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The Silicon Valley startup ecosystem in the 21st century: entrepreneurial resilience and networked innovation

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The Silicon Valley startup ecosystem in the 21st century: entrepreneurial resilience and networked innovation

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THE SILICON VALLEY STARTUP ECOSYSTEM IN THE 21
ST
CENTURY:
ENTREPRENEURIAL RESILIENCE AND NETWORKED INNOVATION

by

Nahoi Koo
________________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMMUNICATION)

December 2018

Copyright 2018 Nahoi Koo

ii
Acknowledgements

The past six years at Annenberg have been an unforgettable journey. It was not a smooth
one, but I can for sure say that every moment was meaningful as it allowed me to grow as a
scholar and a person. Finishing this dissertation was unquestionably the most arduous and
challenging task. It was only made possible by indispensable contribution of many individuals to
whom I am greatly indebted.
I thank God for carrying me through all this time and sending me so many amazing
people to help through my time at Annenberg. I had the most caring and supportive advisor who
spent countless hours on my dissertation. Thank you, Professor Manuel Castells for everything. I
would also like to thank my dissertation committee members, Professors Jonathan Aronson,
Peter Monge, and Mike Ananny for their guidance on my research.
I am extremely grateful for the financial support I received at the University of Southern
California. My dissertation research has been sponsored by the International Balzan Foundation
with the funds attributed to Professor Manuel Castells' Balzan Prize 2014. The Annenberg
School for Communication provided the education and funding during my entire Ph.D. The
Korean Studies Institute and the Graduate School also funded my research and conference trips.
My family has been the source of my strength – the reason why I did not give up. Thank
you, Mom, Dad, and my brother Yunhoi for your unconditional love. You are the most important
people in my life and I love you so much. I also thank my friends and colleagues who have
always been there for me. Thank you, my Annenberg friends for our six years of friendship and
collegiality. Without you, I would not have made it this far. I also extend my deepest gratitude to
my church community for all your prayers and support these past several years.

iii
Last but not least, I would like to thank my grandparents who played such important role
in defining who I am today. Both my grandfathers worked in the journalism industry during the
time of dictatorship in South Korea. My paternal grandfather was a leading journalist at one of
the most influential Korean language daily newspapers, the Dong-a Ilbo. My maternal
grandfather ran a small local newspaper company in the heart of Seoul. Both of them believed in
the freedom of press and more importantly valued education. As a communication scholar, I
hope I can carry their legacy and continue to make them proud.

iv
Table of Contents
Acknowledgements ii
List of Tables v
List of Figures vi
Abstract vii
Chapter 1: Introduction 1
Chapter 2: Explaining Entrepreneurial Resilience in the 21
st
Century Silicon Valley 27
Chapter 3: Building a Startup as an Organizational Emergence 48
Chapter 4: Networks Structure and Impact on Success of Startups 70
Chapter 5: Silicon Valley as a Milieu of Innovation: Continuity and Change 92
Chapter 6: Conclusion 112
References 123

v
List of Tables
Table 1.1. List of fieldwork sites in Silicon Valley 21
Table 1.2. List of international fieldwork sites and descriptions 24
Table 2.1. Codes of themes and subthemes of entrepreneurial resilience 32
Table 2.2. Dynamic entrepreneur and static person 33
Table 3.1. Panelists comments and their descriptions 64
Table 3.2. The best 8 startups selected for the Startup Conference 2016 Pitch Competition 65
Table 4.1. Core startups in Silicon Valley 81
Table 4.2. Rank chart of the most central startups in Silicon Valley 81
Table 4.3. Core startups in other regions 83
Table 4.4. Rank chart of the most central startups in other regions 84
Table 4.5. Core startups in Silicon Valley and other regions 86
Table 4.6. Rank chart of the most central startups in Silicon Valley and other regions 87
Table 4.7. Network level information 89
Table 5.1. Top 5 dominant sectors (Hoovers) 102
Table 5.2. Top 5 dominant industries (Hoovers) 102
Table 5.3. Top 5 dominant industries (NAICS) 102
Table 5.4. Top 5 dominant industries (SIC) 102
Table 5.5. Top ten Silicon Valley startups by valuation 103

vi
List of Figures
Figure 1.1. Investments and deals from Q1 2002 to Q1 2008 in Silicon Valley 3
Figure 1.2. History of Silicon Valley 4
Figure 3.1. Startup development phases from idea to business and team to organization 51
Figure 3.2. Illustration of how the venture capital industry works 53
Figure 4.1. Network of co-invested startups in Silicon Valley 80
Figure 4.2. Network of co-invested startups in other regions 82
Figure 4.3. Network of co-invested Fortune’s 2016 unicorns in the United States 85
Figure 5.1. Top ten regions by total investment amount 108

vii
Abstract

During the first decade of the 21st century, Silicon Valley experienced two major
economic crises - the dotcom crisis in 2001 and the financial crisis in 2008. Parallel to the theory
of discontinuous waves of business cycles, the crises served as an exogenous shock that
restructured the regional economy and sparked a new wave of innovation. Silicon Valley was
able to bounce back both times. What’s more, it has entered into a new phase of development
characterized by a massive explosion of startups, sharp increase in venture capital investment,
broadening diversification of industry base, and global expansion of entrepreneurial networks.
Each of the data chapters in this dissertation focused on different aspects of Silicon Valley’s
continuity and change under these new conditions.
Consequently, findings from each chapter expand the scope of research on
entrepreneurship and innovation by addressing the processes and outcome of innovation in a
broader context of a milieu of innovation. The study chose the startup ecosystem as a relevant
object of study and theoretical perspective of the current startup scene in the 21st century Silicon
Valley. The study first classified entrepreneurial resilience as a telling feature of successful
entrepreneurship at an individual level. Next, it identified the processes involved in building a
startup at an organization level. Then it examined the network level features of a group of highly
successful startups and identified how they contribute to entrepreneurial performance. Last, the
study evaluated the continuity and changes of Silicon Valley as a milieu of innovation in the 21st
century.
The structure of this dissertation, which consists of four interconnected parts, provides a
holistic evaluation of the current Silicon Valley startup ecosystem and offers insight into its

viii
broader influence in the global economy. Each chapter develops its own theoretical framework
and methodology to address its own set of research questions. Chapter 2 reviews explanatory
factors essential for entrepreneurial resilience in today’s Silicon Valley startup scene. Chapter 3
explores key organizational conditions and relational dynamics involved in building a startup.
Chapter 4 examines network structures and effects on startups’ investment performance and
innovation capacity. Chapter 5 evaluates how Silicon Valley has changed over time yet still
persists to be the milieu of innovation in the 21
st
century. Finally, the concluding chapter
combines the findings of the four data chapters and points out some commonalities that emerge
across these chapters in light of broader implications and future research directions.

1
Chapter 1: Introduction

The objective of this dissertation is to contribute to the understanding of the interaction of
entrepreneurship, innovation, and regional growth in the context of the 21st century information,
communication, and technology (ICT) startup industry. The dissertation concerns the inner
workings of the industry by examining the recent organizational, technological, and cultural
transformations of Silicon Valley – the leading milieu of technological innovation. Furthermore,
it investigates the new wave of networked innovation in the region. By focusing on both the
processes and outcomes of innovation production, the dissertation develops a research design
that allows for a systematic empirical assessment of the Silicon Valley startup ecosystem in four
progressive layers.
First, this study explores the individual, behavioral, and cultural conditions for
entrepreneurial resilience that allows creative individuals to thrive and innovate in the startup
ecosystem. Second, it traces organizational processes of building a startup and investigates
relational dynamics among different stakeholders involved in the processes. Next, the study
analyzes networks of startups in terms of their structure and influence. Last, it explicates the
concept of a milieu of innovation on the basis of these first three layers. In doing so, the
dissertation makes a case that a holistic approach of accessing the practices of entrepreneurship
in Silicon Valley unveils the very process of innovation production and offers insight into its
global influence.
Silicon Valley epitomizes the global hub of innovation in which thousands of venture-
backed startups cluster and form networks of technological and business linkages. According to
the 2015 Startup Genome Report, there are 14,000 to 19,000 startups and 1.7 to 2.2 million high-

2
tech workers in Silicon Valley, the highest of all top 20 global startup ecosystems. It captures
approximately 45% of the top 20’s venture capital investments and 30% of exit value of the top
20’s absolute total growth (Herrmann et al., 2015). To account such vibrancy of the startup scene
in Silicon Valley, the term “ecosystem” is used throughout this dissertation. The Silicon Valley
startup ecosystem is composed of a group of interconnected entrepreneurial entities that
continuously interact with one another and co-create an environment conducive to innovation.
There is abundant research on startups in Silicon Valley as the primary growth engine of
the new information economy (Hall, 1998; Saxenian, 1996; Rogers & Larsen, 1984; Janeway,
2012). However, the scope of the research is often limited to either the micro-level analysis of
specific entrepreneurial entities such as individual startups or venture capital firms, or the
regional analysis of Silicon Valley in the latter half of the 20th century. Existing theoretical
investigations of entrepreneurship and innovation in the literature lack empirical evidence of the
startup scene in the recent decade. In fact, Silicon Valley’s startup ecosystem involves complex
interactions among multiple entrepreneurial entities and has undergone enormous change since
the dot com era.
To address the gap, this dissertation provides the aforementioned four-level evaluation of
Silicon Valley with a particular focus on its growth and development over the past decade since
the 2008 global financial crisis. This time period serves as a significant marker of new conditions
for the Silicon Valley startup ecosystem. Most importantly, the collapse in financial markets has
directly impacted the supply of venture capital invested in startups. The time of the crisis also
coincides with the introduction and mass adoption of touch screen smartphones and tablets with
mobile operating systems that have fundamentally altered users’ digital experience. With the

3
advent of new technologies, alternative economic practices such as new forms of currency,
collaborative consumptions, and data-driven economies began to emerge.
Unlike the utter collapse of its regional economy following the dot-com bubble in 2001,
the 2008 financial crisis has been less detrimental to Silicon Valley. While the crisis has
damaged various economic sectors and industries in other regions, Silicon Valley was able to
recover quickly and flourish with increased labor demand and venture capital funding. In May
2017, the average unemployment rate in Silicon Valley hit a 17-year low of 2.8%, which is 1.5%
lower than the national unemployment rate (Joint Venture, 2017). According to the MoneyTree
Report from PricewaterhouseCoopers (2015), the annual amount of venture capital investment
deployed across the United States has doubled from $30 billion in 2008 to $60 billion in 2015.
As can be seen from Figure 1.1, Silicon Valley has been receiving the highest level of funding
for all regions and accounted for more than half of the total investment amount in the United
States.

Figure 1.1. Investments and deals from Q1 2002 to Q1 2008 in Silicon Valley
Source: PwC MoneyTree Report (2018)

4
The strong recovery of Silicon Valley in the aftermath of the crisis has led to an
entrepreneurial explosion described by The Economist (2014) as a Cambrian moment of startup
evolution. A series of boom and bust cycles followed by a period of solid growth in Silicon
Valley sheds light on mainstream theories of innovation economics, which contend a priori that
economic development is promoted by entrepreneurship and innovation moving through the long
waves of business fluctuations. Figure 1.2 bellow maps the history of Silicon Valley and
illustrates its five waves of business cycles. Each wave, characterized by different negative
externalities, features unique key players and new technological innovations and proceeds
rapidly lasting less than a decade.
Figure 1.2. History of Silicon Valley
Source: Startup Genome (2015)

As such, Silicon Valley’s growth trajectory provides empirical support for the theory of
long waves of business cycles and thus contribute to the exiting literature on entrepreneurship

5
and innovation. In the remaining sections of this introductory chapter, theories of innovation
economics will be discussed and critiqued to situate this dissertation research within the body of
existing relevant literature. In particular, Schumpeterian entrepreneurship is used as a theoretical
framework to explicate important concepts and develop research questions with respect to
Silicon Valley. Then, an executive summary of research design and methods will be explained in
terms of the four-level analysis mentioned earlier. Finally, a brief summary of the organization of
the dissertation will be provided.
Economic Theories of Entrepreneurship and Innovation
Schumpeter's (1939) seminal work on entrepreneurship and innovation foregrounds
innovation as a major disequilibrating force in a system of economic process and development.
The so-called creative destruction is an organic process induced by innovation that
revolutionarizes the entire economic structure from within, constantly bringing changes to
capitalist economy. In the most fundamental sense, innovation is defined as "the setting up of a
new production function" (Knudsen, Becker, & Swedberg, 2011, p. 297). By means of the
production function, Schumpeter characterizes innovation as dependent on this function which
describes the way in which quantity of product varies with quantities of factors.
More specifically, considering economic production as "combining productive services",
innovation denotes new combinations of factors that not only increases the physical output but
also decreases marginal costs of production. Such logic however, is based on the premise that the
innovating is difficult due to routinized old production functions. In light of this, Schumpeter
(1942) depicts five telling features of innovation as: (1) the creation of a new good or new
quality of good; (2) the creation of a new method of production; (3) the opening of a new market;
(4) the capture of a new source of supply; and (5) a new organization of industry – creation or

6
destruction of a monopoly. The following sub-sections explicate each of these features and
discuss how it serves as an important basis that shape and propagate the Silicon Valley startup
ecosystem.
Creation of a New Good
Already in the 1980s, Silicon Valley was the birthplace of "pocket calculators, video
games, home computers, cordless telephones, laser technology, microprocessors, and digital
watches" (Rogers & Larsen, 1984, p. 28). By the end of the 1980s, "Silicon Valley was the home
of increasingly diversified networks of its specialized equipment, component, subsystem, and
software producers" (Saxenian, 1996, p. 122). And it continues to produce new technological
innovations that get quickly adopted by users world-wide. As Saxenian points out:
As firms in the computer and semiconductor industries rejected the model of
stable cost-based competition for a strategy of creating new markets by
constantly introducing new products and applications, they dramatically
shortened product cycles. This new competitive environment privileged Silicon
Valley's regional network-based system, with its capacity to promote
experimentation, learning, and the pursuit of multiple technological trajectories
(p. 131).
New Method of Production
Silicon Valley pioneered a new method of production on the basis of efficiency.
Semiconductor firms employed the use of "low-cost, low-volume, flexible mini-fabs that could
quickly process short runs of different designs on a single line" (Saxenian, 1996, p. 120). This
allowed chipmakers in Silicon Valley to introduce "state-of-the-art products faster than their
more integrated competitors" (p. 121). Unlike its competitor Boston's Route 128, Silicon Valley's
method of production was characterized by "its professional and technical networks rather than
around the individual firm" (p. 30). Local producers, while competing against each other, gained
from mutual learning and adjustment provided by Silicon Valley's collaborative methods of

7
production. Information also plays a key role in the new method of production. Roger and Larsen
(1984) note:
Silicon Valley is an almost perfect example of an information society, in which
a majority of the workforce engages in gathering, processing, or distributing
information, or in making information technology" (Rogers & Larsen, 1984, p.
28).
Opening of a New Market
As suggested by Saxenian (1996), new technological products from Silicon Valley
created new markets for consumers and producers alike. It is equally important to address the
new labor market generated by Silicon Valley's unique network-based environment for
competition as well. The organizational culture of Silicon Valley was characterized by hard-
work ethics and meritocracy, rewarding workers based off of their work performance.
Accordingly, job-hopping for these workers is a way to advance their career and earn more
money, and "unlike elsewhere, in Silicon Valley job-hopping provides greater opportunity for
advancement than staying with the same company (Rogers & Larsen, 1984, p. 88). As a result,
Silicon Valley is characterized by unusually high labor market turnover and job mobility. In
evaluating this phenomena, Rogers and Larsen claim that:
High job mobility is a boon or a disaster, depending on one's perspective. For
employees, the assurance of being able to leave one company and move to
another with an increase in salary provides a form of security. [...] companies
have a quite different view of job-hopping; the loss of experienced employees is
a major problem. A constant staff turnover creates difficulties in establishing
consistency in internal operations. When an engineer who is key to a design
project leaves, much of the thinking behind the project leaves with him. [...] The
high rate of job change is perhaps encouraged by the companies as they urge
employees to aspire better position (p. 88)
New Source of Supply
The origin of Silicon Valley begins with semiconductor production, and the region itself
derives its name from high-purity silicon used in transistors and other semiconductor devices

8
(Silicon Valley, 2015). With the increasing popularity of semiconductor devices in the 1970s,
computer-software and other high-technology industries also began to emerge. Over time,
information and talents became the main supply for knowledge that produced numerous
entrepreneurial ideas and innovations. As Rogers and Larsen argue, "certainly objects [were]
involved, but in most cases the information is much more critical than the material objects" (p.
275).
New Organization of Industry
In comparison to its competitor Boston's Route 128, Silicon Valley had no prior
industrial history. This, however, worked toward its advantage because Silicon Valley's "distance
from established economic and political institutions facilitated experimentation with novel and
productive relationship" (Saxenian, 1996, p. 27). This allowed engineers and entrepreneurs to
create a more flexible industrial system that was "organized around the region and its
professional and technical networks rather than around the individual firm" (p. 30). The resulting
environment was conducive to spread technological skills and know-how within the region.
Furthermore, its decentralized and fluid networks created more opportunities as:
The region's social and professional networks operated as a kind of meta-
organization through which engineers, in shifting combinations, organized
technological advance. Individuals moved between firms and projects without
the alienation that might be expected with such a high degree of mobility
because these relationships remained intact" (p. 37)
Such an organization of the high technology industry was also characterized by: (1) highly
skilled employees, many of whom are scientists and engineers; (2) a fast rate of growth; (3) a
high ratio of R&D expenditures to sales; and (4) a worldwide market for its products" (Rogers &
Larsen, 1984, p. 29). Another unique feature of the Silicon Valley high-tech industry is its
proximity to a research university, which also accounts for its success. This also provides a solid

9
infrastructure for scholars and practitioners to collaborate and benefit from one another.
Moreover, startup firms benefit from clustering in this region because:
A new born firm is necessarily very fragile in its dependence on suppliers,
financiers, markets, and other parts of its infrastructure. Logically, start-ups are
most likely to be formed and succeed in an area where the infrastructure already
exists. infrastructure is a basic reason for the principle of agglomeration, the
tendency for firms in an industry to gather in the same area (p. 232).

Renewed Interest in Schumpeterian Entrepreneurship
Following the 2008 global financial crisis, there has been a renewed interest in Joseph
Schumpeter's pioneering theories of the entrepreneur (Knudsen, Becker, & Swedberg, 2011).
Schumpeterian entrepreneurship has become a foundational theoretical framework for studies on
entrepreneurship and innovation. His well-known definition of entrepreneurship reads as
follows: "The carrying out of new combinations we call 'enterprise'; the individuals whose
function it is to carry them out we call 'entrepreneurs'" (Schumpeter, 1983, p. 74). Though his
ideas were not much recognized by his contemporaries who viewed his work as outdated and not
in synch with the then-fashionable Keynesianism, the recent economic downturn has rekindled
academic and political interest in the importance of entrepreneurship and innovation.
Schumpeter’s proposition on economic instability as an integral part of economic
development was dismissed by his contemporaries who gave much attention to saving and
capital accumulation as the factor of economic growth (Mokyr & Oxford University Press,
2003). However, the evolutionary dynamics of social and economic change implicated in
Schumpeter's theories has become an important topic that is high on the agenda today. Hence, his
basic intuitions allow scholars and politicians alike to return to the theoretical precepts that could
be used to analyze a number of important issues in the modern economy. Application and further
development of Schumpeter's ideas could provide useful insights into how the recent economic

10
downturn could be "followed by a period of creative construction that leads the way to a new,
prosperous economy" (Knudsen, Becker, & Swedberg, 2011, p. 36).
Schumpeter observes economic development as a dynamic process and thus, it is crucial
to understand the circular flow of economic life that proceeds routinely on the basis of past
experience (Schumpeter, 1939). He also highlights the importance of entrepreneurs as an active
agent of change who plays a key role in economic progress by innovating and creating new
combinations in production (Schumpeter & Elliott, 1982). Economic competition therefore
involves mainly the dynamic innovations of entrepreneurs. The innovative entrepreneurs take
risks and introduce new innovations to stimulate economic activity, through which the old are
replaced by creative destruction.
Economic Definition of Innovation
Since Schumpeter's pioneering work on innovation economics, it has been well established
that innovation plays a crucial role in promoting economic growth and development. However,
economists like Freeman takes a step further and argues that innovation is also critical in terms of
increasing quality of life. He articulates:
[...] innovation is of importance not only for increasing prosperity, but also in
the more fundamental sense of enabling men to do things which have never been
done before at all. It enables the whole quality of life to be changed for better or
for worse. (Freeman, 2013, p. 3)

This study concurs with Freeman's view that innovation offers more than economic implications
of increasing prosperity and productivity growth. Innovation, more importantly, empowers
individuals to transform their respective society by means of creative ideas. In addition to
individual empowerment, innovation serves as a catalyst for fundamental improvements in the
quality of life. In justifying this argument, this dissertation develops a more contemporary

11
definition of innovation used in economics to elucidate how and why innovation entails both
economic and social dimensions.
Following the Schumpeterian approach, contemporary economists routinely define
innovation as "the first commercial application or production of a new process or product"
(Freeman, 2013, p. 110). Before delving into the dynamic features of innovation, however, it is
important to note the important distinction between inventions and innovations. While the former
concerns novel ideas or models for "a new or improved device, product, process, or system," the
latter is accomplished only when these inventions become commercialized in the market (p. 5).
Not all inventions become innovations as commercialization requires understanding the market
where a product or service can be bought and sold. Hence, it follows that:
At one extreme there may be cases where the only novelty lies in the idea for a
new market for an existing product; at the other extreme, there may be cases
where a new scientific discovery automatically commands a market without any
further adaptation or development. The vast majority of innovations lie
somewhere in between these two extremes and involve some imaginative
combination of new technical possibilities and market possibilities. Necessity
may be the mother of invention, but procreation still requires a partner. (p. 110)

In discussing innovation as commercialization of invention, Freeman carefully analyzes
the interplay between technical novelty and the market logic. Innovation is a twofold process that
involves productive efforts by both individuals and firms. For instance, the original invention
could occur in a university or by a smaller firm, but the actual innovation results from marketing
and investment activities initiated by larger firms. Taking this novel invention to market
"requires an array of incremental product innovations, such as continual improvements in
automobile transmission, together with innovations in product processes" (Breznitz & Cowhey,
2012, p. 127). Without considering the market demand and production costs, even the most
qualified and enthusiastic scientist-investor or engineer is less likely to succeed as an innovator.

12
Therefore, as suggested by Freeman (2013), successful innovation requires "the coupling
between science, technology, innovative investment and the market" (p. 214). In terms of cause
and effect, innovation "embodies a specific balance between public-good aspects and private
(i.e., economically appropriable) features", and its varying levels of the "private appropriation"
could be seen as "both the incentive to and the outcome of the innovative process" (Freeman,
1990, p. 126). Although innovation could take forms other than technological, Freeman mainly
focuses on the new technologies as innovations that create new industries with a new production
function. Freeman's selection of a wide range of industries covers various kinds of innovations
including product innovations, process innovations, energy innovations and materials innovation
(p. 19).
Characterized by "an important all pervasive low-cost input, often a source of energy,
sometimes a crucial material, plus significant new products and processes and a new
infrastructure," innovation that marks each technological revolution renders "an upheaval in the
whole fabric of the economy and of propelling a long-term upsurge of development" (Perez,
2003, p. 8). Technological revolutions that come along with innovation also accelerates
productivity for economic activities, which leads to regenerating the productive system and
increasing the general level of efficiency and competitiveness. Concerning these economic
dimensions:
[...] economists have always recognized the central importance of technological
innovation for economic progress. The famous first chapter of Adam Smith's
Wealth of Nations plunges immediately into discussion of 'improvements in
machinery' and the way in which division of labor promotes specialized
intentions. Marx's model of the capitalist economy ascribes a central role to
technical innovation in capital goods - 'the bourgeoisie cannot exist without
constantly revolutionizing the means of production'. Marshall had no hesitation
in describing 'knowledge' as the chief engine of progress in the economy
(Freeman, 2013, p. 3).

13
To survive in a competitive capitalist environment, economic agents are "forced to adapt
to technological change" by re-organizing their innovative activities (Archibugi et al., 1999, p.
536). Following the logic of creative destructive, innovation stimulates a new kind of effective
competition, "which commands a decisive cost or quality disadvantage" (Knudsen, Becker, &
Swedberg, 2011, p. 317). This relates to Arthur's (1999) analysis of the economy as an evolving
complex system. Complexity portrays the economy as "process dependent, organic, and always
evolving " (p. 107). In such an environment, it is the technological breakthrough that allows
positive feedbacks or increasing returns. In light of modernization, the scale of this outcome
tends to be global (Conrad et al., 2009). Technological innovation not only serves as "a vehicle
for the diffusion of information and knowledge across borders" but also adapts to the competitive
market that is constantly changing in an international environment (Archibugi et al., 1999, p.
535). As a result, innovators who implement new technologies need to embrace the rapid change
that comes with the rise of global competition, which often poses increased uncertainty and
insecurity.
Discontinuous Waves of Business Cycles
Focusing on how growth, change, and entrepreneurship interact, Schumpeter uses a
theory of discontinuous evolution to explain that although inventions and discoveries take place
continuously in stochastic patterns, their transformation into entrepreneurial innovations occurs
in distinct and discontinuous waves (Schumpeter, 1939). This is because the economic, political,
and cultural resistance of the status quo make it difficult and tedious to penetrate the existing
market. Institutions also make innovations go through legal processes to be approved for their
commercial use. Moreover, because new innovations entail a high degree of uncertainty, it takes
time to test and revise them before their full adoption.

14
Hence, a certain accumulation of innovation power is required to break such resistance
against change. Schumpeter's theories of business cycle have their roots in the Walrasian general
equilibrium approach. His notion of the entrepreneur stems from Walras' general equilibrium
theory in which entrepreneurs are presented as passive figures. Schumpeter disagrees with
Walras and makes entrepreneurs the chief agent of change that brings disequilibrium in a
competitive economy. His famous phrase “the perennial gale of creative destruction” suggests
that capitalism would come to an end gradually in the long run through these changes as
innovations make old inventions to become outdated (Schumpeter, 1939).
Kondratieff Waves
The phenomenon of Kondratieff long cycles explains technological innovations and long-
run economic progress in the midst of complex economic environment. Lasting about half a
century, each business cycle is unique "because of the variety of technical innovations as well as
the variety of exogenous events such as wars, gold discoveries or harvest failures" (Freeman,
2013, p. 208). The initial success and profitability of innovations in the early period of the cycle
attracts more firms to enter into the sector leading to a band-wagon effect, acting as "a signal to
the swarms of imitator" (p. 215). The resulting maturation and standardization of the
technologies in these new industries, however, soon reduce their competitiveness in the market.
This also decreases productivity as the "economies of scale are exploited and the
pressures shift to cost-saving innovations in process technologies," and more over, "capital-
intensity increases and employment growth slows down or even stops altogether" (Freeman,
2013, p. 209). At this point, new innovation takes over and marks the beginning of the next long
run, which leads to the destruction of the previous combinations - creative destruction. To
illustrate this repetitive cycle, Hall and Preston (1988) provide vivid examples from history. The

15
first Kondratieff lasting from the late 18th century to mid-19th century created a new industry of
coal, iron, and steam. Next, the second Kondratieff in the 19th century was characterized by
railways and steamships. The third Kondratieff is attributed to the electrical revolution in the
20th century. Last, the fourth Kondratieff came with the recent development in new information
technologies.
Each cycle was accompanied by introduction of new technological innovation. into the
economic system replacing the old. As examples of creative destruction:
Arkwright's water frame was a clear threat to hand spinner both in England and
in India. The Liverpool-Manchester railway announced the demise of the horse-
drawn carriage for long-distance passenger travel, affecting various occupations
from innkeepers to veterinarians (Perez, 2002, p. 23).
It is also noteworthy that each of these cycles emerged from specific geographical domains
(Castells & Hall, 1994; Freeman, 2013; Hall, 1988). For instance, in the third Kondratieff,
metropolitan regions like Berlin, New York-Boston, and London were the center of global
innovation. In terms of the geography of new information technologies, the fourth Kondratieff,
Silicon Valley has been hailed as the leading milieu of innovation since the 1970s. That these
geographical domains were able to outpace other regions as the central locus of innovation
demonstrates the importance of finding one's own niche in a competitive environment. This also
brings to the question of how these key innovators successfully maintain their competitive edge
in the maturing industries.
Based on a series of empirical case studies, Freeman (2013) explores the "extreme
complexity of the interfaces between advancing science, technology, and a changing market" and
suggests the following: (1) strong in-house professional R&D; (2) performance of basic research
or close connections with those conducting such research; (3) the use of patents to gain
protection and to bargain with competitors; (4) large enough size to finance fairly heavy R&D

16
expenditures over long periods; (5) shorter lead-times than competitors; (6) readiness to take
high risks; (7) early and imaginative identification of a potential market; (8) careful attention to
the potential market and substantial efforts to involve, educate and assist users; (9)
entrepreneurship strong enough effectively to coordinate R&D, production and marketing and;
(10) good communications with the outside scientific world as well as with customers (p. 112).
Limitations of Economic Approaches
By definition of innovation, setting up a new production function indicates new
combinations of input by employing existing supplies of productive means in the economic
system. Therefore, the new kind of production function allows more output with less input,
increasing economic productivity and efficiency. While there are various kinds of innovation,
economists mainly concern technological innovation as the engine of economic growth and
progress. According to Adam Smith, this could be an improvement in machinery and for Karl
Marx, technological innovation is the revolutionary means of production machinery. As a result
of increasing returns induced by innovation, the economy becomes more competitive and
complex, constantly evolving through an organic process (Arthur, 1994). Also, with new cycles
of innovation comes a destruction of the old.
Innovators must therefore strive to maintain their competitive edge in the economy, and
the aforementioned historical and empirical cases illustrate the importance of one's geography
location and offer practical suggestions for firms to better succeed in the competition. Missing in
this economic analysis, however, is the role of entrepreneurship that enacts the
commercialization of an invention feasible and also, the culture of the market that shapes
innovation. Focusing solely on the economic dimension of innovation is thus limited because as
mentioned earlier, innovation not only promotes economic prosperity but also enhances quality

17
of life and empowers individuals. Hence, it is important to understand how innovation interacts
with entrepreneurship and culture.
Cultural and Social Dimensions of Innovation
The synergistic interaction between entrepreneurship, culture, and innovation stimulates
creative destruction and accounts for large-scale changes in the economy. Such an interaction
also shows that the importance of the geography still holds in terms of innovative milieu - a term
coined by the French geography Philippe Aydalot in 1986
1
. To illustrate how culture engenders
the geography of innovation, Hall (1998) presents an analytical comparison of Florence and
London:
In Florence the new capitalists were the promoters of culture; in London,
entrenched in the City, some of them formed a Puritan backwoods movement,
trying repeated to shut the theaters down, and eventually succeeding. In Florence
the artist-craftsmen were traditional medieval guild members, with the values
that that implied; in London they were new-style capitalist adventurers, coming
from roots in trade, conscious that they were on the road to fame and fortune,
and very much in a hurry - even to the sloppiness of their production methods.
The capitalism is of a distinctively alter kind than the fourteenth-century Italian
variety: art is now unambiguously a commodity, analogous to Hollywood in the
1930s or to commercial television anywhere in the 1990s (p. 157).
In a more contemporary context, Silicon Valley's culture of "relative openness, the fast
pace of business activity, and the cooperative practices" distinguishes the region as a milieu of
innovation (Saxenian, 1996, p. 111). Silicon Valley's entrepreneurs adopted a flexible business
model based on networks, which departed from the vertical, corporate models of their
predecessors in Route 128. This unique culture allowed diversification of the regional economy
and markets for a wide range of new technological innovations. Entrepreneur's close social
relations with venture capitalists is also heavily depicted in the Silicon Valley model of

1
The term coined by Philippe Aydalot was conceptually developed through a series of
exchanges in Berkley between Aydalot, Manuel Castells, and Peter Hall in the 1980s.

18
entrepreneurship. Schumpeter makes it clear in his argument that "successful entrepreneur
become capitalists (or landowners), while unsuccessful ones presumably become workers or
managers (Schumpeter, 1939, p. 78). In other words, capital accumulation for financing
innovation is a crucial aspect of entrepreneurship. But the role of venture capitalists, who provide
financing for innovation, serves beyond economic purposes.
In Silicon Valley, venture capitalists become closely involved in "advising entrepreneurs
on business plans and strategies, helping find co-investors, recruiting key managers, and serving
on boards of directors" (Saxenian, 1996, p. 39). Venture capitalists also play the role of
knowledge brokers "who acquire and create through personal (and generally local) networks
about industries, market conditions, entrepreneurs, and companies through a constant process of
interaction and observation" (Zook, 2005, p. 3). In a sense, their capability to create and manage
social network for the transmission of tacit knowledge is more valuable than providing
investment.
The cultural and social dimensions of innovation could be contradictory to the
conventional economic theory of rational decision making. Because venture capitalists too are
humans, non-market factors could influence their decisions based on irrationality. For instance,
the dot-com economy in the 1990s was characterized by irrational exuberance (Shiller, 2005).
The "widespread faith in the transformative nature of the Internet and the avarice and ambition of
those involved in the dot-com firms" led to excessive speculation (Zook, 2005, p. 8). During this
period, venture capitalists invested heavily in Internet companies with little prospect for success
and viability, which eventually precipitated the collapse of the dot-com bubble. Consequently,
the resulting downturn devastated the economy with layoffs, bankruptcies, and a stock market
collapse.

19
Research Questions
This introductory chapter has thus far located the topic of Silicon Valley’s startup
ecosystem in a broader theoretical framework of entrepreneurship and innovation and examined
how they interact with various economic and social dimensions discussed in the existing
literature. More specifically, it has elaborated on how innovation enhances business productivity
and competitiveness in light of Schumpeterian entrepreneurship, Kondratieff cycles, and
innovative milieus and their socio-cultural characteristics. By explicating on the concept of
entrepreneurship and innovation through theoretical grounding in innovation economics, the
chapter has situated this dissertation study within a disciplinary framework and context.
Moreover, the importance of both economic and social dimensions of entrepreneurship
and innovation has been equally stressed throughout the analysis. In both dimensions, there is a
positive feedback loop that reinforces their synergetic relationship with innovation. In terms of
setting up a new production function, entrepreneurship and innovation increase productivity and
promote long term economic growth. The economy, in return, creates a competitive environment
to foster innovation. Likewise, in terms of commercializing invention, innovation is dependent
on its inventor and entrepreneur alike. While innovation produces lasting changes in culture, the
socio-cultural context of the inventor and the entrepreneur shapes innovation as well. To account
such interactive nature of innovation production, the study uses the term “networked
innovation”.
Overall, taking both of these dimensions into consideration, this dissertation argues that
the growth of Silicon Valley’s startup ecosystem in the 21
st
century is fueled by entrepreneurial
resilience of its actors who produce networked innovation through synergistic interactions in a
self-reproducing milieu of innovation. To advance this argument, the dissertation explores

20
various aspects of both the processes and outcomes of innovation production using four units of
analysis – dynamic entrepreneur, startup organization, entrepreneurial network, and innovative
milieu. Each of these will be analyzed in its own chapter supported by relevant empirical
evidence. Accordingly, the guiding research questions are as follows, and these guiding
questions will be approached by a set of more concrete research questions and hypotheses in
each of the data chapters:
1. How and why does entrepreneurial resilience serve as an important characteristic of
dynamic entrepreneurs? What are the conditions for entrepreneurial resilience in
Silicon Valley? How does entrepreneurial resilience contribute to the production of
innovation?
2. What are the organizational processes involved in building a startup? How do ideas of
individual entrepreneurs get translated into business models? What are the relational
dynamics between entrepreneurs and other key stakeholders and how do they evolve
as startups expand?
3. How are networks of startups structured and who are the key players? How does
geographical proximity influence startup networks?
4. Why does Silicon Valley continue to be the leading milieu of innovation? How has it
changed over the recent decade?
Research Design and Methodology
In addressing the aforementioned guiding research questions, this dissertation uses mixed
methods. The qualitative component of the dissertation is based on four years of fieldwork in the
startup industry. Multi-sited ethnography, participant-observation, and interviews were
conducted at startup conferences, VC workshops, pitch competitions, industry expos and

21
networking events in Silicon Valley between 2014 and 2017. Table 1.1 lists the field sites in
Silicon Valley. The purpose of observing and participating in startup events was to closely
examine social interactions that take in professional settings of entrepreneurial activities, in
which I could observe the most natural behaviors of entrepreneurs as they networked and
discussed business ideas among themselves. Because the goal of this dissertation is to capture a
holistic understanding of the Silicon Valley startup ecosystem, the multi-sited ethnography of
large-scale events was the most appropriate and effective method to gather objective data.
Table 1.1. List of fieldwork sites in Silicon Valley
Event Name Date & Location Description
1 Glenbrook Payments
Workshop: Bitcoin

March 26, 2014
Mountainview, CA

A workshop organized by Glenbrook
Partners on Bitcoin, Data in Payments,
B2B Payments followed by questions
and discussion on new forms of currency
and payment initiatives.
http://glenbrook.com
2 TiEcon May 5-7, 2016
Santa Clara, CA

Entrepreneurship conference organized
by the Indus Entrepreneurs (TiE)
dedicated to fostering entrepreneurship
through mentoring, networking, and
education.
http://www.tiecon.org/
3 Finovate Spring May 10-11, 2016
San Jose, CA
Financial technologies conference in a
unique, short-form, demo-only format
that attracts large, high-impact audiences
of senior financial and banking
executives, venture capitalists, industry
analysts, and startups. Participants get to
vote for Best of Show winners.
http://spring2016.finovate.com/eventbroc
hure
4 Internet of Things Expo May 10-12, 2016
May 14-17, 2017
The largest Internet of Things (IoT) event
on topics such as smart homes, connected
healthcare, wearable devices,

22
Santa Clara, CA autonomous cars, etc. Over 10,000
people and 200 exhibitors attend the expo
that combines IoT, Future Connected
Cars, Wearable World Congress, and
Apps World North America.
http://iotworldevent.com/
5 Vator Splash Spring May 12, 2016
Oakland, CA
Startup event and competition that brings
together high-caliber speakers who talk
about how to build and scale successful
companies, how their industries are
changing and the opportunities those
changes are creating.
http://events.vator.tv/splash-spring-may-
2016/
6 The Startup Conference May 19, 2016
May 17, 2016
Redwood City, CA
One of the largest conferences in Silicon
Valley, with 1,500 entrepreneurs and
investors, who learn how to pitch VCs,
find co-founders, launch product to the
press, reach early adopters and
influencers, etc.
http://thestartupconference.com
7 Maker Faire Bay Area May 20, 2016
San Mateo, CA
A science/county fair where tech
enthusiasts, engineers, educators, science
clubs, and commercial exhibitors gather
to celebrate the Maker Movement
http://makerfaire.com
8 EMTech Digital May 23-24, 2016
San Francisco, CA
Conference organized by MIT Tech
Review that highlights the latest research
on artificial intelligence techniques,
including deep learning and speech and
image recognition.
http://www.technologyreview.com/emtec
h/
9 Silicon Valley Open
Doors
May 25-26, 2016
May 24-25, 2016
Mountain View, CA
Technology investment conference that
brings in a mix of 2000 investors and
entrepreneurs and provides pitch sessions
of 35 preselected entrepreneurs and

23
startups.
http://www.svod.org
10 Startup Pitchfest May 10, 2017
Palo Alto, CA
Pitch coaching session by VC Taskforce
to give input and direction to
entrepreneurs who get to practice their
elevator pitch with venture capitalists in
Silicon Valley.
http://www.vctaskforce.com
11 Augmented World
Expo
May 30 - June 1,
2017
Santa Clara, CA
Industry conference and expo
showcasing speakers, startups, and
organizations who are using Augmented
Reality and Virtual Reality. There were
4,700 attendees, 351 speakers, and 212
exhibitors.
http://www.augmentedworldexpo.com/

Multi-sited ethnography also opened more avenues for other naturalistic methods of
evidence gathering. In-depth case studies of startup competitions and pitch sessions uncover the
behind-the-scenes inner workings of the industry and illuminate how these activities influence
the production of innovation in the startup ecosystem. The analysis employs triangulation and
utilizes a particular type of evidence, be it ethnographic, participant-observation, process-tracing,
or field research (Gerring, 2007). An individual case is “a spatially delimited phenomenon (a
unit) observed at a single point in time or some period of time [and] each case may provide a
single observation or multiple (within-case) observations” (p. 19). This dissertation also deals
with international case studies from Berlin, Copenhagen, Naples, and Seoul where additional
fieldwork interviewing scholars and industry professionals in entrepreneurship and technological
innovation was conducted. Table 1.2 displays a list of international fieldwork.

24
Table 1.2. List of international fieldwork sites and descriptions

Location

Date

Description

1 Seoul, South Korea June 2015

Interviewed over a dozen U.S.-educated South
Korean returnees who work for startups or
multinational conglomerates in Seoul and Pangyo
Techno Valley.
2 Naples, Italy

July 2016

Participated in the 32
nd
European Group for
Organizational Studies Colloquium and presented
during the “Entrepreneurial Origins of
Organizational Routines and their Impact on the
Development of Organizations” panel.
3 Copenhagen, Denmark July 2017

Participated in the 33
rd
European Group for
Organizational Studies Colloquium and presented
during the “Geopolitical Implications of
International Business” panel.
4 Berlin, Germany December
2017

Participated in TechCrunch Disrupt that featured
the Startup Battlefield competition, a 24-hour
Hackathon, Startup Alley, and Hardware Alley.

For the networks chapter, company data of successful startups in Silicon Valley and other
regions were collated from industry reports, company databases, and the U.S. patent office.
Sample was selected from the 2016 Fortune’s list of unicorn startups ranked by valuation, which
is based on a combination of data from PitchBook, CB Insights, news reports, and their own
investigation. In 2016, the list ranked 174 domestic and global startups as successful private
companies with post-money valuations of $1 billion or more. Due to limited data access for
investment activities of foreign companies, 96 domestic startups were selected for a series of
network analyses.
Next, investors and total investment funds for each of these startups were identified from
Thomson One, an online database that provides company information including the company
financials, ratios, filings, and reports formerly found in the Investext database. Investors for these
sample startups are those who have participated in any of the funding rounds since the

25
establishment of the company. In total, 367 investors funded the sample startups. Along with
investor information, attributes of each startup such as the company location, history, and
industry sector were collected as part of the data from Hoover’s Online database that delivers
information on private and public companies and their industry analyses. Patent assignment
information was collected from the United States Patent and Trademark Office.
The collected data were organized in two-mode, affiliation matrices, consisting of
investors in the column dimension and startups in the row dimension. UCINET 6.0 was used to
analyze the data and transform the two-mode affiliation matrix to one mode data (S. P. Borgatti,
Everett, & Freeman, 2002). In the process of transforming the two-mode affiliation matrix to 1
mode data, the original affiliation matrix was multiplied by its transpose matrix. This constructed
a startup-by-startup network based on a shared investor so that the network displays connections
between all of the startups. Three separate network graphs were produced using the Netdraw
network visualization tool, which is embedded in UCINET 6.0.
Organization of the Dissertation
The structure of this dissertation, which consists of four interconnected parts, provides a
holistic evaluation of the current Silicon Valley startup ecosystem and offers insight into its
broader influence in the global economy. Each chapter develops its own theoretical framework
and methodology to address its own set of research questions. Chapter 2 reviews explanatory
factors essential for entrepreneurial resilience in today’s Silicon Valley startup scene. Chapter 3
explores key organizational conditions and relational dynamics involved in building a startup.
Chapter 4 examines network structures and effects on startups’ investment performance and
innovation capacity. Chapter 5 evaluates how Silicon Valley has changed over time yet still
persists to be the milieu of innovation in the 21
st
century. Finally, the concluding chapter

26
combines the findings of the four data chapters and points out some commonalities that emerge
across these chapters in light of broader implications and future research directions.

27
Chapter 2: Explaining Entrepreneurial Resilience in the 21
st
Century Silicon Valley
“I’m convinced that about half of what separates the successful entrepreneurs from the non-
successful ones is pure perseverance.”
- Steve Jobs, 1995

Entrepreneurs always played an integral role in fostering innovation and economic
growth in Silicon Valley. Indeed, the origin of Silicon Valley’s high-tech industry is best
understood by tracing the successful endeavors of some of its most prominent entrepreneurs.
Notably, William Hewlett and David Packard, the co-founders of Hewlett-Packard Company,
started their company in their garage in Palo Alto in the 1940s after graduating from Stanford
University in electrical engineering under the direction of Professor Fred Terman. William
Shockley, who along with Terman is credited with being the father of Silicon Valley, invented
the transistor at Bell Labs and founded the Shockley Semiconductor Laboratory in Mountain
View in the 1950s. Later, Shockley’s employees create many companies such as Fairchild
Semiconductor and Intel. In time, Silicon Valley became a regional milieu of tech innovation and
attracted young, passionate entrepreneurs from everywhere. Its long line of tech entrepreneurs
spans from Apple’s Steve Jobs to Mark Zuckerberg of Facebook, the biggest icons of the
contemporary tech industry (Silicon Valley, 2015).
Entrepreneurs in Silicon Valley’s tech industry are unique because they have certain traits
that distinguish them from other entrepreneurs. What makes them particularly distinctive is their
resilience against entrepreneurial odds. Here resilience means the willingness and ability to
thrive in challenging circumstances. Examples of resilience include but are not limited to the
adaptability to external changes, the capacity to take action and manage resources in advance of
an expected event, and the ability to come up with dynamic strategies as circumstances change

28
(Hamel & Välikangas, 2003). Pursuing an entrepreneurial endeavor rides on resilience because it
involves risk and uncertainty and thus, requires perseverance and delayed gratification for future
gains. As a result, visionary entrepreneurs who consistently outperform their industry rivals as
competition increases stay persistent by means of entrepreneurial resilience. At the regional
level, entrepreneurial resilience has been a vital source of innovation and the prime engine of
economic growth in Silicon Valley. This has been salient throughout the history of Silicon
Valley especially during the turbulent times such as the dot-com crisis and the financial crisis of
2008. Taking these into account, this chapter explores the various dynamics that constitute
entrepreneurial resilience in the current new wave of innovation in Silicon Valley.
To start, consider the meaning of entrepreneurial resilience in the context of
contemporary Silicon Valley startup ecosystem. In the innovation and entrepreneurship
literature, entrepreneurial resilience is generally defined in two ways (Hedner, Abouzeedan &
Klofsten, 2011). Entrepreneurial resilience may refer to one’s ability to recover and bounce back
from various setbacks. Or, entrepreneurial resilience may concern how one continues to function
and navigate through adverse circumstances. The former emphasizes the importance of reducing
risks and re-establishing equilibrium, while the latter focuses on maintaining the status quo in the
face of future uncertainties. In describing entrepreneurial resilience, I combine both definitions to
capture a broader meaning. Hence, here entrepreneurial resilience predominantly relates to
survival and the leveraging of a startup which depends on a set of diverse factors including: (1)
individual; (2) behavioral; (3) cultural; (4) organizational; and (5) institutional conditions.
To examine each of these conditions and discuss how and why they lead to
entrepreneurial resilience, Chapter 2 proceeds in three interconnected sections. First, it provides
a theoretical base through a brief overview of the Schumpeterian entrepreneurship and it

29
elaborates how entrepreneurial resilience can be explained by Schumpeter’s notion of the
dynamic entrepreneur. The limitations of Schumpeter’s theory are noted and suggestions to
better understand entrepreneurial resilience by introducing empirical evidence from my
fieldwork on Silicon Valley’s startup ecosystem. Next, it details the field sites used for this study
and it explains how the data was analyzed. Third, the individual, behavioral, and cultural
conditions that constitute entrepreneurial resilience in today’s Silicon Valley startup scene are
assessed. Organizational and institutional conditions are discussed in later chapters.
Schumpeterian Entrepreneurship
The Schumpeterian theory of entrepreneurship foregrounds the entrepreneur as a catalyst
of economic progress. The entrepreneur carries out innovation that make existing inventions
obsolete through the process of creative destruction. In discussing the entrepreneur’s role in
revolutionizing the economic structure, Schumpeter notes:
individual acts of entrepreneurship set off wavelike macro changes in the
economy that disrupt the circular flow of the steady state. The bursts of creative
energy unleashed by entrepreneurs cause this flow to be temporarily destabilized
until it has the time to adapt, only to be disestablished again by other innovations
(Knudsen et al., 2011, p. 16).
Schumpeter defines an entrepreneur as the man of action who constantly innovates and
creates consumer demand in the market by introducing new combinations in production. His
portrayal of the man of action can be ascribed to the difference in two types of mankind -
dynamic entrepreneur versus static person. He sees a rigid dichotomy in human nature that
differentiates the entrepreneur from the non-entrepreneur. In contrast to the static person, the
dynamic entrepreneur breaks out of equilibrium and does what is new. He is active and
energetic, a leader who puts together new combinations, feels no inner resistance to change. He
battles resistance to his actions, makes intuitive choices among a multitude of new alternatives,

30
and is motivated by power and joy in creation. He may command no resources but borrows from
a bank.
In his later work, Schumpeter conceptualizes entrepreneur as the middleman between the
producers and the consumers in the market. Incorporating the mechanisms of social dynamism
into the interaction between economic actors, Schumpeter (1983) depicts entrepreneurship as a
social process of economic relations among human actors. The dynamic entrepreneur and the
social mechanisms of Schumpeterian entrepreneurship provide a useful lens through which to
examine the conditions that make up entrepreneurial resilience in the economic setting of the
technology startup ecosystem in Silicon Valley. In particular, close attention is paid to the
dynamic entrepreneur, which is used as a theoretical basis to argue that entrepreneurship stems
from a nexus of individual characteristics such as personality, beliefs, and values.
However, individual characteristics alone are not sufficient to explain entrepreneurial
resilience. Becoming a resilient entrepreneur also requires behavioral patterns and actions that
create a social process of competitive entrepreneurial activities. One must fulfill a set of cultural,
organizational, and institutional conditions to paint a complete portrait of an entrepreneur. To
support this argument, findings from my fieldwork in Silicon Valley are presented as evidence to
illustrate that becoming an entrepreneur requires a multi-step growth progression. There may be
some variations arising from cultural, educational, and professional background, but the social
process of becoming an entrepreneur who thrives in the competitive Silicon Valley startup
ecosystem follows a similar path.
Methodology
Chapter 2 pays close attention to the ethnographic field data I collected at various
industry conferences, networking events, startup competitions, and pitch workshops in Silicon

31
Valley. The purpose of my fieldwork was to examine social interactions that take place in
specific, physical locations of startup activities where entrepreneurs and other relevant actors
come to learn, share ideas, and connect with each other. Hence, I developed a multi-sited
research strategy to follow the entrepreneurs and other relevant actors in various professional
settings (Marcus, 1995). Engaging in multi-sited ethnography exposed me to the inner workings
of the current Silicon Valley startup industry. It also allowed me, as both an attendee and
participant of the conferences, to observe and understand how individual characteristics of
Silicon Valley entrepreneur relate to and interact with patterns of their behaviors and actions
under certain cultural conditions. My use of ethnographic and other qualitative methods such as
participant-observation and open conversational interviews with the Silicon Valley entrepreneurs
and venture capitalists allowed me to get close to the issues I wanted to understand.
In addition to field observations, I spent a large amount of time getting to know the
entrepreneurs and conducting open conversational interviews at these sites (Gray, 2002). In the
early stages of my research, I used unstructured interviews to gather information from the
participants’ perspectives due to my inexperience and lack exposure to the professional
environments. As I observed and interacted with Silicon Valley entrepreneurs and venture
capitalists, open conversational interviews helped to get them to open up and share their life
stories. After collecting some interview data, I narrowed the focus to seek specific information.
When more data was needed, I returned to some events including the IoT Expo, the Startup
Conference, and Silicon Valley Open Doors to explore whether the perspectives of a particular
group changed over time. This allowed me to organize my data across a number of grids and
develop comparative dimensions to understand a new generation of entrepreneurs.

32
I took rigorous field notes which were coded and analyzed using NVivo software. The
analytic plan in this part of the study consisted of three steps. First, to truncate the data to a
manageable size, I scanned my field notes for sections and phrases that concerned the topic of
entrepreneurial resilience. Second, I used open coding to categorizes a particular phenomenon by
scrutinizing data, and then I selected phrases according to themes that emerged from my research
questions (Strauss & Strauss, 2008). Third, I used the constant comparison technique to refine
the nodes and examined the data again to extract meanings (Glaser & Strauss, 1973).
In this chapter, the individual, behavioral, and cultural conditions for entrepreneurial
resilience were explored. Data from my field notes were compared and analyzed using the
theme-oriented strategy (Huberman & Miles, 1994). Accordingly, sub-themes were assigned
under the three conditions and phrases selected for sub-themes were coded with rich explanation
and examples of the studied phenomenon. Table 2.1 displays the organization of themes and
subthemes in this study.
Table 2.1. Codes of themes and subthemes of entrepreneurial resilience
Themes Subthemes
Individual conditions Dynamic entrepreneur
Silicon Valley entrepreneur
Expectations from venture capitalists
Emotional resilience

Behavioral conditions Pitch deck
Networking
Daily routines

Cultural conditions Values and beliefs
Meritocracy
Gender

33
Individual Conditions
Dynamic Entrepreneur
Schumpeter delineates the entrepreneur as the man of action by presenting two types of
human nature - dynamic entrepreneur and static person. His analysis is a useful framework to
organize my field observations of individual qualities that build entrepreneurial resilience.
Overall, characteristics of the dynamic entrepreneur can be divided into three broad categories.
The first concerns the entrepreneur’s personality traits such as being dynamic, active, energetic,
etc. The second deals with behavioral traits that include disrupting the equilibrium, doing what is
new, making intuitive choices, and seeking external resources. Last, there are cultural traits that
reflect the entrepreneur’s values and beliefs. The dynamic entrepreneur embraces change, battles
resistance to his actions, and finds power and joy in creation. Table 2.2 summarizes noticeable
features of the dynamic entrepreneur in contrast to the static person.
Table 2.2. Dynamic entrepreneur and static person
Dynamic Entrepreneur Static Person
● Dynamic
● Breaks out of equilibrium
● Does what is new
● Active, energetic
● Leader
● Puts together new combinations
● No inner resistance to change
● Battles resistance to his actions
● Makes an intuitive choice among a
multitude of new alternatives
● Motivated by power and joy in
creation
● Commands no resources but
borrows from a bank
● Static
● Seeks equilibrium
● Repeats what already exists
● Passive, low energy
● Follower
● Accepts existing ways
● Strong resistance to change
● Hostile to new actions of others
● Makes a rational choice among existing
alternatives
● Motivation exclusively by needs and
stops when these are satisfied
● Commands no resources and has no use
for new resources

Source: Knudsen et al. (2011)

34
The remainder of Chapter 2 extends the analysis of Schumpeterian entrepreneurship in
the context of Silicon Valley’s startup ecosystem. By analyzing my interviews and field notes, I
present the findings from my fieldwork in Silicon Valley in relation to personality, behavioral,
and cultural characteristics. These characteristics create the necessary conditions for
entrepreneurial resilience, which also depends on how personal, behavioral, and cultural
conditions interact with one another at different levels.
Silicon Valley Entrepreneur at First Glance
To explore the explanatory role of the unique traits of Silicon Valley entrepreneurs in
terms of Schumpeter’s dynamic entrepreneur, my initial fieldwork took place in and around the
venues where professional startup events took place. I focused on startup conferences where
entrepreneurs and venture capitalists from Silicon Valley gathered to network and discuss current
trends in their industry. TiEcon startup conference 2016 in the Santa Clara Convention Center
was my first site of participant observation
2
. TiEcon is one of the largest entrepreneurship
conferences in the region. It is organized by a non-profit organization founded in 1992 by
successful entrepreneurs, corporate executives, and senior professionals in Silicon Valley to
foster new generations of entrepreneurs through mentorship and networking.
The following excerpt from my field diary describes my first impression of a professional
industry event:
Early in the morning on the first day of TiEcon, dozens of cars were already in
line waiting to enter the parking lot which was half full even before the event
started. As I walked into the convention center from the parking lot, I was
surrounded by a group of entrepreneurs greeting each other and making friendly
introductions. Some of them seemed to know each other already and there was
a bustle of chatter and noise in the lobby as they were eagerly engaging in
discussion about new ideas and business plans. Standing in line at the

2
TiEcon 2016 featured more than 4,700 entrepreneurs and 230 speakers.

35
registration desk, I saw people shaking hands, giving hugs, and even taking
pictures together.
Most of the attendees were middle-aged men dressed in business casual attire or informally in
jeans and tennis shoes with backpacks. There were a few female attendees in the male-dominated
crowd, who were also busy conversing with other attendees. In contrast to male attendees,
however, they were dressed in more formal professional attire such as dresses and heels. Because
TiEcon was founded by Indian American entrepreneurs and investors, there was a large presence
of Indian attendees.
When the official program commenced, the auditorium was already full. As the emcee
announced the start of the event, Jay Visvanatha, executive director of TiEcon Silicon Valley
3
,
entered the stage and made a welcome speech. What struck me during the opening statement was
the sense of optimism and hope. For instance, the speaker commented, “[this is] where you will
find the next Elon Musk and Mark Zuckerberg.” The CEOs of the most powerful tech companies
were constantly introduced as bona fide tech celebrities and the paragon of success in the tech
startup world throughout the entire conference. Parallel to the theme of the conference, “Dream.
Change. Inspire.”, there seemed to be an expectation of ideal personality traits that symbolize the
resilient Silicon Valley entrepreneur.
Furthermore, it was suggested that anyone who is able to develop these traits could rise to
become one of the world’s most influential tech icons. At other similar conferences, it was
common for speakers to refer to Silicon Valley tech giants and make bold claims that startups
participating in these conferences were “tomorrow’s next Google and Facebook” (Vator, 2016).

3
TiE is a global network of 61 chapters in North America, Asia, and Europe with over
11,500 members across 18 countries. Each chapter has its own executive director.

36
Hence, the lexicon frequently used by the keynote speakers and panelists implied the valued
personality traits of a resilient entrepreneur. For example, the term “disruptive” was repeatedly
emphasized in many of the keynote speeches, suggesting that resilient entrepreneurs must break
the existing equilibrium and bring about change. “Disruption” was also frequently used to
describe the new influential technologies produced by resilient entrepreneurs.
To investigate the discourse relevant to entrepreneurial resilience, I took extensive
fieldnotes at conference sessions that focused on entrepreneurship. Many of these conferences
offered multiple tracks based on specific topics on entrepreneurship and innovation such as new
emerging technologies and venture capital investments. For example, the TiEcon
entrepreneurship track
4
explored topics on entrepreneurship such as funding options, sales
executions, and market competition. Analysis of my field notes data with NVivo’s coding
reference count shows that “users,” “products”, and “businesses” were the three most frequently
used words by conference speakers on topics relating to entrepreneurship and innovation.
Likewise, at the core of Silicon Valley’s startup business model is determination to innovate
products that fit the needs of the market and enhance user experience.
Attendees of the entrepreneurship track were diverse, including aspiring and experienced
entrepreneurs and investors. One participant I spoke to traveled from Toronto to attend TieCon
to gain insights and knowledge. For an engineer dealing with mobile device management, the
conference was an educational investment about the startup scene and preparation for his career

4
TiEcon organizes conference sessions around different themes. Key tracks offered in
2016 were Internet of Things, Future of Cloud & IT, Data Economy, Entrepreneurship, Trending
Technologies, etc.

37
path to becoming an entrepreneur. Throughout my fieldwork, I encountered motivated
entrepreneurs willing to spend serious money to attend professional conferences in Silicon
Valley, and many of them were based in other regions. Despite high registration fees and travel
costs, most conference attendees found this a necessary investment for their career. Their
rationale was that the benefits of attending the conferences outweighed the opportunity costs. An
attendee at Finovate Spring told me, “It is cheaper to attend this one big event and network than
to go individually to all these startups and venture capitalists across the country.”
Expectations from Venture Capitalists
Some panelists and speakers of the TiEcon entrepreneurship track were former
entrepreneurs turned venture capitalists. To minimize risk and maximize return on investment,
venture capitalists must judiciously invest their funds and therefore look for a specific set of
personality traits from the entrepreneurs. As former entrepreneurs, venture capitalist who spoke
at the entrepreneurship track sessions shared a more holistic view on what builds entrepreneurial
resilience. Robert Siegel, a General Partner at XSeed Capital, co-founded Weave Innovations
Inc. acquired by Kodak. He notes that “being a CEO is a lonely job and identified optimism,
leadership, and responsibility as key traits that he looks for in entrepreneurs. Similarly, Sudip
Chakrabarti, a Partner at Lightspeed and also a former entrepreneur, listed three key traits of a
resilient entrepreneur.
5

First, venture capitalists look for entrepreneurs who thrive. “I try to tease out what makes
them hungry,” said Chakrabarti. He explained, “You are used to getting no’s [and] you have to
have that belief and a chip on your shoulder.” It is more common for entrepreneurs to get their

5
Siegel and Chakrabarti spoke on the Psychology of Entrepreneurs Panel.

38
ideas rejected by key stakeholders. Only a small portion of them are funded by venture
capitalists. Second, resilient entrepreneurs have a strong locus of control. “They believe that they
can make some of those actions happen, not dependent on others.” Third, resilient entrepreneurs
are capable of improvising and listening to their investors or customers. Chakrabarti noted that
while entrepreneurs may “start out as a passionate visionary [or an] artistic type going against
everyone,” resilient entrepreneurs are those who are able to make the transition from “a
passionate artist to a maniacal customer pleaser.” Chakrabarti credited his experience as a failed
entrepreneur as aiding his becoming a good investor and identifying factors that develop
entrepreneurial resilience.
Building Emotional Resilience
Another factor that contributes to entrepreneurial resilience is the capacity to manage
feelings and impulses. At Vator Splash, the premier entrepreneur conference in Oakland, Bart
Garrett who regularly counsels Silicon Valley entrepreneurs on emotional issues described that
entrepreneurs are not only self-driven and freethinking, but they also have a deep desire and
unrelenting passion to pursue their work
6
. Having counseled numerous entrepreneurs who
struggle with emotional and psychological issues, Garrett stressed the importance of emotional
stability as a direct contributor to entrepreneurial resilience. He stated:
I am deeply convinced that if you’re a better person, then you’ll create a healthier
culture in your startup, and through that you’ll see it coming good. Being a better
person, you need to pay attention to your emotions as a thermostat [...] You
spend a lot time developing your management team, but not yourself [...] You’re
creating the culture of your culture. Building a common good in social health
forms the very beginning of your startup.

6
Garrett led a session called “Fear, Anger, Apprehension, Self-Loathing, Jealousy,
Dissatisfaction, Etc.”

39
One big unexpected challenge of being a first time young entrepreneur is overcoming a
series of rejections. Jason Soll, CEO of Cape Productions recalled his early experience in Silicon
Valley: “One of the most difficult part of the job is walking out of the 20th investor meeting and
get a no and go into the 21st meeting with a smile. You have to be able to come back to your team
after those horrible meetings and make sure to keep them confident.” Every week felt like multiple
weeks going through countless rounds of pitches and he was afraid of letting his colleagues and
partners down. He confessed, “I always thought that I have the ability to talk about both the peaks
and valleys, I but realized that there are only a few people whom you can turn to talk about these.”
What made Soll bounce back was his openness and willingness to take in tough criticism. After
every single meeting, he incorporated his investors’ feedback and revised his presentation. This
allowed him to progress. He eventually came to enjoy asking for advice from the board members
and investors.
Behavioral Conditions
Funding Pitch Deck
Apart from their unique personality traits, entrepreneurs engage in unique business
routines to access resources that necessitate their survival in the ecosystem. I analyze these
activities as a pattern of entrepreneurial actions. One activity that I examined is a startup pitch
deck, a brief presentation by entrepreneurs to provide a quick overview of their business plan and
appeal to venture capitalists for funding. Startup pitch decks to venture capitalists follow a set
formula. First, their presentation always begins with identifying a problem in the world. Second,
they offer a solution by introducing their product. Third, they elaborate on their business model
and explain the user experience. Fourth, to prove their credentials, they list past awards and

40
accomplishments such as partnerships with big tech firms and also discuss their current stage of
funding. Finally, they introduce their team members’ educational and professional backgrounds.
For those who enter startup competitions, their presentation time is restricted. At some
events, microphones get turned off if the presenters go overtime. Presenters must ensure that
their pitch fits within the allotted time, a few minutes. There follows a short Q&A session during
which the entrepreneurs engage with the judges and venture capitalists to receive feedback and
provide clarification on the pitch deck. Benefits from winning these competitions are prize
money plus recognition so that they can attract more investors and develop new business
opportunities. Therefore, it is crucial to rapidly maximize their appeal and entrepreneurs must
find creative ways to hook their audience during the presentation.
For instance, Gregory Keough, CEO of Finova Financial used a talking doll as his
customer to demonstrate the company’s business model of online lending as he presented on the
first day of Finovate Spring 2016. The conference was a two-day event in a unique, demo-only
format that featured more than 70 startups in financial technologies industry showcasing their
companies to large audiences of investors, industry analysts, and senior financial executives.
Each day of the conference was divided into four sessions comprised of up to a dozen back-to-
back presentations. It was crucial for presenters to use strategies to stay engaged with their
audience given the intense competition and the short attention span of most attendees.
Networking
In addition to pitch decks, there are multiple networking sessions where startups
showcase their products at their own exhibit booth in a casual setting throughout the conference
schedule. Venture capitalists and potential clients walk around the room and stop by the booths
of their interests to gain more information about the product or discuss possible business

41
opportunities. Each startup booth was represented by a few people, usually the company’s CEO,
CTO, and/or CMO. Each of them rotated answering different questions and as they answered
each question, they introduced their areas of expertise to provide additional credibility to their
answer. The conversations usually ended with exchanging business card and contact information.
At some conferences, networking was more digitalized. For example, EMTech Digital launched
a special mobile application for attendees to network with one another online.
At the WHOVA app featured Event Bulletin Board attendees engaged in real-time group
messaging during the conference and accessed the list of attendees and their contact information.
Attendees discussed a wide range of issues including the Wi-Fi password, networking, and
presentation feedback. For example, Hope Reese posted, “Anyone have a good picture of Alan
Packer I could use? I will give you photo credit.” Kalus Obermeier used the application for
networking opportunities. He posted, “Anyone working on applying AI to optimize medical
device supply chain? Please contact me.” These activities show importance of networking
indicates, an indicator of entrepreneurial resilience that can be augmented by expanding their
professional network and exchanging information and other key resources.
Daily Routines
Almost every entrepreneur in Silicon Valley whom I spoke to told me that finding a
healthy work life balance is nearly impossible, especially during the early stages of a startup.
Eager, driven, young entrepreneurs work arduously to get their company going and reach a
milestone. The younger the company, the greater the number of roles and responsibilities they
play at work, both official and unofficial. Working long hours has consequences on their
personal life and relationships. Amanda Bradford, the founder and CEO of a dating app, noted
that although she started a dating app company, her own dating life became non-existent after

42
founding the company. Leading a small and lean team, she was playing a number of different
roles in the company. She was working weekends and late at night and she was willing to put in
the time and effort and make the sacrifices for the company. This possibly explains how most
successful startups have multiple co-founders. Indeed, a strong work ethic has always been a
telling feature of the Silicon Valley entrepreneurs.
Rogers and Larsen (1984) made journalistic observations about the day-to-day work life
of the high-technology entrepreneurs in the 1980s: “Not only are the hours long and the pace
fast, but Silicon Valley workdays are intense. Rarely do people come to work and sit around
chatting about news, weather, or sports” (p. 138). This legacy has continued for the past few
decades. Nascent entrepreneurs have limited resources such as information, human capital,
investment, social network, and time. Moreover, high cost of living forces young entrepreneurs
to reside in business incubators and cohabitated office spaces where work life balance is nearly
impossible.
Nancy Hua, Founder of Apptimize, recalled the early days of her company, and said,
“We went through everything that happened in Silicon Valley Season 1 [...] I had people staying
over at my place all the time [and] we worked all the time. And we worked the most when things
were going well.” Max Mullen, Co-founder of Instacart, shared pictures of his first office - his
kitchen. He said, “If you’re starting a company your house is going to be your first office.”
Showing the picture of his second office, a 750 sq. ft. room in SoMa, he recalled, “We filled it
with cheap IKEA furniture that we built ourselves and we filled it with people until it became a
fire hazard.” Mullen emphasized that office spaces, regardless of size or function, reflect the
culture that entrepreneurs set up physically and their values and beliefs.

43
Cultural Conditions
Values and Beliefs
Entrepreneurs are vision driven individuals who find joy in solving real-world problems.
They are motivated by curiosity and by the greater social impact of making the world a better
place to live. Therefore, building a startup always begins with a vision-driven idea. Creative
ideas address the problems that arise from the changing technological and economic landscape.
They accommodate the varied needs and expectations of their future users. Therefore, founders
must first identify a problem in the market when they have interesting business ideas. Then, they
refine their initial creative ideas to find solutions through conducting research on the market and
potential consumers.
For instance, Kensho, a fin-tech startup, uses machine intelligence in finance to identify
nuanced precedents ad watch market behavior to detect risk. The vision is to create a ubiquitous
and efficient financial system. Its CTO Matt Taylor explained the company’s rationale behind
the product that it is to create transparency in the financial market and prevent another global
financial crisis. Similar to Kensho, TaskRabbit used the 2008 economic downturn as its
entrepreneurial opportunity to create a new wave for people to work by sharing the skills and
services they have. Leah Busque, Founder and CEO of TaskRabbit, realized that people look for
flexibility in tough economic times and even as the economy improves, the mindset of
freelancing remains. Success of Kensho and TaskRabbit also illustrate that because these
entrepreneurs are resilient, they were able to use the crisis towards their advantage.
In the long run, the founder’s vision serves a greater cause than economic success. As the
company grows, the founder’s values and beliefs are reflected in their office spaces, hiring
process, business products, etc. This exemplifies that the company culture that is created and

44
maintained throughout the lifecycle of a startup depends on the founder’s personality. As a
result, it is common to see a decline in company performance after the founder leaves or
changes. This has been prevalent throughout the history of Silicon Valley and the famous story
of Steve Jobs getting fired and recruited back highlights that the founder plays a significant role
in creating the company culture and, consequently, its resilience. Soon after Apple went public,
John Sculley, the marketing genius behind Pepsi-Cola, was recruited as its CEO alongside Steve
Jobs. Soon afterwards, Apple’s board removed Jobs from the company. Apple began to struggle
without its founder and eventually returned Jobs back to the company as CEO, who then drove
the company to its greatest successes. Jay Elliot (2012), former Senior Vice President of Apple,
described how Jobs’ own set of values were incorporated into creating the company culture:
Steve and I spent a lot of time discussing the core of the company’s values. He
kept emphasizing that they had to reflect the nature of a start-up company and
of a company driven by innovation, entrepreneurship, and products that truly
satisfied the user. He wanted the company to be based on values, and he wanted
values that would remind everyone not to compromise the integrity of the
product in the name of profit. He wanted the company to be an innovator and a
premier manufacturer of personal computers — but as the value leader, not the
price leader (p. 15).
Meritocracy
Values and beliefs serve as a vital source of motivation for entrepreneurs and as a basis
for direction in their business, but the complete assessment of entrepreneurial resilience is merit
based. The majority of entrepreneurs and their team members at these conferences were highly
educated with multiple advanced degrees from prestigious universities. Likewise, the
overwhelming majority of the invited speakers at these conferences were accomplished elites
with strong educational background from top universities. Conference moderators often
introduced the speakers by highlighting the schools they had attended: Harvard, Wharton, MIT,
Stanford, etc. Moreover, entrepreneurs’ performance in the industry was evaluated based on their

45
past achievements such as the number of funding rounds, the amount of investment, partnership
with notable institutions, and awards from other startup competitions.
Gender
At almost all the conferences I attended, somebody addressed the gender gap in the
industry during the Q&A session. Most female entrepreneurs complained about the
discriminatory environment with regard to venture capital financing. It was an uncomfortable
question since most attendees were male investors and entrepreneurs. Often the answers were
unsatisfactory. For instance, one speaker suggested not working with people with gender biases.
Another suggested that venture capitalists enjoy working with women because there are so few
of them; this reinforced the existing problems and left female participants dissatisfied. Female
entrepreneurs wanted to be treated and evaluated equally as their male counterparts, and actively
organized efforts by female entrepreneurs and venture capitalists to help each other.
For example, Golden Seeds, a venture capital firm founded by ex-wall street female
investors, now focuses exclusively on the women entrepreneurs. They realized that startups with
female founders received less than 3 to 4 percent of the venture funds. Kathy Downing,
Managing Director of Golden Seeds, explained that as women angels became more prevalent,
more female entrepreneurs were funded. There was also cultural difference. Indian female
entrepreneurs seemed to face less inequality. The CEO/Founder of one AI company observed
that growing up in India, she never perceived engineering as a gendered subject. There were
always an equal number of female and male students in her classes. Furthermore, she was
constantly encouraged and supported by her family and colleagues to pursue her career. Beyond
cultural variations, gender was more evenly distributed among the younger generation. One
elementary school teacher attending the Maker Faire Bay Area noted that the gender ratio among

46
his elementary school mathematics and science students was balanced and that his female
students often performed better in building technology for his science class activities.
Discussion
This chapter reviewed explanatory factors that may be essential for entrepreneurial
resilience in today’s Silicon Valley startup scene. Because entrepreneurship is a social process of
economic relations among individuals, both individual and social mechanisms help explain
entrepreneurial resilience. My field observations found that three conditions – individual,
behavioral, and cultural – were critical. The interplay between them helps explain how and why
entrepreneurial resilience develops under different conditions. To begin, personality traits of the
dynamic entrepreneur in Silicon Valley was characterized by optimism, passion, leadership, and
openness. These characteristics provide the basis for behavioral patterns and cultural values,
beliefs, and norms.
Because entrepreneurs must constantly negotiate inconsistencies such as limited job
security for high reward, maintaining emotional stability and making shrewd decisions help them
endure hardship and overcome obstacles. These individual traits are exemplified by a number of
Silicon Valley tech celebrities who thrived in the competitive environment and innovated
disruptive technologies. Examples in this chapter illuminated the daily efforts of the Silicon
Valley entrepreneurs who strive to reach their goals and dreams. Again, individual conditions for
entrepreneurial resilience are necessary for success. Behaviors such as pitch decks and
networking activities require these personality traits of the dynamic entrepreneur and open access
to resources, including funds, support, information, etc.
Individual and behavioral conditions for entrepreneurial resilience are equally
foundational for cultural conditions that reflect the entrepreneur’s values and beliefs.

47
Entrepreneurial values and practices were discussed and shared among entrepreneurs, venture
capitalists, and other stakeholders in the Silicon Valley startup ecosystem. They go hand in hand
because only by executing a set of business practices are they able to meet cultural expectations
and accepted norms in the industry. Taking these conditions into consideration, chapter 3
examines organizational conditions that foster organizational growth of a startup company. It
argues that entrepreneurship is a multi-dimensional process and entails different steps and
phases. In addition, it shows how building a business that thrives in the Silicon Valley startup
ecosystem is a collective effort of different actors and industry stakeholders.

48
Chapter 3: Building a Startup as an Organizational Emergence
“Organizing is a process; an organization is the result of that process.”
- Elinor Ostrom, 1990

Entrepreneurial resilience is built on characteristics of individual entrepreneurs who come
up with ideas to create innovation, but building a startup entails organizational processes that
involve a range of actors. A business that thrives in the Silicon Valley startup ecosystem is a
collective effort of different actors and stakeholders in the industry, not a one-man show. These
actors and stakeholders include team members, investors, venture capitalists, legal experts, and
board of trustees. This chapter explores the organizational processes underlying entrepreneurs’
management of resources and interaction with stakeholders. It also discusses theory development
related to the role of strategic communication in building a startup as the entrepreneurs expand
their organizational networks. More specifically, it considers the dynamic relationships between
Silicon Valley entrepreneurs and venture capitalists because venture capitalists exert huge
influence on the funding, the vision, and the growth of a startup.
This chapter argues that traditional theories and methods in organizational studies may
not help understand the organizational processes of building an entrepreneurial startup that
requires unique organizational conditions. It explains what is meant by building an organization
and describes how building a startup encompasses different stages of development as an
organizational entity. Next, the chapter provides an overview of the Silicon Valley startup
ecosystem, its actors, and their roles in the organizational processes of building a startup
company. Then, an overview of the startup ecosystem is provided and the methodology for
collecting and analyzing the empirical data in Silicon Valley is detailed. Using empirical

49
evidence, an analysis of how Silicon Valley entrepreneurs collaborate and interact with their
team members, venture capitalists, and other stakeholders to build and expand their companies is
provided. Finally, relational dynamics between entrepreneurs and venture capitalists is examined
through the lens of their multiplex ties, pitch conversations, and coaching workshops.
Understanding Organizational Processes
To define organizational processes, four bodies of theory from the organizational
communication literature are discussed and synthesized (Littlejohn, 1989). First, organizations
are structured to undertake various functions. Second, organizations are composed of human
relations based on cooperation – the medium through which individual capabilities can be
combined to achieve superordinate tasks. Third, organizations are built upon communication
which facilitates members to cooperate and work toward a shared purpose and common goal.
Last, organizations are led by select individuals who make important decisions and promote
cultures that are embedded in individual values and beliefs. These activities construct an
organization that consists of two or more people with different functions but share collective
goals. Likewise, building a startup entails organizational processes of coordinating
entrepreneurial tasks and activities by different actors through mechanisms of structure,
relations, communication, and cultures.
However, unlike established organizations, startups lack resources, networks, financial
capital, and market presence (Ozcan & Eisenhardt, 2009). As new entrants in the industry,
startups that compete with existing incumbents in the industry must engage in high-risk ventures
and encounter enormous challenges such as institutional pressures, resources shortages, and
market uncertainty. During the early stages especially, startups must develop organizational
resilience to survive and thrive in a highly volatile and competitive environment. Startups differ

50
in terms of size or scale. Compared to more established, well-resourced organizations, startups
emerge as small groups of people who specialize in different skillsets. By definition, groups are
lean and flexible with an interactional structure (Shaw, 1971).
Two or more persons in a group interact with one another and influence and are
influenced by each other person. As a result, the most effective mode of leadership in small
groups is a team management style that maximizes concern for production and establishing
relationships both internally and externally. The process of building a startup is an organizational
emergence because from idea to product and business model, startups undergo different stages of
development phases to become a real growing business and organization. In the initial stage,
entrepreneurs deal with developing novel ideas and transforming them into a feasible product
and business plan. In the venture capital industry, this is known as the seed stage during which
entrepreneurs receive financial support from friends, family, and angel investors.
After 18 months in business, startups enter early-stage financing and start seeking series
A funding – the first significant round of venture. During this stage, products and/or services are
in testing and the company may or may not be generating measurable revenues. Next is the
expansion stage during which there are commercially available products and/or services, and the
company achieves significant revenue growth. Entrepreneurs are ready for series B and C
funding rounds in which they sell preferred stock to investors in exchange for their investment.
Startups in this stage are less than three years old. Finally, during the later stage, products and/or
services are widely available and there is strong, on-going revenue with positive cash flow.
Furthermore, the company is likely to be profitable and may even include spin-offs with Series D
and E+ funding rounds (PwC, 2015).

51
Figure 3.1 below elaborates on the startup development phases from idea to business and
team to organization. It illustrates how startups seek resources and support as they evolve
through six development stages of ideating, concepting, committing, validating, scaling, and
establishing. First, ideating portrays entrepreneurial ambition to introduce a product or service
for a target market. This concerns initial ideas of the company’s founder. Second, the founder
conceptualizes the company’s mission and vision with initial strategies for attaining the
objectives. At this stage, there are two or three team members with complementary skills. Third,
the company develops an initial product or service version, with a committed co-founding team
that shares the common vision, values, and goals. Fourth, the company iterates and tests
assumptions for validated solution to demonstrate initial user growth in the market. Fifth, it
attracts significant funding and begins to scale up in terms of user growth, market traction, and
profitability. Finally, the company becomes an established organization that can be expected to
continue or make exit(s) in the market. It has achieved great growth and is now able to easily
attract financial and other necessary resources (Startup Commons, 2017).

Figure 3.1. Startup development phases from idea to business and team to organization
Source: Startup Commons (2017)

52
Silicon Valley Startup Ecosystem
Since the 1970s Silicon Valley has been the milieu of innovation for entrepreneurial
growth and success, attracting numerous venture-backed startups to cluster and form networks of
technological and business linkages. Originated as a center of semiconductor production, Silicon
Valley produced many influential technologies and applications from computer software and
Internet services to smart phones and electric cars. Part of what drives these technological
innovations is the unique venture capital model propagated by Silicon Valley's technology
community over the past generation. The Silicon Valley startup ecosystem epitomizes the model
entrepreneurship that is not only conducive to innovation but also generates and recycles new
wealth in the economy into new ventures, helping successful startups grow into corporate giants
like Apple and Google. The Silicon Valley model of venture capital introduced new ways of
financing a nascent business, which traditionally required access to inherited wealth or
government support. Venture capital investments allow innovative ideas to raise money to
finance new products, process, and/or services by sharing the risks and the benefits of those
investments between the investor and the investee (Saxenian, 1996).
The Schumpeterian theory of entrepreneurship also elaborates on the importance of other
economic agents that assist in the production of innovation. As entrepreneurs introduce
innovations, they seek financing through bank credit, which enables them to grow (Schumpeter,
1939). In Capitalism, Socialism, and Democracy (1961), Schumpeter states that big businesses
are efficient drivers of economic development improvements. Institutions actively participate by
approving innovations through legal processes for their commercial use. Although dynamic
entrepreneurs are at the center of fostering economic growth and development, they rely on other
economic agents who can finance their innovative ideas to become an actual product or service.

53
Therefore, Schumpeter conceptualizes entrepreneurs as the middlemen between the producers
and the consumers in the market.
Figure 3.2 presents a simplified pictorial representation of how venture capital industry
works (Zider, 1998). Of the four main players - entrepreneurs, investors, investment bankers, and
venture capitalists - this chapter emphasizes entrepreneurs and venture capitalists because they
are directly involved in the production of innovation in Silicon Valley. Typically, venture
capitalists will agree to invest certain amount of money in exchange for a percentage of
preferred-equity ownership position. In their deal, they receive provisions that offer downside
protection such a liquidation preference for the company's assets and blocking or disproportional
voting rights over decisions like the sale of the company or the initial public offering (IPO). In
addition, investors get to increase their stakes in successful ventures. This model still persists in
Silicon Valley; however, since the economic crises of 2001 and 2008 it is more common for
venture capitalists to jointly engage in syndicated investments to reduce risks (Janeway, 2012).

Figure 3.2. Illustration of how the venture capital industry works
Source: Zider (1998)

54
Fired and Hisrich (1994) devised universal criteria common to venture capital
investments in threefold. First, venture capital decision making depends on the company's
business concept that reflects significant potential for earnings growth and demonstrates
competitive advantage and ideas that either works already or can be introduced to market within
a few years. Second, the startup company must display strong management skills with integrity,
qualified leadership, and willingness to receive additional management. Last, investment
decisions are based on the company's potential for high rate of return and its ability to offer an
exit opportunity. Similarly, Cumming (2006) identifies determinants of venture capital portfolio
size, which are: (1) the characteristics of venture capital funds such as the type of fund, fund
duration, total fundraising, and the number of venture capital fund managers; (2) the
characteristics of the startup company, including its stage of development, technology, and
geographical location; (3) the nature of the financing transaction from staging and syndication to
capital structure; and (4) market conditions that externally affect the company's business
performance.
Venture capitalists employ the strategy of syndicated investment, in which multiple
investors to jointly invest in multiple rounds of funding. As Janeway (2012) articulates:
The convention model was rationally designed to spread risk across investing
firms and through time. At start-up, typically two or more funds would invest in
the A Round, with no contractual commitment to make a follow-on investment.
Each subsequent round would be priced on the basis of then-current conditions:
both internal progress against benchmarks, such as product development and
customer acquisition, and the state of the external economic and financial
environment. And each round would be open new investors, although
preemptive rights to invest would likely have been secured by the venture
capitalist in previous rounds (p. 110).

In light of the inner workings of the venture capital industry, this chapter now shifts to
the analysis of my empirical data and examines the organizational emergence of Silicon

55
Valley startups as they recruit their team members, introduce new products and/or
services, expand their organizational networks, and attain venture capital funding.
Methodology
In tandem with the methodology used in previous chapter, observations of entrepreneurial
activities are drawn from multi-sited ethnography, participant observation, and interviews in
various professional settings such as startup conferences, pitch competitions, industry expos, and
networking events in Silicon Valley. During my fieldwork, I took field notes and voice recorded
select conversations with permission. After completing the final round of fieldwork, I conducted
additional literature review to match my empirical data with findings from existing theoretical
texts and case studies. Next, I used open coding to select phrases according to themes that
emerged from my research questions for this chapter on organizational processes of building a
startup in Silicon Valley (Strauss & Corbin, 1998). Data were then compared and analyzed using
the theme-oriented strategy (Huberman & Miles, 1994). In this chapter, I pay more attention to
the relational dynamics between the entrepreneurs and venture capitalist.
The last section of this chapter presents an in-depth case studies of startup competitions
and pitch sessions to illustrate how venture capitalists and entrepreneurs use strategic
communication to negotiate and liaise with each other. Scrutinizing each of these case studies
allows my analysis to further examine specific attributes of strategic communication and how
they may influence the production of innovation in the startup ecosystem in Silicon Valley.
Likewise, case study is a naturalistic method of evidence gathering that employs triangulation
and utilizes a particular type of evidence, be it ethnographic, participant-observation, process-
tracing, or field research (Gerring, 2007). An individual case “connotes a spatially delimited
phenomenon (a unit) observed at a single point in time or some period of time [and] each case

56
may provide a single observation or multiple (within-case) observations (p. 19). All in all, my
case studies consist of an intensive study of different cases of startup competitions and pitch
coaching sessions with venture capitalists, and by doing so, I aim to “generalize across a larger
set of cases of the same general type” (p. 65).
Expansion of Network
Team Building
In building a startup, one of the primary roles of an entrepreneur is to put together an
efficient team that works towards the company’s vision. Accordingly, entrepreneurs must set the
organizational cultures and values for their team members. Team building is a process that
involves hiring the right people with the right culture fit, and it progresses over time. As the
company grows, it needs additional board members and it also considers outsourcing skills to
external specialists. Externally, they need to understand their user-base and generate increased
consumer traction. Moreover, they need to target the right market with sophisticated contingency
plans and exit strategies.
Throughout each stage, entrepreneurs connect with venture capitalists to receive
investment funding. The role of venture capitalists extends beyond financial provision as they
take pride in seeing the growth of startups they invest in. Venture capitalists also act as
knowledge brokers "who acquire and create through personal (and generally local) networks
about industries, market conditions, entrepreneurs, and companies through a constant process of
interaction and observation" (Zook, 2005, p. 3). As per my observation, unlike entrepreneurs,
venture capitalists are risk-averse and act as mediators bringing entrepreneurs back to the real
world and helping them to contemplate the feasibility of their business.

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Speakers at startup conferences giving tips to young entrepreneurs stressed the
importance of finding the right people to work with. Parallel to the findings from previous
chapter, cultural conditions are crucial in the hiring process. The founder sets the vision for the
team and hire the right people who can work autonomously. At the early stage, the leader must
be able to deal with ambiguity singlehandedly. As the company expands, however, it needs more
specialists and a diverse set of thinkers who can challenge each other in a committed team (B.,
Wong, personal communication, May 19, 2016). Leaders must embrace authenticity and think
about the values before hiring anyone (S., Rubin, personal communication, May 19, 2016).
Financial interest becomes a secondary concern and doing “good” for the company culture
comes first. Therefore, leaders must look for people who will protect their vision. Team
members must possess the right skill sets for each of their roles, and the founder must actively
search for these people in the industry. As Bardin (2016) shared some tips on building a good
team:
The question to ask is, do they have the right expertise you’re looking for? You
have to hire strong engineers and PR Communication specialists who are very
outspoken and has access to different networks. You must be out there to meet
and talk to people in the industry. Hiring someone who worked in big companies
like Google and Facebook doesn’t guarantee your success (N. Bardin, personal
communication, May 25, 2016).
Ability to assess a potential employee during an interview is critical. Howard (2016)
suggests group interviews are more effective because they result in more data and consensus.
During the interviews, candidates should be able to prove their interpersonal skills. He
explained:
Test everyone! Have them do a day in the life. Check references that the
candidate didn’t give you because who wouldn’t give good references? Go
check their LinkedIn and see who they’ve worked with. There must be a culture
fit. Tolerating assholes will ruin your culture. Culture fits will be there longer.
Ask board members when was the last they fired a CEO and why. EQ is an
important criterion. Watch out their handshake, stay away from those that talk

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fast [and those with any] dysfunctional behavior (R. Howard, personal
communication, May 12, 2016).

Board Members
In Silicon Valley, venture capitalists are closely involved in "advising entrepreneurs on
business plans and strategies, helping find co-investors, recruiting key managers, and serving on
boards of directors" (Saxenian, 1996, p. 39). The biggest single mistake founders make in the
organizational process of building a startup occurs when they assemble their board. Howard
(2016) in his session, “Protecting Yourself as a Startup Founder” in Vator Splash Spring 2016,
proposed the strategy of allocating common board seats and investing more time on vetting the
potential candidates versus all others. He used the analogy of ‘Go to Vegas, get married’ to
describe the consequences of breaking up with board members because after they join the
venture, they would never want to leave the company. He said, “Avoid bloated boards so you
can run your company. Why give up control at such an early stage? Don’t add board members
unless you absolutely should.” To maintain their leadership control, junior entrepreneurs should
choose their advisors first before assembling their board members. Howard continued,
First have them as advisors and work with 12 months to build trust. Smaller
boards are more efficient in serving you. Be transparent and tell them how you
would like to run the board meetings. Don’t let them tell you how to run the
meeting. Explain your expectations of them. Explain how your run the board
meetings.
The speaker gave other practical tips such as choosing operators over MBA graduates
when deciding on a board member. It is crucial to have a board member who is adept at running
a business, which is distinct from being visionary or passionate. Howard also suggested going to
partners over associates when entrepreneurs hit a roadblock because junior associates often lack
the experience and knowledge necessary to reflect the business in a more critical way during the
initial stages of a startup. In contrast, partners are more experienced and able to solve things

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more quickly. Howard concluded the session with a strong recommendation that an entrepreneur
should never rush filling a board seat.
In addition, the speaker emphasized never to recruit someone as a board member without
any prior working relationship. Board members are not entrepreneurs themselves; however, they
legitimize and enforce certain forms of business perspectives which often contradict what
entrepreneurs strive to achieve. While most startup boards permit the founders to run the
business and interfere guide and assist them on a need to basis, board members usually prefer
conservative business practices that minimize and/or prevent risk. Inadvertently, this might curb
entrepreneurial drive and freedom and business expansion of budding entrepreneurs with
creative ideas or innovative products.
Venture Capitalists
It is common in Silicon Valley that the startup’s board members will also serve as
venture capitalists investing in creative business concepts. Relational dynamics between
entrepreneurs and venture capitalists exemplify a tension between creating new innovations and
finding resources to make them marketable. Entrepreneurship pushed by venture capitalists thus
becomes strategically oriented towards the company’s growth and profitability. Throughout the
life cycle of a startup, venture capitalists and entrepreneurs negotiate these tensions through a
mutual understanding that entrepreneurship not only requires dexterity dealing with multiple
obstacles but also realistic goals to succeed in the industry.
With the Internet and other new forms of digital technology, there is a lower entry barrier
to a highly competitive market at a global scale. Anyone with a promising idea is now able to
find resources and launch a startup, but new entrants face challenges competing against a handful
of tech giants and other incumbents that already dominate the global market. Hence,

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entrepreneurs and venture capitalists openly converse the problem of increased risk and
competition and so, address the need for more capital and other resources to thrive in the market.
These values and practices are widely discussed and accepted in the Silicon Valley startup
ecosystem.
Venture capitalists consider themselves as entrepreneurs’ partners as they carry the same
risks. By communicating realistic expectations with the venture capitalists, entrepreneurs find
ways to deliberate over inconsistencies in the industry such as limited job security for high
reward. As discussed in the previous section, emphasis on team dynamics and choosing the right
board members illustrates that entrepreneurship is process-oriented and relationship-focused. It is
a multi-dimensional organizational process with multiple sets of actors playing different roles.
Investing in meaningful relationships can benefit entrepreneurs in the long run. Budding
entrepreneurs often assume that they will get funding if they have a marketable idea. During a
panel on Early Stage Funding, panelist Branko Cerny, CEO and Founder of SquareOne,
identified this as a black box myth:
Investors think through the consequences of their decision. They worry, ‘Am I
going to get into trouble if this investment goes wrong?’. You must have a post-
exit strategy. Don’t make a mistake by fundraising too early. It’s better to go out
there when it’s almost too late with too many proof points. Don’t go pitching 25
partnerships, aim for three to five.
Another panelist Philip Meyer-Schmeling, CEO of Velolock, agreed that seeking venture capital
funding is more than a financial interest. He advised, “If you want to do something, there will
always be people who will be willing to give you money. You will get the money if you are
doing something right.” Similarly, Shaukat Shamim (2016) stressed the importance of value
proposition:
Regardless of what you do, there are always people who say no to you. Don’t
become a sales person. It won’t make a difference much. You make the company.

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Investors come. As an entrepreneur, there’s only few help you can get. Focus on
value proposition (S. Shamim, personal communication, May 19, 2016).
When asked about what happens to the entrepreneur’s relationship with investors if the
company fails, Cerny answered, “I’m going to do anything to make the company successful and
if it doesn’t work, it’s okay. They shouldn’t blame you for failing.” Shamim added that investors
are aware of the high-risk nature of venture capital and therefore understands that not all startups
will succeed in generating return on investment. Meyer-Schmeling advised nascent entrepreneurs
to build a great social network and said, “Failure is preventable by sharing information and
asking more for advice. Be communicative.”
From the venture capitalist’s point of view, entrepreneurs these days face a new challenge
because performance expectations in every stage of startup growth have gone up in general.
Startups must present data or metrics to effectively raise capital, and venture capitalists, too,
must actively engage in seeking and identifying high-growth investable startups by attending and
participating in startup events such as hackathons, demo days, and tech crunch disrupt. One of
the panelists at Vator Splash 2016 encouraged startups to approach venture capitalists more
proactively:
Execute your business, become a top 100 mobile app, and get public relations
specialists to write about you and get some traction. Get VCs to find you. VC’s
would like to approach companies. In the early stage, if you ask for money, you
get advice on your business model. But if you ask for advice, although not
always, you might get some money if you’ve got great metrics. Focus on quality
of your business. Valuation is just a small component. Key thing in venture
market is growth, worst things - business few million dollars in revenue even if
profitable, not growing. Most companies need to figure out how to grow as fast
as possible, but also build success with a great business (Personal
communication, May 12, 2016).

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Entrepreneur-VC Dynamics
This chapter now shifts the focus to the case studies that closely examine the
organizational interactions between the entrepreneurs and venture capitalists during the startup
pitch decks. These interactions are shaped by strategic communication, which I define in this
context as a communicative process that resolves discrepancies between the stakeholders.
Entrepreneurs present in ways that convince venture capitalists of the company’s worth and
profitability without giving up the essence of their original ideas. On the other hand, venture
capitalists rationalize and evaluate the business projects to reduce risks and maximize their
expected return on investment. Deliberations from each side ultimately converge and result in
organizational emergence of a new firm. In the following case studies, I elaborate how strategic
communication evolves in the organizational process of establishing cooperative relations
between the entrepreneurs and venture capitalists and allows startups to secure necessary
resources for their growth and development. As part of my first case study, I begin this session
with my field observations of a startup pitch event in Silicon Valley, which train entrepreneurs
with skills to perform well in startup competitions.
Pitch Workshop
StartUP Pitchfest Silicon Valley is a monthly startup event organized by the VC
Taskforce that creates programs for startups looking for funding. In addition to Pitchfests,
StartUp Academy is provided throughout the year and it features a “structured program of
workshops and pitch events as well as face time with investors organized specifically to help
companies understand how to best talk to investors about their companies and begin the funding
process” (VC Taskforce, 2017). In May 2017, I attended some of its events sponsored by
Pillsbury Law in Palo Alto – a law firm that specializes in emerging growth companies and

63
frequently hosts startup-related events. The following is an excerpt from the handout distributed
during the workshop:
We’ll give you guidelines for your elevator pitch! We’ll give you coaching!
We’ll give you investors! You do the rest! AND it won’t cost you thousands of
dollars. When you get in front of investors don’t WASTE your valuable time …
OR THEIRS!
Throughout the workshop it was stressed that pitching is “communication that entices investors
enough that they want to meet you again the second time” (VC Taskforce, 2017). Effective
strategic communication is decisive to secure venture capital funding because it facilitates the
building of rapport and trust between entrepreneurs and venture capitalists. As indicated in the
workshop handout:
Many entrepreneurs haven’t refined their “What it is we do” statement enough.
Most of the time in front of investors is devoted to uncovering what the business
does. We can help you with that. Many entrepreneurs don’t disclose valuable
information about the pain point they are addressing, their team, or their
background. We can help you with that. Attending our coaching session!
PREPARE TO PITCH to investors.
It is equally important that venture capitalists make the effort to build trust and evaluate
the quality of entrepreneurs and their business ideas. There was a separate set of instructions for
the venture capitalists participating in the workshop:
Please put on your “general investor’ hat and focus your ratings on the
fundability of the business as well as the elevator pitch. Some things to consider:
Did the pitch capture your attention? Does the venture sound like a “fundable
deal” (even if it’s not in your space)?
The key to the effective communication between the two is the mutual understanding of the
exchanged information and furthermore and the ability to capture the underlying meaning of the
received information by making sound inferences and interpretations. The following is a table
presented in the workshop handout that guides strategic communication between the

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entrepreneurs who pitch their ideas and the venture capitalists who provide fundability feedback
in response to their pitch deck presentations.
Table 3.1. Panelists comments and their descriptions

Panelist Comment

What the Comment Really Means

“Your idea is still … an idea.” Your business idea and pitch are not
sufficiently clear. Please take some time
to flesh it out, ask your friends to give you
feedback, and try again!

“It’s certainly interesting, but … “ I can see where you are going, but you
may want to revisit your presentation in
order to make me look twice.

“That sounds like something I haven’t heard
before…”
Your pitch is intriguing, but I’m not yet
convinced.

“You have it all … almost.” You’ve definitely got my attention – let’s
keep talking!

“This is very solid. I’m excited about it.” Your pitch/business concept was
outstanding. This is very solid, and I will
ask my partners to include you in our next
meeting.

Source: VC Taskforce StartUP Pitchfest Silicon Valley (2017)

The Startup Conference 2016 Pitch Competition
The annual Startup Conference is a large-scale professional event in Silicon Valley with
over 1,500 participants and provides useful business opportunities for entrepreneurs and
investors. In comparison to my other field sites, the Startup Conference was one of the more
culturally, racially, and ethnically diverse conferences. There was a huge presence of
international startups from around the world, and many of them participated as speakers in the
panel sessions, networking booths, and startup competition. One month before the event, over
one hundred startups submitted their applications through AngelList, an online platform for

65
startups to raise early stage investment, to enter the startup competition. There were two
requirements for startups to be eligible to apply. The competition was open to startups that were
less than 4 years old and have raised less than $2 million in funding. As indicated on the official
website, the competition especially encouraged young and less experienced startups to
participate: “There is a sweet spot in our heart for self-funded startups, so even if you are one
solo entrepreneur in your garage, as long as you came up with something great, do apply!”
(Startup Conference, 2017).
During the conference, the best eight startups were given the opportunity to deliver their
elevator pitch on stage in the morning session. Using their mobile phones, the audience voted the
top 3 pitches that qualified for the afternoon panel with venture capitalists who then decided the
“Best Startup” of the year. The winner was designated as the IBM global entrepreneur and
received full access to the IBM resources for startups including expert mentors, developer tools,
educational resources, and so on. In May 2016, eight startups that entered the competition were
Airmule, Bitwage, iGrow, Fogo, Lucid, Overnest, People Loop, and SPLT. Table 3.2 below
presents a brief summary of these startups.
Table 3.2. The best 8 startups selected for the Startup Conference 2016 Pitch Competition

Presentation #

Company Name

Description

1 Airmule An app that connects air travelers with TSA-approved
shipping partners to transport stuff in their bags

2

Bitwage A bitcoin payroll and international wage payment service

3 iGrow Organic farm in Indonesia funded by the customers

4

Fogo Mobile app for outdoor enthusiasts that tracks

5 Lucid 3D camera that enables VR and AR content creation with
real-time image processing

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6

Overnest Secure team communication application providing
encrypted messaging, archiving, and search for innovative
teams

7

People Loop Cloud-connected recruiter network for the global staffing
industry

8

SPLT Enterprise-first carpooling and ride sharing platform that
connects employees for their commute

In the afternoon session, the three startups selected for the final round – iGrow, People
Loop, and SPLT – pitched to the investors who deliberated in front of these startups acting as
partners of the same venture capital firm and then chose one startup to reward the top prize.
Startups presented the same material again in front of the judges, and the judges gave instant
feedback. For example, investors asked a series of questions regarding the business model of
SPLT, an employee carpooling platform: “Could you give us some idea of traction? How do you
determine the cost of your product? What is the total lifetime value of a customer? What is a sale
cycle? What has your history been? How would you work with the insurance companies?”
Investors probed entrepreneurs with questions about their companies to dig into the reasoning
behind the company’s particular sales and marketing strategy, which allows investors to evaluate
the risk factors and profitability of the startups. In response, entrepreneurs answer these
questions by providing proof points as to the viability of their business model in the competitive
tech industry. For instance, SPLT responded to the above questions one by one by providing
more details on their business performance:
There is an annual fee to administer the program via public-private partnership.
We are working with the local government to externalize a lot of the costs […]
Sale cycle is really short, around 45 days in the Bay Area. We make a 3-year
contract but would like to have additional two years. The lifetime value of a
customer would be several hundreds of thousands of dollars.

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After the Q&A session, the judges discussed among themselves to evaluate each of the
three finalists and choose the best startup. They held the meeting on the auditorium stage and
pretended that they were partners of the same venture capital firm. The facilitator of the session
began the discussion: “So, these three companies are pretty different guys. What do you think?”
The judges were split between SPLT and iGrow. Those who voted for iGrow appreciated the
social impact of its innovative business model – customers investing to help farmers in Indonesia
to sell their crops. In sharp contrast, those who voted against iGrow argued that there was greater
risk involved from the revenue side.
One of the investors explained: “It feels a little bit challenged as a business model
whereas SPLT makes sense. People want to commute with people you know.” He further
articulated that riding sharing among employees creates an opportunity to make a big difference
in the company culture. Another investor concurred, “iGrow is a bet on a bet on a bet. A lot of
things need to go right.” There was also a concern that Indonesian currency fluctuations. In
rebuttal, an investor who supported iGrow argued, “iGrow has enough domain expertise.
Certainly, it knows more about farming in Indonesia than we do.” Another investor supporting
iGrow added: “I love iGrow. I think the intention is good and wonderful, and having a social
impact is a must to be successful as a business.”
Eventually, majority of the judges voted for SPLT as the winner of the 2016 Startup
Conference Pitch Competition. Overall, there was a split between voting for an innovative
business model and a stable revenue-generating business. Carey Lai, one of the judges, explained
why she chose SPLT over iGrow: “Is disruption always the best thing? Take the demand and
apply the technology. People are more upset about traffic, and SPLT entered the market in the
right time. I love what iGrow is doing, but the path from here to significant revenue is a very

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hard path. On the other hand, bringing technology to facilitate the carpooling process rings ease
to the very manual and outdated process.”
Throughout this competition, I observed two different types of strategic communication.
On the one hand, there was strategic communication between entrepreneurs and VCs during the
pitch deck. On the other hand, there is strategic communication among VCs as they deliberated
on investment decisions. The former involves both entrepreneurs and venture capitalists
negotiating the tensions between innovation and investment. Entrepreneurs convince their
investors that their ideas are marketable and viable in the commercial market, and venture
capitalists probe into the feasibility of their business models. In contrast, the latter concerns how
venture capitalists communicate among themselves as they discuss the risk factors surrounding
the profitability and risk involved in the venture business.
Discussion
In summary, this chapter has sought to establish is the key organizational process of
entrepreneurship as a set of relationships established between entrepreneurs and other
stakeholders in the industry. A series of organizational interactions shaped by strategic
communication between two sets of actors facilitate negotiating the tensions between opposing
perspectives on what counts as disruptive innovation in the market. Ultimately, as arguments
from both sides converge and tensions get resolved, there is an emergence of startups as
organizational entities. The organizational processes discussed in this chapter reflect how a
startup emerges and evolves as it interacts with other actors in Silicon Valley’s startup
ecosystem. It is crucial that for a startup to succeed in the long run, it must strive to cooperate
and compromise with the stakeholders and maintain stable relations with them. As Ostrom
describes (1990):

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Because of the repeated situations involved in most organized processes,
individuals can use contingent strategies in which cooperation will have a
greater chance of evolving and surviving. Individuals frequently are willing to
forgo immediate returns in order to gain larger joint benefits when they observe
many others following the same strategy […] Changing the positive and
negative inducements associated with particular actions and outcomes and the
levels and types of information available can also encourage coordination of
activities (p. 39).
As illustrated in the last section of this chapter, the ultimate convergence indicates who
gets to remain and survive in the ecosystem. The case study of the 2016 Startup Conference
Pitch Competition demonstrates that not the most innovative project gets funded by the venture
capitalists. Overall, the criteria of avoiding risk had a greater weight than social impact.
Therefore, startups who were not able to convince their investors that their firms will deliver
solid returns ultimately got eliminated in the funding process. One might concern that if the logic
of risk-averse systematically prevails, although it may yield to generating profits and growth of a
company, it might hinder the production of disruptive innovation in the market. However, we
know that there is still a great deal of innovation from Silicon Valley startups emerging through
venture capital funding which is channeled towards innovative projects despite the risk. How
innovation from Silicon Valley continues to thrive and impact the consumers at a global scale
will be discussed more thoroughly in the following chapters.

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Chapter 4: Networks Structure and Impact on the Success of Startups
“The fundamental difference between a social network explanation and a non-network explanation
of a process is the inclusion of concepts and information on relationships among units in a study.”
- Stanley Wasserman and Katherine Faust, 1994

Social networks are essential for successful entrepreneurship at both individual and
organizational levels. Startup founders in Silicon Valley invest heavily in establishing social ties
to seek new information, resources, and venture opportunities. In particular, networking with
venture capitalists at industry conferences, pitch competitions, and startup expos exemplifies a
business routine that yields entrepreneurial resilience of nascent founders. At the organizational
level, venture capitalists play a decisive role in the formation and growth of a startup company.
The Silicon Valley venture capital model allows innovative ideas to raise money and finance
new products, processes, and/or services. Hence, venture capitalists are directly involved in the
production of innovation as they share the risks and the benefits of their investments.
Besides financial provision, venture capitalists act as knowledge brokers who advise
startups on management plans and strategies. Venture capitalists often employ the strategy of
syndicated investment through which multiple investors jointly participate in different rounds of
funding. This creates a network of startups connected by shared investors. The goal of this
chapter is to achieve a systematic mapping of startup networks formed by syndicated investment.
Because co-funded startups receive common information, advice, and resources from their
shared investors, highly connected startups in the network accrue more benefits from not only
gaining financial support but also receiving business mentorship. Chapters 2 and 3 have
illustrated that networking with venture capitalists is an important aspect of entrepreneurship in
Silicon Valley.

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Chapter 2 examined how forming social ties to venture capitalists fulfills the behavioral
condition for entrepreneurial resilience through their wealth of experience and portfolios of
successes and failures. Chapter 3 then identified a set of relationships between entrepreneurs and
other stakeholders, most notably the venture capitalists, that stimulates the organizational
processes of building a startup company in Silicon Valley. Taking these findings into
consideration, this chapter endeavors to unveil the network structure of startups and identify its
key players. To examine how networks play a particularly crucial role in Silicon Valley, the
chapter presents an analytical comparison of co-investment networks of startups in Silicon
Valley and other regions.
The plan of this chapter is as follows. The first section begins with some general
overview of the social networks approach to entrepreneurship, and the second section explains its
relevance to the analyses of the Silicon Valley startup ecosystem. In doing so, it specifies and
develops three hypotheses to be tested in this networks study. Next, the methodology section
details the data, measures, procedures, and analyses. The fourth section then provides a
comparative analysis of co-invested startup networks in Silicon Valley and other regions by
empirically illustrating some of the key differences that exist in the network structures. Key
players in each of the networks are also identified and compared on the basis of their attributes.
Finally, the last section discusses the implications of these findings.
Integrating Network Theories and Analysis into Research on Entrepreneurship
Emerged in the 1990s, network research within the field of entrepreneurship is fairly new
(Hoang & Antoncic, 2003). Its theoretical root is unique because:
Drawing on a broader revitalization of the field of economic sociology, scholars
began to question the widely held view that entrepreneurs, as economic actors,
were isolated and that the entrepreneurial process was distinct from other social
phenomena. Instead, entrepreneurs were seen as intimately tied, through their

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social relationships, to a broader network of actors. It became the task of scholars
to examine the causes and consequences of embeddedness in the entrepreneurial
process p. 167).
Network research on entrepreneurship broadly concerns sociological and economic
investigations of entrepreneurial entities and activities such as venture capital investments,
startups, and entrepreneurs. Studies focus on how social networks influence the flow of
entrepreneurial resources like information and financial capital. For instance, Burt's (1992)
classic work on structural holes illuminates that entrepreneurs gain competitive advantages by
filling in the gap between two individuals with complementary resources.
In terms of links between nodes, there are social networks of entrepreneurs and affiliation
networks of joint ventures. The former employs data collection techniques that involve survey
questionnaire and egocentric data (Greve & Salaff, 2003; Hoang & Antoncic, 2003). Researchers
collect and analyze data by asking the respondents to describe their relations with other members
in the network. Affiliation networks of joint venture activities could be analyzed by collecting
two-mode data that consist of co-investment participation. Syndicated investment allows
researchers to capture the "interaction among the venture capitalists that jointly finance a target"
(Sorenson & Stuart, 1999, p. 1567). Two-mode data obtained from archival sources such as
business reports are converted to a network using matrix algebra. Network researchers use
databases such as Thomson Financial's Venture Economics database to collect their data
(Bygrave, 1988; Sorenson & Stuart, 1999; Hochberg, Ljungqvist, & Lu, 2007, 2010).
Recent studies on entrepreneurial networks test how network structures can produce
outcomes beyond individuals' market transactions and impact a firm's performance and also, re-
structuring of the market. Scholars in finance and business management integrate network
analysis into more traditional economics by using econometrics to explore the effects of network
structure on entrepreneurial performance (Bygrave, 1988; Hochberg et al., 2010). For example,

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to understand effect of firm networks on fund performance, Hochberg et al. (2007) estimated five
separate regression models by adding five centrality measures - outdegree, indegree,
betweenness, and eigenvector. Investment performance was measured by exit rates, defined as
the fraction of portfolio companies that went public or got acquired by another company.
Findings suggest that better-networked firms are associated with better fund performance.
There is also a line of research that focuses on region-specific networks of
entrepreneurship. Liu and Chen (2014) capture the distinct features of China's venture capital
networks in their empirical analysis by adding a firm's guanxi or relationship with the Chinese
state as a dummy variable in their econometric model. They also computed network centrality of
a syndication relationship between VC firms as one of their independent variables. Results
indicate that such unique cultural and political characteristics of China's venture capital
environment make networks more important for China compared to the venture capital networks
in the United States in terms of investment performance.
In a similar manner, Hillmann and Aven (2010) observe how partnership networks and
reputation help new ventures to mobilize capital during the industrialization of late imperial
Russia from 1869 to 1913, during which entrepreneurs who were supported by the tsar could
enjoy economic and political privileges over others by the corporate law of 1836. They coded
affiliation networks of co-founding ties among founders and the companies in whose founding
teams these individual founders participated. They also compared the role of reputation using a
binary measure that equals one if collaboration is continued and zero otherwise. The findings are
as follows:
First, the Russian corporate network was indeed fragmented into a widespread
and a well-connected core and a periphery that consisted of hundreds of scattered
small components without any relational bridges connecting them. [...] second,
homophily based on common ethnicity, regional origins, and shared experience

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in similar industries guided the composition of founding teams within the
isolated and tight-knit clusters in the periphery. in contrast, the network core
embedded entrepreneurs who came from more diverse ethnic and regional
backgrounds and who invested in diverse industrial sectors [...] third,
cooperation with founding partners who have earned a reputation of successful
entrepreneurship in the past did indeed raise the amount of capital mobile for
present ventures (p. 488).
In both cases, a network’s unique political and cultural settings engendered a unique
environment for entrepreneurship. For China, it is the cultural norm of guanxi and China's recent
transition into a more entrepreneurial friendly economy that induced the shape of its network
structure. Likewise, in the late imperial Russia, cultural values such as reputation and ethnic
diversity intermingle with its industrial transition, thereby creating a synergistic effect on the
entrepreneurial networks. Following the approach of these region-specific studies, this chapter
concerns the unique network structure of the Silicon Valley startup ecosystem. Social network
analysis is useful for this research because it “represents a ‘structuralist’ approach to
organizations, fields, and communities, which complements an ‘individualist’ approach” (Kilduff
& Tsai, 2003). Moreover, the outcome of networking can be examined via innovation capacity
and investment performance of co-invested startups.
Social Networks Approach to Examine the Silicon Valley Startup Scene
Existing network studies on Silicon Valley pay attention to the overall network structure
of its economic actors and their impact on regional development. Castilla, Hwang, Granovetter,
and Granovetter (2000) analyze three different social networks in Silicon Valley – founders of the
semiconductor industry, venture capital firms, and IPO deals in the information retrieval services
industry. The co-investment network of 129 venture capital organizations between 1958 and 1983
presents a cluster of highly connected venture capital firms that includes some of the oldest and
still the most influential venture capital firms in Silicon Valley today, such as Crosspoint Venture

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Partners and Kleiner Perkins. Similarly, Castilla (2003) compares the network structure of venture
capital firms in Silicon Valley to that of venture capital firms in Route 128. His findings illustrate
that firms in Silicon Valley are more densely connected and that funding invested locally by
Silicon Valley firms are much higher.
The implications of these study are that the particular structure of venture capital
networks in Silicon Valley is unique and has fostered its regional growth. Following these
studies, this chapter argues that because Silicon Valley is the leading center of venture capital
funding, so is its startup networks. As discussed in previous chapters, networking with venture
capitalists plays a crucial role in the success of a startup at both individual and organizational
levels. Thus, it is proposed that with regard to syndicated investment, startups in Silicon Valley
are better networked than startups in other regions.
Hypothesis 1: Startups in Silicon Valley are more central in the co-investment network
than startups in other regions.
Methodology
Data
In network studies of entrepreneurship, affiliation networks of joint venture activities are
analyzed by collecting two-mode data that consist of syndicated investments. Two-mode data are
usually obtained from “archival sources, such as annual reports, and then converted to a network
using matrix algebra" (Valente, 2010, p. 48). However, it is often difficult to obtain public
information of startup companies because "young, private entrepreneurial ventures typically fall
outside the scope of [most] organizations' activities" (Sorenson & Stuart, 1999, p. 1548). To
address this limitation, the current study examines a group of highly successful startups that are
widely recognized in the industry. Sample of this study is thus selected from the 2016 Fortune’s

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list of unicorn startups ranked by valuation – based on a combination of data from PitchBook,
CB Insights, news reports, and their own investigation.
In 2016, the list ranked 174 domestic and global startups as successful private companies
with post-money valuations of $1 billion or more. Due to limited data access for investment
activities of foreign companies, this study selects 96 domestic startups for a series of network
analyses. Next, investors and total investment funds for each of these startups are identified from
Thomson One, an online database that provides company information including the company
financials, ratios, filings, and reports formerly found in the Investext database. Investors for these
sample startups are those who have participated in any of the funding rounds since the
establishment of the company. In total, 367 investors funded the sample startups. Along with
investor information, attributes of each startup such as the company location, history, and
industry sector were collected as part of the data from Hoover’s Online database that delivers
information on private and public companies and their industry analyses. Patent assignment
information was collected from the United States Patent and Trademark Office.
Measures
The variables of primary interest for this study are those measuring the network
embeddedness of each startup, which was operationalized as one’s centrality scores in the co-
investment network. Degree centrality is defined as the number of investors the startup shares
with other startups in the network. Betweenness centrality, which measures the extent to which
one node acts as a bridge for other nodes. It is calculated as the number of pairs of projects
whose geodesic paths contain one funder divided by the total number of geodesic paths between
any two projects in the network. Eigenvector centrality reflects the extent to which nodes with
which one node is associated are influential. Closeness centrality is the reciprocal of farness, the

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sum of the lengths of the geodesics to all other nodes. Each startup’s eigenvector centrality is a
function of the sum of centralities of other startups to which they are connected. In each network,
a group of densely connected nodes are identified as the core (Freeman, 1979).
Network level information is gathered from computing a network’s average degree,
degree centralization, density, number of components, connectedness, average distance, and
diameter (Wasserman & Faust, 1994). Average degree measures the average of actor centrality
scores in the network whereas degree centralization measure “quantifies the range or variability
of the individual node indices” (p. 180). It is synonymous with the “dispersion or heterogeneity
of an actor index” (p. 181). Density is the percentage of potential connections that actually exist
in a network.
Connectedness and reachability between startups in the co-investment network help to
understand the transfer of investment funds, information, and other through shared investors. The
number of components indicates the number of subgraphs in which there is a path between all
pairs of nodes. The average distance, the mean length of any shortest path between nodes,
measures how far apart each pair of nodes is. Connectedness indicates whether each pair of
startups is joined by some path. The diameter of a network is the length of the longest geodesic
or path between two startups (Wasserman & Faust, 1994).
Innovation capacity is measured by the number of patent assignments, and investment
performance is measured by the amount of funding each startup has accumulated from its
venture rounds. The four control variables were company characteristics – experience, industry
sector, location, and era. The history or the age of a company is a proxy for its experience. The
founding year of each company is subtracted from the current year, 2018 to compute the history
variable that accounts its age. A dummy for the software sector was included in the model to

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account for the fact that software has become the most dominant sector in the tech industry. A
dummy variable for location was added to differentiate companies that are located in Silicon
Valley and other regions.
Procedures and Analysis
The collected data were organized in two-mode, affiliation matrices, consisting of
investors in the column dimension and startups in the row dimension. Existence of a co-
investment relationship was marked by 1s, and the absence of it was marked by 0s. UCINET 6
was used to analyze the data and transform the two-mode affiliation matrix to one mode data (S.
P. Borgatti, Everett, & Freeman, 2002). In the process of transforming the two-mode affiliation
matrix to 1 mode data, the original affiliation matrix was multiplied by its transpose matrix. This
constructed a startup-by-startup network based on a shared investor so that the network displays
connections between all of the startups. Three separate network graphs were produced using the
Netdraw network visualization tool, which is embedded in UCINET 6.0.
Centrality scores for each startup were calculated using UCINET 6.0 (S. P. Borgatti,
Everett, & Freeman, 2002). The most central nodes were identified based on the resulting output
of the degree, closeness, eigenvector, and betweenness scores. Network-level information was
also gathered in this analysis for a systematic comparison of different networks as a whole. Core-
periphery structure was computed and identified which startups belong in the core of the
network. The first graph presents a network of 53 startups in Silicon Valley that are linked
through syndicated investments of 255 venture capitalists. Second is a network of 43 startups in
other regions linked by 194 venture capitalists. These two graphs are used for a comparative
analysis of startup networks in Silicon Valley and other regions.

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Finally, the third graph illustrates a network of both Silicon Valley and non-Silicon
Valley startups. There are altogether 96 startups linked by 367 investors. A UCINET attribute
file was created with a single column dummy code 1 = startup in Silicon Valley, and 0 = startup
in other regions. A node-level two-sample t-test was performed in UCINET 6.0 to test hypothesis
1, determining the differences between the mean degree centrality of startups in Silicon Valley
and other regions (S. P. Borgatti, Everett, & Freeman, 2002).
Comparative Analysis of Startup Networks in Silicon Valley and Other Regions
Network of Startups in Silicon Valley
Figure 4.1 presents the co-investment network of startups in Silicon Valley. Each link
between the nodes represents a shared investor, and because it is non-directional all relationships
are reciprocated. The core-periphery analysis identifies 18 companies as a network core. In terms
of node attributes, half of these companies are in the prepackaged software industry. The other
half are in other information communication and technology sectors (ICT) including information
retrieval, computer programming, communication service, and etc. Seven companies were
established before the 2008 economic recession. On average, 1.2 billion dollars were invested for
each company with a mean valuation of 7.26 billion dollars. In addition, each startup on average
was assigned to 42 patents. Table 4.1 provides more details on company information in terms of
sector, founding year, investment performance, valuation, and innovation capacity.

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Figure 4.1. Network of co-invested startups in Silicon Valley (N=53)
The five most central nodes are ranked in terms of degree, betweenness, eigenvector, and
closeness centrality scores. Table 4.2 presents their rank chart. In terms of degree centrality,
Airbnb shares the highest numbers of investors with other startups, followed by Instacart,
Prosper Marketplace, Stripe, and Uber. Prosper Marketplace has the highest betweenness score,
which means that some pairs of startups are connected only through Prosper Marketplace.
Twilio, a cloud communications platform founded in 2008, is the only company in the rank chart
that does not belong to the network core. However, it has the second highest betweenness score,
followed by Docusign, Lookout, and Airbnb. Airbnb is the most central node in the network in
terms of eigenvector centrality, which captures that it is well connected to others with high
connectivity. Following Airbnb are Instacart, Prosper Marketplace, Slack, and Uber. For
closeness, Prosper Marketplace is the most central node, followed by Twilio, Docusign,

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Lookout, and Airbnb. This indicates that the average distance of Prosper Market from all other
startups in the network is the shortest.
Table 4.1. Core startups in Silicon Valley
Company Sector Year Investment Valuation Patent
Airbnb Information retrieval 2008 3304.74 m 25 b 6
Aliphcom Electronic component 1998 561.98 m 3.3 b 182
AppDynamics Prepackaged software 2008 314.50 m 2 b 29
Docker Computer programming 2008 239.85 m 1 b 0
Docusign Prepackaged software 2004 517.44 m 3 b 24
Dropbox Computer processing 2007 582.25 m 1 b 271
Github Prepackaged software 2008 350.50 m 2 b 4
Houzz Information retrieval 2009 613.60 m 2.3 b 1
Instacart Prepackaged software 2012 877.87 m 2 b 0
Lookout Prepackaged software 2007 282.34 m 1 b 99
MarkLogic Prepackaged software 2003 179.24 m 1 b 4
Nextdoor Information retrieval 2007 188.11 m 1.1 b 0
Okta Prepackaged software 2009 232.17 m 1.2 b 11
Pinterest Information retrieval 2009 1368.10 m 11 b 2
Prosper Marketplace Personal credit 2006 432.22 m 1.9 b 1.9
Slack Prepackaged software 2009 788.50 m 2.9 b 0
Stripe Prepackaged software 2010 358.27 m 7 b 5
Uber Communication service 2009 10862.61 m 62 b 108

Table 4.2. Rank chart of the most central startups in Silicon Valley
Rank Degree Betweenness Eigenvector Closeness
1 Airbnb Prosper Market Airbnb Prosper Market
2 Instacart Twilio Instacart Airbnb, Stripe
3 Prosper Market Docusign Prosper Market Uber
4 Stripe Lookout Slack Houzz
5 Uber Airbnb Uber Instacart

Network of Startups in Other Regions
The network graph of co-investment connections among startups in other regions is
illustrated in Figure 4.2. Unlike Figure 4.1, in which all nodes in the network are fully connected,

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there are five isolates in Figure 4.2. In other words, there are no overlapping investors for Avant,
MediaMath, NJoy, NantHealth, and Zeta Interactive. Moreover, the network graph in Figure 4.2
as a whole is loosely connected compared to the Silicon Valley network. Still, one can single out
a group of ten startups that are more densely connected to one another. UCINET’s core-
periphery analysis identifies Domo, DraftKings, the Honest Company, Jet, Magic Leap, Moderna
Therapeutics, Oscar Health, Snapchat, Sprinklr, and Warby Parker as the network core.

Figure 4.2. Network of co-invested startups in other regions (N=53)

Interestingly, all of these core startups in this network are founded after the 2008
economic recession. Similar to Silicon Valley startups, prepackaged software is the most
dominant industry. However, more than half (60%) of the core startups are in other industries,
ranging from pharmaceutical and medical service plans to catalog industries, which is more

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diverse than Silicon Valley startups. In terms of location, three are located in New York, two in
Southern California, two in Boston, and the rest in Utah, New Jersey, and Florida. On average,
these companies are valued around $3.12 billion, invested $966.77 of venture capital funds, and
assigned to 18 patents – all lower than that of Silicon Valley companies. Table 4.3 displays more
information on these startups.
Table 4.3. Core startups in other regions

Table 4.4 presents a rank chart of the five most central nodes in this network. In terms of
degree centrality, Oscar Health is the most central node, followed by Snapchat, Magic Leap, Jet,
and Warby Parker. Snapchat has the highest betweenness centrality score, followed by Jet, Vox
Media, Oscar Health, and Fanatics. While Vox Media – a digital media company in Washington,
D.C. founded in 2004 – and Fanatics – a online sports retailer in Florida founded in 1995 – are
not part of the network core, they still play an influential role as a bridge in the middle of many
paths connecting other startups. Intarcia Therapeutics, a biopharmaceutical company based in
Boston and founded in 1995, has the highest eigenvector centrality despite not being in the
network core, followed by Domo, Jet, DraftKings, and Magic Leap. These companies are well
connected to other influential startups in the network. Jet and Oscar Health, with the lowest
Company State Sector Year Investment Valuation Patent
Domo UT Prepackaged software 2010 725.56 m 2 b 35
DraftKings MA Information retrieval 2011 542.01 m 1.2 b 0
HonestCo CA Catalog 2011 289.82 m 1.7 b 2
Jet NJ Catalog 2014 570 m 1.4 b 0
MagicLeap FL Prepackaged software 2011 2343.5 m 2 b 48
Moderna MA Pharmaceutical 2009 1595.1 m 3 b 34
Oscar Health NY Medical service plan 2013 727.50 m 1.5 b 0
Snapchat CA Prepackaged software 2011 2423.8 m 16 b 54
Sprinklr NY Prepackaged software 2009 236.9 m 1.2 b 5
Warby Parker NY Catalog 2010 213.5 m 1.2 b 2

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closeness centrality score, have the shortest geodesic distances from every other startup in the
network. The next four companies with the shortest average distances are Magic Leap, Snapchat,
WeWork, and Warby Parker. WeWork – a New York based company founded in 2010 – is
another company with a high centrality score that does not belong to the network core.
Table 4.4. Rank chart of the most central startups in other regions
Rank Degree Betweenness Eigenvector Closeness
1 Oscar Health Snapchat Intarcia Therapeutics Jet, Oscar Health
2 Snapchat Jet Domo MagicLeap
3 MagicLeap Vox Media Jet Snapchat
4 Jet Oscar Health DraftKings WeWork
5 WarbyParker Fanatics MagicLeap WarbyParker

Network of Startups in Both Silicon Valley and Other Regions
In a network of startups in both Silicon Valley and other regions there is only one isolated
node – NantHealth, a healthcare company based in Culver City. In Figure 4.3, Avant,
MediaMath, NJOY, and Zeta Interactive, which are isolates in Figure 4.2, are connected in the
combined network. For example, Zeta Interactive – a data-driven marketing company in New
York – shares an investor with a Silicon Valley company, Social Finance. MediaMath – a
marketing software company in Santa Monica – is connected to Credit Karma, Survey Monkey,
and Eventbrite, all based in Silicon Valley.

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Figure 4.3. Network of co-invested Fortune’s 2016 unicorns in the United States (N=96)

Table 4.5 below summarizes company information of the core startups in the combined
network. There are 26 companies, of which 20 are based in Silicon Valley and 6 in other regions
including Utah, New York, New Jersey, Florida, and Southern California. Prepackaged software
is again the most dominant industry, with 54% of the core startups. Next is the ICT industry,
with 35%. The remaining 11% are in the catalog, personal credit, and medical services
industries. Eight companies were founded before the 2008 economic recession, all but one of
which are based in Silicon Valley. The only startup that was founded before 2008 that was not
located in Silicon Valley was MongoDB, a prepackaged software company based in New York.
Startups located in other regions – Domo, Jet, MagicLeap, Oscar Health, and Snapchat – are
founded after 2008. This is consistent with the finding above that core companies in other
regions are younger.

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Table 4.5. Core startups in Silicon Valley and other regions

In terms of financial performance and innovation capacities, core startups in the
combined network (M = 6.16 billion, SD = 2.12 billion) are valued higher than the core startups
in the other regions network (M = 3.12 billion, SD = 4.56 billion) but less than the core startups
in the Silicon Valley network (M = 7.26 billion, SD = 14.84 billion). The amount of total
investment funds raised by core startups in the combined network (M = 1.16 billion, SD = 2.12
billion) is similar to that of core startups in Silicon Valley (M = 1.23 billion, SD = 2.51 billion),
Company State Sector Year Investment Valuation Patent
Airbnb SV Information retrieval 2008 3304.74 m 25 b 6
Aliphcom SV Electronic component 1998 561.98 m 3.3 b 182
AppDynamics SV Prepackaged software 2008 314.50 m 2 b 29
Docker SV Computer programming 2008 239.85 m 1 b 0
Docusign SV Prepackaged software 2004 517.44 m 3 b 24
Domo UT Prepackaged software 2010 725.56 m 2 b 35
Dropbox SV Computer processing 2007 582.25 m 1 b 271
Github SV Prepackaged software 2008 350.50 m 2 b 4
Houzz SV Information retrieval 2009 613.60 m 2.3 b 1
Instacart SV Prepackaged software 2012 877.87 m 2 b 0
Jet NJ Catalog 2014 570 m 1.4 b 0
Lookout SV Prepackaged software 2007 282.34 m 1 b 99
MagicLeap FL Prepackaged software 2011 2343.5 m 2 b 48
MarkLogic SV Prepackaged software 2003 179.24 m 1 b 4
MongoDB NY Prepackaged software 2007 311 m 1.6 b 12
Nextdoor SV Information retrieval 2007 188.11 m 1.1 b 0
Nutanix SV Computer programing 2009 315.25 m 2 b 44
Okta SV Prepackaged software 2009 232.17 m 1.2 b 11
Oscar Health NY Medical service plan 2013 727.50 m 1.5 b 0
Pinterest SV Information retrieval 2009 1368.10 m 11 b 2
Prosper SV Personal credit 2006 432.22 m 1.9 b 1.9
Slack SV Prepackaged software 2009 788.50 m 2.9 b 0
Snapchat CA Prepackaged software 2011 2423.8 m 16 b 54
Stripe SV Prepackaged software 2009 788.50 m 2.9 b 0
Uber SV Prepackaged software 2010 358.27 m 7 b 5
Zenefits SV Communication service 2009 10862.6 m 62 b 108

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which is higher than those in other regions (M = .97 billion, SD = .84 billion). On average, core
startups in the combined network own approximately 36 patents (SD = 64.54), which is more
than those in the other region network (M = 18, SD = 4.56) but less than those in the Silicon
Valley network (M = 41.55, SD = 76.16).
Table 4.6 provides a rank chart of the most central startups in both Silicon Valley and
other regions. It is important to note that all of the central startups are based in Silicon Valley. In
terms of degree centrality, Airbnb is the most central node, followed by Uber, Instacart, Prosper
Marketplace, Stripe, and Slack. Prosper Market has the highest betweenness centrality score,
followed by Airbnb, Docusign, Uber, and Social Finance. Airbnb is also the most central node
considering its connection to other influential nodes in the combined network. Next is Instacart,
Prosper Marketplace, Uber, Stripe. In terms of closeness centrality, Airbnb is again the most
central with the shortest average distance from all other startups in the network, followed by
Uber, Prosper Marketplace, Stripe, Houzz, and Instacart. To summarize, Airbnb shares the
highest numbers of investors with other startups, is the most central node with its connection to
other central nodes, has the shortest geodesic distance from other startups.

Table 4.6. Rank chart of the most central startups in both Silicon Valley and other regions
Rank Degree Betweenness Eigenvector Closeness
1 Airbnb Prosper Airbnb Airbnb
2 Uber, Instacart Airbnb Instacart Uber, Prosper
3 Prosper Docusign Prosper Stripe
4 Stripe Uber Uber Houzz
5 Slack Social Finance Stripe Instacart

Interestingly, some companies become more connected in the combined network of
startups in both Silicon Valley and other regions. In terms of core startups, Nutanix – a Silicon

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Valley company that offers a virtualized datacenter platform – and MongoDB – a database
company based in New York – did not belong to the core in their own separate network but are
now part of the core in the combined network. Similarly, Social finance, a financial company
which did not have a particularly high betweenness centrality score in the Silicon Valley
network, is now one of top five bridges in the combined network. This implies that Social
Finance plays a crucial role in connecting companies in Silicon Valley with companies in other
regions. Furthermore, it is one of the most central nodes regarding betweenness centrality
although it does not belong to the core of the combined network, which again reinforces its role
as a bridge. This indicates that startups in other regions that are invested by venture capitalists
who invest in Silicon Valley startups enjoy the benefits of network connections in the overall
combined networks.
Network Level Analysis
The summary of network-level information on the three networks examined in this
chapter is given in Table 4.7 in the following order: (1) network size; (2) average degree; (3)
degree centralization; (4) density; (5) components; (6) connectedness; (7) average distance; and,
(8) diameter. Network size indicates the number of startups in a network. There are 53
companies in the Silicon Valley network, 43 companies in the network of startups in other
regions, and 96 companies in the combined network. Startups in the combined network have the
highest average degree of 28.3, followed by those in Silicon Valley (M = 21.6) and other regions
(M = 7.81). In other words, on average, a startup in the combined network of both Silicon Valley
and other regions shares approximately 28 co-investment links with other startups in the
network.

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Table 4.7. Network level information
Measure Silicon Valley Other Regions Both
Size 53 43 96
Average degree 21.623 7.814 28.396
Degree centralization 0.367 0.279 0.394
Density 0.685 0.216 0.437
Components 1 6 2
Connectedness 1 0.779 0.979
Average distance 1.632 1.999 1.773
Diameter 3 5 4

Degree centralization and density describe how compact each network is. While density
captures the overall cohesion, degree centralization measures the extent to which the cohesion is
organized around the most central point. It is calculated by dividing the variations in the degrees
of nodes by the maximum possible degree variation (Freeman, 1979). All three networks
examined in this chapter are somewhat decentralized; links are somewhat evenly distributed
among most nodes. Degree centralization of the Silicon Valley network is 36.7% while that of
the other regions network is 27.9%, and that of the combined network is 39.4%. Density of the
Silicon Valley network is the highest as 68.5% of all possible pairs of startups are co-invested by
one or more of the investors. In contrast, the network of startups in other regions is rather sparse
with less than half (21.6%) of all possible startup pairs connected through syndicated investment.
Density of the combined network is also less than half (43.7%) but higher than that of the other
regions network.
The Silicon Valley startup network is fully connected with just one component, a
subgraph in which there is a co-investment link between all pairs of startups. Startups in other
regions are more scattered with 77.9% connectedness and six components in the network. In the
combined network of startups in both Silicon Valley and other regions, there are two
components, and connectedness increases to 97.9%. The average distance of the Silicon Valley

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startup network is the shortest, which shows that on average, startups in this network are 1.63
steps away from all other startups, assuming that they could reach one another. In this sense all
three networks are cohesive, because all the nodes are linked within two steps – 1.999 steps in
the other regions network and 1.773 in the combined network. The diameter of a network is the
shortest for the Silicon Valley network and the largest for the other regions network. The length
of the largest geodesic between any pair of nodes in the former is 3 whereas it is 5 in the latter.
Node Level Analysis
The node-level t-test analysis in UCINET examined whether Silicon Valley companies
have more co-investment ties. To reiterate, hypothesis 1 proposed that with regard to syndicated
investment, startups in Silicon Valley are better networked than startups in other regions. This
hypothesis was tested by comparing the normed freeman degree centrality of companies in
Silicon Valley and other regions. In the combined network of all domestic startups in both
Silicon Valley and other regions, being located in Silicon Valley is used as a binary variable
coded 1 for companies in Silicon Valley and 0 for companies in other regions.
The default of 10,000 trials created the permutation-based sampling distribution of the
difference between the two means for this test, and the scores on normed Freeman degree
centralization are randomly permutated for each of these trials. The output of this test reports that
the average normed degree centrality of companies in Silicon Valley (M = 51.755, SD = 33.469)
is 22.848 units higher than the average normed degree centrality of companies in other regions
(M = 28.907, SD = 24.168). The difference is also statistically significant, p < 0.001, thereby
supporting hypothesis 1. Startups that are located in Silicon Valley in comparison to startups in
other regions are better connected in terms of co-invested venture capital funding. In other

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words, startups in Silicon Valley are more central in the co-investment network than startups in
other regions.
Discussion
This section has examined the network structures and their effects on a company’s
innovation capacity and investment performance by conducting a series of network analyses and
testing three different hypotheses. Findings suggest that at the network level, Silicon Valley co-
investment network is much denser and cohesive compared to the network of startups in other
regions. At a node level, Silicon Valley companies were more central and influential in the
combined network of startups in Silicon Valley and other regions. Furthermore, isolated startups
in the network of other regions became connected in the combined network, indicating that
Silicon Valley startups play an influential role in other regions as well. The next chapter explores
why and how Silicon Valley continues to be the center of innovation and its regional growth over
the past few decades.

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Chapter 5: Silicon Valley as a Milieu of Innovation: Continuity and Change
“Silicon Valley continues to reinvent itself as its specialized producers learn collectively and
adjust to one another's needs through shifting patterns of competition and collaboration.”
- Saxenian, 1996

In previous chapters, I have explored systematically the current startup scene in Silicon
Valley by treating individuals, organizations, and networks as different units of analysis. By
doing so, I discovered that individual actors in the Silicon Valley startup ecosystem develop
entrepreneurial resilience by means of engaging in risky ventures, alternating between successes
and failures, and committing to their ongoing business activities. Moreover, each startup
undergoes a multi-step, incremental expansion from a creative idea to an independent
organizational entity with various actors. Startups that outperform their competitors are likely to
be more networked through venture capitalists who not only provide financial support but also
influence their business ideas and executions.
These findings provide a comprehensive understanding of the actual processes that make
up the functioning of entrepreneurship and innovation production unique to the Silicon Valley
startup ecosystem. To supplement these findings, I now shift the focus to examining the larger
regional context of Silicon Valley as a milieu of innovation. This chapter is hence dedicated to a
regional-level analysis by concerning the broader institutional, cultural, and economic conditions
for Silicon Valley to become and remain as the global center of startups and innovation. In
particular, it investigates new regional-level features that explain why and how Silicon Valley in
the post-2008 economic crisis persists to be the milieu of innovation, attracting startups and
venture capitalists from around the world.

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While there is ample research on the regional significance of Silicon Valley, existing
studies in the social sciences mostly focus on the birth and development of Silicon Valley in the
second half of the 20
th
century. Over the course of recent years, however, Silicon Valley has
undergone a series of rapid, multiple, and systematic changes. Therefore, the so-called regional
advantage of Silicon Valley has been deeply transformed economically, technologically, and
culturally. The aim of this chapter is to first re-establish Silicon Valley as the milieu of
innovation in the 21
st
century by considering the following set of conventional questions that are
routinely asked in this research domain. How do the processes of network formation and
entrepreneurship relate to the milieu of innovation? What are the factors that make Silicon
Valley still unique as a regional cluster conducive to innovation? Why does spatial proximity
still play a crucial role in the age of globalization and digitalization? These questions re-evaluate
the existing characteristics of Silicon Valley in lieu of its recent transformations.
Next, the chapter presents new features of Silicon Valley corresponding to the recent
startup scene of the early 21
st
century. The general questions to be explored in this chapter are:
What are the original factors of milieu of innovation that persist in the current Silicon Valley
startup ecosystem? How has Silicon Valley been transformed in the aftermath of the 2008
economic crisis? By illustrating how and why Silicon Valley as a milieu of innovation is
reproducing and rewriting its existing features conducive to innovation, this chapter argues that
milieus of innovation evolve through time. Before delving into the analysis of Silicon Valley as a
milieu of innovation, this chapter first begins with defining the term. Then it provides a brief
historical background of Silicon Valley. Next are the sections that detail my fieldwork
methodology and findings. In addition to analyzing and interpreting my ethnographic data, I also
present industry data from business reports and databases to assess the new wave of innovation

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in Silicon Valley. Finally, I discuss the new features and impact of Silicon Valley as the enduring
global milieu of innovation in the 21
st
century.
Conceptualizing Milieu of innovation
Hippolyte Taine's notion of artistic milieu states that milieux exist because of the artists
who reside in the same area and generate a particular kind of milieu-specific talent. Such a talent
engenders "tastes and styles, producing not only the great revolutions in the human imagination,
but also more subtle differences between styles, between schools, between nations" (Hall, 1998,
p. 15). Analogously, Tornqvist in 1978 coined the term creative milieu to illustrate that regional
context matters when it comes to creativity. The four features of creative milieu are: information
transmitted among people; knowledge that consists in the storage of the information in
memories; competence in certain activities in terms of the demands of region-specific external
environment; and creativity which synergizes the first three activities and create something new
out of them.
Examples from history demonstrate that centers of creativity:
[...] tend to be at the intersection of different cultures, where beliefs, lifestyles,
and knowledge mingle and allow individuals to see new combinations of ideas
with greater ease [...] To achieve creativity in an existing domain, there must be
surplus attention available. This is why such centers of creativity as Greece in
the fifth century B.C., Florence in the fifteenth century and Paris in the
nineteenth century tended to be places where wealth allows individuals to learn
and to experiment above and beyond what was necessary for survival
(Csikszentmihalyi, 2013, p. 8).
Creativity is the essential source for the very first step of innovation, and it is closely associated
with culture. According to Csikszentmihalyi (1996), creativity is produced from the interaction
of "a system composed of three elements: a culture that contains symbolic rules, a person who
brings novelty into the symbolic domain, and a field of experts who recognize and validate the
innovation" (p.6). In the context of Silicon Valley startup ecosystem, this could be also viewed

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as an interaction between entrepreneurial culture, startups, and venture capitalists, all of which
are essential for innovation production.
There is a positive feedback loop between culture and creativity as creativity enriches
culture and vice versa. Similarly, in describing the impact of new technological innovation, Perez
(2002) asserts, "societies are profoundly shaken and shaped by each technological revolution
and, in turn, the technological potential is shaped and steered as a result of intense social,
political, and ideological confrontations and compromises" (p. 22). More importantly, the
sociological relevance of this profound change is that it "entails a shift in the way of life" (p.
185). The cultural perspective adds to a great extent to the foundational understanding of the
innovative milieux and their significance in the production of innovation dictated by one’s social
environment. Castells, Hall, and Aydalot in the early 1980s identified milieu of innovation as “a
specific set of relationship of production and management, based on a social organization that by
and large shares a work culture and instrumental goals aimed at generating new knowledge, new
processes, and new products” (Castells, 2010, p. 419).
Castells and Hall (1994) conducted a study on the formation, structure, and dynamics of
four distinct kinds of technological milieux of innovation: industrial complexes, science cities,
technology parks, and the research/manufacturing centers. Their findings indicate that success of
a milieu is closely associated with its networking capacity, which generates innovation synergy.
Castells (2010) further argues that high-technology-led industrial milieu of innovation needs
“spatial proximity [as] a necessary material condition for the existence because of the nature of
the interaction in the innovative process” (p. 419). In accordance with the analyses of Castells
and Hall, I find that the defining characteristic of an innovative milieu is the geographical
proximity between different entities that contribute to the production of innovation and attract

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more creative talents. In the case of Silicon Valley, its flexible network-based system created a
milieu of innovation, “organized around the region and its professional networks rather than
around the individual firm” (Saxenian, 1994). In her more recent work, Saxenian (2014)
accounts the new economic dynamism of Silicon Valley and also addresses its challenges such as
stagnant employment and growing inequality. The following section traces the history of Silicon
Valley to derive its classical milieu of innovation model as a preparatory step for later sections
that evaluate the transformations of Silicon Valley in the 21
st
century.
The Rise of Silicon Valley as a Milieu of Innovation
Silicon Valley derives its name from the high-purity silicon used in transistors and other
semiconductor devices (Silicon Valley, 2015)
7
. In the 1970s, when the Santa Clara county was
dubbed Silicon Valley, many of the largest semiconductor firms like Fairchild and Shockley
Labs had their headquarters in the region (Saxenian, 1996). Discovery of these firms were often
unintended. For instance, the founding of the Hewlett-Packard Company could be attributed to
Stanford electrical engineering professor Frederick Terman "who encouraged his graduate
students William Hewlett and David Packard to commercialize an audio-oscillator that Hewlett
had designed while working on his master's thesis" (p. 20). With the increasing popularity of
semiconductor devices, computer-software and other high-technology industries also began to
emerge. Over time, however, information became the raw material for knowledge that produced
numerous entrepreneurial ideas and innovations. As Rogers and Larsen argue:
In Silicon Valley one can create value out of thin air: Information and innovation
combine to produce economic value [...] Information is the Valley's resource
(Rogers & Larsen, 1984, p. 276).

7
Although the region was initially named after the actual material of its products, Silicon
Valley later evolved to symbolize and communicate innovative ideas and creative talents.

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Originated as a center of semiconductor production, Silicon Valley soon became the
milieu of innovation, producing some of the most influential modern technologies and
applications from computer software and Internet services to smart phones and electric cars. Part
of what drives these technological innovations is the unique entrepreneurial culture propagated
by Silicon Valley's technology community over the past generation. Culture of Silicon Valley
not only shapes its entrepreneurs and innovations, but also its economy at large. The startup
scene in Silicon Valley portrays how its culture is deeply embedded in the technologies that are
produced. The Silicon Valley work ethic values meritocracy and work performance to determine
success (Rogers & Larsen, 1984). Consequently, the culture of Silicon Valley enhances
competition and efficiency in production.
Culture also influences one’s job mobility and networking capacity in Silicon Valley. In
particular, the phenomenon of transnational brain circulation depicts the new phase of
globalization and migration in entrepreneurship. Conventional wisdom concerns international
migration of the highly skilled workforce from poor to rich countries as the brain drain
phenomenon (Saxenian, 2007). Saxenian's empirical study, however, suggests that these workers
are now returning home to transfer technical and institutional know-how by starting new
companies and establishing business relationships. As illustrated in her book, The New
Argonauts, a number of Taiwanese Americans relocate to Taiwan after years of working
experience in Silicon Valley and are able to utilize their cultural familiarity and bi-lingual
capabilities to their advantage. Moreover, they continue to network with Silicon Valley
entrepreneurs and venture capitalists as they expand their business globally.
The success story of Silicon Valley, however, is seen an anomaly from the perspective of
conventional product cycle theory in economics, which describes "the logic of industrial

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evolution and location in mass production industries that compete on the basis of minimizing
manufacturing costs" (Saxenian, 1996, p.131). The geographical center of computer innovation
shifted to Silicon Valley in the late 1980s, "despite Route 128's longstanding concentration of
technology, skill, and expertise in computer systems architecture and design" (p. 130). Silicon
Valley was able to outpace Route 128's initial leading edge and rapidly emerge as the central
locus of innovation in maturing industries because of its network-based business model with
increased synergy. As Saxenian explains:
Product cycle theory cannot explain why Silicon Valley did not decline, but
rather adapted, when the semiconductor industry matured [...] Competition
based on continuous innovation, however, undermines the logic of industrial
maturity implicit in the life cycle model. As firms in the computer and
semiconductor industries rejected the model of stable cost-based competition for
a strategy of creating new markets by constantly introducing new products and
applications, they dramatically shortened product cycles. This new competitive
environment privileged Silicon Valley's regional network-based system, with its
capacity to promote experimentation, learning, and the pursuit of multiple
technological trajectories (p. 131).
While Silicon Valley’s entrepreneurial and innovation activities grew out organically,
there were institutional factors that further bolstered its efforts to create more synergy and
productivity in the startup ecosystem. First, Silicon Valley’s sophisticated venture capital
industry provides startups more leeway to raise money for their businesses. Second, as the
number of firms grew in the region grew, so has its related professional networks such as
professional services in law, banking, real estate, research, and more. Third, a legal system
emerged to support the high rates of mobility between firms. Fourth, top research universities
continuously produced more talents with their well-developed educational training system. Last,
various professional and technical organizations emerged and enhanced cooperation among
stakeholders (Saxenian, 2014).

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The Enduring Power of Silicon Valley
The notion of Silicon Valley as a milieu of innovation is not entirely novel as it has long
been examined by scholars and widely reported in news media. On the one hand, there is a
consensus that the processes of network formation and entrepreneurial activities enabled by
spatial proximity and institutional support in the region create a synergetic environment
conducive to innovation. On the other hand, the entrepreneurial culture of Silicon Valley that
promotes creativity and encourage collaboration among talents contribute to resource
management and innovation production. Under the new economic, cultural, and institutional
conditions of the 21
st
century, however, the existing network-based, synergistic model of Silicon
Valley startup ecosystem has been adjusted.
For instance, while startups in Silicon Valley continue to generate ground-breaking
disruptive technologies and products, the current wave of innovation is driven mainly by the
software industry with a highly diverse business base including communications, healthcare,
aerospace, education, etc. Correspondingly, the industry’s network structure has become much
more developed, as networks have grown in size and complexity at a global scale. Silicon Valley
has also grown geographically, as startups have spread beyond the South Bay and expanded into
San Francisco and the East Bay. Furthermore, the unique entrepreneurial culture of Silicon
Valley has now been incorporated into large established technology companies such as Google
and Facebook, which offer flexible hours, egalitarian management structure, casual dress code,
and other perks such as free food and laundry service. In addition to discussing the new
dimensions of Silicon Valley’s existing features, this section further explores the emergent
features of its milieu of innovation corresponding to the changing startup landscape of the early
21
st
century.

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Methodology
Observations of Silicon Valley as a milieu of innovation are drawn from multi-sited
ethnography, participant-observation, and interviews in various professional settings such as
startup conferences, pitch competitions, industry expos, and networking events in both Silicon
Valley and other regions. Because this chapter also deals with international case studies, some
parts of the analysis refers to my international fieldwork in Berlin, Copenhagen, Naples, and
Seoul where both scholars and industry professionals in entrepreneurship and technological
innovation were interviewed. In Copenhagen and Naples, I attended an annual academic
conference and participated in a track that explored topics on entrepreneurship and globalization.
Berlin hosted a regional conference of Silicon Valley’s TechCrunch event, geared towards
European startups.
Notes were taken during the interview and conference session. Select conversations were
voice recorded with permission. Interviews conducted in Korean were voiced recorded and later
translated select parts that were used for the analysis. After completing the final round of
fieldwork, additional literature review was conducted to match the empirical data with findings
from existing theoretical texts and case studies. Open coding strategy was used to select phrases
according to themes that emerged from my research questions for this chapter on Silicon Valley
as a milieu of innovation (Strauss & Corbin, 1998). Data were then compared and analyzed on
the basis of overlapping themes (Huberman & Miles, 1994).
A New Wave of Innovation
Since its inception, Silicon Valley has undergone a series of booms and busts – most
notably the dotcom era of the 1990s and the economic recession following the 2008 financial
crisis. Following each boom and bust cycle came a new wave of innovation with a more

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diversified industrial base as well as increased global presence. It is notable that the number of
unicorns, successful private companies with post-money valuations of $1 billion or more, has
soared dramatically over the past few years. From 2005 through 2008, only three startups
became unicorns whereas in 2014 alone there were 55 new unicorns (TechCrunch, 2016). To
investigate the current wave of innovation, I selected 178 startups from the 2016 CrunchBase
Unicorn Leaderboard by TechCrunch
8
- a leading online publisher of technology industry news.
These companies are either unicorns, exited unicorns, or emerging unicorns. Exited unicorns are
companies that went public through IPO or got acquired by bigger firms. Private companies with
a valuation of between $500 million and $1 billion are called emerging unicorns.
Dominant Sectors and Industries
Analysis of these companies using Hoover’s online database (2016) indicates that
approximately half the companies were in the software and consumer internet sectors. In terms
of their industries, which describes a more specific categorization, 36 companies were in
information technology services and 12 were in computer software. According to the North
American Industry Classification System (NAICS), 20 of the companies engaged in custom
computer programming services, 13 in computer systems design services, and 13 in software
publishers. Similarly, Standard Industrial Classification (SIC) system by the United States
Department of Labor search of these companies showed that computer software development
was the most dominant sector. Tables 5.1 through 5.4 below detail more industry information
about these companies.

8
In 2015 CrunchBase introduced the Unicorn Leaderboard to keep track of privately held
companies that have passed the billion-dollar valuation mark.

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Table 5.1. Top 5 dominant sectors (Hoovers)
Sector Number of Companies
Software 54
Consumer Internet 19
Media 16
E-Commerce 12
Retail 9

Table 5.2. Top 5 dominant industries (Hoovers)
Industry Number of Companies
Informational technology services 36
Computer software 12
Business services sector 9
Telecommunications services 8
Lending 6

Table 5.3. Top 5 dominant industries (NAICS)
Industry Number of Companies
Computer programming services 20
Computer systems design services 13
Software publishers 13
Wired telecommunications carriers 9
All other information services 8

Table 5.4. Top 5 dominant industries (SIC)
Industry Number of Companies
Computer software development 8
Computer software development and
applications
6
Customer computer programming services 5
Online database information retrieval 5
Application computer software 4

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Diverse Industry Base
As can be seen from the tables above, software is by far the leading sector in the 2016
global startup scene. However, these software companies extend their business into a diverse set
of industries that do not necessarily relate to one another. For instance, while more than half of
the top ten Silicon Valley companies by valuation in 2016 were the software sector, all of them
belonged to different industries. Table 5.7 presents the list of these companies and their
corresponding sectors and industries. Overall, there is a huge broadening of field-specific
applications of software products to meet the market demand in different industries ranging from
transportation and travel to finance, insurance, and lending. What is considered disruptive in the
new wave of innovation can therefore be attributed to how one implements its technology in the
market that is most suitable to its business product.

Table 5.5. Top ten Silicon Valley startups by valuation
Company Valuation Sector Industry
Uber $62 b Software Computer software
Airbnb $25.5 b Software Hotels & motels
Palantir $20.5 b Software Information tech
Pinterest $11 b Media Media
Dropbox $10 b Software Managed application
Theranos $9 b Health Medial equipment
Lyft $5.5 b Software Taxi & limousine
Stripe $5 b Finance Finance & insurance
Zenefits $4.5 b Software Insurance agencies
SocialFinance $4 b Finance Lending

A similar pattern emerged from my fieldwork data, implying how startups are expanding
technological application into a wider range of industries. At the Internet of Things (IoT) World
Expo in Santa Clara, over 350 thought leaders and 200 exhibitors came together to discuss the

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monetization of the IoT revolution. The focus was on the creation and advancement of real-
world solutions using IoT products, rather than just considering how the technology itself can be
improved. The Expo exhibited IoT products in the following industries: manufacturing and
supply chain, artificial intelligence (AI), health care, smart cities and transportation, energy and
agriculture, connected cars, and smart home. The Augmented World Expo featured the latest
technology advancements across the extended reality (XR) market, including block chain, web
XR, augmented reality (AR) cloud, AI, future of vehicles, and smart glasses.
Startup conferences such as TiEcon, Silicon Valley Open Doors (SVOD), and
TechCrunch Disrupt offered multiple tracks and a diverse array of sessions to discuss the
application and monetization of new technological innovations. There were six different tracks in
TiEcon: AI platforms, financial technology, IoT, cyber security, health technology, and block
chain. For the pitch competition at SVOD participating startups were grouped into six different
industries: (1) enterprise software, (2) machine learning, AI, and education technology, (3) big
data, data analytics, and application and tools, (4) social impact and mission driven, (5) AR/VR,
3D applications, and (6) consumer internet, mobile application, and consumer IoT. TechCrunch
Disrupt organized off the record sessions where participants could interact with invited speakers
from Silicon Valley and discuss innovative use cases in AI, machine learning, crypto currencies,
block chain, biotechnology, robotics, and financial technology.
Industry leaders are well aware of the need for a broader understanding of the industry
impact in the new wave of innovation. Shantanu Narayen, CEO of Adobe Systems, described
that in the current experience business wave of enterprise disruption, success of innovation
depends on meeting customer demands and getting the right product to the right person. Vishal
Sikka, CEO of Infosys, accentuated that innovation must be pervasive and that enterprises must

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continuously re-invent information technology and system landscape through discovering more
useful applications of existing technology. Tye Brady, chief technologist at Amazon Robotics,
introduced robotics as a new field of innovation with abundant use cases that employ
“computing, sensing, and the actuation systems to perform one or more tasks in the physical
environment across a spectrum of intelligent behavior,” which ultimately extends human
capabilities and improves quality of life by adding intelligence.
Expansion of Silicon Valley Networks
The advancement and expansion of technological innovation in the Silicon Valley startup
ecosystem have led to the growth of its entrepreneurial networks in threefold. First, networks
have become more sophisticated and complex with increased collaboration and interaction
between startups, venture capitalists, established companies, and other professionals. Second,
networks spread geographically, and Silicon Valley now covers a larger area up to the East Bay
and San Francisco area. This has created a renewed culture of tech companies in Silicon Valley.
Third, the region has also become the global hub of startups and innovation, and its domestic
startups are also expanding global. As a result, the overall network structure has grown in both
size and complexity. In the following sections, I base my analysis of field data on Gordon’s
(1994) argument that:
Regions and networks in fact constitute interdependent poles within the new
spatial mosaic of global innovation. Globalization in this context involves not
the leaving impact of universal processes but, on the contrary, the calculated
synthesis of cultural diversity in the for of differentiated regional innovation
logics and capabilities (p. 46).
Despite increased competition and maturing industry, startup activities continue to
flourish in terms of both quantity and quality. With more accessible resources and information,

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entry barrier is lower allowing products to be built faster and cheaper. As Eric Feng, venture
capitalist from Kleiner Perkins Caufield and Byers, argues:
In years past, just building a product was necessary and sufficient. Today,
building a product is necessary but not sufficient. That is intriguing [because it]
makes entrepreneurship accessible but more competitive.
Lower entry barrier and increased competition leads to an evolution of the startup ecosystem
with more complex networks composed of dynamic entities. Networking sessions in the region
invite participants from different professions such as engineering, law, medicine, media, etc. For
instance, Silicon Valley corporate attorney Todd Rumberger gave legal advice to startups at an
information session organized by Foley & Lardner LLP in Palo Alto. TiEcon invited Sam
Liccardo, Mayor of San Jose, as one of the main speakers to discuss the city’s infrastructure and
safety issues. Liccardo invited startups to form partnerships with government, universities, and
NGOs to collaborate on issues like working on improving the city’s transportation system.
Richard Murdock, co-founder of RONA Holdings, closely works with and offers
mentorship to startups in Silicon Valley. He observed multiple stages of networking for different
startups depending on their stage of growth. For a young startup, its network is confined by its
limited ties to venture capitalists and mentors. Mature startups network with accountants,
lawyers, and insurance companies to build the company infrastructure. A wide range of networks
in Silicon Valley provides resources and information access to nascent startups. Angel investor
Ariel Poler highlighted diversity of networks in Silicon Valley where many philosophies and
approaches co-exist to allow startups to find the approach that works for them and customize
learnings to their unique situation. Berlin-based investors at TechCrunch Disrupt identified
networks of corporate giants and startups as a determining factor of the evolution of the Silicon
Valley startup ecosystem. Due to high job mobility in Silicon Valley, corporate giants produce

107
well-trained employees who leave to start their own companies, which establishes more ties
between big firms and startups in terms of shared knowledge and experience.
Silicon Valley has also expanded its region physically along with the growth of its
networks. Startups have spread beyond the South Bay and crowd into San Francisco and the East
Bay. The new generation of entrepreneurs, the so-called millennials, enjoy the city life and
cheaper options for office space such as WeWork’s shared workspaces. Those whose offices are
located in the South Bay still live in the city as companies like Apple and Google offer employee
buses to commute. Furthermore, the unique entrepreneurial culture of Silicon Valley has now
been incorporated into large established technology companies such as Google and Facebook,
which offer flexible hours, egalitarian management structure, casual dress code, and other perks
such as free food and laundry service. As Regis McKenna who is known as Silicon Valley
marketing guru, describes, “it has grown geographically as well as in concept” (Mazur & Miles,
2007).
In fact, new regions of Silicon Valley territories perform well and even surpass the
original South Bay Silicon Valley. According to the PwC Money Tree Report Q4 2017, San
Francisco has become the region with the highest annual funding level at $15.8 billion in 2014,
exceeding Silicon Valley by $4 billion. In 2015 the amount reached $26.7 billion, while Silicon
Valley was at $10 billion. The number of venture funding deals also illustrates the geographical
expansion of Silicon Valley startup ecosystem. In 2011 San Francisco for the first time closed
more venture deals than Silicon Valley with 710 new deals, which was 65 more than that of
Silicon Valley. Within a few years, the number of deal activities in San Francisco increased to
over 1000 while Silicon Valley remained below 800. In 2015, San Francisco had a record-
breaking number of deals at 1,246 while Silicon Valley had 746.

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Figure 5.1 below displays total investment amount for top ten regions in the United States
in 2016 and 2017. San Francisco tops the list in both Q4 of 2016 and Q4 of 2017. The New
York Metropolitan area ranks the second in the last quarter of 2017 and shows a significant
decrease in the total investment amount from the previous quarter. Throughout all quarters,
however, total investment amount accumulated by startups in both San Francisco and Silicon
Valley is the highest. It is interesting to note that startups in major cities have become a popular
destination for venture capital firms while Silicon Valley startups continues to perform well. This
also supports Silicon Valley’s expansion into San Francisco, which captures the enduring power
and influence of the region.

Figure 5.1. Top ten regions by total investment amount

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New Phase of Globalization
Transnational brain circulation has been a prominent feature of globalization and
migration in Silicon Valley. Saxenian’s study on the New Argonauts, examine the global
circulation of immigrant workers in Silicon Valley who relocate back to their original home
countries to establish new businesses while they continue to network with Silicon Valley startups
and venture capitalists to share resources. A new emergent trend that I observed during my
fieldwork in Silicon Valley is a large influx of international startups seeking venture capital
funding and business expansion in the U.S. market. By coming to Silicon Valley, startups that
are already established and operating in other countries seek to network with American venture
capitalists and recruit local talents for their U.S. office. This is a sharp contrast from the existing
immigrant and domestic entrepreneurs in Silicon Valley who instead seek to go global.
Therefore, global networking has become more crucial than ever before for both American and
foreign startups in Silicon Valley.
Being able to acquire investment from the U.S. venture capitalists is a huge asset for
international startups. Because most American investors in Silicon Valley have themselves been
entrepreneurs in the past and also currently serve as company board members and advisors, they
engage in all facets of new startups and their business activities. With their entrepreneurial
background and experience, they are able to offer better business advice and solutions to
international startups that move to Silicon Valley. With most of venture capital funding coming
from Silicon Valley, American investors are able to think big and manage large-scale venture
capital funds with startups in various industries. Moreover, the U.S. market is more competitive
and mature, which poses another set of challenges for international startups. Access to the

110
Silicon Valley network thus allows these startups to find the right resources and information as
they tap into the U.S. market.
Noam Bardin, Founder and CEO of Waze that was acquired by Google, is hailed as one
of Business Insider’s 100 Stars of Silicon Valley and is originally from Israel. He spoke at the
SVOD conference in 2016 about the classic mistakes foreign companies make when they move
to Silicon Valley. International startups often focus on the headquarter office back home while
letting U.S. business person to run the U.S. office. Bardin stressed that “the person who moves
[to Silicon Valley] has to be the founder,” and elaborated that founders must create their own
Silicon Valley network. As he described, “the biggest problem is that we didn’t go to school here
[and] hiring strong engineers is hard if you didn’t go to Stanford or MIT with them.”
Accordingly, he advised, “the first person you should hire is someone in PR and
Communications who’s very outspoken [and] being out there who meet and talk to people in
Silicon Valley.” CEOs and founders have the responsibility to travel back and forth and manage
both offices efficiently.
Johannes Reck, Founder and CEO of GetYourGuide, from Berlin echoed Bardin’s point
that “American VCs are used to having billion-dollar businesses [and] they have the vision that a
company can change the world.” During the early stages of her company’s funding rounds, Reck
had to fly to Silicon Valley back and forth to meet the American investors. Alicia Navarro, Co-
Founder and CEO of Skimlinks, based in London also emphasized the importance of networking
with American investors who can get startups connected with new clients and media outlets. Ana
Izquierdo, CEO of Talent Clue, based in Barcelona also testified the benefits of networking with
American investors:
After raising the seed fund, we started contacting [American investors] because
our goal was not to build a Spanish company but build an international company.

111
All of my investors were from Spain, but we’ve been talking to investors in the
UK and US. [We] expanded to three countries really fast.

Discussion
This chapter has examined how Silicon Valley persists to be the milieu of innovation by
analyzing its regional-level features unique to the current 21
st
century startup scene. First, it
emphasizes that globalization has been renewed and broadened through transnational networking
and expansion of global market. Second, there has been a transition from production-driven
model to application-driven business model of innovation. In terms of the industrial base, it has
shifted from micro-electronics and advanced electronics to multiple forms of software products,
which are more process-oriented and applied in diverse domains. Also, startups in Silicon Valley
have diversified their industrial base, going into areas of energy, education, transportation, and
healthcare. Transitioning from production driven milieu of innovation to consumption driven
innovation relating to demands coming from multiple dimensions of society, Silicon Valley is in
fact deeply transformed both industrially and culturally. Moreover, with an expansion of global
networks, structures and dynamics of entrepreneurship now entails a model of innovation that
relies more onto its labor force and consumers around the world with increased competition and
interaction between firms.

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Chapter 6: Conclusion

Findings from chapters 2 through 5 demonstrate the power of generating four progressive
layers of analysis - individual, organizational, network, and regional - with various sources and
types of data to evaluate Silicon Valley as a leading milieu of innovation in the 21st century. The
overall research design employed the comprehensive ecosystem approach to scrutinize the
interactions among entrepreneurship, innovation, and regional growth in the contemporary
information, communication, and technology (ICT) startup industry. By considering both the
processes and outcome of networked innovation, this dissertation study was able to delve into
different aspects of the current startup scene in Silicon Valley and closely examine how it
operates and expands. The Silicon Valley startup ecosystem continues to reproduce itself over
time in a manner that enhances productivity, efficiency, and creativity of entrepreneurial entities
that not only thrive in competitive situations, but also work together to produce networked
innovation at a global scale.
In concluding the dissertation, this final chapter brings these findings together into a
summary and places them in the framework of the theories of increasing returns and new
economic geography proposed by economists Brian Arthur and Paul Krugman. These two
economists challenged mainstream economics on a variety of fronts, most prominently by
arguing against the law of diminishing marginal returns and advocating the importance of spatial
structures for economic development. The relevance of these two economic theories addresses
the broader implications of the dissertation findings beyond the Schumpeterian entrepreneurship
and business cycles of innovation discussed in the introductory chapter. In fact, it takes a step

113
further into addressing how entrepreneurship and innovation produce increasing returns in the
economic geography of clusters.
Summary
During the first decade of the 21st century, Silicon Valley experienced two major
economic crises - the dotcom crisis in 2001 and the financial crisis in 2008. Parallel to the theory
of discontinuous waves of business cycles, the crises served as an exogenous shock that
restructured the regional economy and sparked a new wave of innovation. Silicon Valley was
able to bounce back both times. What’s more, it has entered into a new phase of development
characterized by a massive explosion of startups, sharp increase in venture capital investment,
broadening diversification of industry base, and global expansion of entrepreneurial networks.
Each of the data chapters in this dissertation focused on different aspects of Silicon Valley’s
continuity and change under these new conditions.
Consequently, findings from each chapter expand the scope of research on
entrepreneurship and innovation by addressing the processes and outcome of innovation in a
broader context of a milieu of innovation. The study chose the startup ecosystem as a relevant
object of study and theoretical perspective of the current startup scene in the 21st century Silicon
Valley. The study first classified entrepreneurial resilience as a telling feature of successful
entrepreneurship at an individual level. Next, it identified the processes involved in building a
startup at an organization level. Then it examined the network level features of a group of highly
successful startups and identified how they contribute to entrepreneurial performance. Last, the
study evaluated the continuity and changes of Silicon Valley as a milieu of innovation in the 21st
century.

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Entrepreneurial Resilience in the 21
st
Century Silicon Valley
Chapter 2 reviewed explanatory factors that are essential for entrepreneurial resilience in
today’s Silicon Valley startup scene. Entrepreneurial resilience was defined as the willingness
and ability to thrive in difficult circumstances. Because entrepreneurship is characterized by a
social process of economic relations among individuals, both individual and social mechanisms
were examined to explain entrepreneurial resilience. Analysis of field data found that three
conditions – individual, behavioral, and cultural – were critical. The interplay between them
showed how and why entrepreneurial resilience develops. Findings also showed that venture
capitalists play a crucial role in all three conditions.
In terms of individual condition, dynamic entrepreneurs in Silicon Valley carry distinct
personality traits characterized by optimism, passion, leadership, and openness. As
entrepreneurship involves risk and uncertainty, maintaining emotional stability and making
shrewd decisions allow them to endure hardship and overcome obstacles. These individual traits
are exemplified by a number of Silicon Valley tech celebrities who thrived in the competitive
environment and innovated disruptive technologies. This provided the basis for behavioral and
cultural conditions that require a set of entrepreneurial activities and cultural values, beliefs, or
norms.
Behavioral condition is characterized by unique entrepreneurial routines such as pitching
and networking. Startup founders in Silicon Valley also work long hours, and it is quite common
for nascent entrepreneurs to reside in cohabitated office spaces where work life balance is nearly
impossible. The founder’s values and beliefs get incorporated into the company culture, which is
maintained throughout the lifecycle of a startup. While values and beliefs serve as a vital source
of motivation for entrepreneurs and as a basis for direction in their business, the complete

115
assessment of entrepreneurial resilience is merit based and is culturally constructed. For instance,
entrepreneurs’ performance in the industry is often evaluated based on their investment
achievements and educational background. Female entrepreneurs faced more challenges due to
gender discrimination in the venture capital industry.
Building a Startup as an Organizational Emergence
Chapter 3 examined organizational conditions that foster organizational growth of a
startup company. The chapter identified the key organizational process of entrepreneurship as a
set of relationships established between entrepreneurs and other stakeholders in the industry.
Organizational interactions shaped by strategic communication between entrepreneurs and
venture capitalists facilitate negotiating the tensions between opposing perspectives on what
counts as disruptive innovation in the market. Ultimately, as arguments from both sides converge
and tensions get resolved, there is an emergence of startups as organizational entities. It is crucial
that for a startup to succeed in the long run, it must strive to cooperate and compromise with the
stakeholders and maintain stable relations with them.
The case study of the 2016 Startup Conference Pitch Competition exemplified that not
the most innovative project gets funded by the venture capitalists. Overall, the criteria of
avoiding risk had a greater weight than social impact. Therefore, startups that were not able to
convince their investors that their firms could deliver solid returns ultimately got eliminated in
the funding process. One might concern that if the logic of risk-averse systematically prevails,
although it may yield to generating profits and growth of a company, it might hinder the
production of disruptive innovation in the market. However, there is still a great deal of
innovation from Silicon Valley startups emerging through venture capital funding which is
channeled towards innovative projects despite the risk.

116
Networks Structure and Impact on Success of Startups
Chapter 4 explored the network structures and their effects on a company’s innovation
capacity and investment performance by conducting a series of network analyses and testing
three different hypotheses. Findings suggest that at the network level, Silicon Valley co-
investment network is much denser and cohesive compared to the network of startups in other
regions. At a node level, Silicon Valley companies were more central and influential in the
combined network of startups in Silicon Valley and other regions. Furthermore, isolated startups
in the network of other regions became connected in the combined network, indicating that
Silicon Valley startups play an influential role in other regions as well. Overall, startups in
Silicon Valley are better networked compared to startups in other regions.
Silicon Valley as a Milieu of Innovation
Chapter 5 investigated how Silicon Valley persists to be the milieu of innovation by
analyzing its regional-level features unique to the current 21
st
century startup scene. First, it
emphasized that globalization has been renewed and broadened through transnational networking
and expansion of global market. Second, there has been a transition from production-driven
model to application-driven business model of innovation. In terms of the industrial base, it has
shifted from micro-electronics and advanced electronics to multiple forms of software products,
which are more process-oriented and applied in diverse domains. Startups in Silicon Valley have
diversified their industrial base, going into areas of energy, education, transportation, and
healthcare.
Transitioning from production driven milieu of innovation to consumption driven
innovation with demands coming from multiple dimensions of society, Silicon Valley is in fact
deeply transformed both industrially and culturally. Moreover, with an expansion of global

117
networks, structures and dynamics of entrepreneurship now entails a model of innovation that
relies more on the labor force and consumers around the world with increased competition and
interaction between firms. Silicon Valley has also expanded its region physically along with the
growth of its networks. Startups have spread beyond the South Bay and crowd into San
Francisco and the East Bay. The expanded region continues to attract domestic and international
startups that seek venture capital funding and other resources.
Increasing Returns
The traditional economic principle of diminishing marginal returns states that additional
variable factor input in the production function will decrease the marginal physical product after
a certain point (Pass, Davies, & Lowes, 2006). In other words, the initial high marginal return on
investment declines with more capital input. Eventually, products that get ahead in a market face
the limitations of scarce resources and thus, reach an equilibrium of price and market share. In
the early 1980s, economist Brian Arthur challenged this conventional view with his famous
model of increasing returns. He developed a view of complex economy based on positive
feedback, which results in multiple equilibria, and described it as "process dependent, organic,
and always evolving" (Arthur, 1994, p. 107).
In such an environment, it is the technological breakthrough that allows positive
feedbacks or increasing returns. Arthur (1990) stresses that technology changes the means of
production which further changes the character of the economy and its operation. In a
knowledge-based economy, creating high-technology products reduces marginal costs and
increases profits. For example, products such as computers, automobiles, and aircrafts require
initial high costs in research and development. However, additional production becomes cheaper
with increased experience and resources in the manufacturing process. He explains:

118
Not only do the costs of producing high-technology products fall as a company
makes more of them, but the benefits of using them increase. Many items such
as computers or telecommunications equipment work in networks that require
compatibility; when one brand gains a significant market share, people have a
strong incentive to buy more of the same product so as to be able to exchange
information with those using it already (p. 93).
The positive feedback loop creates a network effect because the more people adopt a new
technology, the more it improves in terms of quality and future adoption. Winner of the
competition in positive feedback economy is however unpredictable due to random stochastic
events and nonlinearities, especially in an emerging market. Referring to several historical
instances, Arthur (1990) argues that technological standards get locked-in regardless of technical
superiority because of the initial widespread adoption. For instance, DOS locked in for over a
decade although there was a better alternative such as the Macintosh operation system. Under
increasing returns, one product or one firm with a selection advantage monopolizes the market
by chance.
Random chance also influences the geographical settlement of companies. Industrial
concentration in one location is self-reinforcing as it depends on the preferences of early entrants
rather than regional superiority. Arthur (1990) elaborates:
The first firm to enter the industry picks a location based purely on geographic
preference. The second firm decides based on preference modified by the
benefits gained by locating near the first firm. The third firm is influenced by
the positions of the first two firms, and so on. If some location by good fortune
attracts more firms than the others in the early stages of this evolution, the
probability that it will attract more firms increases. Industrial concentration
becomes self- reinforcing (p. 95).
He identifies Silicon Valley as an illustrative case of how early chance concentration of
influential firms has developed the configuration of the modern milieu of innovation. If
early entrants like Hewlett and Packard had preferred other locations, the center of
technological innovation might be somewhere else.

119
Economic Geography and Industrial Clusters
Nobel prize-winning economist Paul Krugman (1991) concurs with Arthur’s view on
imperfect competition, multiple equilibria, and increasing returns. He further develops a model
of geographical concentration of manufacturing in a core-periphery pattern, which is
differentiated into an industrialized core and an agricultural periphery. In many countries, the
majority of the population resides in a few metropolitan industrial clusters. Emergence of a core-
periphery pattern often depends on “pecuniary externalities associated with either demand or
supply linkages rather than purely technological spillovers” (p. 485). Similar to Arthur’s notion
of chance and positive feedback economy, Krugman points that the initial conditions of the core
determine its formation.
The interaction among economies of scale, transportation costs, and market demand
altogether creates positive feedback economy in which concentrating production feeds on itself.
Initially, increasing returns provide incentives for firms to cluster in the core. Being in the core
minimizes transportation costs, and firms will concentrate where there is a large market demand.
The location decisions of firms themselves also determine the location of large markets because
the market grows where the production is concentrated. This interaction creates positive
feedback economy with increasing returns, which results in an economic phase transition from
dispersed activity to a core-periphery pattern. Krugman overall argues that spatial dimensions of
the economy must be considered to address important economic questions.
Silicon Valley and Network Effects
The initial development of Silicon Valley as the core industrial cluster occurred largely as
a result of the geographical preference of its early entrants. In the early 20
th
century, Silicon
Valley had no prior industrial history and its genesis was largely fueled by government

120
sponsored research projects at Stanford University. The agglomeration of technology companies
began in the mid 20
th
century as Frederick Terman, the dean of Stanford’s Engineering School,
advised his students to form companies in Palo Alto. Most notably, William Hewlett and David
Packard founded the Hewlett-Packard company, which continues to be one of the most
recognized firms in the region. Since then, technology spillovers and clustering attracted more
firms and made concentration of economic activities possible.
As Silicon Valley emerged as the paragon of high-tech industry, Arthur’s notion of
positive feedback loop played a critical role in producing increasing returns via new
technological innovations. Agglomeration of startups and venture capital firms in the region
facilitated rapid adoption of new technologies and methods of production with reduced marginal
costs. Return on investment increased and so did productivity and profit opportunities. As more
entrants clustered in the region, there were more resources available for entrepreneurs and
venture capitalists. New entrants were able to enjoy the technological standards built by existing
incumbents who also benefited from fresh ideas that newcomers brought to the network. The
growth of Silicon Valley as a milieu of innovation is thus characterized by the network effects of
positive feedback.
Network effects continue to play a key role in the growth and expansion of Silicon Valley
in the 21
st
century. As findings from this dissertation indicate, individual entrepreneurs are able
to find more resources in a competitive market with lower entry barrier. Despite increased
competition, however, the industry may be dominated by a few key players because
technological standards get locked-in. Hence, for a nascent startup to build up market share, the
founder must first build up user base and lock in the market. Case studies of pitch competitions
in Silicon Valley showed that it is not necessarily the technological superiority that attracts

121
venture capital funding. Instead, investors are often interested in business products that can
easily penetrate the market and resonate with consumer demand.
The main source of technological innovations produced in Silicon Valley comes from
ideas of dynamic entrepreneurs. Knowledge, in an economic sense, differs from other physical
resources because it is non-exhaustible. For instance, sharing of ideas and information with
others does not deplete one’s knowledge. In fact, increased cooperation and collaboration among
creative individuals could add more value to the existing knowledge and even produce new
insights. This further leads to the positive feedback loop and increasing returns, as accumulated
knowledge and experience reduce the costs of production and increases productivity which
further generates more networked resources conducive to innovation. An example of this is the
dominance of software companies. The technological standards established by a few pioneers get
adopted into diverse business models and platforms.
As such, the workings of today’s Silicon Valley startup ecosystem illustrate the
limitations of conventional economic principles in explaining the new wave of innovation. In
particular, the aftermath of the economic crises has transformed the milieu of innovation in terms
of its geography, economy, and culture. Production of innovation is also largely shaped by social
values and processes. To account these conditions, this dissertation has closely examined how
entrepreneurship, innovation, and regional growth interact at different levels. The empirical
findings suggest that Silicon Valley startup ecosystem continues to reproduce itself over time in
a manner that enhances productivity, efficiency, and creativity of entrepreneurial entities that not
only thrive in competitive situations, but also work together to produce networked innovation at
a global scale.

122
Nevertheless, it is important to acknowledge that to further advance this argument other
aspects of innovation must be discussed. For instance, the role of the state in fostering national
systems of innovation and advancing new frontiers for technological advancements is equally
significant. How national systems of innovation operates at a national and global level will
provide a bigger picture to understand the structural importance of innovation. Future studies
arising from this dissertation project may pay more attention to innovation policies that foster
startup activities. It may also include comparative studies of other global milieus of innovation
with a focus on how certain features of Silicon Valley gets replicated and reproduced in other
regional contexts.

123
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Creator Koo, Nahoi (author)

Core Title The Silicon Valley startup ecosystem in the 21st century: entrepreneurial resilience and networked innovation

School Annenberg School for Communication

Degree Doctor of Philosophy

Degree Program Communication

Publication Date 10/26/2020

Defense Date 05/01/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag entrepreneurial resilience,milieu of innovation,networks,OAI-PMH Harvest,startup ecosystem

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Advisor Castells, Manuel (committee chair), Ananny, Mike (committee member), Aronson, Jonathan (committee member), Monge, Peter (committee member)

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