias i s OK i f not hidden3.06
Site has profess iona l
look?
It should look l i ke
someone cares 2.86
Total 60.09 0
19 Dec 2001: The criteria and weighted values are based on a survey input from 66 analysts. For
details see: http://daxrnorman.googlepages.com/analysis
3 Feb 2012: Excel Spreadsheet which adds auto‐sum was produced by Bill Welch, Deputy Director,
Center for Intelligence Research Analysis and Training, Mercyhurst College.
26 Jan 2013: Trust Scale and Web Site Evaluation Worksheet is in the PUBLIC DOMAIN .
Enter URL:
117
Appendix B
Competitive Indicators and Warning Analysis Template
Firm:
Indicators Yes/No Date Score
Funding Attributes (5 possible points )Seed Capital
Up to $1 million (1) 0$1 million or greater (1) 0
Equity Share50% or more (1) 0
Investor Financing (2) 0Firm Attributes (4 possible points )
Organized business plan (1) 0Innovative product (1) 0Team founding (2) 0
If team fractures (-2) 0Founder Attributes (6 possible points )
Previous entrepreneurial experience (2) 0Previous industrial work experience (1) 0Previous management experience (1) 0Education
Bachelor's degree or above (1) 0Relevent social network (1) 0
Funding 0Firm 0Founder 0
Total Score 0 /15
Indications & Warning Analysis - Startups
118
Appendix C
Comparison of Indicators of Success: Palo Alto Networks, ISC8, and Cryptine Networks.
Indicators of Success
Evidence and Date of Indicator
Palo Alto
Networks ISC8, Inc.
Cryptine
Networks
Fu
nd
ing
Seed Capital up to $1M Yes
May 2005
Yes
Dec. 2010
Yes
Nov. 2005
Seed Capital Greater than
$1M No
Yes
Dec. 2010 No
Equity Share Greater than
50% No No No
Investor Financing Yes
January 2006
Yes
Nov. 2013 No
Fir
m
Organized Business Plan Yes
August 2007 No No
Innovative Product Yes
August 2007
Yes
May 2013 No
Team Founding Yes
February 2005
Yes
Nov. 2011
Yes
January 2002
Founding Team Fracture No Yes
March 2013
Yes
Nov. 2005
Fou
nd
er
Previous Entrepreneurial
Experience
Yes
January 2000 No No
Previous Industrial Work
Experience
Yes
January 1990
Yes
January 2006
Yes
August 1999
Previous Management
Experience
Yes
February 2004
Yes
June 1982
Yes
April 2001
Bachelor’s Degree or Above Yes
May 1995
Yes
May 1961
Yes
May 2001
Relevant Social Network Yes
March 2005
Yes
October 2009 No
119
Appendix D
Palo Alto Networks – Competitive Indicators and Warning analysis
Firm:
Indicators Yes/No Date Score
Funding Attributes (5 possible points )Seed Capital
Up to $1 million (1) yes May-05 1$1 million or greater (1) no 0
Equity Share50% or more (1) no 0
Investor Financing (2) yes Jan-06 2Firm Attributes (4 possible points )
Organized business plan (1) yes Aug-07 1Innovative product (1) yes Aug-07 1Team founding (2) yes Feb-05 2
If team fractures (-2) no 0Founder Attributes (6 possible points )
Previous entrepreneurial experience (2) yes Jan-00 2Previous industrial work experience (1) yes Jan-90 1Previous management experience (1) yes Feb-04 1Education
Bachelor's degree or above (1) yes May-95 1Relevent social network (1) yes Mar-05 1
Funding 3Firm 4Founder 6
Total Score 13 /15
Indications & Warning Analysis - Startups
Palo Alto Networks
120
Palo Alto Networks – Decision Stairway
121
Appendix E
ISC8, Inc. – Competitive Indicators and Warning analysis
Firm:
Indicators Yes/No Date Score
Funding Attributes (5 possible points )Seed Capital
Up to $1 million (1) yes Dec-10 1$1 million or greater (1) yes Dec-10 1
Equity Share50% or more (1) no 0
Investor Financing (2) yes Nov-13 2Firm Attributes (4 possible points )
Organized business plan (1) no 0Innovative product (1) yes May-13 1Team founding (2) yes Nov-11 2
If team fractures (-2) yes Mar-13 -2Founder Attributes (6 possible points )
Previous entrepreneurial experience (2) no 0Previous industrial work experience (1) yes Jan-06 1Previous management experience (1) yes Jun-82 1Education
Bachelor's degree or above (1) yes May-61 1Relevent social network (1) yes Oct-09 1
Funding 4Firm 1Founder 4
Total Score 9 /15
Indications & Warning Analysis - Startups
ISC8, Inc.
122
ISC8, Inc. – Decision Stairway
123
Appendix F
Cryptine Networks – Competitive Indicators and Warning analysis
Firm:
Indicators Yes/No Date Score
Funding Attributes (5 possible points )Seed Capital
Up to $1 million (1) yes Nov-05 1$1 million or greater (1) no 0
Equity Share50% or more (1) no 0
Investor Financing (2) no - 0Firm Attributes (4 possible points )
Organized business plan (1) no - 0Innovative product (1) no - 0Team founding (2) yes Jan-02 2
If team fractures (-2) yes Nov-05 -2Founder Attributes (6 possible points )
Previous entrepreneurial experience (2) no - 0Previous industrial work experience (1) yes Aug-99 1Previous management experience (1) yes Apr-01 1Education
Bachelor's degree or above (1) yes May-01 1Relevent social network (1) no 0
Funding 1Firm 0Founder 3
Total Score 4 /15
Indications & Warning Analysis - Startups
Cryptine Networks
124
Cryptine Networks – Decision Stairway