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How social and human capital create financial capital in crowdfunding projects
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How social and human capital create financial capital in crowdfunding projects
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Content
HOW SOCIAL AND HUMAN CAPITAL CREATE FINANCIAL CAPITAL IN
CROWDFUNDING PROJECTS
by
Li Lu
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 2014
Copyright 2014 Li Lu
ii
Dedication
To my family.
iii
Acknowledgements
First and foremost, I would like to express my deepest gratitude to my advisor, Dr. Janet
Fulk. Thank you for believing and caring. Your generous support made this dissertation possible.
I also want to thank my other two committee members, Dr. Peter Monge, and Dr. Lian Jian.
Thank you for providing valuable feedback to the project and guiding me through countless
puzzles I encountered. I am also in debt to Dr. Dmitri Williams for generously sharing his advice
on conducting good research and great research opportunities.
Many friends made this graduate school journey special. Some of you show me how to
get it done beautifully, and some of you are always there when I need you.
I am incredibly grateful for having a wonderfully supportive family. My mom has always
been a role model for me. Her diligence and courage give me inspiration and strength. My
husband has been extremely supportive during this five-year long-distance relationship. Going
through dissertating with him assures me how lucky I am to be married to this man.
iv
Table of Contents
Acknowledgements ........................................................................................................................ iii
List of Tables ...................................................................................................................................v
List of Figures ................................................................................................................................ vi
Abstract ......................................................................................................................................... vii
Chapter 1: Introduction ....................................................................................................................1
Chapter 2: Conceptual Framework ................................................................................................11
Definition of Crowdfunding ..............................................................................................11
A Typical Reward-Based Crowdfunding Project ..............................................................14
Crowdfunding as Networks ...............................................................................................15
Social Capital and Human Capital .....................................................................................18
The Mediating Role of Knowledge Exchange ...................................................................38
Displayed Passion and Displayed Preparedness ................................................................43
Chapter 3: Methods ........................................................................................................................58
Data Source and Data Collection Procedure ......................................................................58
Measures ............................................................................................................................65
Data Analysis .....................................................................................................................77
Chapter 4: Results ..........................................................................................................................78
Data Description ................................................................................................................78
Social Capital and Human Capital .....................................................................................79
Mediating Effects of Knowledge Exchange ......................................................................85
Displayed Passion and Displayed Preparedness ................................................................95
Chapter 5: Discussion, Limitation and Future Directions .............................................................98
Discussion ..........................................................................................................................98
Limitations and Future directions ....................................................................................118
Practical Implications.......................................................................................................123
Conclusion ...................................................................................................................................126
Bibliography ................................................................................................................................127
v
List of Tables
Table 1. Summary of All Hypotheses and Results 56
Table 2. Intercoder Reliability for Displayed Passion (DPS) and Display Preparedness (DPP)
between Two Coders 72
Table 3.Coding Scheme and Examples for Knowledge Exchange 73
Table 4. First Order Correlations and Descriptive Statistics 81
Table 5. Regression Coefficients for Hierarchical Regression Models Regarding Social Capital
and Human Capital 84
Table 6. Regression Coefficients for Hierarchical Regression Models Regarding Displayed
Passion and Displayed Preparedness 97
Table 7. Regression Coefficients for Regression Models in Post-Hoc Analysis 100
vi
List of Figures
Figure 1. Affiliation Networks of Crowd-funders and Crowdfunding Projects 20
Figure 2. One Project, Formlab 1 Consisting of Four Crowd-funders 21
Figure 3. Ties between Projects Because of Shared Crowd-funders 22
Figure 4. Ties between Crowd-funders Because of Co-funding 23
Figure 5. Overall Network Structure Aggregating Ties among Projects, among Crowd-funders,
and between Projects and Crowd-funders 24
Figure 6. An Example of Comment Section of a Project on CrowdInno 69
Figure 7. Parameter Estimation for Mediating Effects of Knowledge Exchange on Degree
Centrality (Structural Embeddedness) and Funding Amount 91
Figure 8. Parameter Estimation for Mediating Effects of Knowledge Exchange on Betweenness
Centrality (Juntional Embeddedness) and Funding Amount 92
Figure 9. Parameter Estimation for Mediating Effects of Knowledge Exchange on Eigenvector
Centrality (Positional Embeddedness) and Funding Amount 93
Figure 10. Parameter Estimation for Mediating Effects of Knowledge Exchange on Human
Capital and Funding Amount 94
Figure 11. Scatterplot of Degree Centrality by Funding Amount 101
Figure 12. Scatterplot of Betweenness Centrality by Funding Amount 102
Figure 13. Scatterplot of Eigenvector Centrality by Funding Amount 103
vii
Abstract
This dissertation explores factors explaining crowdfunding success by examining how
resources residing in crowds can be transformed into financial capital. It aims to answer the
following two questions from an entrepreneur’s perspective: What attributes should I look for in
potential investors to increase my chance of being funded? What is the best way to pitch my idea
given certain funder characteristics?
Results from this study suggest that projects with high social capital in the form of
network embeddedness, including structural embeddedness, junctional embeddedness, and
positional embeddedness, are likely to attract more funding. Further, the rate of facilitating
effects of network embeddednesses on attracting funding increases initially and then decreases.
Human capital residing in the crowds in the form of the number of experienced crowd-funders
predicts funding success as well. What’s more, the amount of knowledge exchange within the
crowd is the underlying mechanism translating benefits of social capital (and human capital) into
funding success. Specifically, knowledge exchange can fully explain benefits of junctional
embeddedness and human capital, and can account for partial effects of structural embeddedness
and positional embeddedness. Displayed passion and preparedness in entrepreneurs’ pitches both
aid entrepreneurs to attract more funding. Yet, displayed preparedness fully mediates the
relationship between displayed passion and funding success, meaning that it is only through the
pitch content or its substance that crowds are willing to contribute. Lastly, human capital within
the crowd attenuates the positive effects of both displayed passion and displayed preparedness in
the pitch on funding success.
1
Chapter 1: Introduction
Acquiring necessary resources is vital for organizations to survive and thrive (Barney,
1991; Wernerfelt, 1984). Acquiring financial capital is critical for new entrepreneurs to move
beyond ideas or scale up (Cassar, 2004). Considering the substantial demand for investment from
numerous entrepreneurs and the comparatively scant financial resources from investors such as
venture capitalists (VCs) and angel investors (Cassar, 2004), investors’ investment decision
processes have been under scrutiny for a long time. Indeed, previous VC investment decision
literature has devoted considerable effort to identifying factors in the investment decision process
from the investor’s point of view. This effort has resulted in the development of a number of
influential models delineating factors that influence VCs’ decisions such as market research,
competitor analysis and characteristics of the entrepreneurs, e.g., their related experience,
personality and management skills (e.g., Hall & Hofer, 1993; Macmillan, Siegel, & Narasimha,
1985; Tyebjee & Bruno, 1984). The general message delivered reads, using Macmillan et al.’s
(1985) analogy: when you decide which horse to bet on, you should analyze the horse (the
product), the race (the market), the odds (the financial criteria), and the jockey (the entrepreneur).
Undeniably, these models can be quite helpful for potential investors when selecting
which entrepreneurs to invest in; however, these models are not particularly constructive to
entrepreneurs who are at the funding stage. Given a particular entrepreneur with a particular
product in hand, more constructive questions might be: who is more likely to invest in me,
investor A or investor B? What attributes should I look for in potential investors to increase my
chance of being funded? What is the best way to pitch my idea to them given their characteristics?
Motivated by these questions, the current dissertation aims to extend the financial resource
acquisition literature by shifting the focus from investors’ needs to entrepreneurs’ needs. To do
2
so, this study investigates how funder attributes, particularly social capital and human capital,
affect entrepreneurs’ ability to attract financial resource. Moreover, this is done in a novel
context: crowdfunding.
Compared to traditional ways of soliciting funds from friends and family or VCs,
crowdfunding provides an alternative for entrepreneurs to collect funds for their start-up ventures
(Giudici, Nava, Lamastra & Verecondo, 2013; Ley & Weaven, 2011). Crowdfunding taps into a
large group of undefined individuals, or crowds, who evaluate a project based on available
information and potentially provide funds to entrepreneurs through online platforms. Essentially,
entrepreneurs initiate an open call for funding on a crowdfunding platform to potential funders,
who can be anyone in the world with funds and Internet access, and potential funders who like
the project will offer funds in exchange for a certain kind of reward. Because crowdfunding
provides a unique opportunity to observe an unprecedentedly wide range of funder attributes, this
context can be particularly amenable to detecting how funders’ attributes affect funding
outcomes.
In addition to being theoretically intriguing, crowdfunding has been shown to be an
influential financing approach for the economy. According to a recently published crowdfunding
industry report by Massolution, crowdfunding platforms collectively raised $2.7 billion dollars
and helped to realize more than 1 million campaigns across the globe in 2012
1
. The report also
predicted that crowdfunding will have an 81% increase in 2013 and reach $5.1 billion. The rapid
growth of crowdfunding has been driving policy makers to pass new laws to accommodate this
emerging phenomenon. The JOBS Act, signed into United States law on April 5, 2012, removed
restrictions that businesses had to seek investment from only accredited investors and exempted
small businesses from the costly procedures. Now businesses can solicit funding from the public
1
Retrieved from http://research.crowdsourcing.org/2013cf-crowdfunding-industry-report
3
online up to $1 million per year in equity. In summary, crowdfunding is expected to
fundamentally alter the way people finance their businesses.
Taken together, the current dissertation thus proposes to focus on how funders’ attributes
influence funding outcomes in the context of crowdfunding. Specifically, this dissertation aims
to answer the following four research questions: 1) What type of crowd-funders should
entrepreneurs target in order to achieve their funding goal effectively? 2) What is the underlying
mechanism by which resources residing in a crowd are transformed into financial capital? 3) Do
crowd-funders respond to entrepreneurs’ pitches differently than VCs do? 4) What attributes of
the crowd affect their reactions to these pitches?
To answer these questions, this dissertation will draw upon three research streams: social
capital (Coleman, 1988; Granovetter, 1985) and human capital theory (Becker, 1993), and the
knowledge transfer (Argote & Ingram, 2000) and displayed entrepreneurial passion literatures
(Chen, Yao & Kotha, 2009). First, this study will employ social capital theory (Granovetter,
1985) and human capital theory (Becker, 1993) to examine how resources residing within
crowd-funders can be effectively transformed into financial capital in crowdfunding projects.
Resources here denote all facilitating forces that help people to evaluate and offer funding for
crowdfunding projects. Specifically, through social capital theory, this study explores how the
social contexts in which crowdfunding projects are embedded shape projects resource
acquisitions. Moreover, human capital theory is used to investigate how crowds’ previous task
experience affects the focal project’s funding success. Different from many previous studies
which have reported fruitful results on how entrepreneurs’ social capital and human capital
affect their ability to gain investment (e.g., Baron & Markman, 2000; Davidsson & Honig, 2003;
4
Stam & Elfring, 2008), this study proposes to examine how crowd-funders’ social capital and
human capitals influence entrepreneurial fundraising.
A more detailed review of social capital will be provided in the literature review section,
however, briefly, social capital refers to the value that network actors can get from the network
position and ties they hold (Burt, 2001). According to social capital theory (Adler & Kwon, 2002;
Nahapiet & Ghoshal, 1998; Payne, Moore, Griffs & Autry, 2011), the social constituents in
which the crowd is embedded create and further transfer resources for crowdfunding projects.
This social context is derived from crowd-funders’ naturally evolving funding history on the
online platform. Further, as information technologies broaden the possibilities of economic
exchange, how individuals select adversely becomes more and more critical because they lack
the information banks or certain high profile VCs have in order to assess the risk. Accordingly,
social relationships formed on these online platforms increasingly become an important source
for individuals to look for signals (Lin, Prabhala, & Viswanathan, 2013).
Although classical sociological research has laid the foundation on the relationship
between firms’ social relationships and their capacities of acquiring financial capital (e.g., Baron
& Markman, 2003; Davidsson & Honig, 2003; Sacks, Ventresca, & Uzzi, 2001; Uzzi, 1999;
Uzzi & Gillespei, 2002), the crowd-based fundraising, whereby funding behaviors are voluntary
and not constrained by top-down formal organizational structure, invites new theorizing on this
relationship. To explain, the idiosyncrasy of crowd-based fundraising, i.e., raising funds from a
crowd with high enthusiasm but less expertise than a few highly trained individuals (Ley &
Weaven, 2011), makes the role of social constituents within the crowd even more intriguing.
This is because crowd-funders can concurrently fund multiple projects on one crowdfunding
platform, which results in an interwoven network of direct and indirect relationships among
5
crowd-funders. These relationships serve as channels for passing on resources among crowd-
funders. Thus, social capital theory, by highlighting the role of social relations, is particularly
informative for investigating the relationship between social capital and received financial
capital in a crowdfunding context.
At the same time, scholars have provided initial evidence that in the States the
syndication network, or venture capitalists’ co-investment network, is a giant densely-connected
cluster (e.g., Birmingham, 2006; Castilla, 2003) and well-connected VCs outperform those with
less connections (e.g., Hochberg, Ljungqvist, & Lu, 2007; Sorenson, & Stuart, 2001). These
findings provide more support for the general premise that social contexts shape economic
exchanges, such as funding behaviors. Yet, findings from the observation of VC behaviors might
not be universal in all funding scenarios. To start, gaining funding through VC exhibits distinct
features compared to that through crowdfunding in three ways. First, the funding amount is
oftentimes bigger in VC than in crowdfunding context. For example, Hochberg and associates
(2007) examined 3,469 VC funds managed by 1974 VC firms in the States and reported that the
average sample fund was as high as $64 million. Mollick (2014) examined the universe of
projects (48,526) on Kickstarter from 2009 to 2012, a leading crowdfunding platform, and
reported that an average Kickstarter project raised $7892. Second, funding from VC is driven by
pure monetary motivations, while funding from crowdfunding combines monetary consideration
and other motivations, such as identification and altruism as well (Gerber, Hui, & Kuo, 2012).
Distinct motivations conceivably will lead to different behaviors. Third, the VC community has
been shown to be a tightly connected community in which venture capital firms specialized in
certain domains are linked to each other through co-investment (Birmingham, 2006) while
6
limited information about crowdfunding community is known. Thus, it might not be accurate to
assume that findings from VC literature should be generalizable to crowdfunding without testing.
Further, although a number of recent crowdfunding studies have considered social
influence when predicting funding success, these studies generally have a relatively narrow take
on social influence. For instance, scholars have tackled how earlier contributors’ behaviors
affected subsequent contributions (e.g., Burtch, Ghose, & Wattal, 2013; Kuppuswamy & Bayus,
2013), and some scholars reported that geographic dispersions and resultant cultural difference
plays a role in contributing behaviors as well (e.g., Agrawal, Catalini, & Goldfarb, 2011; Burtch,
Ghose, & Wattal, 2014). Yet, social constituents go beyond individual actors’ immediate
surroundings and tap into a broader social context in which entities are embedded. This study
will particularly explore how crowdfunding projects’ network embeddedness within the
crowdfunding platform predicts the amount of funding that projects attract.
Human capital theory on the other hand, focuses on how knowledge, expertise and skills
embodied in individuals will affect their decision-making process and performance (Becker,
1993; Hitt, Bierman, Shimizu, & Kochhar, 2001). Similar to social capital, human capital is
derived from crowd-funders’ previous funding history. However, the human capital aspect of
previous funding history addresses how domain expertise residing in the crowd, the content
aspect, affects the funding success while social capital captures the structural dimension of
network structure’s effect on funding outcomes.
Second, this research will utilize research on knowledge transfer (Argote & Ingram, 2000;
Reagans & McEvily, 2003; Tsai, 2001) and explore whether knowledge exchange is the
mechanism responsible for the realizing the potential of social capital and human capital on
entrepreneurs’ funding success. Indeed, the link between sources of social capital or human
7
capital and its possessor’s various outcomes has been extensively explored. Studies have shown
that social capital can facilitate individuals’ job hunting (e.g., Granovetter, 1995) and work
performance (e.g., Burt, 1992; Moran, 2005), as well as companies’ product innovation (e.g.,
Tsai & Ghoshal, 1998) and organizational performance (e.g., Baum, McEvily, & Rowley, 2012;
Soda, Usai, & Zaheer, 2004). Moreover, scholars often conjecture that advantageous information
access is why, at least to some extent, social capital and human capital realizes resource
mobilization (e.g., Grewal, et al., 2006; Tsai, 2001; Uzzi, 1999; Uzzi & Gillespie, 2002).
However, the mediating role of knowledge exchange has rarely been empirically tested with the
exception of Wu (2008). Yet Wu’s (2008) study only focused on one dimension of social capital:
the relational aspect of social capital; so the mediating role of information exchange on the
structural dimension of social capital remains under-explored. To fill this gap, the current study
proposes to empirically investigate whether knowledge exchange between entrepreneurs and
crowd-funders mediates the relationship between social capital (and human capital) and the
crowdfunding success.
In a crowdfunding context, this means that the reason why some crowdfunding projects
attract more funding than others is because knowledge exchange in these projects, driven by
projects’ substantial social and human capital, is more effective at transforming potential funders
into actual funders. It is reasoned that it is through this knowledge exchange process that the
potential of crowds’ social capital (or human capital) is realized, meaning that entrepreneurs’
credentials and project quality are appropriately gauged, the fit between potential funders and the
project is analyzed, and thus the potential hazards from information asymmetry between
entrepreneurs and potential funders (e.g., Venkataraman, 1997) is mitigated.
8
Third, since interactions between entrepreneurs and potential funders are highly
influential to funding decisions, how effectively entrepreneurs deliver their business plans has
been receiving increasing attention (Chen, et al., 2009; Galbraith, McKinney, DeNoble, &
Ehrlich, 2014). This study will expand on previous research regarding the entrepreneurial
passion and preparedness displayed during a business pitch (Chen, et al., 2009) into the
crowdfunding context, and investigate how laymen funders react to these features of business
presentations. Similar to traditional business pitches, entrepreneurs seeking funding on
crowdfunding platforms also need to rely on a pitch, oftentimes a video, to present their ideas
and try to persuade potential funders of their value. The entrepreneurship literature has started to
examine what characteristics of the pitch can effectively convince potential investors, and
existing scholarship has reported that displayed passion and preparedness are critical factors
affecting funding decisions (e.g., Cardon, 2009; Chen, et al., 2009; Elsbach & Kramer, 2003).
This study will join this endeavor and expand on previous literature by investigating how laymen
investors respond to presenters’ displayed passion and preparedness, and what other factors
might moderate how crowd-funders react to the pitch.
Through these efforts, this dissertation aims to contribute to two broad research streams.
First, this study adds to the financial resource acquisition literature by examining how investors’
attributes, including social capital and human capital residing in the crowd, shape entrepreneurs’
funding outcomes in the crowdfunding context. This focus is distinct from the traditional
investment decision-making process which analyzes all factors such as the market, the
entrepreneurs and the product but not attributes of the investors; in contrast, this study explores
what attributes of investors will make a difference in investment and thus can potentially provide
an expanded picture of the funding process from the viewpoint of entrepreneurs.
9
In particular, this study provided empirical evidence that crowds with high social capital
and domain expertise would attract the most funding. The crowd as a whole, despite a lack of
individual domain expertise compared to VCs (Ley & Weaven, 2011), could identify project
quality and respond to an entrepreneurs’ pitch in a similar way to VCs or angel investors: both
displayed passion and displayed preparedness positively predicted the amount of funding
received, but displayed preparedness mediated the relationship between displayed passion and
the funding success. Further, human capital attenuated the positive effects of displayed passion
and displayed preparedness on funding success.
Second, this dissertation has the potential to contribute to the social capital and human
capital literature. Existing scholarship on the relationship between social capital and entities’
outcomes tends to center on the effects of network structure and resource content separately
(Adler & Kwon, 2002). Although social capital theorizes that both resource content and the
network structure in which resources are embedded are critical for generating social capital,
many previous studies exclusively focus on the structural component (e.g., Grewal, et al., 2006;
Ransbotham, et al., 2012) and overlook resource content. Increasingly, more and more
researchers have realized the drawback of such practices (e.g., Hansen, Mors, & Lovas, 2005;
Moran, 2005), but more empirical studies are needed to provide a more comprehensive picture of
the relationship between social capital and social actor’s effectiveness. Furthermore, this study
also empirically examined and verified that knowledge exchange between entrepreneurs’ and
crowd-funders partially mediated the relationship between projects’ network embeddedness and
their funding success, and fully mediated the relationship between crowds’ human capital and
their funding success.
10
This empirical evidence was based on identification and analysis of 134 crowdfunding
projects that exclusively focus on one subject, three-dimensional (3D) printing technology, that
were launched on a leading crowd-funding platform, CrowdInno (a pseudonym), over the past
five years. Keeping the project content relatively consistent can control important confounds in
the analysis and thus grants more room to detect the effects of social influence. Crowdfunding
projects’ network position was constructed from 134 projects’ crowd-funders’ complete previous
funding relationships on CrowdInno. Human capital of the crowd was constructed from all
crowd-funders’ previous funding experience. To capture knowledge exchange, authentic public
comments exchanged between entrepreneurs and crowd-funders were coded. Coders also
assessed displayed passion and displayed preparedness in each entrepreneur's pitch video.
The rest of the dissertation is organized as follows. First, since crowdfunding is a
relatively new phenomenon, its definition and a review of its characteristics are provided as an
empirical base for further theoretical reasoning in the beginning of Chapter 2. The next section
reviews previous literature on social capital and human capital and proposes hypotheses
regarding their relationship with crowdfunding projects’ funding success. Next, the mediating
role of knowledge exchange for the above two relationships is discussed. The following section
presents literature on how displayed passion and preparedness affect funding decisions and
expands it to the crowdfunding context. Chapter 3 provides the data source, the process of data
collection and detailed variable descriptions. Chapter 4 presents the results. Finally Chapter 5
discusses theoretical and practical implications of these results.
11
Chapter 2: Conceptual Framework
Definition of Crowdfunding
Crowdfunding is one of many forms of crowdsourcing. Crowdsourcing has been
recognized as a new engine to harness distributed productivity from a large group of individuals
(Howe, 2009; Terwiesch & Xu, 2008). As Howe (2009) put it, “crowdsourcing isn’t a single
strategy. It’s an umbrella term for a highly varied group of approaches that share one obvious
attribute in common: they all depend on some contribution from the crowd. But the nature of
those contributions can differ tremendously” (p. 280). Howe (2009) further identified four
categories of crowdsourcing applications: crowd creation where individual efforts are gathered
from a crowd to achieve an innovative outcome, such as creating a commercial using fans inputs;
crowd voting, where individual judgments are aggregated to form an opinion about something,
such as voting for the best T-shirt design on Threadless; crowd wisdom, where people’s
knowledge or expertise are harvested to predict one outcome, such as stock market prediction
platforms, and crowd funding.
Belleflamme, Lambert and Schwienbacher (2011) defined crowdfunding as an activity
that “involves an open call, essentially through the Internet, for the provision of financial
resources either in form of donation or in exchange for some form of reward and/or voting rights”
(p. 5). At its heart, crowdfunding involves entrepreneurs who “tap into the crowd” to raise funds
for their endeavors. As an emerging financing approach, crowdfunding provides an alternative to
traditional fundraising approaches.
Traditionally, start-up ventures’ capital financing options include bootstrapping, friends
and family, business angels and pre-seed and seed funding (Ley & Weaven, 2011).
Bootstrapping denotes a number of methods to minimize using external financial institutions
12
such as banking. A well-known form of bootstrapping is the use of private credit cards, for
instance. In addition, friends and families are oftentimes a source of funding as well (Agrawal,
Catalini, & Goldfarb, 2011). Indeed, getting funding from friends and families can be considered
as utilizing ones’ social capital, yet this is inherently different from crowdfunding where funds
largely come from individuals without personal connections. Business angels are individuals
with considerable capital and they are motivated to invest in exchange for potentially high
returns (Osnabrugge, 2000). Pre-seed and seed venture capital firms oftentimes offer funds to
entrepreneurs in their early period in exchange for part of a business. Previous research has
shown that entrepreneurial ventures at early stages have a higher probability of failure due to the
“liability to newness,” a concept coined by Stinchcombe (1965). Conventional funding at this
stage is thus prone to higher risk and is quite limited compared to the increasing demand for new
products. Accordingly, crowdfunding offers a much needed alternative, in addition to traditional
financing, to fund entrepreneurs during their early periods.
Crowdfunding is a multifaceted phenomenon and Hemer (2011) categorized
crowdfunding activities into five sub-categories based on the form of rewards. According to
Hemer (2011), Crowd donations denote those crowdfunding activities that solicit pure donations
to projects. Spot.Us provides such an example. Spot,Us is a non-profit organization aiming to
bring journalists, news publishers and citizens together. People can donate to fund journalism
projects covering issues they would like to see through an online platform. Crowd sponsoring
refers to crowdfunding activities where people contribute to projects and get sponsorship in
return. Crowd sponsoring is particularly prominent among sports fans endorsing their favorite
teams or fellow fans. Crowd pre-selling (or pre-ordering) describes crowdfunding projects
where the dominant exchange relationship is pre-selling or pre-ordering. Oftentimes project
13
initiators ask for funds to finish a deliverable product which can be given to crowd-funders later.
For example, project initiators can create an album, a film, or a technological artifact. Well
known online platforms of this type include Sellaband, Kickstarter, and Indiegogo. Crowd
lending is another type of crowdfunding form, where crowdfunding occurs in the form of loans.
Kiva.org is such an example which allows people to lend money to especially low-income
entrepreneurs or students across the globe. Lastly, crowd equity denotes crowdfunding activities
where the return of contributed funds is in the form of equity. This type of crowdfunding
conceivably will become more influential as The JOBS Act has been passed, which permits
ventures to borrow funds from individuals in the form of equity.
The above classification of crowdfunding activities, based on the form of rewards,
affords us a comprehensive understanding of crowdfunding as an emerging phenomenon, and we
can thus view crowdfunding activities as a continuum whereby the left end represents pure
donation-based crowdfunding activities and the right end represents pure financial return-based
crowdfunding activities. Other crowdfunding activities in the middle of this spectrum are those
in which funders can receive a certain kind of non-financial reward such as a thank-you note, a
T-shirt, recognition on the project’s website, or a pre-ordered product in exchange for their
financial support. The current study chooses to examine these intermediate reward-based
crowdfunding for the following two reasons. First, Massolution’s (2012) report mentioned that
reward-based crowdfunding is the most popular type in online platforms and is growing fast.
Second, pure donation-based crowdfunding activities are examined more extensively through the
lens of public goods or philanthropic behaviors (e.g., Andreoni, 2006) and finance driven
crowdfunding activities might follow rational choice behaviors more closely (e.g., Herzenstein,
Dholakia, & Andrews, 2011). In contrast, social interactions will conceivably impact the reward-
14
based crowdfunding community more prominently and hence reward-based crowdfunding fits
this dissertation’s focus better. The next section describes how a crowdfunding project unfolds
and then presents crowdfunding activities as networks.
A Typical Reward-Based Crowdfunding Project
Across various types of crowdfunding platforms, reward-based crowdfunding projects
share considerable similarity. Typically, depending on the form of capital that is sought,
entrepreneurs choose a suitable online platform to carry out a project. Many online crowdfunding
platforms allow people to create accounts with little entry cost. After creating an account,
entrepreneurs will initiate a project by making a pitch to the crowd. In this pitch, the entrepreneur
will specify what the project is about, e.g., producing a new album or a new technological
artifact, why crowd-funders should contribute funding such as supporting a cause or exchanging
for a reward, and how much financial capital is needed. The entrepreneur often provides
information regarding his or her credentials such as related past experience or sample products to
increase the project’s attractiveness. Many online crowdfunding platforms allow entrepreneurs to
use texts, pictures and videos to promote their projects. An increasing number of online
platforms also allow project initiators to use third-party services, especially social media such
Facebook and Twitter, to promote their campaigns.
During the funding phase, most online platforms enable and encourage communications
between entrepreneurs and potential crowd-funders. These communications sometimes are open
to the public, and sometimes only accessible to funders. In this way, potential crowd-funders
have a chance to inquire about any questions or concerns they have for the entrepreneur and the
entrepreneur can use this opportunity to clarify questions and even update his or her project
according to feedback he or she receives. The communication process plays a critical role since
15
when making funding decisions, potential funders are consistently seeking more information to
reduce their uncertainty in terms of the quality of the project, the credential of entrepreneurs and
the fit between the project and their needs. These communications do not happen only between
entrepreneurs and crowd-funders, but among crowd-funders as well. Crowd-funders oftentimes
discuss strengths and weaknesses of the project, compare similar projects and provide
suggestions to each other.
When the funding phase ends, the project might be successfully funded or not.
Depending on the pre-determined funding mechanisms, some entrepreneurs get whatever amount
of money they have solicited, while some can only access it if their funding goal is reached.
Then, if successful, entrepreneurs will use the funding to carry out the promised project. During
this process, entrepreneurs are expected to provide timely updates to crowd-funders regarding
the project’s progress. In the end, the crowd-funded project is regarded finished when the
promised deliverable is received by crowd-funders. For instance, a new album is created and
delivered to crowd-funders.
Crowdfunding as Networks
In order to grasp how resources residing in crowds can be transformed into financial
resources for a project, it is helpful to reveal where these resources originate and how they are
transferred from one project to another. It is argued that crowd-funders’ past funding experience
and resultant networks created by past funding relationships serve as a resource for future
projects. Crowd-funders contribute to multiple projects, and in doing so link these projects
indirectly to each other through the common funders. It is through shared crowd-funders that the
accumulated knowledge and norms within the community get created and passed down to new
projects; co-funding new projects establishes new relationships among crowd-funders which, in
16
turn, re-shape the project network structure within the community. The following section
specifies how using a network lens can help us to understand resource flow in crowdfunding, and
then develops hypotheses based on this argument.
Since crowd-funding solicits funds in an open call to virtually anyone with Internet
access, crowd-funding breaks the boundary of locality and kinship to a large extent (Agrawal, et
al., 2011). In this way, online crowdfunding platforms serve an intermediary role between fund-
seekers and crowd-funders to facilitate collaborations between these two parties (Ordanini,
Miceli, & Pizzetti, 2011). A collaborative relationship between an entrepreneur and a crowd-
funder has two features. First, it is a self-organizing process. Like an open-source software (OSS)
project collaboration (e.g., Arranz & Fdez de Arroyable, 2013; Grewal, et al., 2006), funders
voluntarily choose the projects to which they want to contribute. Second, this collaboration
relationship goes beyond pure financial contribution. Previous research on motivation to
participate in crowdfunding provides support for this aspect. For instance, Gerber and her
colleagues (2012) interviewed 11 entrepreneurs and crowd-funders and concluded that
entrepreneurs desire to raise funds, to establish relationships with possible collaborators, to
receive validation for their innovation, and to potentially expand their publicity through social
media. Crowd-funders contribute because they want to obtain rewards, support the cause, and
engage in a trusting and creative community.
Take a typical crowd-funded 3D printer project as an example. An entrepreneur might
start a campaign to build affordable 3D printers for small business owners on an online
crowdfunding platform. In this project, the entrepreneur might simply want to raise funds for a
3D printer business, or the entrepreneur might want to establish relationships with other 3D
printer users for future collaboration, or the entrepreneur might initiate the project out of
17
frustration that the 3D printers available on the market are too expensive for small business
owners. Similarly, a crowd-funder offering funds to this 3D printer project might simply want to
contribute to a technological innovation; the funder might want to pre-order a 3D printer for a
jewelry design business; or perhaps the funder wishes to find others who also engage in 3D
printing business for networking.
Thus, collaborative relationships between entrepreneurs and crowd-funders aggregate
economic exchange, altruistic donation, and social relations. Given the multifaceted nature of
crowdfunding activities, social capital theory, especially through a network lens, is particularly
valuable in analyzing the effect of social constituents on crowdfunding success. That’s because
social networks tap into the relational nature of collaboration, and this perspective has the
advantage of integrating the properties of both the nodes and the multifaceted relationships in the
collaboration network (Borgatti & Halgan, 2011; Monge & Contractor, 2003).
As crowd-funders contribute to multiple projects, they constitute facilitating forces for
multiple projects. The affiliation between actors and events can be represented through a two-
mode network (Wasserman & Faust, 1994, see Figure 1). In two-mode networks, relationships
among nodes in one mode might be associated with linkages in the other mode. That means that
a relationship exists between two crowd-funders if they fund the same crowdfunding project.
Similarly, a relationship exists between two crowdfunding projects if they share at least one
crowd-funder.
From the lens of networks, crowdfunding projects can be seen as aggregations of crowd-
funders all contributing to one project (as Figure 2 shows). Crowdfunding projects are connected
through shared funders (as Figure 3 shows). Ties exist among crowd-funders because they have
funded at least one common project (as Figure 4 shows). And a whole network consists of inter-
18
connected projects (local clusters) associated with crowd-funders who contributed to multiple
projects (as Figure 5 shows).
Accordingly, a crowdfunding platform can be viewed as a giant network consisting of
thousands of projects connected through co-funders. Oftentimes, this giant network contains
multiple clusters, which can be viewed as sub-communities. For instance, on Indiegogo, a
leading reward-based crowdfunding platform, projects are classified into different categories,
such as Animals, Education, Food, Religion, Technology and Sports, etc. It is plausible that these
categories are the bases where network clusters form. These sub-communities attract crowd-
funders and grow bigger; at the same time, crowd-funders might become loyal members to
certain sub-communities out of their interest and keeps contributing. In this way, the whole
platform or the network sustains its activity.
Social Capital and Human Capital
Conceptualizing social capital
As one of the most popular concepts in social science in the past two decades, social
capital has become an umbrella term which garners a variety of meanings (for reviews, see Adler
& Kwon, 2002; Payne, Moore, Griffs & Autry, 2011). The most noted difference in
conceptualizing social capital comes from Coleman (1988) and Burt (1992). Coleman (1988)
tended to highlight the collectivity in the social capital concept and proposed the following
definition: “it [social capital] is a not a single entity but a variety of different entities, with two
elements in common: they all consist of some aspect of social structures, and they facilitate
certain actions of actors—whether persons or corporate actors—within the structure” (p. 98). By
contrast, Burt (2005) emphasized individual actors’ network position could bring network actors
distinctive advantages and defined social capital as “the advantage created by a person’s location
19
in a structure of relationships” (p. 5). Despite this discrepancy, social capital research shares
common ground because both perspectives agree that social capital grants its possessors an
advantage by allowing them to favorably mobilize resources (Burt, 2001). Previous studies have
repeatedly shown that social capital can bring its possessors positive outcomes such as greater
innovation (Tsai & Ghoshal, 1998), increased knowledge transfer activities (Maurer, Bartsch, &
Ebers, 2011; Wei, Zheng, & Zhang, 2011), and better organizational performance (Baum, et al.,
2012; Powell, Koput, & Smith-Doerr, 1996).
Social capital theory argues that one source of social capital is an actors’ network
position (Burt, 1992; Lin, 2001). For instance, Nahapiet and Ghoshal (1998) viewed social
capital as “the sum of the actual and potential resources embedded within, available through, and
derived from the network of relationships possessed by an individual or social unit” (p. 243).
Adler and Kwon (2002) also acknowledged that “Its [social capital] source lies in the structure
and content of the actor’s social relations. Its effects flow from the information, influence, and
solidarity it makes available to the actor” (p. 23). The next section will discuss one particular
facet of network structure: network embeddedness and provide reasoning why it can explain
crowdfunding projects’ funding success.
20
Figure 1. Affiliation Networks of Crowd-funders and Crowdfunding Projects.
Kyle
Gail
Herb
Mei
Rob
Guo
Chris
Formlab 1
RepRap
B9Creator
Zim
The Peachy
21
Figure 2. One Project, Formlab 1, Consisting of Four Crowd-funders.
Kyle Gail
Mei
Rob
Crowd-funders for Formlab 1 3D printer
22
Figure 3. Ties between Projects Because of Shared Crowd-funders.
Formlab 1
RepRap
B9Creator
Zim
The Peachy
23
Figure 4. Ties between Crowd-funders Because of Co-funding.
Kyle
Rob
Gail
Mei
Guo Chris
Herb
24
Figure 5. Overall Network Structure Aggregating Ties among Projects, among Crowd-funders,
and between Projects and Crowd-funders.
Formlab 1
RepRap
B9Creator
Zim
The Peachy
Kyle
Rob
Gail
Mei
Guo
Chris
Herb
25
Network embeddedness
Like many other key words in social science such as social capital, structure and status,
researchers have distinct interpretations of the concept of “embeddedness”. To facilitate
introducing the current paper’s view on embeddedness, this concept’s origin and history will be
briefly reviewed before its definition in this study is provided.
Scholars typically credit Polanyi for introducing the term embeddedness in The Great
Transformation (1944) when discussing the interplay between economic exchange and social
structures. Granovetter (1985) further articulated this notion and differentiated under- and
oversocialization (or under-embeddedness and over-embeddedness; Monge & Contractor, 2003).
Under-embeddedness refers to situations whereby actors’ behaviors are largely independent of
social institutions such as political power and cultural influence and over-embeddedness denotes
situations whereby actors’ behaviors do not have much autonomy but are largely determined by
their surrounding structures (Monge & Contractor, 2003). Granovetter’s (1985) contribution lies
in recognizing that economic behaviors are motivated and constrained by the surrounding social
structures simultaneously. As Granovetter (1985, p. 487) put it, “Actors do not behave or decide
like atoms outside a social context, nor do they adhere slavishly to a script written for them by
the particular intersection of social categories that they happen to occupy. Their attempts at
purposive action are instead embedded in concrete, ongoing systems of social relations.”
Granovetter’s essay (1985) stimulated an immense amount of research demonstrating that
economic exchanges are embedded in social constituents (e.g., Uzzi, 1996; Uzzi, 1999).
Following Granovetter’s lead (1985), Zukin and DiMaggio (1990) broadened this
concept and used “embeddedness” to refer to “the contingent nature of economic action with
respect to cognition, culture, social structure, and political institutions” (p. 15). Zukin and
26
DiMaggio’s (1990) further articulated four mechanisms through which economic exchange is
contextualized within social constitutes. As their names suggest, the cognitive dimension refers
to how “the structured regularities of mental processes” affect economic behaviors (p.15) and
cultural embeddedness denotes how “shared collective understandings” shape economic
reasoning (p. 17). Further, political embeddedness refers to the extent to which economic
institutions are impacted by “a struggle for power that involves economic actors and nonmarket
institutions” (p. 20), and structural embeddedness refers to “the contextualization of economic
exchange in patterns of ongoing interpersonal relations” (p. 18).
Among these four dimensions, structural embeddedness has dominated research in
organizational studies (Dacin, Ventresca, & Beal, 1999) and is particularly relevant to the current
study. Essentially, structural embeddedness captures the extent to which social structures can
contextualize economic activities. To grasp the social structures, scholars have increasingly
turned to social network analysis and mainly taken two approaches to construe structural
embeddedness: network content and network structure. First, some scholars have focused on how
different types of relationships, i.e., the content of ties, in the network affect actors’ performance.
For instance, Uzzi (1996) compared distinct returns between embedded ties or repeated ties and
aim’s-length ties or one-time-transaction ties on dress apparel firms’ survival. Moran (2005)
found a positive relationship between 120 product managers’ relational embeddedness and their
innovation-related performance. Second, some scholars have explored how network structure, or
the configuration of ties in the network, impact actors’ performance. For instance, Rotolo and
Petruzzelli (2013) studied 203 scientists in an Italian academic community and examined how
their network positions were linked to their productivity.
27
This brief review of the development of embeddedness as a research construct highlights
two points. First, when it comes to examining social structures’ constitutive forces, structural
embeddedness and social capital are integrated and jointly depict how social constituents create
and mobilize resources for its possessor. Specifically, originating from economic sociology,
Granovetter’s (1985) analysis did not turn to social capital at all when discussing embeddedness.
Similarly, Zukin and DiMaggio (1990) did not employ social capital when articulating four
mechanisms of embeddedness. At the same time, scholars started to extend social capital into
social phenomenon beyond individuals, such as units in organizations (e.g., Tsai & Ghoshal,
1998) and firms (e.g., Burt, 1992; Gulati, 1998). Uzzi initially started to integrate social capital
research into his embeddedness studies (e.g., Uzzi, 1996). This integration should not be
surprising due to two reasons. First, structural embeddedness and social capital research both
heavily rely on social networks. They both maintain that resources of the actor stem from and
transfer through the network (Dacin, et al., 1999). Second, the mechanisms through which
structural embeddedness brings value to actors are quite similar to social capital. For instance,
Uzzi (1997) listed three features of embedded ties: trust, fined-grained information transfer and
joint problem-solving arrangements, and these features can be adequately used to describe
advantages of social capital. Taken all together, the current state of the literature is that scholars
increasingly employ these two research streams simultaneously to explain the interconnectedness
between network actors’ behaviors and their social structures (e.g., Dacin, et al., 1999; Grewal,
et al., 2006; Mallapragada, Grewal, & Lilien, 2012; Monge & Contractor, 2003; Moran, 2005).
Second, extending from the original focus on the interplay between actors’ economic
activities and its social institutions, researchers have been using embeddedness in a broader sense
and applying it to a more general frame of reference: how actors’ social structures shape various
28
outcomes (e.g., Ransbotham, Kane, & Lurie, 2012; Rotolo & Petruzzelli, 2013). For instance,
Moran (2005) examined managers’ key contact network in their work environment and showed
that the structural embeddedness in managers’ contact network, as measured by whether actors’
contact network is closed or not, positively predicted their sales performance. Accordingly, the
tenet of structural embeddedness has evolved to focus on the architectural nature of social ties in
general (Arranz, & Fdez de Arroyabe, 2013; Grewal, et al., 2006).
One recent application of structural embeddedness was to investigate the impact of the
network position of collaborative emerging entities such as Wiki pages (Wang & Zhang, 2012),
OSS projects (Grewal et al., 2006; Hahn, Moon, & Zhang, 2008) and user-generated content
(Mallapragada, et al., 2012). The social constituents in which these collaborative emerging
entities are embedded undeniably influence their outcomes, and their network positions can be
gauged by examining their creators’ existing web of relationships. This is because it is their
creators who possess and potentially activate these resources through past experiences which
shape the focal project’s outcomes. For instance, to examine R&D projects’ structural
embeddedness in EU Network of Excellence, Arranz and Fdez. de Arroyable (2013) obtained
affiliation networks consisting of projects and partners. Then they transformed the two-mode
network into two valued matrices, calculated project centrality and examined its effect on project
performance. .
Along this line, the current study uses structural embeddedness to investigate how
crowdfunding projects’ network position can impact their funding success. Crowdfunding
projects’ network positions are established using crowd-funders’ naturally evolving funding
relationships (see Figure 3). To explain, the funding community can be viewed as an evolving
collaborative network of crowd-funders whose web of relationships are shaped by their co-
29
funding experience over time. Concurrently, the resultant co-funding networks impact the
crowdfunding projects’ funding success since this network contains and transmits the resources.
Taken all together, crowdfunding projects’ structural embeddedness refers to the extent to
which a crowdfunding project is connected to other projects through crowd-funders’ co-funding
relationships. Following previous literature (Grewal, et al., 2006), structural embeddedness is
further classified into three sub-categories: structural, junctional and positional embeddedness.
Structural embeddedness assesses the influence of the project within the overall network
through its shared co-funders with other projects. A project with high structural embeddedness
shares many co-crowd-funders with other crowdfunding projects in the community. This is
operationalized through the degree centrality of projects (e.g., Arranz & Fdez. de Arroyabe, 2013;
Grewal, et al., 2006) and can be interpreted as the “expansiveness” and “popularity” of the
project within the community (Monge & Contractor, 2003). Junctional embeddedness denotes
the extent to which projects connect with other projects which are not connected within the
whole network. A project with high junctional embeddedness can connect to other projects in the
network more efficiently. This is operationalized through betweenness centrality (e.g.,
Arranz&Fdez. de Arroyabe, 2013; Grewal, et al., 2006). Lastly, positional embeddedness
captures the degree to which projects connect with other embedded projects. A project with high
positional embeddedness will connect with many other embedded projects. This is
operationalized through eigenvector centrality (e.g., Grewal, et al., 2006).
Effects of embeddedness on crowdfunding success
Previous research on network embeddedness has accumulated considerable evidence
showing that being moderately embedded in a network is the most beneficial to an actor’s
performance. As predicted by Granovetter (1985), neither under-embeddedness nor over-
30
embeddedness benefits actors’ performance much. For example, Uzzi (1996) found that firms
entrenched in transaction networks have higher survival chance than those engaged in arm’s-
length exchange; however, the facilitating effects of embeddedness diminishes after it reaches a
certain point. He argued that the negative effect of over-embeddedness was a result of trading in
a close circle only, which weakened the trader’s ability to access diverse and innovative
information. Uzzi (1999) examined more than 1000 firms and their partnerships and concluded
that firms with moderate amounts of embeddedness are more likely to get loans from banks.
Either under- or over-embeddedness can be detrimental to organizations.
Following a similar reasoning, Lechner, Frankenberger, and Floyd (2010) studied
strategic initiative units from five big corporations and concluded that their structural
embeddedness had an inverted U-shaped relationship with their performance. In the same vein,
Rotolo and Petruzzelli (2013) examined 203 scientists in an Italian academic community and
reported that scientists could gain performance benefits through holding central positions in the
community; however, the benefits of centrality vanished after hitting a threshold. They reasoned
that over-embeddedness was costly since it required extensive effort to maintain the ties.
Applying similar reasoning to collaborative emerging entities, Grewal and associates
(2006) examined affiliation networks in the OSS community and reported that OSS projects’
network embeddedness has strong facilitating effects on projects’ technical and commercial
success. Mallapragada and his colleagues (2012) studied 817 OSS projects from SourceForge
and reported that projects’ embeddedness decreased product release time. Arranz and Fdez de
Arroyable (2013) found positive relationships between R&D project network embeddedness and
project performance.
31
Adapting this logic to the crowdfunding context, it is argued that project embeddedness
should have an inverted U-shaped relationship with the amount of funding projects received:
namely moderate amount of embeddedness will be associated with high funding amount while
under- or over-embeddedness will be related to lower funding amount. It is reasoned that the
positive effect of moderate embeddedness comes from advantageous information access (Burt,
1992) and signaling effect (Spence, 1973). First, there is an information asymmetry between
entrepreneurs and potential funders in terms of the true quality of the project and entrepreneurs
(Venkataraman, 1997), so potential funders are constantly looking for information to reduce their
uncertainty. Thus available information, e.g., a comparison between two similar projects or the
fit between the project and potential funders’ need, plays an important role when potential
funders making a funding decision. Previous studies (e.g., Arranz & Fdez de Arroyable, 2013)
have reported that junctional embeddedness would generate information access the most since
high junctional embeddedness assures the project is connected with other projects which are not
well connected, which makes it more likely to receive diverse information and broker novel
information from other parts of the community. Additionally, high structural embeddedness
suggests that the project shares many co-funders with other projects in the community, and these
shared crowd-funders can be good information sources to channel diverse information from other
projects as well (e.g., Arranz & Fdez de Arroyable, 2013). Further, high positional
embeddedness suggests the focal project is connected to other highly embedded projects and thus
might be more likely to receive information from peer projects.
Another aspect of information access advantage from high embeddedness comes from
crowd-funders’ willingness to share their information. Badaracco (1991) differentiated two types
of knowledge: migratory knowledge and embedded knowledge. The former is easier to relocate
32
with encoding methods such as books, data files and other types of documents, while the latter is
more challenging to transfer since it resides in the intertwined relationships and particular norms.
Accordingly, embedded knowledge is easier to be transferred when embedded relationships exist
between two network participants. When projects are highly embedded, their tight connections
with other projects will likely result in a cooperative norm. That’s because highly embedded
projects are more likely to create an environment where crowd-funders can see a lot of familiar
funders and be aware of the desired norm, and thus feel more comfortable about sharing their
input with other fellow funders, compared to projects where everyone is practically a stranger to
each other. What’s more, in highly embedded projects what they is more likely to be embedded
knowledge, which is often regarded more valuable.
With this information access advantage, entrepreneurs have the opportunity to improve
the project quality by learning from its funders and following best practices in the community.
When project quality improves, the crowdfunding project can attract more funding. On the other
hand, with high project embeddedness and well-connected crowd-funders in the project, new
funders can also better assess the quality of the project, credential of the entrepreneur and the fit
between their needs and the focal project, which can increase focal projects’ funding success. For
instance, it is quite common that entrepreneurs do not have all the answers to potential crowd-
funders’ questions, such as whether the product will be compatible with certain computer
systems or what’s the best practice to ship liquid to a foreign country. In this situation, crowd-
funders who have funded other similar projects can be an outstanding information source.
Moreover, compared to entrepreneurs who have motivation to hide or even twist information to
their own advantage, other funders’ information should be perceived as more trustworthy
because they have higher benevolence or more motivation to do good for the fellow crowd-
33
funders than the entrepreneur (Mayer, Davis, & Schoorman, 1995). Scholars have shown
emerging evidence from VC firms syndication behaviors. Specifically, VC firms often co-invest
out of the consideration of information sharing (e.g., Bygrave, 1988) and being highly connected
within the network indeed facilitates their performance, as measured by the companies’
performance they have invested (e.g., Hochberg, et al., 2007). Sorenson and Stuart (2001) found
that being embedded in the syndication network could mitigate VC firms’ propensity of
investing locally and within the same industry and they reasoned that this occurred because
embeddedness expanded firms’ information access by breaking the usual geographic and
industry boundary.
Moreover, high structural and junctional embeddedness also make it more efficient to
spread the information, and generate even more attention for projects. Previous literature on
innovation diffusion lends support to this prediction showing that embedded projects can more
successfully disseminate project information (Abrahamson & Rosenkopf, 1997). Crowd-funders
often recommend projects they think might be a fit to other funders. For instance, in a project
pitching to deliver consumer 3D printers to funders, experienced funders might recommend
another project that produces filament for 3D printers. In this way, the filament project attracts
more funders through existing funders.
What’s more, from a signaling theory perspective (Spence, 1973), a large amount of
shared crowd-funders (structural embeddedness) signals projects’ popularity and subsequently
might bring in more funding. Likewise, both higher junctional and positional embeddedness
indicate that crowd-funders of a focal project have contributed to other projects, and to
influential projects in the case of positional embeddedness. It means projects with high
embeddedness attract crowd-funders with related task experience, and thus are expected to pick
34
high quality projects. Therefore, projects in which they participate are conceivably more
attractive than others.
However, the positive effect of embeddedness might not last forever; some studies have
shown that it diminishes after it reaches a threshold, and then it might be detrimental (e.g.,
Mallagrapada et al., 2012; Uzzi, 1996; Uzzi, 1999). In a crowdfunding context, over-
embeddedness might mean that a lot of funders for the focal project are within an established
community, and it fails to attract new funders. It is plausible that over-embedded projects may be
extremely esoteric and thus only appeal to experienced funders who are already experts in their
domain, and the public cannot grasp it and thus are not interested. In this way, these specialized
projects might be able to only attract a small amount of funders and thus may not reach high
funding success. Further, when crowdfunding projects are over-embedded, it suggests that many
funders have funded multiple projects or perhaps are funding multiple projects simultaneously.
That means it is likely they can devote less attention, possibly less financial resource to any
given project. In this way, resources that can be mobilized from these funders can be quite
limited. Taken altogether, it is proposed:
H1: Crowdfunding projects’ network embeddedness will have an inverted U-shaped
relationship with the amount of funding they attract.
H1a: Crowdfunding projects’ structural embeddedness will have an inverted U-shaped
relationship with the amount of funding they attract.
H1b: Crowdfunding projects’ junctional embeddedness will have an inverted U-shaped
relationship with the amount of funding they attract.
H1c: Crowdfunding projects’ positional embeddedness will have an inverted U-shaped
relationship with the amount of funding they attract.
35
Human capital
“Human capital is embodied knowledge and skills” (Becker, Murphy, & Tamura, 1993, p.
324). In this study, human capital describes the stock of experience, knowledge and expertise
regarding crowdfunding that the crowd has. It is a collective feature rather than an individual one,
meaning it describes a quality of the crowd as a whole. The reason why human capital is critical
to creating value lies in the fact that humans can constantly improve their skills from education,
training and task related experience, and naturally transform acquired knowledge from one
domain to another (Farjoun, 1994). Argote and Ingram (2000) pointed out that “move of people”
is one important mechanism for knowledge transfer within organizations. At the same time,
Grant (1996) claimed that knowledge is one of the most critical resources for firms. Thus, human
capital has been viewed as one critical asset for organizations (Lepak & Snell, 1999). Taken
together, task-related experience grants employees enhanced ability to master the task, and
organizations gain benefits through effective utilization of human capital.
Not surprisingly, previous research has documented how task-related experience can lead
to improved human capital. For instance, through two experiments, Littlepage, Robison, and
Reddington (1997) reported that experience with similar tasks can lead to improved individual
proficiency, which resulted in better group decision making. Hitt and associates (2001) suggested
that people might acquire explicit knowledge through formal training, such as advanced degrees
and training program, and gain tacit knowledge, a type of knowledge regarded as more valuable
since it’s challenging to imitate (Polanyi, 1967), through “learning by doing”. They reported that
firms’ human capital, operationalized through the quality of law school graduates and their
experience as a partner in firms, contributed to law firms’ performance.
36
To note, project network embeddedness and their human capital are interconnected but
conceptually distinct. Because both of these constructs are construed from crowd-funders’
interwoven co-funding relationships, they tap into how resources residing in the crowd can be
translated into financial resources. Yet, they are conceptually distinct. Human capital emphasizes
the content of such ties, i.e., the stock of domain expertise, while network embeddedness taps
into the overall network structure’s influence throughout the community on the focal project
more. It is conceivable that some projects might be high on human capital but low on junctional
or positional embeddedness. Therefore, this dissertation will examine these two constructs
separately.
Effects of human capital on crowdfunding success
In the crowdfunding context, the stock of funders’ previous funding experience,
especially on related projects, is one indicator of the amount of human capital residing in the
crowd. It can be viewed as an asset for both the entrepreneur and fellow potential funders. Take
3D printing technology as an example of one domain again, and compare two crowds: one crowd
consisting of 100 individuals with one person having 3D printing related project funding
experience, while another crowd including 100 individuals has 50 persons with 3D printing
related project funding experience. It seems reasonable to predict that the latter crowd might
possess more domain expertise or human capital on 3D printing than the former.
As mentioned earlier, entrepreneurs usually hold more information about their product
and themselves than potential funders (Venkataraman, 1997), and they have a vested interest to
strategically present the information, or even hide information to make their product more
desirable than it actually is. Potential funders need to gather and analyze available information to
gauge the quality of the project and credentials of the entrepreneurs. As mentioned earlier,
37
crowd-funders with extensive funding experience are more competent doing so than
inexperienced funders.
Further, experienced funders are good information sources when both the entrepreneurs
and other crowd-funders have questions for two reasons. First, experienced crowd-funders can
provide information in a timely and customized manner. It is through experienced crowd-funders
that the history and accumulated expertise in the community gets passed down. Compared to
inexperienced funders who might spend effort gathering information from other sources,
identifying the quality of the information and applying information to the situation in hand,
experienced funders can offer insights from their own experience and their experience can be
applied to other funders without much modification. For instance, experienced crowd-funders
can provide detailed comparisons between two similar 3D printer projects including their first-
hand user experience and offer customized suggestions based on potential funders’ needs.
Second, as mentioned earlier, experienced crowd-funders are likely to be perceived by other
funders as not having a motivation to deceive other crowd-funders, so that information from
them may be seen as more trustworthy than that from the entrepreneur. For instance, a potential
funder might put more weight on an experienced crowd-funder’s evaluation of the project than
words from the entrepreneur. In addition, according to signaling theory, potential crowd-funders
might infer that crowdfunding projects with a large amount of experienced funders have good
quality (Spence, 1973). Therefore, it is proposed:
H2: Human capital residing in the crowd, in the form of the number of experienced
funders, will be positively related to the amount of funding a crowdfunding project
attracts.
38
The Mediating Role of Knowledge Exchange
Embeddedness and knowledge exchange
The previous section laid out the rationale for why crowdfunding projects’ network
embeddedness would have an inverted U-shaped relationship with their funding success. This
section will explore what intermediary mechanism is responsible for realizing this relationship.
One often speculated mechanism through which structural embeddedness affects entities’
resource acquisition is advantageous information access (e.g., Adler & Kwon, 2002; Grewal, et
al., 2006; Tsai, 2001; Uzzi, 1999; Uzzi & Gillespie, 2002), particularly unique information.
Before diving into the specific reasoning, it is important to note that knowledge exchange is not
completely independent from social capital. To explain, social capital contains not only the
network structure, but the resources embedded in the network (e.g., Inkpen & Tsang, 2005;
Nahapiet & Ghoshal, 1998). In a sense, knowledge exchange can be viewed as one resource
valuable to the crowd, and thus can be viewed as one indicator of social capital. However, in
order to clearly delineate the relationship between sources of social capital and its effects,
existing scholarship has examined social capital and knowledge transfer as two different
constructs (e.g., Inkpen & Tsang, 2005; Yli-Renko, Autio, & Sapienza, 2001). The current study
hence follows previous research and argues that the network position of crowdfunding projects
determines the potential resources projects can access, and the extent to which knowledge is
exchanged among each crowd predicts how much social capital is indeed realized. In sum,
acknowledging that knowledge exchange can be an indicator of social capital, this section will
discuss how knowledge exchange as a separate construct mediates the relationship between
structural embeddedness and the funding success.
39
The reason why it is worth investigating knowledge exchange as the mechanism for
translating the potential of social capital into financial capital is three-fold. First, the amount of
financial resources a crowdfunding project can raise depends on every potential funder’s
decision and their decision to a large extent is a function of the range and content of information
they can access regarding this crowdfunding project. Hence, at the individual crowd-funder level,
information access matters. Second, the link between knowledge exchange and an entity’s ability
to gain competitive advantage has been investigated extensively in organizational learning
literature (e.g., Kogut & Zander, 1992; van Wijk, Jansen, & Lyles, 2008). With knowledge being
recognized as one of the most critical resources for organizations (Grant, 1996), researchers have
repeatedly shown that knowledge exchange activities, including knowledge transfer and
information seeking are positive associated with organizations’ performance (e.g., Argote &
Ingram, 2000; Lane, Salk, & Lyles, 2001) and innovation (e.g., Powell, et al., 1996; Tsai, 2001).
Third, the effects of distinct network structures on knowledge exchange have been explored (e.g.,
Ingram & Roberts, 2000; Inkpen & Tsang, 2005; Reagans & McEvily, 2003; Tsai, 2001). For
example, scholars have reported that network range, the extent to which the actor can reach
different parts of the network, and network cohesion, the extent to which actors are connected
with nodes who are highly connected, (Reagans & McEvily, 2003) and network centrality (e.g.,
Tsai, 2001) can foster knowledge exchange for organizations. Applying these general findings to
the crowdfunding context, information access is critical for potential funders to make funding
decisions, and previous literature has explored the link between network structure and an entity’s
knowledge exchange behaviors and the link between an entity’s knowledge exchange behaviors
and its performance respectively. Therefore, the current study proposes to add to the literature by
investigating the mediation effect of knowledge exchange in the relationship between social
40
network actors’ embeddedness and their performance, defined in this context as crowdfunding
projects’ funding success.
In this study, knowledge exchange refers to the extent to which entrepreneurs and crowd-
funders seek and share information within a given crowdfunding project. Both information
seeking and sharing aspects are included since knowledge exchange is a reciprocal process
whereby knowledge sharing is often driven by seeking endeavors. This mediation effect has not
been explored in the literature with the exception of Wu (2008). Through surveying 108 Chinese
manufacturing firm owners, Wu (2008) found that information sharing indeed mediated the
effect of three dimensions of social capital, including embedded ties, trust and repeated
transactions, on firms’ competitiveness improvement. Yet, in Wu’s (2008) study, the mediation
effect tested was on information sharing only, and it was examined in the relationship between
firms’ competitiveness improvement and relational dimension of social capital, such as trust,
repeated transactions and network ties. This study will expand this line of research by exploring
the mediating role of knowledge exchange, including both information seeking and information
sharing, in the relationship between project network embeddedness and the funding success.
Moreover, from a methodological point of view, one shortcoming in the knowledge
exchange literature is that much research investigating knowledge sharing has been relying on
self-reported survey data without objective validation (van Wijk, et al., 2008). In contrast, this
study will overcome this shortcoming by analyzing the content of public communications
between entrepreneurs and crowd-funders on crowdfunding platforms to capture knowledge
exchange. This method has the advantage of increasing the external validity of the study.
Specifically, the reason why knowledge exchange mediates the relationship between
project embeddedness and funding success is two-fold: because it facilitates information reach
41
and willingness to seek and share information. With heightened information access, crowd-
funders can make more informed funding decisions. Since these two reasons are mentioned
earlier in the section on relationship between network embeddedness and funding success, they
will be briefly recapped here. First, highly embedded projects will have an extensive range to
reach other projects in the community (e.g., Arranz & Fdez de Arroyable, 2013), which provides
them information access advantage to improve the project quality and communication with
crowd-funders. This is achieved by having shared crowd-funders who broker information from
other projects to focal projects. Second, high embeddedness also indicates that a cooperative
norm is more likely to be developed around the project and thus crowd-funders are more willing
to contribute and seek information. Compared to projects with few linkages with other projects,
highly embedded projects contain funders who have had funding experience and may bring in
the shared norms within the community, e.g., what funders expect from entrepreneurs, what
technical details should be explained and in what format, and what communication activities
entrepreneurs should conduct to engage the funders. With this shared norm, cooperative
behaviors are more likely to happen and these experienced funders are more willing to seek and
provide information to the project. In contrast, when crowd-funders lack common experience,
they may be hesitant to seek information since no one prefers to be viewed as inexperienced. In
this case, they will be less likely to help each other with inquiries and prefer to leave them to
entrepreneurs without a cooperative norm. Hence, with extensive information access, highly
embedded projects can potentially analyze the quality of the projects and credentials of
entrepreneurs better, which should lead to more informed funding decisions.
To note, the proposed main relationships between three types of network
embeddednesses and the funding success are curvilinear, and the hypothesized effect of
42
knowledge exchange conceivably mediates this curvilinear relationship such that the benefits of
knowledge exchange increases initially due to growing novel and diverse information projects
can access. However, when information exchanged starts to get repetitive, the value of incoming
information decreases. Eventually the cost of maintaining network embeddedness exceeds the
benefits of incoming information, and then over-embedded projects funding success might suffer
accordingly. Therefore, it is proposed:
H3a: The amount of knowledge exchange will mediate the inverted U-shaped
relationship between crowdfunding projects’ structural embeddedness and the amount of
funding they attract.
H3d: The amount of knowledge exchange will mediate the inverted U-shaped
relationship between crowdfunding projects’ junctional embeddedness and the amount of
funding they attract.
H3c: The amount of knowledge exchange will mediate the inverted U-shaped
relationship between crowdfunding projects’ positional embeddedness and the amount of
funding they attract.
Human capital and knowledge transfer
The above section has established that the human capital within a crowd should
positively predict the amount of funding that a crowd attracts. This section explores the
intermediary mechanism through which it happens. What processes intervene to help transfer
human capital into financial capital in crowdfunding projects?
Human capital is knowledge residing in people’s mind. Without the knowledge exchange
process, human capital is simply a stock of experience and competence that is not being
transferred into usable information (Nonaka, 1994). It is through knowledge exchange that
43
experienced crowd-funders leverage information within the crowd and accordingly allow the
crowd to sort through the information asymmetry and identify project quality (Venkataraman,
1997).
Specifically, people with domain expertise are better equipped to evaluate projects
accurately because they can ask the right questions to garner information from the entrepreneur
to make a comprehensive assessment. In addition to seeking information on behalf of the crowd,
individuals with domain expertise can offer their insights to the crowd through providing their
evaluation of the project and the entrepreneur, and even answering questions from other crowd-
funders. With their extensive funding experience on related projects, they can bring in good
practices from other projects and offer suggestions to entrepreneurs and other funders through
serving as knowledge broker (Pawlowski & Robey, 2004). Additionally, their knowledge
seeking and knowledge sharing behaviors could plausibly encourage other inexperienced funders
to engage in similar behaviors, and eventually identify the quality of the projects more accurately.
In this way, individuals with domain expertise influence others’ contribution decision by
leveraging knowledge among the crowd. Taken all together, it seems reasonable to predict that
human capital residing in the crowd helps to improve the project quality and the fit between the
project and funders through knowledge exchange. Therefore, it is proposed:
H4: The amount of knowledge exchange within the crowd will mediate the relationship
between human capital residing the in crowd, in the form of the number of experienced
funders, and the amount of funding a crowdfunding project attracts.
Displayed Passion and Displayed Preparedness
Entrepreneurial passion
44
Previous sections have shown that social capital and human capital residing in the crowd
attract funding for a crowdfunding project through knowledge exchange between the
entrepreneur and funders and among funders. This section explores how laymen crowd-funders
react to entrepreneurs’ oral business presentations (or the “pitch”), particularly displayed passion
and preparedness, and how human capital residing in the crowd moderates this relationship.
Entrepreneurial passion was chosen as the characteristic of entrepreneurs’ business
presentation in this study for three reasons. First, the fact that the importance of making a good
pitch to potential funders has been emphasized by almost every crowdfunding platform
highlights the significance of the pitch for crowdfunding campaigns. For instance, Indiegogo
advises all entrepreneurs that “First impressions are everything, and that’s why the most
important aspect of your crowdfunding campaign is your pitch video”
2
. Second, essentially,
crowd-funders’ decision making process can be viewed as a persuasion process, whereby an
entrepreneur pitches an idea to a large group of individuals and aims to convince them that this is
a good idea and hence they should provide funds to realize the idea. Considerable literature in
persuasion has shown that the affective state in which communicators present the information
plays a critical role in how persuasive the message is perceived to be (Schwarz, 1991).
Particularly relevant to the current study is how characteristics of effective business plan
presentation (Galbraith, et al., 2014) affect entrepreneurs’ ability to solicit funds. Although
limited, existing scholarship has provided initial insights on how entrepreneurs’ displayed
passion affects their abilities to acquire financial resources (e.g., Cardon, 2009; Chen, et al.,
2009). Third, compared to traditional venture decision making, crowdfunding decisions have two
unique characteristics which make the effect of pitch quality even more prominent. First, because
2
Retrieved from https://go.indiegogo.com/blog/2013/02/five-ways-to-power-up-your-crowdfunding-pitch-
video.html
45
the potential funders for crowdfunding projects are not a few VCs with domain expertise on the
focal topic, but rather are a large and undefined crowd of individuals, entrepreneurs need to be
careful when considering the balance between presenting the content in technical detail but also
in an accessible way. Moreover, not all funders are contributing to exchange for a monetary
reward (Gerber, et al, 2012). Therefore, instead of being driven by financial return alone, their
participation might be influenced by how they resonate with the pitch and especially what
affective state the pitch put them in. Taken together, it is reasonable to expect that displayed
passion during the pitch plays an important role affecting potential funders’ funding decision.
Furthermore, this investigation goes beyond a simple extension of the theory into a new
context; it also invites an opportunity for new theory building. Specifically, existing scholarship
on business plan presentation has been focusing on the characteristics of the pitch itself, e.g., the
displayed passion and preparedness, or the impression management skills; however, only a
limited amount of research has examined how characteristics of audience interact with the pitch
features and how this interaction will affect funding success. The exceptions include Galbraith,
et al., 2014; Mitteness, Cardon, & Sudek, 2010; and Mitteness, Sudek, & Cardon, 2012, all of
which are reviewed in this section.
Drawing on existing literature on VC funding decisions, especially the effectiveness of
business plan presentations, this study will particularly investigate how entrepreneurs’ displayed
passion and preparedness affect crowd-funders’ funding decision. The next section will define
displayed passion and preparedness, propose hypotheses on the relationship between displayed
passion and preparedness in the pitch and crowdfunding success, and further explore how
domain expertise in the crowds moderates those relationships.
46
Passion is considered a “strong inclination toward an activity that people like, that they
find important” (Vallerand, Mageau, Ratelle, Leonard, et al, 2003, p. 757). This construct is
often considered a strong motivational resource (Cardon, et al., 2009; Smilor, 1997). Some
scholars view passion as a personal trait (e.g., Baum & Locke, 2004) and others study it as a
positive state (e.g., Cardon, et al., 2009). Specifically, entrepreneurial passion is defined as
“consciously accessible, intense positive feelings experienced by engagement in entrepreneurial
activities associated with roles that are meaningful and salient to the self-identity of the
entrepreneur” (Cardon, et al., 2009, p. 517).
Entrepreneurial passion has received increasing attention since this construct not only
identifies an important characteristic of entrepreneurs, but also links entrepreneurs’ behavioral
inclinations to entrepreneurial role identities (Cardon, et al, 2009). For instance, Smilor (1997)
noticed that “perhaps the most observed phenomenon of the entrepreneurial process is the
passion of the entrepreneur” (p. 342). Cardon, Zietsma, Saparito, Matherne and Davis (2005)
discussed entrepreneurial endeavors as “a tale of passion”. Cardon and her colleagues (2009)
argued that entrepreneurial passion can lead to more effective opportunity recognition and
business growth through creative problem solving, persistence and absorption. Although
empirical studies on entrepreneurial passion’s effects are limited, scholars have shown that it
contributes to business growth indirectly through either entrepreneurs’ competence and strategy
(Baum, Locke, & Smith, 2001) or better communicated business growth vision (Baum & Locke,
2004).
Displayed passion and preparedness
When it comes to influencing funders’ decision making, entrepreneurial passion is
examined through the lens of displayed passion. It means when potential funders make funding
47
decisions, the extent to which entrepreneurs exhibit passion towards the venture, particularly
during the pitch, will play an important role (Chen, et al., 2009). Many previous studies treat
displayed passion as a one dimensional construct focusing on the affective state solely (e.g.,
Baum & Locke, 2004; Baron, 2008). Through a lab experiment and a field study, Chen and his
colleagues (2009) expanded entrepreneurial passion into a two dimensional concept: it
incorporates an affective aspect, displayed passion, and a cognitive aspect, displayed
preparedness. Specifically, displayed passion refers to the extent to which entrepreneurs exhibit
intense positive feelings toward their venture, and the displayed preparedness denotes the extent
to which entrepreneurs’ presentation shows their thorough consideration of the business plan.
The current study adopts this multidimensional conceptualization of entrepreneurial passion and
examines how these two aspects, displayed passion and preparedness, affect crowdfunding
projects’ funding success.
Existing scholarship has reported mixed results concerning the relationship between
displayed passion and VCs’ funding decisions. Some studies found that displayed passion
facilitates investment funding decisions. For example, Elsbach and Kramer (2003) explored how
creativity was assessed in the film and television industries at Hollywood by observing 28
pitches and interviewing 36 informants, and they found that displayed passion was an important
signal to categorize the pitcher into the “creative” category, which is a critical criteria for the
final funding decision. Mitteness and her colleagues (2012) studied 3502 evaluations resulting
from 241 companies screened by 64 angels and concluded that perceived passion rated by angel
investors did contribute to entrepreneurship’s funding potential such that entrepreneurs
displaying more passion were more likely to proceed to the next round.
48
Further, research from emotional contagion theory can offer some insights on why
displayed passion in the pitch might facilitate entrepreneurs to acquire financial resources (Baron,
2008). When the entrepreneur is passionate about his or her venture, recipients of the pitch or
potential funders can feel passionate or positive about the proposal due to emotional contagion
through emotional mimicry (Hatfield, Cacioppo, & Rapson, 1994). Concurrently, research on
affect and persuasion, particularly the feelings-as-information theory has shown that people rely
upon their affective state to infer the merits of information they receive (Schwarz & Clore, 1988).
Put it simply, when people feel good, they are more likely to attribute, often mistakenly, their
positive mood to the message they are processing. Research from Affect Infusion Model (AIM)
lends support to this argument (Forgas, 1995) as well. According to AIM, affect influences
individuals’ cognitive processing and this effect tends to exacerbate in complex situations which
demand elaborated cognitive processing. When people are in a positive affective state and trying
to make a complicated decision, they are more likely to select and process stimuli congruent with
their affective state. Therefore, according to AIM, people in a positive affective state tend to
engage in activities that keep them happy, such as helping others. With regard to investment
funding decisions, this means that when potential funders feel positive or passionate about the
proposal, they tend to think the venture in a positive way and thus will be more likely to offer
funds. For instance, Grichnik, Smeja and Welpe (2010) conducted an online experiment with 146
participants from young entrepreneurial firms and found that participants experiencing positive
emotions tended to evaluate business opportunities more positively.
On the other hand, some research also has shown that entrepreneurial passion might have
limited or no influence on investment funding decisions. Cardon, Sudek and Mitteness (2009)
coded 60 videotaped business pitches made to a big angel investment group and concluded that
49
displayed enthusiasm was not related to whether the project can proceed to the next funding
stage or not. Galbraith and associates (2014) analyzed 22 videotaped business presentations
made by real-life entrepreneurs in high-technology companies to the U.S. Department of Defense
and had these presentations rated by panel reviewers from the granting agency. They found that
displayed passion during the presentation was associated with more favorable ratings of the
proposal’s commercial potential but not technology merits.
In a crowdfunding situation, it is conceivable that displayed passion will have a positive
effect on potential funders’ funding decision for two reasons. First, the potential funders for
crowdfunding projects are not all domain experts. Anyone with an Internet connection can be a
crowd-funder. To this group of laymen funders, technical jargons and sophisticated market
analysis in a business plan might not be particularly persuasive; rather, an accessible and
passionate speech might be more convincing. Moreover, because many funders participate in the
donation or reward-based crowdfunding not out of financial investment reason; rather, they
might participate due to their belief in a cause, or identification with the community, or simple
enjoyment of innovations (Gerber, et al, 2012). Therefore, instead of being driven by the
financial return, their participation might be influenced by how the pitch is delivered to them and
especially how they resonate with the emotional appeal of the entrepreneur.
Further, when the entrepreneur displays high passion in his or her pitch, he or she will
attract more funders, especially new ones without previous funding experience. That’s because
pitches with high displayed passion conceivably appeal to more potential funders than those with
low displayed passion because people are driven to maintain their positive affect (Schwarz &
Clore, 1988). Additionally, new funders are expected to be affected by affect more since they
have less related experience to rely on and when people make decisions with less information
50
available, they tend to rely on affective cues more (Forgas, 1995). Taken altogether, it is
proposed:
H5: Displayed passion in the pitch video will be positively related to (a) the amount of
funding they attract, (b) the number of total backers, and (c) the number of new backers.
Conversely to displayed passion, displayed preparedness has constantly been shown to
positively predict funding decisions. Showing passion towards the new venture can signal
entrepreneurs’ strong motivation and their willingness to do whatever it takes to overcome
obstacles and achieve their goals; yet, passion alone cannot build an entrepreneurship. As Smilor
(1997) commented that “effective entrepreneurs are dreamers who do” (p. 342). Since a lot of
entrepreneurial endeavors occur in new emerging market, the risk for entrepreneurship is
inherently higher. Thus, an information asymmetry lies between the entrepreneurs and potential
funders in terms of the quality of the project, the management team, the market research and
competitive analysis, etc. Accordingly, a key challenge many entrepreneurs face is to reduce the
uncertainty potential funders have in mind by providing needed information in a persuasive
manner. To do this, entrepreneurs should show clear evidence that they have carefully studied
the market and target audience, analyzed competitors on the market, and packaged the pitch in a
coherent and logical way. This aspect of entrepreneurial passion is demonstrated through
displayed preparedness.
There is some emerging empirical evidence showing that displayed preparedness has a
positive effect on funding success. For example, Chen and his colleagues (2009) invited 55
investors as competition judges to assess 31 business plans and found that displayed
preparedness positively affected investment decisions. In Galbraith, et al.’s (2014) study,
although displayed passion’s effect is mixed, entrepreneurial preparedness strongly facilitates
51
reviewers’ evaluation of the business proposal. Similarly, in Cardon, Sudek and Mitteness’ (2009)
study, displayed preparedness was positively related to firms moving forward in the funding
phase. Pollack, Rutherford and Nagy (2012) coded 113 successful pitches from 98 episodes of
Shark Tank and Dragon Den, both of which are reality TV shows featuring entrepreneurs
pitching ideas to a panel of potential investors, and confirmed that entrepreneurial preparedness
successfully predicted the amount of funding they received.
When it comes to crowdfunding, it seems reasonable to follow previous scholarship (e.g.,
Cardon, et al., 2009; Chen, et al., 2009; Galbraith, et al., 2014) and propose that preparedness
will positively predict the amount of funding attracted, the number of funders, and the number of
new funders as well. That’s because when people make funding decisions, the substance in the
pitch signals whether or not the entrepreneur can deliver the project the way they envision the
project to be, e.g., their market research will ensure potential funders that their product is
innovative and competitive and related domain expertise in the team increases funders’
confidence that the product will be successfully delivered. Without these quality signals through
preparedness, pure passion might not be able to get the project all the way to funding success.
Moreover, displayed preparedness in the pitch should attract new funders to the project as well.
It is plausible that new funders are brought to the crowdfunding platform through media attention
or “word of mouth”, but what makes them to become a funder lies in the substance in the pitch.
Support from crowdfunding studies starts to emerge as well. After examining more than 48,526
projects on Kickstarter, Mollick (2014) concluded that project preparedness positively predicted
the funding success. Therefore, it is proposed:
52
H6: Displayed preparedness in the pitch video will be positively related to (a) the amount
of funding they attract, (b) the number of total backers, and (c) the number of new
backers.
Relationship between displayed passion and displayed preparedness
As a first step to examine effects of business presentations on investment funding
decisions, displayed passion and displayed preparedness aim to capture the extent to which
entrepreneurs communicate their business vision in a passionate and logical manner. To advance
this endeavor, the current study further explores the relationship between these two factors. In
other words, do displayed preparedness and displayed passion interact when affecting funders’
decision?
The current study proposes that displayed preparedness might have an amplifying effect
on the positive link between displayed passion and the funding amount. Entrepreneurs’ thorough
consideration of their business plan (preparedness) might be able to ensure potential funders
experiencing positive emotion due to displayed passion that the entrepreneur can deliver the
product the way it is presented through the cognitive route. Thus, compared to pitches with low
displayed passion, displayed preparedness will have an even stronger effect on projects with high
displayed passion. Thus, it is proposed:
H7: Displayed preparedness in the pitch video will positively moderate the relationship
between (a) displayed passion and the amount of funding they attract, (b) displayed
passion and the total number of funders, and (c) displayed passion and the number of new
funders such that displayed preparedness will have a stronger effect on projects with high
displayed passion, compared to those with low displayed passion.
Moderating effect of human capital
53
Previous reasoning has discussed how potential crowd-funders would react to business
pitches on a crowdfunding platform, particularly the effects of displayed passion and displayed
preparedness, and this section will explore how crowd-funders’ characteristics, especially their
domain expertise, moderate the positive relationships between displayed passion (and displayed
preparedness) and the funding amount.
How crowd-funders react to the pitch depends on not only characteristics of the pitch
itself, but also features of the message recipients, the crowd-funders. There is scant evidence
showing that the fit between investor attributes and entrepreneurs looking for funding, or
“personal chemistry” (Hisrich & Jancowicz, 1990) contributes to explain the heterogeneity of
investors’ reactions to the same pitch. For instance, Mitteness and associates (2010) found that
investors are not homogenous and those investors high on social perceptiveness and extraversion
regarded displayed passion as more important than other investors. Mitteness and her colleagues
(2012) further showed that angel investors who are older, have stronger mentor motivation and
more intuitive cognitive style compared to the analytic style, reacted to displayed passion more.
Galbraith, et al. (2014) reported that presentation characteristics interacted with panel
background such that displayed passion had a bigger effect on reviewers with business
experience, compared to reviewers who were scientists or engineers.
These studies start to establish the foundation that investor attributes play an important
role in how investors react to pitches; however, the characteristics of investors that have been
examined so far are rather static, such as demographics, personality traits, and cognitive style;
yet, one critical contingency that directly influences funding decision-making, i.e., potential
funders’ domain expertise or human capital, has been overlooked. As discussed earlier, human
capital residing in the crowd denotes the extent to which the crowd as a whole possesses
54
knowledge or experience in one particular field. Based on this premise, this study attempts to
answer the question: how does the domain expertise residing in a crowd affect the crowd’s
reaction to displayed passion and preparedness in a pitch?
First, this study argues that human capital, in particular, domain expertise in the crowd,
will attenuate the positive effect of displayed passion on the funding success. Studies from
effects of mood on persuasion, especially the role of positive affect in Elaboration Likelihood
Model (ELM, Petty & Cacioppo, 1986) can lend support to this argument. According to ELM,
people engage in either the central or peripheral route when processing information, depending
on their motivation and ability. When individuals possess high processing ability, they tend to
engage in the central route of persuasion, whereby people put more weight on the quality of the
argument more than other factors, such as the way the information is presented. Studies
examining effects of affect on cognition have confirmed the prediction that mood can serve as
peripheral cues and their impact can be compromised when individuals are highly motivated or
have high capacity to process the information (Schwarz, Bless, & Bohner, 1991).
In the crowdfunding context, it is argued that crowd-funders’ extensive funding
experience equips them with enhanced ability to pick out high quality projects and entrepreneurs
who can deliver their promise. Entrepreneurs have information advantage in terms of the true
quality of the project in the funding phase; experienced crowd-funders are skilled at identifying
reliable signals and unreliable signals from entrepreneurs. Thus, experienced funders will not
make their funding decisions relying heavily on the video in which entrepreneurs conduct self-
promotion and employ powerful emotions to carry away potential funders; rather they will look
into the technical details of the projects, seek factorial information from entrepreneurs to make
an informed decision themselves. Therefore, it is proposed:
55
H8: Human capital residing in the crowd, in the form of the number of experienced
funders, will have a negative moderation effect on the relationship between displayed
passion and the amount of funding attracted.
When it comes to the effect of human capital on displayed preparedness, the current
study makes a similar prediction. Intuitively it might seem reasonable to predict that experienced
crowd-funders can more easily recognize the extent to which entrepreneurs have taken actions
towards their passion, such as their R&D development, market research and competitor analysis
and thus human capital might have an amplifying effect on the positive effect of displayed
preparedness on the funding success.
Yet, all this information, signaling entrepreneurs’ preparedness of the entrepreneur is
intentionally selected and edited by the entrepreneurs. After all, the displayed preparedness in the
pitch video is one cognitive dimension of the overall entrepreneurial passion. It is plausible that
entrepreneurs “perform” in the pitch in the way they want potential crowd-funders to perceive
them: passionate and competent. With this possibility in mind, crowds with high human capital
might be more likely to critically process the message and analyze the credential and factorial
information about the project rather than the pitch itself. Through the public communication
process, experienced funders’ assessment will affect other potential funders without domain
expertise. Taken all together, it is predicted:
H9: Human capital residing in the crowd, in the form of number of experienced crowd-
funders, will negatively moderate the positive relationship between displayed
preparedness and the amount of funding attracted.
All hypotheses are summarized in Table 1.
56
Table 1. Summary of All Hypotheses.
Hypotheses
Network embeddedness
H1a: Crowdfunding projects’ structural embeddedness will have
an inverted U-shaped relationship with the amount of funding they
attract.
H1b: Crowdfunding projects’ junctional embeddedness will have
an inverted U-shaped relationship with the amount of funding they
attract.
H1c: Crowdfunding projects’ positional embeddedness will have
an inverted U-shaped relationship with the amount of funding they
attract.
Human capital
H2: Human capital residing in the crowd, in the form of the
number of experienced funders, will be positively related to the
amount of funding a crowdfunding project attracts.
Mediating effect of knowledge exchange
H3a: The amount of knowledge exchange within the crowd will
mediate the inverted-U shaped relationship between crowdfunding
projects’ network structural embeddedness and the amount of
funding they attract.
H3d: The amount of knowledge exchange within the crowd will
mediate the inverted-U shaped relationship between crowdfunding
projects’ junctional embeddedness and the amount of funding they
attract.
H3c: The amount of knowledge exchange within the crowd will
mediate the inverted-U shaped relationship between crowdfunding
projects’ positional embeddedness and the amount of funding they
attract.
H4: The amount of knowledge exchange within the crowd will
mediate the relationship between human capital residing the in
crowd, in the form of the number of experienced funders, and the
amount of funding they attract.
Displayed passion and preparedness
H5: Displayed passion in the pitch video will be positively related
to (a) the amount of funding they attract, (b) the number of total
backers, and (c) the number of new backers.
H6: Displayed preparedness in the pitch video will be positively
related to (a) the amount of funding they attract, (b) the number of
total backers, and (c) the number of new backers.
H7: Displayed preparedness in the pitch video will positively
57
moderate the relationship between (a) displayed passion and the
amount of funding they attract, (b) displayed passion and the total
number of funders, and (c) displayed passion and the amount of
new funders such that displayed preparedness will have a stronger
effect on projects with high displayed passion, compared to those
with low displayed passion.
Moderating effect of human capital
H8: Human capital residing in the crowd, in the form of the
number of experienced funders, will have a negative moderation
effect on the relationship between displayed passion and the
amount of funding attracted.
H9: Human capital residing in the crowd, in the form of number of
experienced crowd-funders, will have a negative moderation effect
on the relationship between displayed preparedness and the amount
of funding attracted.
58
Chapter 3: Method
Data Source and Data Collection Procedure
These hypotheses were tested using publicly available online data collected from a
leading crowdfunding platform focusing on innovation, CrowdInno (a pseudonym). The reason
why this platform was chosen is three-fold. First, CrowdInno is a typical and thriving
crowdfunding platform. By March 2014, CrowdInno passed 1 million in pledges from 5.7
million funders across the world. According to CrowdInno’s posted statistics in 2013, twenty
nine percent of these funders are repeat funders, meaning they have funded more than 1 project
on CrowdInno. Thus, CrowdInno has cultivated a healthy and thriving community devoted to
crowdfunding, particularly for innovation projects. Second, information regarding the project
and users, including the pitch video and entrepreneurs and funders’ activities related to funding
or creating a project is publicly available. This feature makes it possible to test ideas about how
funders’ previous funding experiences in a crowdfunding platform and pitch features shape a
focal crowdfunding project’s success. Third, this platform encourages active communication
between entrepreneurs and crowd-funders. CrowdInno emphasizes the importance of
communication repeatedly on the site. For example, on its FAQ and Help pages, e.g., How to
successfully get funded, the critical role of communication is emphasized multiple times.
CrowdInno also develops advanced technical features which afford rich communications among
users. Entrepreneurs and funders can communicate publicly in two ways. First, entrepreneurs can
initiate communication with funders by providing an Update. Updates serve as project blogs,
devoted to inform funders of the progress of a project, e.g., a new technical feature is added to
the product, or the entrepreneur has successfully located a manufacturer for the project. Second,
entrepreneurs and funders can exchange information in the Comments section. The Comments
59
section is where entrepreneurs and funders exchange messages. For example, funders might
inquire about some technical details on a 3D printer, and either the entrepreneur or other funders
can respond.
To recap, this dissertation explores how crowd-funders’ characteristics influence
crowdfunding projects’ funding success. Specifically, it investigates how funders’ previous
funding relations embedded within the crowdfunding platform affect a focal project’s success
from a social capital and human capital perspective. Furthermore, this research examines how
funders react to pitch videos and how funder attributes interact with displayed passion in the
pitch video to influence people’s funding decisions. That means it is necessary to collect
information from three sources: 1) project information including the pitch video, the amount of
funding they attracted, and other project attributes such as funding duration and funding goal, etc.
2) information regarding all funders from the focal projects, and 3) all funders’ comprehensive
funding history on CrowdInno. To carry out this idea and make the data collection manageable at
the same time, this dissertation limited the scope of projects to one type of project on CrowdInno,
specifically, all 3D printing technology related projects that have been launched on CrowdInno
since this platform has been active (2009) up to April 7
th
, 2014. The reason why this type of
project was chosen and detailed selection criteria will be described after this emerging
technology is introduced briefly.
Three Dimensional (3D) Printing Technology
Three dimensional (3D) printing refers to the process of making a 3D object from a
digital model. A typical 3D printing process starts with a digital file, and this file will instruct a
3D printer to print the object layer by layer. Since most 3D printed objects are made by
successive layers of plastic, metal, clay, wood, or other materials this technology is also called
60
“additive manufacturing”, in comparison to traditional manufacturing methods such as cutting
and assembling (i.e., subtractive process). The last decade has witnessed a rapid growth of 3D
printing. According to Wohlers Associates, a leading firm studying 3D printing technology, the
3D printing market was worth $2.2 billion in 2012, and 28.3% of it involved production-ready
items instead of prototypes. McKinsey Global Institute (MGI) nominated 3D printing as one of
top 12 disruptive technologies that will change the world, and MGI predicts that 3D printing will
have an economic impact ranging between $200 to $600 billion by 2025
3
.
So far, 3D printing technology has brought significant changes to a wide range of
industries, from jewelry design, to dental crown and bridges, to human organs, and to foot wear
design. NASA successfully tested a 3D printed rocket engine in 2013 aiming to reduce cost for
U.S. space hardware use
4
. A recent study conducted at Michigan Technological University
concluded that at-home 3D printers can bring investment return in thousands of dollars
5
.
Harvard Business Review predicted that 3D printing would change the way of manufacturing so
fundamentally that “China will have to give up on being the mass-manufacturing powerhouse of
the world.”
6
With the increasing impact of 3D printing technology on technological innovation and
economics, the 3D printing industry itself is experiencing rapid change as well (de Jong & de
Bruijn, 2013). The first functioning 3D printer was invented by Chuck Hull in 1984. Not
surprisingly, early 3D printing systems were expensive to purchase and maintain. Consequently,
big 3D printing companies, such as Stratasys have been targeting big corporations, such as 3M
and General Motors. According to de Jong & de Bruijn, (2013), early 3D printer was sold for
3
http://www.mckinsey.com/insights/business_technology/disruptive_technologies
4
http://www.nasa.gov/press/2013/august/nasa-tests-limits-of-3-d-printing-with-powerful-rocket-engine-
check/#.Uw54_oUXcds
5
http://whatsnext.blogs.cnn.com/2013/07/31/study-at-home-3-d-printing-could-save-consumers-thousands/
6
http://hbr.org/2013/03/3-d-printing-will-change-the-world/ar/1
61
around $250,000, and three decades since this technology’s birth, the 3D printing system price
has been dramatically decreasing, ranging from $10,000 to $30,000. With more and more
companies entering the lower end of the market and 3D printing systems becoming more user-
friendly, more medium or small sized ventures will likely benefit from this technological
innovation (de Jong & de Bruijn, 2013). Some people reason that this change results from many
key 3D printing patents expiring
7
, and others argue that the vibrant user innovation stemming
from the 3D printer user community is partially responsible for the rapid changes in 3D printing
industry (de Jong & de Bruijn, 2013).
An outstanding example of user community’s innovation is the RepRap (replicating rapid
prototype) project. RepRap is an initiative dedicated to produce a 3D printer capable of self-
replicating. This open source project was founded by Dr. Adrian Bowyer at Bath University in
2005 and is operated under GNU General Public License
8
. Soon RepRap attracted many
voluntary contributors across the world and has produced various 3D printer models
9
. The goal
of this project is to empower users, any user in the world, with the ability to produce everyday
objects, and possibly shift the paradigm in consumer product design and manufacturing.
Makerbot, for example, is a well-known company transforming these open source 3D printers
into commercialized 3D printers. Taken together, the 3D printing industry is a rapidly growing
nascent industry: from the demand side, many medium and small-sized business owners are
motivated to invest in affordable 3D printers; from the supply side, 3D printing technology is
experiencing a rapid growth and has benefited from open collaborative innovation from the
active user community (de Jong & de Bruijn, 2013).
7
http://3dprintingindustry.com/2013/12/29/many-3d-printing-patents-expiring-soon-heres-round-overview/
8
http://en.wikipedia.org/wiki/RepRap
9
http://reprap.org/wiki/RepRap_Family_Tree
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Taken together, collecting data focusing on 3D printing technology-related projects
exclusively on CrowdInno has the following three advantages. First, 3D printing technology-
related projects feature knowledge-intensive entrepreneurial endeavors, whereby the focus of the
current dissertation, i.e., expertise residing in the crowd and knowledge exchange between
entrepreneurs and crowd-funders, plays a critical role in explaining the funding success. In
particular, as a nascent industry, evaluating the quality of 3D printing related projects, e.g., 3D
printer, electronic boards or the software generating the digital files, demands relatively unique
expertise compared to assessing projects targeting the mass, e.g., a new vase design. Thus, this
industry particularly offers a promising test bed for examining the research questions in this
study.
Second, keeping the content of the projects relatively constant can control confound
variables and make it easier to detect the effects of research variables in the current study. That’s
because it is plausible that different projects may have different funding mechanisms. For
example, domain expertise might exert different influence on a project appealing to the general
public, say a vase with a novel design, as compared to a project targeting people with specialized
needs, say a filament extruder, a machine that can turn raw materials into usable filament for 3D
printers. Therefore, keeping the content of projects similar helps to detect the effect of funders’
previous funding networks on project funding success.
Third, 3D printing technology related projects provide a unique opportunity to observe
the dynamic process of funders getting attracted to CrowdInno and migrating to different
projects. This happens because, first, as a leading crowdfunding platform focusing on innovation,
CrowdInno has accumulated a big group of crowd-funders who are passionate and
knowledgeable on technological innovation. This group of potential funders is viewed as a rare
63
asset to entrepreneurs since these funders can be excellent sources to beta-test their products,
receive feedback from, and network with (Brabham, 2010). This vibrant and experienced user
community enables CrowdInno to attract a large amount of entrepreneurs to initiate projects on
CrowdInno and new funders to the community subsequently. Currently, 3D printing technology
is one nascent industry and is increasingly growing (de Jong & de Bruijn, 2013), its user
community is hence still in the process of emerging and expanding. Compared to a relatively
saturated market, 3D printing technology related projects offers an opportunity to observe how
crowd-funders migrate to CrowdInno and to different projects over time.
Project Collection Criteria
In order to get a comprehensive list of all 3D printing-related projects on CrowdInno, the
following procedures were carried out. First, since 3D printing technology related projects
include different types of projects on CrowdInno, ranging from 3D printers, to 3D printer
filaments, to electronic boards, to computer software, and to 3D scanners, using “3D printing” as
a key word generated limited projects; thus, “3D” was used as a key word to search for all 3D
related projects on CrowdInno, which yielded 829 projects. Then, the researcher carefully read
through all projects’ descriptions, videos and reward information to identity projects whose main
purpose is to produce 3D printing related products. Examples of excluded projects include
projects focusing on 3D animation, 3D graphics design or 3D printed objects, e.g., 3D printed
jewelry.
These endeavors led to 135 projects that fall into three categories: 1) 3D printer projects
in which the main reward is a functioning 3D printer; 2) accessories of 3D printer projects in
which the main rewards are accessories of 3D printers, such as 3D printing materials, 3D printer
extruders, software for operating 3D printers, and other 3D printing related hardware and
64
software projects; and 3) projects that aim to promote 3D printing technology to a broader
community, and examples include projects proposing to make 3D printing learning materials,
open 3D printing shops, or get a 3D printer for the entrepreneur him/herself. Among these 135
3D printing technology-related projects, one project was removed since it received $0. So the
final sample includes 134 projects.
As argued earlier, ties between projects indicate whether two projects share common
crowd-funders. Hence, to capture the network position of crowdfunding projects, it is necessary
to extract information on all crowd-funders of these 134 projects. As a result, after identifying
these 134 projects, public information on every crowd-funder was collected as well, including
their location, tenure on CrowdInno, and all previous projects they contributed to up till the focal
project. In addition, information on project description, entrepreneurs, projects’ updates and
comments were collected as well. It is important to note, this study only collected publicly
available information from users’ profile and project updates. Private information including users’
real life identifiable information and their transaction records, and private updates (entrepreneurs
can make Updates only viewable to funders) were not collected. User privacy was fully protected
in the current study.
Network Data Construction
After identifying the 134 3D printing technology related projects and all crowd-funders
for these projects, a two-mode network, consisting of 134 projects and all crowd-funders for
these 134 projects, was constructed. Each cell indicates whether the crowd-funder contributed to
the project or not. With this two-mode network, a project adjacency matrix was constructed, each
cell indicating how many crowd-funders a pair of projects shares. This project adjacency matrix
will be referred as project network in this study.
65
Measurements
Dependent variables.
Amount of funding attracted. Projects’ actual amount of funding received was collected
to measure how successful these crowdfunding projects are. Foreign currencies were all
converted to dollars. Because the distribution of amount of funding received is not normal
(Skewness = 4.72, Kurtosis = 25.79), following other scholars’ practice and this variable was
transformed using the logarithm function (e.g., Mollick, 2014). So the dependent variable in the
data analysis was log-transformed amount of funding projects attracted.
Number of total funders. The total number of funders was listed on the project page.
Number of new funders. It was calculated as the number of funders who had not created
or funded any other project on CrowdInno before the focal project.
Independent variables.
Network Embeddedness. Following the previous literature (Arraz, &Fdez. de Arroyabe,
2013; Faust, 1997; Grewal, et al., 2006), three types of centrality were employed to capture
projects’ network embeddedness. First, the valued project network was dichotomized. Because
the mean of valued cells, each cell indicating the number of shared funders in this matrix, is 4.02,
Five was used as the cutting point, meaning the cell will be coded as 1 if the shared number of
funders between two projects is equal or bigger than 5, and it is coded 0 otherwise. These
centralities are calculated using statnet package in R (Handcock, Hunter, Butts, Goodreau, &
Morris, 2008).
Structural embeddedness was operationalized as degree centrality for a project, defined
as the number of ties the project has with other projects in the network (Freeman, 1979).
66
Junctional embeddedness was operationalized as 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 (Freeman, 1979).
Positional embeddedness was operationalized as eigenvector centrality, which reflects
the extent to which nodes with which one node is associated are influential. Each project’s
eigenvector centrality is a function of the sum of centralities of other projects to which they are
connected.
Human capital. Human capital aims to capture the stock of domain expertise residing in
the crowd. In the current study, domain expertise particularly focuses on funders’ knowledge or
past experience with 3D printing technology related projects. It is reasonable to expect that
funders who has funded successful 3D printing-related projects possess more expertise than
those who did not. Thus, every funder’s funding history was examined and how many successful
3D printing technology-related projects (projects that reached their funding goals are considered
successful) they have funded before the focal project was identified. For each project, the sum of
funders with at least one successful 3D printing-related project history was calculated to indicate
the domain knowledge residing in the crowd.
Displayed passion and preparedness. To evaluate displayed passion and preparedness,
the pitch videos of all 134 projects were analyzed. Among these 134 projects, 10 of them did not
include a pitch video on their project page. Further, 13 of them did not have a presenter in their
video. For example, some videos simply show how a 3D printer functions. These 23 projects
were excluded. Thus, the final data regarding the pitch characteristics includes 111 pitch videos.
67
Displayed passion and preparedness were assessed using an established 11-item scale
developed by Chen, et al. (2009). These 111 videos were analyzed by the researcher and one
research assistant, each coding about 80 projects which led to a 28 projects (25%) overlap. At
first, following Lombard, Snyder-Duch and Bracken’s (2002) recommendation, the researcher
and a research assistant both coded 25% of the data to establish consistency in coding the videos.
Inter-coder reliability for each item was calculated (see Table 2). Since the intraclass correlation
coefficients (ICCs) for all items using a two-way random model (Shrout & Fleiss, 1979), are
acceptable (above .70) (Lombard, et al., 2002), the researcher and the research assistant
continued to code the video individually. The differences between two coders were resolved
through discussion.
The six-item scale for displayed passion (Cronbach’s α =.94) and 5-item scale for
displayed preparedness (Cronbach’s α =.90) both exhibited high reliability. Thus the final score
for both scales were the average across the items.
The Mediator.
Knowledge exchange. To capture the amount of knowledge exchange between
entrepreneurs and crowd-funders, this study focused on the public communications between
them on CrowdInno’s comment section. As mentioned earlier, the comment section functions as
a message board for entrepreneurs and potential funders. Entrepreneurs and all funders can leave
a message to all, and all messages are publicly available. Figure 6 shows an example of the
comment section for one project on CrowdInno. The advantage of focusing on the public
communication on CrowdInno lies in its high external validity. These messages are authentic
communications between entrepreneurs and funders. They organically evolved within the
projects as the project proceeded. Because knowledge exchange is generally regarded as a more
68
socially desirable behavior, data obtained through coding the public communication is less liable
to social desirability bias, compared to the traditional method gauging knowledge exchange with
surveys or interviews.
69
Figure 6.An example of comment section of a project on CrowdInno.
70
The process by which a more detailed coding scheme was generated by both building
upon previous literature and using an inductive approach. Since the comments between the two
parties are essentially social interactions, a synthesis coding list for social interactions from
Weltzer-Ward (2011) was consulted. This list summarized all reported social interactions
categories in the literature, such as giving information, giving opinion, asking for information,
and asking for opinion, etc. Concurrently, given the scale of the current study, 134 projects with
average 113 comments each, a manageable coding scheme was needed. Since the current study
specifically focuses on the extent to which knowledge was exchanged during the conversation,
these types were selected: information exchange, including giving/seeking information,
giving/seeking suggestions as the initial coding scheme for the pilot coding. Then with the
guidance of this list, the researcher coded around 100 comments from 2 projects, and recorded
categories for these comments, and the top three sub-categories as a form of knowledge
exchange are: information seeking, information sharing and suggestion making. Detailed
explanations for each are provided below. Table 3 contains detailed description and examples for
each of them.
Information sharing. This variable captures whether the comment contributes knowledge
to help potential funders make funding decision, such as answering inquiries, sharing project-
related information, comparing different options, sharing personal experience with 3D printing
or other printers, and their funding decisions and why.
Information seeking. This variable captures whether the commenter is asking for more
information in order to make a funding decision, such as asking for technical specifics of the
product and asking for clarification from the entrepreneur.
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Suggestion making. This variable describes whether the commenter takes an initiative and
makes a suggestion or proposes ideas for the crowd and the entrepreneur to consider related to
the funding outcome, such as an idea for a stretch goal and a proposal to adding another level of
reward. Quickly, a stretch goal of a crowdfunding project is a new funding goal set by the
entrepreneur when the original funding goal is met and it usually proposes an improvement to
the current project and seeks more funding in exchange.
The unit of coding is every comment. Every comment is coded for these three dimensions
for yes (1) or no (0) by coders. Because there was no word limit for the comment length,
comments might be quite long, include multiple paragraphs or ideas. So it is possible that a
commenter might ask for more information about the product, and answer inquiries from earlier
commenters at the same time. Therefore, these three dimensions are not mutually exclusive. It is
possible that one comment scores three points since it serves all these three functions regarding
knowledge exchange.
These comments were coded by two research assistants. Before they started coding
independently, they coded a representative sample together to assess whether they could evaluate
the content reliably. Following Lombard, et al.’s (2002) suggestion, a representative sample of
the data for assessing intercoder reliability should be no greater than 300 units. These coders
each coded 3 projects which contained 347 comments in total.
Since all three variables are nominal, Cohen’s kappa was calculated to estimate
intercoder reliability (Lombard, et al., 2002). All three variables achieved decent intercoder
reliability: for knowledge sharing Cohen’s kappa = .76; for knowledge seeking, Cohen’s kappa
= .80; and for suggestion making, Cohen’s kappa = .77. After establishing the intercoder
reliability, two coders each coded half of comments.
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The final value for each project on knowledge exchange is the sum of all comments’
score across these three dimensions. Further, since this variable is highly skewed (Skewness =
5.74, Kurtosis = 39.79), it was log transformed as well. To account for the fact that 17 projects
had zero total comments, one was added to every project’s value to make sure the resultant log
value was a valid number.
Table 2. Intercoder Reliability for Displayed Passion (DPS) and Display Preparedness (DPP)
between Two Coders.
Items
ICC for
Intercoder
Reliability
DPS1: The presenter(s) had energetic body movements. .70
DPS2: The presenter(s) had rich body language (e.g., hand gesture). .70
DPS3: The presenter(s) showed animated facial expression. .85
DPS4: The presenter(s) used of a lot of gestures. .70
DPS5: The presenter(s) face lit up when he/she talked. .75
DPS6: The presenter(s) talked with varied tone and pitch. .81
DPP1: The presentation content had substance. .84
DPP2: The presentation was thoughtful and in-depth. .86
DPP3: The presentation was coherent and logical. .70
DPP4: The presenter(s) articulated the relationship between the
business plan and the broader context.
.85
DPP5: The presenter(s) cited facts to support his/her argument. .70
Note: Both displayed passion and displayed preparedness scales (5 point Likert scale, where 1 =
strongly disagree, and 5 = strongly agree) were adopted from Chen, Yao & Kotha, 2009. ICCs
reported here were calculated using two-way random effects model and results of single
measures.
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Table 3.Coding Scheme and Examples for Knowledge Exchange.
Sub-categories Definition Examples
Information
sharing
This variable captures
whether the comment
contributes knowledge to
help potential funders to
make funding decision.
One funder asked: “I'm new to this whole 3D
printing genre although I'm familiar with what it
does. I for some reason was thinking of going for
broke and ordering something like 8-10 extra
spools. For some reason people are being more
conservative. I;m in Singapore and have no idea
how easy/hard it would be to get materials, can
anyone tell me why I shouldn't purchase too
many?”
One funder asked: “Any ideas what you mean by
the ABS bed lasting only a few hundred prints and
having to replace it? And it being super cheap?”
One funder asked: “@M3D How much will spools
of filament cost after the [CrowdInno]. Will it
remain at the $12 cost?”
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Information
seeking
This variable captures
whether the commenter
is asking for more
information in order to
make a funding decision.
An entrepreneur responded: “‘What about the
shipping costs? When and how much do we have to
submit for that?’ We will collect shipping payments
about 1 month before your reward tier delivers,
allowing us to get accurate address info and
hopefully better shipping rates.”
One funder responded to anther funder regarding
difference between two types of filament: “There
was a lengthy discussion about that here yesterday.
Short answer: Ask Google for "PLA vs ABS".
Slightly longer answer: ABS is sturdier, but emits
toxic fumes during printing, and tends to warp if
there's no heated printbed. PLA is less flexible and
less sturdy (i.e. it breaks more quickly under
stress), but it's easier to print with, smells better
(non-toxic) and has a glossier finish.”
One funder responded to another funder: “X/Y is
the native resolution of the projector (XGA:
1024x768) divided by the X/Y projected area
(102.4 x 76.8 mm for 0.1 mm, 100 micron setting).
Calibrating X/Y for a given projector setting, 100
microns for example, is accomplished by projecting
a 10 micron x 10 micron grid pattern onto a printed
pattern of actual size and adjusting until they match
and are focused.”
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Suggestion
making
This variable describes
whether the commenter
takes an initiative and
makes a suggestions or
propose ideas for the
crowd and the
entrepreneur to consider
related to the funding
outcome
One funder suggested: “I think there are still people
who wanted to order filaments but couldnt do it in
time, there should be a backerkit campaign for
those who still wanted to and have backed the
project and im pretty sure theres a demand for it.”
One funder suggested: “As we are already way over
the $50k, the only stretch goals should be the
option for a finer nozzle/higher resolution.
Anything else will only cause chaos, delay and
frustration in the end. Since the project will run for
a while, we should consider available/state of the
art competitors, which will have higher resolution.
But anything else that takes away from the
concentration on the final goal should be left out.
Make that a project that delivers what was
promised in time.”
One funder commented: “I have two suggestions
and one question: 1. You state that the printer is
also optimized to print with ABS yet in the first 3
seconds of your video I already see a part warping
upwards from the bed... Printing with ABS without
a heated bed and an enclosed case isn't that viable.
Don't set the expectations too high! 2. M3D and
OctoPrint running on an Raspberry Pi would be the
perfect match. Make sure to contact its creator Gina
so she could add support for your printer. Heck you
could also sell another addon with an Raspberry Pi
with OctoPrint pre-installed. And now with the
impending release of the new RPI you could even
add it to the printer directly. Question: Does the
build plate involve painters tape or kapton tape?
Does it need to be re-applied after a new print?”
Control variables. Funding goal. This variable indicates how much money
entrepreneurs proposed to solicit from the crowd. This number reflects entrepreneurs’ realistic
anticipation of the project because CrowdInno adopts the all-or-nothing model, i.e.,
entrepreneurs access the funds only if the project reaches the funding goal. Further, Mollick
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(2014) examined universe data from Kickstarter and concluded that funding goal was negatively
related to the odds of project attracting enough funding. Following the previous literature, this
variable is included in the model after being log-transformed (e.g., Mollick, 2014).
Duration of crowdfunding project. The date projects were launched on CrowdInno and
the date they finished collecting funds were recorded. The length of projects was recorded by
days. This variable was included since previous studies have suggested that funding duration has
a negative effect on project being successfully funded (Etter, Grossglauser, &Thiran, 2013;
Mollick, 2014).
Project category. When entrepreneurs launch their projects on CrowdInno, they can
select which categories their project is listed under, such as Hardware, Open Software, and
Design. A preliminary analysis showed that projects belonging to Hardware category were
significantly different from projects in other categories, such as Open Software, Technology, and
Video Games. It might be due to the fact that Hardware projects would promise a hardware
product to be manufactured and delivered to funders. Projects in other categories did not have
much heterogeneity. So project category was dummy coded to indicate whether the project
belongs to the Hardware category (1) or not (0).
Market maturity. Because 3D printing technology is still in a young stage, and this study
captures its development in CrowdInno, the extent to which this market is mature enough to have
a sustainable consumer base might affect projects’ funding success. It is plausible that the timing
of projects entering CrowdInno plays a role on how much funding they can attract such that
earlier projects showed that 3D printing was an emerging and promising market so they attracted
more investors and entrepreneurs accordingly. Following previous research on crowdsourcing
markets (e.g., Walter & Back, 2011; Yang, Chen & Pavlou, 2009), market maturity, i.e., the
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overall age of the specific segment of CrowdInno was coded to control for this effect. This
variable was constructed by counting the months from the month the first 3D printing
technology-related project appeared on CrowdInno until the time the focal project was launched
on CrowdInno.
Entrepreneur tenure. It is plausible that the experience entrepreneurs have with
CrowdInno can facilitate their success such that they can learn from other entrepreneurs’
successful or failed experience and familiarize themselves with expectations from crowd-funders.
Therefore, the date that entrepreneurs joined CrowdInno was recorded. Subtracting the starting
date of every project with creators’ joining time indicates how long (months) the project creator
has been a member of CrowdInno.
Entrepreneurs previous creating experience. To control for whether the entrepreneur
has created projects on CrowdInno before, the number of projects entrepreneurs created prior to
the focal project was recorded. If a creator has created at least one project before the focal project
it was code as 1, otherwise it was coded as 0.
Data Analysis
Hierarchical linear regression models were employed to test hypotheses regarding the
effects of network embeddedness and human capital on the amount of funding received (H1 &
H2), and the effects of displayed passion and displayed preparedness on funding outcomes (H5-
9). The mediating effects of knowledge exchange (H3 & H4) were tested using Structural
Equation Modeling (SEM; Bollen, 1989).The SEM analysis was conducted with the AMOS 20
program (Byrne, 2001).
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Chapter 4: Results
Data Description
Among the 134 projects, 59% (79) of them were successful (i.e., reaching their funding
goals), relatively higher than 48% reported in Mollick’s (2014) systematic examination of
universal projects on CrowdInno for over two years. These successfully funded projects, in total,
raised $23,326,779 pledges and each project received an average of $174,080. These 134
projects attracted 76,329 crowd-funders in total and 32,619 of them are new funders to the
community, meaning they have never backed or created projects before. In terms of project
categories, 54.5% of the projects (73) belong to Hardware, 26.9% of the projects (36) belong to
Technology, and the following 18.61% projects (25) belong to Design, Open Software, or Video
Games.
These 134 projects were launched on CrowdInno across five years. The earliest project
was launched in October 2009, and most projects (121) were launched during or after 2012.
Further, these 134 projects were created by 124 unique entrepreneurs, and 10 entrepreneurs
created two projects each in this dataset
10
.On average, each project attracted 570 funders, and
243 of them were new funders to the community. Projects’ funding campaigns last for 32.7 days
on average. Entrepreneurs’ average tenure with CrowdInno when they started the project was 8.5
months. During the funding phase, entrepreneurs and funders exchanged an average of 127.8
comments, among which entrepreneurs would contribute an average of 23.4 and funders would
contribute an average of 104.4.
The following is a description of these 134 projects’ crowd-funders’ funding history.
These 76,329 crowd-funders’ average tenure with CrowdInno was 10 months and they funded
10
Because 92.5% of projects were created by different entrepreneurs and all the independent variables are at project
level, it means 92.5% of my data do not have within group variance. Therefore, I decided not to run a mixed model,
but controlled entrepreneurs’ previous creating experience as a dummy variable in the model.
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879,625 projects (693,783 successful projects) in total. Among the total of 76,329 crowd-funders,
43,323 of them (56.8%) were repeat funders, meaning they funded at least 1 project before the
focal 3D printing related project, and 42,520 of them funded at least 1 successful project.
Compared to the general CrowdInno statistics, i.e., 29% of funders are repeat backers, the
funders in this study show relatively higher loyalty to CrowdInno.
The network composed of these 134 3D crowdfunding projects has a density of .21 and
the average degree is 27.8. It means on average, a project would have 27.8 ties with others in the
network. First order correlation and data description are presented in Table 4.
Social Capital and Human Capital
Among all multiple regression models reported below, variance inflation factors (VIFs)
were calculated to check for multicollinearity. The VIFs values in all models are below 5,
indicating the models are not liable to multicollinearity. As described below, the three network
measures were modeled separately to avoid any multicollinearity issues.
Hypothesis 1 involves how project network position affects how much funding the
project attracted. The first step is to present a base model with control variables only, including
funding goal (log), project category, funding duration, entrepreneur tenure, market maturity, and
entrepreneurs’ previous creating experience. The base model explained 30 % of total variance.
he results showed that funder goal is positively related to the amount of funding attracted (β
= .47, p < .01); projects belong to the Hardware category (β = .16, p = .04) are likely to attract
more funding than projects in other categories. Further, market maturity does not have an effect
on the total amount of funding projects received (β = .09, p = .30), while entrepreneurs’ tenure (β
= .04, p = .51), entrepreneurs’ previous creating experience (β = .06, p = .44), and the funding
80
duration (β = .01, p =.86) do not affect amount of funding projects attracted. These results are
summarized in Model 1 in Table 5.
81
Table 4. First Order Correlations and Descriptive Statistics.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. Funding
received (log)
-
2. Funding goal
(log)
.51** -
3. Entrepreneur
tenure (months)
.21* .24* -
4. Hardware
project or not
.16 -.02 .19* -
5. Market maturity .28* .40** .20* -.04 -
6. Funding
duration (days)
.00 .02 -.05 -.02 -.11 -
7. Entrepreneur
previous funding
experience
.09 .02 .12 .09 .09 -.09 -
8. Degree
centrality
.80** .35** .12 .16 .29* .03 .09 -
9. Betweenness
centrality
.46** .13 .13 .13 .03 -.01 .03 .65** -
10. Eigenvector
centrality
.82** .38** .12 .16 .33* .04 .10 .99** .55** -
11. Number of
experienced
funders
.53** .19* .10 .07 .21* .03 .03 .77** .77** .69** -
12. Knowledge
exchange (log)
.85** .45** .12 .13 .18* .08 .15 .82* .49** .83** .58** -
13. Video length
(seconds)
.30** .30** .18* .13 .10 .05 -.14 .19 .04 .21* .10 .29* -
14. Displayed
passion
.22* .02 .06 .02 -.01 .13 .02 .28* .19* .26* .28** .21* .02 -
15. Displayed .60** .32* .11 -.01 .06 .06 -.19* .48** .30* .49** .38** .50** .16 .39** -
82
preparedness
Sample size 134 134 134 134 134 134 134 134 134 134 134 134 134 111 111
Mean 9.86 9.89 8.48 .54 42.85 32.66 .07 27.84 33.21 .07 94.65 3.04 186.38 3.13 3.61
S. D. 2.50 1.49 9.35 .50 10.50 9.41 .26 25.96 84.70 .06 175.08 2.00 111.85 1.15 1.01
Note. *p< .05, **p<.01.
83
H1a, H1b, and H1c propose that network embeddedness would have an inverted U
shaped curvilinear relationship with the funding success. Because these three types of centralities
are moderately to highly correlated, they were entered into the model respectively. To account
for the curvilinear relationship, three types of centralities’ square terms were created. A negative
coefficient for the square terms would indicate an inverted U-shaped relationship. Following
Aiken and West’s (1991) suggestion, centralities are mean centered and their square terms were
created with these centered predictors to avoid multicollinearity. The results showed strong
support to H1a, H1b and H1c.
First, degree centrality (β = .87, p < .001) has a significant and positive effect on the
amount of funding received and its square term (β = -.24, p < .001) has a significant and negative
effect, R
2
= .74, R
2
change = .43, N = 134, suggesting an inverted U-shaped relationship with the
amount of funding attracted. This result provides support to H1a that crowdfunding projects’
structural embeddednesshas an inverted U-shaped relationship with the amount of funding they
attracted. These results are summarized in Model 2 in Table 5.
Second, betweenness centrality (β = .73, p < .001) has a significant and positive effect on
the amount of funding received and its square term (β = -.41, p = .001) has a significant negative
effect, R
2
= .49, R
2
change = .19, suggesting an inverted U- shaped relationship with the amount
of funding received. This result provides support to H1b that crowdfunding projects’ junctional
embeddednesshas an inverted U-shaped relationship with the amount of funding they attracted.
These results are summarized in Model 3 in Table 5.
Third, eigenvector centrality (β = .81, p < .001) has a significant positive effect on the
amount of funding received and its square term (β = -.15, p =.004) has a significant negative
effect, R
2
= .74, R
2
change = .44, suggesting an inverted U- shaped relationship with the amount
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of funding received. This result provides support for H1c: that crowdfunding projects’ positional
embeddedness has an inverted U-shaped relationship with the amount of funding they attracted.
These results are summarized in Model 4 in Table 5. Taken as a whole, the results provide strong
support for the inverted U-shaped relationship between projects’ network embeddedness and the
amount of funding projects attracted.
Hypothesis 2 proposed that human capital residing in the crowd is positively related to
the amount of funding projects attracted. This hypothesis received strong support as well. The
number of funders who funded at least one prior successful 3D printing technology related
projects had a positive effect on the amount of funding received (β = .44, p < .001). These results
are summarized in Model 5 in Table 5.
Table 5. Regression Coefficients for Hierarchical Regression Models Regarding Social Capital
and Human Capital (N = 134)
Sources Model 1 Model 2 Model 3 Model 4 Model 5
Degree centrality .87***
Degree centrality
2
-.24***
Betweenness centrality .73***
Betweenness centrality
2
-.41**
Eigenvector centrality .85***
Eigenvector centrality
2
-.17**
Number of expert funders .44***
Funding goal .47*** .23*** .39*** .22*** .42***
Projects belonging to
Hardware category
.16* .01 .11 .01 .13
Project funding duration .01 -.05 .03 -.05 -.01
Entrepreneur creating
experience
.06 -.001 .04 -.01 .05
Entrepreneur tenure .04 .08 .02 .08 .03
Market maturity .09 -.08 .10 -.11 .01
R
2
.30 .74 .49 .74 .48
R
2
adj
.27 .72 .46 .73 .45
Note: Dependent variable = Log of amount of funding projects received. All coefficients are
standardized coefficients.
*p< .05, **p<.01, ***p< .001.
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Mediating Effects of Knowledge Exchange
First, the mediating effect of knowledge exchange on the curvilinear relationship between
project network embeddedness and the amount of funding they received was tested using SEM.
Model fit indices were assessed according to the AMOS manual (Byrne, 2001). Because the
effects of network embeddedness, including structural embeddedness, junctional embeddedness
and positional embeddedness on the amount of funding attracted are curvilinear, both the linear
effect and quadratic term of embeddedness were entered into the structural model. Below I will
show the results for three types of embeddedness individually. Briefly, degree centrality
(structural embeddedness) and eigenvector centrality (positional embeddedness) both suggested
partial mediating effect of knowledge exchange, while knowledge exchange fully mediated the
effect of betweenness centrality (junctional embeddedness).
This paragraph reports the results on the mediation effect of knowledge exchange on the
relationship between structural embeddedness and funding success. Hypothesis 3a proposed that
the amount of knowledge exchange will mediate the inverted-U shaped relationship between
crowdfunding projects’ network structural embeddedness and the amount of funding they attract.
To start, a null model with all research variables (i.e., structural embeddedness, including both
linear effect and square term, funding amount and all control variables was tested. As expected,
this model received bad fit: χ
2
(36) = 320.20, p < .001, GFI = .69, AGFI = .61, RMSEA = .24.
Then, an initial model with the main effect of the independent variable on the dependent variable,
i.e., the effect of structural embeddedness (including both the linear and the quadratic effects) on
the funding amount was examined. This hypothesized model did not reach a decent fit: χ
2
(28) =
143.19, p < .001, GFI = .83, AGFI = .72, CFI = .60, RMSEA = .18, but the hypothesized
relationships were significant: the linear effect of structural embeddedness on funding amount
86
was significant (β = .81, p < .001), the square term of structural embeddedness also had a
significant effect on the funding amount (β = -.22, p < .001), replicating an inverted U-shaped
relationship between degree centrality and funding amount. Then, the mediator, knowledge
exchange was added to the model, the model overall reached a better fit, χ
2
(27) = 40.08, p = .05,
GFI = .95, AGFI = .89, CFI = .97, RMSEA = .06, and the hypothesized relationships were
significant: the linear effect of structural embeddedness on knowledge exchange was significant
(β = .95, p < .001), the square term of structural embeddedness also had a significant effect on
knowledge exchange (β = -.22, p < .001), and the linear effect of knowledge exchange on
funding amount was significant as well (β = .79, p < .001). The positive effect of structural
embeddedness (the linear effect: β = .41, p < .001; the quadratic term: β = -.14, p = .01) on
funding amount was significant as well. Comparing these two models, the Chi-square difference
test (χ
2
diff
=103.11, p < .005) shows that the second model is significantly better than the previous
one, indicating knowledge exchange indeed has a partial mediating effect on the relationship
between structural embeddedness and the funding amount.
To further improve the second model, with the help of modification indices, control
variables’ relationships with interest variables in the model were adjusted. Specifically, a link
between funding goal and knowledge exchange was added (β = .19, p < .001), and a link
between market maturity and knowledge exchange was added too (β = -.16, p < .001). The final
model reached a good fit to the data, χ
2
(26) = 21.41, p = .72, GFI = .97, AGFI = .93. CFI = 1.00,
RMSEA < 0.01. The final revised model suggested that effects of control variables funding goal
and market maturity were mediated through knowledge exchange as well. The final revised
model explained 80% of the variance in the amount of funding attracted and 75% variance in
knowledge exchange. These results, shown in Figure 7, suggest partial support for H3a in that
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knowledge exchange is a partial but not full mediator between structural embeddedness and the
amount of funding attracted.
This paragraph reports the result of the mediation effect on relationship between
junctional embeddedness and the funding success. Hypothesis 3b proposed that the amount of
knowledge exchange will mediate the inverted-U shaped relationship between crowdfunding
projects’ network junctional embeddedness and the amount of funding they attract. First, a null
model with junctional embeddedness, including both linear effect and its square term, the
funding amount and all control variables was tested: χ
2
(36) = 310.81, p < .001, GFI = .71, AGFI
= .64, RMSEA = .24. Then, an initial model with these same with the main effect of junctional
embeddedness on the funding amount was examined. This initial hypothesized model did not
reach a decent fit: χ
2
(28) = 220.51, p < .001, GFI = .80, AGFI = .68, CFI = .50, RMSEA = .23,
but the hypothesized relationships were significant: the linear effect of junctional embeddedness
on funding amount was significant (β = .62, p < .001), and the square term of junctional
embeddedness also had a significant effect on funding amount (β = -.34, p < .001), replicating an
inverted U-shaped relationship between betweenness centrality and the funding amount. Then,
the mediator, knowledge exchange was added to the model, the model overall reached a better
fit, : χ
2
(29) = 50.48, p = .01, GFI = .94, AGFI = .88, CFI = .95, RMSEA =.08. The linear effect
of junctional embeddedness on knowledge exchange was significant (β = 1.02, p < .001), the
square term also had a significant effect on knowledge exchange (β = -.63, p < .001), and the
effect of knowledge exchange on funding amount was significant as well (β = .79, p< .001).
Further, neither the linear effect of junctional embeddedness (β = .09, p = .39) nor the square
term (β = -.02, p = .85) had an influence on the funding amount. Comparing these two models,
the Chi-square difference test (χ
2
diff
= 170.03, p < .005) shows that the second model is
88
significantly better than the first one, indicating knowledge exchange indeed has a mediating
effect on the relationship between junctional embeddedness and the funding amount.
Modification indices suggested adding one link between control variable funding goal
and knowledge exchange (β = .37, p < .001). Moreover, since junctional embeddedness was not
significantly related to amount of funding attracted, as reported earlier, its linear and square
effects on the funding amount were deleted. With these changes, the model improved further:
χ
2
(30) = 24.88, p = .73, GFI = .97, AGFI = .94, CFI = 1.00, RMSEA <.001. The final revised
model explained 76% variance in the amount of funding attracted and 43% variance in
knowledge exchange. This suggests H3b was supported that knowledge exchange is a full
mediator between junctional embeddedness and funding success. Figure 8 shows the full model.
Finally, this paragraph reports the result on the mediation effect of knowledge exchange
on the relationship between positional embeddedness and the funding success. Hypothesis 3c
proposed that the amount of knowledge exchange will mediate the inverted-U shaped
relationship between crowdfunding projects’ network positional embeddedness and the amount
of funding they attract. First, a null model with positional embeddedness, including both linear
effect and its square term, the funding amount and all control variables was tested was tested:
χ
2
(36) = 289.23, p < .001, GFI = .71, AGFI = .63, RMSEA = .23. Then, an initial model with the
main effect of positional embeddedness on the funding amount was examined. This initial
hypothesized model did not reach a decent fit: χ
2
(28) = 108.34, p < .001, GFI = .85, AGFI = .76,
CFI = .68, RMSEA = .15, but the hypothesized relationships were significant: the linear effect of
positional embeddedness on funding amount was significant (β = .80, p < .001), and the square
term of positional embeddedness also had a significant effect on funding amount (β = -.15, p
< .001), replicating an inverted U-shaped relationship between eigenvector centrality and the
89
funding amount. Then, the mediator, knowledge exchange was added to the model, the model
overall reached a better fit: χ
2
(28) = 41.82, p = .05, GFI = .95, AGFI = .89, CFI = .97, RMSEA
= .06. The linear effect of positional embeddedness on knowledge exchange was significant (β
= .87, p < .001), the square term of positional embeddedness also had a significant effect on
knowledge exchange (β = -.10, p = .05), and the effect of knowledge exchange on funding
amount was significant as well (β = .79, p < .001). Further, the linear effect (β = .39, p <.001),
and square term (β = -.10, p =.02) of positional embeddedness on the funding amount were
significant as well. Comparing these two models, the Chi-square difference test (χ
2
diff
= 66.52, p
< .005) shows that the second model is significantly better than the first one, indicating
knowledge exchange indeed has a mediating effect on the relationship between positional
embeddedness and the funding amount.
Modification indices suggested the addition of mediating effects of amount of knowledge
exchange on the relationships of two control variables to the amount of funding attracted:
funding goal (β = .19, p <.001), and market maturity (β = -.18, p <.001),. The final model
reached a reasonably good fit to the data, χ
2
(26) =21.08, p = .74, GFI = .97, AGFI = .94. CFI =
1.00, RMSEA < 0.01. One interesting change occurred to quadratic term of the effect of
positional embeddedness on knowledge exchange. It became marginally significant (β = -.09, p
= .06). The final revised model suggested that effects of funding goal and market maturity were
mediated through knowledge exchange as well. Their implications will be discussed in the
discussion section. The final revised model explained 81% variance in the amount of funding
attracted and 75% variance in knowledge exchange. These results, shown in Figure 9, suggest
support that H3c for knowledge exchange is a partial but not full mediator of the relationship
between positional embeddedness and the amount of funding attracted.
90
Then the mediating effect of knowledge exchange was tested on the relationship between
human capital and the amount of funding projects attracted as proposed in Hypothesis 4.
Similarly, a null model with human capital, the funding amount and all control variables was
tested: χ
2
(28) = 141.37, p < .001, GFI = .78, AGFI = .72, RMSEA = .17. Then, an initial model
with the main effect of human capital on the funding amount was examined and the model
reached a decent fit: χ
2
(21) = 54.28, p < .001, GFI = .90, AGFI = .83, CFI = .71, RMSEA = .11,
and the main effect of human capital on the funding amount was positive and significant (β = .46,
p < .001). Then the mediator of knowledge exchange was added into the model, and the effect of
human capital on knowledge exchange was significant (β = .53, p < .001), and the effect of
knowledge exchange on funding amount was significant as well (β = .78, p < .05). The effect of
human capital on funding amount was not significant. This model had a reasonably good fit to
the data, χ
2
(23) = 25.74, p = .31, GFI = .96, AGFI = .92. CFI = .99, RMSEA =.03. Comparing
these two models, the Chi-square difference test (χ
2
diff
= 28.54, p < .005) shows that the second
model is significantly better than the first one, indicating the amount of knowledge exchange
fully mediated the relationship between the number of expert funders and the amount of funding
projects attracted. Interestingly, the effect of control variable funding goal (β = .37, p <.001) was
partially mediated by knowledge exchange again. The final revised model, explained 76%
variance in the amount of funding attracted and 42% variance in knowledge exchange. These
results, shown in Figure 10, suggest support for H4 that knowledge exchange fully mediates the
relationship between human capital and the amount of funding attracted.
91
Figure 7. Parameter Estimation for Mediating Effects of Knowledge Exchange on Degree
Centrality (Structural Embeddedness) and Funding Amount
Funding Goal
Project Category
Degree centrality
2
Project funding
duration
Entrepreneur
creating experience
Entrepreneur tenure
Knowledge exchange
Funding Amount
R1
R2
.13**
.02
-.06
-.04
.07
-.16***
-.14**
.41***
.50***
.92**
*
-.20***
.19***
Market maturity
Degree centrality
92
Figure 8. Parameter Estimation for Mediating Effects of Knowledge Exchange on Betweenness
Centrality (Junctional Embeddedness) and Funding Amount
Funding Goal
Project Category
Betweenness centrality
2
Project funding
duration
Entrepreneur
creating experience
Entrepreneur tenure
Knowledge exchange
Funding Amount
R1
R2
.12*
.06
-.06
-.05
.06
.08
.78***
.93***
-.56***
.37***
Market maturity
Betweenness centrality
93
Figure 9. Parameter Estimation for Mediating Effects of Knowledge Exchange on Eigenvector
Centrality (Positional Embeddedness) and Funding Amount
Funding Goal
Project Category
Eigenvector centrality
2
Project funding
duration
Entrepreneur
creating experience
Entrepreneur tenure
Knowledge exchange
Funding Amount
R1
R2
.19**
.02
-.06
-.04
.07
-.18***
-.10*
.39***
.50***
.86***
-.09
.19***
Market maturity
Eigenvector centrality
94
Figure 10. Parameter Estimation for Mediating Effects of Knowledge Exchange on Human
Capital and Funding Amount
Funding Goal
Project Category
Project funding
duration
Entrepreneur
creating experience
Entrepreneur tenure
Knowledge exchange
Funding Amount
R1
R2
.12*
.06
-.06
-.05
.06
.08
.78***
.53***
.37***
Market maturity
Human capital
95
Displayed Passion and Displayed Preparedness
As Hypothesis 5 proposed, the results showed that displayed passion in the video
positively predicted the amount of funding each project received (β = .22, p = .007), controlling
for the effects of project category, entrepreneur tenure, market maturity, funding duration,
funding goal and video length. Thus, H5a received strong support. However, displayed passion
did not affect the total number of funders (β = .06, p = .56) nor the number new funders (β = .02,
p = .87); thus H5b and H5c did not receive support. See Model 1 in Table 6 for the full model.
As predicted by Hypothesis 6, displayed preparedness positively predicted the amount of
the amount of funding each project received (β = .52, p < .001), the number of funders (β= .23, p
= .03), and the number of new funders (β = .21, p = .048). Thus, H6a, H6b and H6c received
strong support. See Model 2 in Table 6 for the full model.
Next, the relationship between displayed passion and displayed preparedness was
examined. Hypothesis 7 proposed that displayed preparedness in the pitch video would
positively moderate the positive relationships between (a) displayed passion and the amount of
funding they attract, (b) displayed passion and the total number of funders, and (c) displayed
passion and the amount of new funders. Because displayed passion did not show an effect on
total number of backers or the number of new backers, the relationship between displayed
passion and displayed preparedness was only tested on the amount of funding attracted.
Following Aiken and West’s (1991) suggestion, a product term of mean centered displayed
passion and displayed preparedness was created to investigate their interaction. To test the
moderation effect, displayed passion and displayed preparedness and the product term were
added to the model. Surprisingly, the interaction between displayed passion and displayed
preparedness was not significant (β = .03, p = .68), and the main effect of displayed passion
96
became insignificant too (β = .02, p = .29) while the effect of displayed preparedness remained
significant (β = .52, p < .001). See Model 3 in Table 6 for the full model.
These results suggest that instead of a moderation effect, displayed preparedness might
have a mediation effect. To investigate this possibility, a mediation test was carried out following
Baron and Kenny’s (1986) suggestion. First, the main effect of displayed passion on the funding
amount was significant as shown earlier (β = .22, p = .007), and then displayed passion also had
a positive effect on displayed preparedness (β = .38, p < .001), controlling the effects of video
length, project category, entrepreneurs’ tenure, entrepreneurs’ experience, market maturity,
funding duration and funding goal. Lastly, both displayed passion and displayed preparedness
were added to the model, and displayed preparedness remained significant (β = .51, p < .001),
while displayed passion became insignificant (β = .02, p = .79), indicating that displayed
preparedness is a full mediator between displayed passion and the funding success. See Model 4
in Table 6 for the full model.
Lastly, the proposed negative moderation effect of crowd human capital on the
relationship of displayed passion (H8) and displayed preparedness (H9) on amount of funding
was examined. Again, following Aiken and West’s (1991) suggestion, main effects of displayed
passion, displayed preparedness and human capital are all mean centered and their product terms
were created with these centered predictors to avoid multicollinearity. As predicted, human
capital had a negative interaction with displayed passion on the amount of funding projects
attracted (β = -.24, p = .01), supporting H8. Further, human capital also had a negative
interaction with displayed preparedness on the amount of funding projects attracted (β = -.39, p
= .001). This result provided support for H9. See Models 5 & 6 in Table 6.
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Table 6. Regression Coefficients for Hierarchical Regression Models Regarding Displayed
Passion and Displayed Preparedness (N = 111)
Sources Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Displayed passion
(DPS)
.22** .02 .02 .06
Displayed preparedness
(DPP)
.52*** .52*** .51*** .22*
Interaction between
DPS and DPP
.03
Human capital (HC) .58*** .68***
Interaction between
DPS and HC
-.24**
Interaction between
DPP and HC
-.39**
Funding goal .55*** .39*** .39*** .40*** .53*** .42***
Projects belonging to
Hardware category
.11 .13 .13 .13 .11 .08
Project funding
duration
-.02 -.00 .00 -.01 -.04 -.05
Entrepreneur creating
experience
.07 .17* .17* .17* .07 .07
Entrepreneur tenure -.05 -.08 -.08 -.08 -.08 -.08
Market maturity .04 .06 .05 .06 -.03 -.03
Video length -.02 -.06 -.06 -.06 .01 -.02
R
2
.39 .57 .57 .57 .58 .69
R
2
adj
.34 .54 .53 .54 .54 .66
Note: Dependent variable = Log of amount of funding projects received. All coefficients are
standardized coefficients.
*p< .05, **p<.01, ***p< .001.
98
Chapter 5: Discussion, Limitation and Future Directions
Discussion
The purpose of this dissertation was to understand the variance of crowdfunding success
from an entrepreneur’s perspective. Toward this end, this study drew upon social capital theory
(Coleman, 1988; Granovetter, 1985), human capital theory (Becker et al., 1993) and research on
entrepreneurial displayed passion (Chen, et al., 2009), and tested a series of hypotheses with 134
crowdfunding projects. Results reported in the preceding chapter offered insights into the
relationships between project embeddedness (and human capital) and the amount of funding
projects attracted, the mediating role of knowledge exchange between entrepreneurs and crowd-
funders, and displayed passion and preparedness and funding success. In this section,
implications of these results are discussed individually first, then a synthesis integrating them
will be provided. Last, future directions and practical implications will be discussed.
Embeddedness
First, consistent with previous literature (e.g., Lechner, et al., 2010; Mallapragada, et al.,
2012; Ransbotham, et al., 2012; Uzzi, 1999), this dissertation found a curvilinear relationship
between three types of network embeddedness, including a) structural embeddedness, b)
junctional embeddedness, and c) positional embeddedness and the amount of funding
crowdfunding projects attracted respectively. Specifically, all three types of centrality’s linear
effects on the funding success were significantly positive, and their quadratic terms have a
significant and negative effect. These coefficients combined suggest a curvilinear relationship
between network embeddedness and the amount of funding projects attracted. Since statistically
curvilinear relationship can be interpreted in multiple ways (e.g., Mallapragada, et al., 2012), a
closer look at this curvilinear relationship was carried out before interpreting these results. First,
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these three centralities and funding success were plotted to visually show the pattern (see Figures
11-13). Interestingly, these plots demonstrate that these curvilinear relationships come from the
changing rate of the positive effect of embeddedness: initially increasing and then decreasing.
Yet, the hindering effect of high embeddedness on funding success as theorized is not obvious to
observe. In order to precisely identify what this curvilinear relationship means, a post-hoc
analysis on this relationship was conducted.
Post-hoc analysis. For each type of embeddedness, all projects were categorized into two
groups using 80 percentile of network embeddedness as the cutting point. Eighty percentile was
chosen because as the Figure 11 and this variable’s frequency table jointly suggest this is where
the rate of the positive effect of structural embeddedness intersects between the linear line and
the Loess line. This means that from this point on, the projected positive linear effect of degree
centrality seems higher than that the curve suggests. Projects belonging to below or equal to 80
percentile or above the 80 percentile of degree centrality were identified, and then the regression
analyses were conducted separately. If high structural embeddedness indeed brings a prohibiting
effect on funding success, it is expected to see a negative coefficient on degree centrality in the
high embeddedness group. However, the results showed positive effects in both groups: in below
or equal to 80 percentile groups, degree centrality has a positive effect on funding success (β
= .70, p < .001); and in above the 80 percentile groups, the effect was still positive (β = .49, p
= .01).Yet, the positive standardized coefficient is smaller in the high embeddedness group. For
the full model results, see Table 7. These analyses were repeated for betweenness centrality and
eigenvector centrality (see Table 7), and the results remain the same: across three types of
embeddedness, the facilitating effect of embeddedness on funding success escalates to a certain
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point, and then its positive effect remains but occurs at a slower rate. In other words, results from
this study did not show the hampering effect of high embeddedness.
Table 7. Regression Coefficients for Regression Models in Post-Hoc Analysis.
Sources Model 1
(First 80
percentile)
Model 2
(Last 20
percentile)
Model 3
(First 80
percentile)
Model 4
(Last 20
percentile)
Model 5
(First 80
percentile)
Model 6
(Last 20
percentile)
Degree
centrality
.70*** .49**
Betweenness
centrality
.40*** .43*
Eigenvector
centrality
.72*** .40*
Funding goal .22** .69 .34*** .31 .23** .66**
Projects
belonging
to
Hardware
category
.02 .03 .10 .10 .02 .03
Project
funding
duration
-.09 .12 -.01 .10 -.10 .14
Entrepreneur
creating
experience
-.03 .27 .05 -.06 -.04 .26
Entrepreneur
tenure
.12 -.11 .07 -.10 .11 -.11
Market
maturity
-.07 -.25 .08 .24 -.10 -.28
R
2
.64 .55 .45 .36 .66 .51
Note: Dependent variable = Log of amount of funding projects received. All coefficients are
standardized coefficients.
*p< .05, **p<.01, ***p< .001.
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Figure 11. Scatterplot of Degree Centrality by Funding Amount.
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Figure 12. Scatterplot of Betweenness Centrality by Funding Amount.
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Figure 13. Scatterplot of Eigenvector Centrality by Funding Amount.
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These results highlight two points regarding the different conclusions scholars draw from
a curvilinear relationship: 1) there might be some discrepancy between the empirical results and
the interpretations some scholars make, and 2) this discrepancy invites new theory building on
the temporal dimension of network embeddedness.
The main difference comes from whether a curvilinear relationship between network
embeddedness and the outcome necessarily demonstrates a hindering effect of network
embeddedness. Following Granovetter’s (1985) seminal piece, many scholars took the idea to
empirical examinations and have shown a curvilinear relationship between network
embeddedness and various outcomes (e.g., Lechner, et al., 2010; Mallapragada, et al., 2012; Uzzi,
1996; Uzzi, 1997). Although these scholars all reported a curvilinear relationship, some of them
interpreted it as high embeddedness hinders the outcome (e.g., Lechner, et al., 2010; Uzzi, 1999),
while others concluded that the rate of the facilitating effect of embeddedness changed (e.g.,
Mallapragada, et al., 2012). Further, most of those who interpreted that high embeddedness
would impede the outcome did not report additional analysis to support their arguments but
inferred based on the curvilinear relationship or projected graphs. This practice potentially might
cause a mismatch between the empirical evidence and the conclusion we draw. Undeniably, a
curvilinear relationship theoretically implies that when the independent variable gets large
enough, its effect on the dependent variable will become negative. However, the way existing
scholarship was reported fails to inform us whether the negative effect occurs as theorized, or the
curvilinear relationship is just a projection and the data collected did not show such a pattern.
Leaving out the possibility that some scholars might draw conclusions from a projected
trend instead of the empirical data, i.e., scholars interpreted the curvilinear relationship as high
embeddedness hampers the outcome when in fact this curvilinear relationship comes from the
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changing rate of embeddedness’ influence, an alternative interpretation stands: one aspect of
embeddedness, the temporal aspect, remains under-explored. Reviewing the contextual
information for the current study can help to make the case. This dissertation examined 134
crowdfunding projects specifically focusing on 3D printing technology, which is a nascent and
fast growing industry. As mentioned earlier, among all 3D printing technology-related projects
launched on CrowdInno between 2009 and 2014 (up to April), there was one project in 2009, one
project in 2010, and 11 projects in 2011. In contrast, during the first four months of 2014,
seventeen projects got launched. Hence the data clearly illustrate that 3D printing technology-
related projects are in their early stage and attracting crowd-funders at an increasing rate.
It is conceivable that the absence of a hindering effect of high embeddedness on
crowdfunding success might be due to the fact that 3D printing technology is still in its early
stage. To explain, when the market is at its early stage meaning the need for the product is
growing and new consumers are consistently attracted to this market, resources within 3D
printing technology-related project community is still growing. Hence the often cited reasons
why high embeddedness impedes the outcome such as limited access to novel information (e.g.,
Uzzi, 1997) and resource waste due to competition (e.g., Lechner, et al., 2010), have not taken
places in this market yet. Therefore, it is plausible that all projects observed in this dissertation
are before the tipping point where the positive effect of embeddedness becomes negative. Taken
altogether, this study would interpret the curvilinear relationship such that in a young community,
network embeddednesses, including structural embeddedness, junctional embeddedness and
positional embeddedness, have a facilitating effect on the amount of funding crowdfunding
projects attracted, and the rate of this positive effect increases initially and then decreases.
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After explicating the interpretation of the curvilinear relationship, below implications of
these results will be discussed. As argued earlier, the positive effect of network embeddedness on
funding success comes from creating and mobilizing resources originated from connections with
other projects in the network. The 3D printing-related project community can be seen as an
ecosystem and projects within this community may have a mutually beneficial relationship. This
is especially true during the early stage of the community when competition has not been a
prominent force yet. For example, the 3D printer projects attract potential consumers for side-
products of 3D printers, such as filament, 3D modeling software and testing kit. At the same time,
as these side-product projects gain popularity, the whole community attracts newcomers and
grows bigger. Hence, with this healthy reciprocal cycle, a highly embedded project suggests its
deep root in the community and its capacity to mobilize resources from other parts of the
community, such as networking and a cooperative norm. These points will be further explained
in knowledge exchange section later since these are manifestations of advantageous information
access; below one example will be shared to show connections with previous projects can bring
business partnership as well.
Launched on March, 3
rd
2014 by Liviu Berechet Anotoni, a visual effects artist and game
designer, Altergaze was a project that aimed to deliver virtual reality goggles to consumers.
Altergaze was designed to put on smartphones and delivered a high quality virtual experience.
Because all parts except for the lenses and screws can be manufactured through 3D printing, this
project called for other entrepreneurs with 3D printers to “crowd-manufacture” the products. It
means as long as an individual owns a functional 3D printer, he or she can partner with this
project and becomes an Altergaze manufacturer. This project received strong support from
crowd-funders of 3D printer projects in the community and many 3D printer funders indeed
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became its funders. In the dataset, this project achieved moderate structural embeddedness (53
percentile) and successfully raised $53,100. This example might be unique, at least for now; yet,
it clearly demonstrates that connecting with the existing community affords focal projects more
ability to mobilize resources, and this resource is not limited to information or a cooperative
norm, but sometimes manufacturing power as well.
Embeddedness and knowledge exchange
Another contribution of this dissertation is to provide empirical support for the mediating
effect of knowledge exchange on the linkage between network embeddedness and funding
success. Specifically, this study demonstrates that knowledge exchanged between entrepreneurs
and crowd-funders partially mediates the relationship between structural embeddedness and the
funding success, and the relationship between positional embeddedness and funding success. In
addition, knowledge exchange fully mediates the relationship between junctional embeddedness
and funding success. This happens because highly embedded projects are equipped with domain
expertise to filter project quality and are more likely to have a cooperative norm in terms of
knowledge exchange.
In addition to the domain expertise experienced crowd-funders can provide, which will
be discussed further in the next section, the benefits of knowledge exchange also manifest
through networking and a cohesive norm brought by high embeddedness. For example, after
crowd-funders “meet” each other several times through co-funding several projects, they often
start building personal relationships. They might start saying “Hi” to each other, attend to each
other’s concerns of the project and recommend projects to each other. For instance, right after
one long-time crowd-funder joked that “hmmmm…surprised David Gaipa isn’t on this one yet….
[CrowdInno] inside joke)”. David Gaipa immediately responded “I should have checked the
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comments earlier. I didn't realize Craig was sitting in here waiting for me. Naturally, I expected
to see him on this one too. ([CrowdInno] inside joke)”. Oftentimes, these online interactions will
evolve to offline hangouts and building self-organized online communities to share their 3D
printing experience, learn from each other and feel the belonging to a group.
Through these online and off-line interactions, people’s passion for 3D printing
technology, do-it-yourself (DIY) culture and maker-space culture get sustained and prosper. In
turn, the 3D printing community on CrowdInno increasingly attracts more new funders and more
financial resources for new projects. With this cohesive norm underlying highly embedded
projects, crowd-funders are expected to spend the effort to share their information voluntarily to
the community. Accordingly, new funders are also more likely to seek information from others.
Two points are worth noting when comparing the distinct effects of knowledge exchange
on the relationship of three types of embeddedness to amount of funding attracted. First,
consistent with previous literature, the mechanism through which junctional embeddedness
facilitates crowdfunding projects to attract funding was empirically confirmed to be information
brokerage (Arranz & Fdez de Arroyable, 2013; Grewal, et al., 2006). Projects with high
junctional embeddedness are those which share co-funders with projects which do not have
many co-funders. In this case, information channeled through these projects tends to be novel
and efficient (Burt, 1992). Hence knowledge exchange fully mediates the relationship between
junctional embeddedness and funding success.
Second, structural and positional embeddednesses also aid funding by channeling in
information from other projects. Yet, the partial mediation suggests that alternative underlying
mechanisms might operate simultaneously. It is possible that structural embeddedness assists
attracting funding through displaying a supportive culture since it indicates the expansiveness of
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the project. Within the 3D printing technology project community, some crowd-funders are very
passionate about 3D printing technologies and believe in its ability to empower individuals to
create complex objects without industrial infrastructures. Thus they oftentimes contribute to 3D
printing technology-related projects simply to show their support for the cause. For example,
many funders expressed that they have several 3D printers already, and they would like to
contribute to the project just to show support. In this way, structural embeddedness might
indicate the extent to which the project is entrenched in the community and carries on the
identity with the community.
When it comes to positional embeddedness, the results show that knowledge exchange
mainly mediates its linear effect and its quadratic term was marginally significant. These finding
suggest that the reason why the rate of facilitating influence of positional embeddedness
decreases eventually cannot be attributed to information access. An alternative explanation might
be that people who funded projects with particularly high positional embeddedness were after the
popularity led by media. These crowd-funders are highly influenced by the “fame” of projects. It
is plausible they do not have a deeper connection with the community but the lead of media
reports. If this is the case, it is reasonable that projects with high positional embeddedness will
have a slower growing rate after it reaches a certain point, but this effect was not manifested
through knowledge exchange.
Human capital and knowledge exchange
This study also provided evidence that the stock of task experience residing in the crowd
positively affects funding success. Because experienced funders can provide needed information
to entrepreneurs and other peer funders in an efficient manner and that information provided by
crowd-funders may be more trustworthy than that from entrepreneurs, experienced crowd-
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funders can leverage that knowledge among potential funders and thus facilitate funding. The
empirical evidence confirmed that knowledge exchange fully mediates the relationship between
human capital and funding success.
On one hand, experienced funders can answer inquiries to help potential funders make
informed funding decisions. For example, when comparing two types of printers one funder
requested those who have laser resin printers to share some experience. Responding to this
request, one crowd-funder responded:
“I have a Pegasus Touch 3D laser resin printer on order. Going by reports from people
who have tried the Pegasus in action, the prints are incredibly detailed (~80 to 100
micron minimum feature size) and the cured resin is very tough and resilient. Compare
this with typical minimum feature size of the nozzle size or higher for most FDM printers.
Actual print cost doesn't work out too high, since the amount of resin used is just
marginally more than the actual volume of each printed model. Some test results with
MakerJuice SubG and SubSF resins ($25 to $55 per liter) are expected to appear on
various blogs in coming months. In any case, MakerJuice will most likely have one or
more Pegasus compatible resins out very soon. Depending on the printer you are
considering, it is worth checking whether MakerJuice already has a compatible product.”
Further, compared to information provided by the entrepreneurs, crowd-funders appear to
trust information from their peers more. For instance, one successful project featuring a desktop
3D printer promised potential funders certain technical accuracy of the printer but refused to
show any high definition prints. The entrepreneur explained the reason why they did this was to
prevent copy-cat as warned by one successful 3D printer entrepreneur on CrowdInno. Many
crowd-funders were interested in the product since it is the cheapest product on the market, but
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were hesitant about the printer quality since it lacked supporting evidence. When the
entrepreneur informed the crowd that he was going to attend a science exhibition in DC area, the
crowd actively sought for local funders in DC area and asked them to attend the exhibition and
report back. Several crowd-funders went and reported back with positive results, which led to a
surge on the funding.
On the other hand, experienced funders also provide suggestions to entrepreneurs, and
sometimes “co-create” a project with entrepreneurs. For instance, in one 3D printer project, one
crowd-funder noticed that “The surface of the printed objects look slightly rough when
magnified.” So this crowd-funder suggested: “Does the resin melt or soften before it chars? I was
wondering if I could use a hair dryer to locally heat the surface such that it anneals and becomes
smooth. Alternatively I have a file and some polishing wheels, but I'd have to really really love
my printed part to go through that much work to make it feel smooth”. The entrepreneur
responded: “What a great idea! This is a great example of what you can use for the liquid
reservoirs and the drain function is a great bonus. Thanks for posting this comment! As for using
heat to smooth the surface of the print, we’re haven’t tried that yet. We will do a quick test and
post the results as this has been brought up a few times. Once again, thanks for your valuable
questions and comments!”
Another example comes from crowd-funders helping with project logistics. When M3D
LLC launched their desktop consumer 3D printer in April 2014 on CrowdInno, this project
attracted more than 3 million dollars within one month. Overwhelmed by the success, the
entrepreneur also realized that shipping their products overseas can be quite challenging.
Complications include regulations regarding shipping liquid (the filament) to another country,
how to calculate value added tax (VAT) when the printer is technically a reward instead of a
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product, and crowd-funders pay different amounts of money for the same product depending on
their shipping dates, etc. Although the printer starts from $199, shipping and VAT are potentially
more than the printer itself. Thus the logistic issues are quite critical to secure funding for the
project. With these complicated issues, the entrepreneur actively sought suggestions from crowd-
funders, especially those with international shipping experience in terms of the best shipping
practice to Europe. Multiple experienced funders shared their experience with similar projects on
CrowdInno and made actionable suggestions to the entrepreneur, such as sharing contact
information of entrepreneurs from previous successful projects and setting up a shipping center
in Europe. By the time this dissertation research is conducted, the conversation continues. Yet, it
is clear that experienced crowd-funders are actively collaborating with entrepreneurs to improve
projects.
More importantly, crowd-funders recognize and acknowledge that their fellow crowd-
funders, especially experienced ones, are asset for the project because they can take on the
responsibility to filter project quality and “look out” for each other. They also strategically put
more weight on evaluations from experienced crowd-funders. For example, one crowd-funder
expressed:
Hey, I agree in part with both John Ecker AMD pclabtech, you're points are definitely
valid, but I think pclabtech - you misread Johns intentions here. As backers, we all need
to look out for each others best interests, there have been many substandard [CrowdInno]
projects that have been cancelled mid way only thanks to the vigil of us regular folks
putting in our money into them. I love the concept, jumped right into it...infact was
bummed when they couldnt give 2 but put in more than the pledge level anyways for
their support BUT I would still like to see a lot more precision prints soon…
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Control Variables
Lastly, the mediation effect of knowledge exchange between the two control variables
and funding success will be briefly discussed. Results from several models (see Figures 7-10)
suggest that funding goal’s positive effect on funding success was partially mediated by
knowledge exchange. This result suggests that as funding goal increases, the amount of
knowledge exchange also grows. The reason why this happens may be that when an entrepreneur
sets a high funding goal, he or she is more motivated to respond to inquiries and attend to
conversations with potential funders. In other words, the expectation and effort entrepreneurs
have for their project is reflected in the funding goal, and then carried out by their
communication practice. Moreover, knowledge exchange also negatively mediates the
relationship market maturity and funding success (see Figures 7, 9 and 10). This means that as a
projects’ launch date gets closer, knowledge exchange exhibits a more positive effect. This
finding demonstrates that, as compared to a project launched earlier on CrowdInno, more recent
projects tend to have more knowledge exchange, which eventually leads to more funding.
Displayed passion and preparedness
This dissertation also explored how displayed passion and preparedness in entrepreneurs’
pitches affected crowdfunding success. Particularly, this study found that both displayed passion
and preparedness are positively related to crowd-funders’ contribution to a project, while
displayed preparedness fully mediates the relationship between displayed passion and the
amount of funding attracted. Further, human capital within the crowd attenuates the positive
effects of displayed passion and preparedness.
It is intuitive to show that displayed passion positively predicted the amount of funding
attracted in the crowdfunding context. Through energetic body movements and varied tones in
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their voice, entrepreneurs convey their excitement for their projects to potential crowd-funders.
However, this study did not find evidence showing displayed passion can attract more crowd-
funders or new funders. These results might suggest that displayed passion has more impact on
funders who would consider contributing in a way that it boosts the amount of funding they
contribute. But displayed passion cannot convert potential funders into real funders. In other
words, displayed passion needs to echo with crowd-funders’ existing intention to contribute but
is not robust enough to create new funders.
Consistent with previous literature (Chen, et al., 2009), displayed preparedness positively
predicted the amount of funding projects attracted, the total number of crowd-funders, and the
number of new funders. This finding confirms that the “substance” entrepreneurs exhibit in their
business plans (Chen, et al., 2009) can be recognized by crowd-funders appropriately. Potential
crowd-funders might come across a project for different reasons such as a media report, a
recommendation by a friend, or because they were simply browsing projects in categories of
their interest. However, it is the effort entrepreneurs put into strengthening the idea that converts
a potential crowd-funder into a real funder.
Further, this study did not find that displayed preparedness could moderate the
relationship between displayed passion and the funding amount; instead, it found that displayed
preparedness fully mediated the positive relationship between displayed passion and the amount
of funding attracted. It shows that the content of the pitch bears the positive effect of the
affection; so without the substance, entrepreneurs with passion may be seen as “talking the talk”
without being able to “walk the walk”. Entrepreneurs can capture potential funders’ attention
through displayed passion, but it is through the content they seal the deal.
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These findings are similar to previous studies showing the effects of displayed passion
and preparedness in venture capital funding decisions; yet, they provide more insights to an
emerging phenomenon where the consumer side, i.e., crowd-funders, is the motor creating values
for firms. The consistency of these results with previous literature (Cardon, et al., 2009; Chen, et
al., 2009; Pollack, et al., 2012) illustrate that the crowds, consisting of laymen crowd-funders,
react to business presentations in a similar manner with experienced VCs: they echo
entrepreneurs’ excitement, they recognize the substance in their business plan, and eventually the
content of the presentation secures funding. Like VCs, crowd-funders can be trusted to assess the
quality of the project and filter information from a pitch appropriately. The moderating effect of
human capital on displayed passion and displayed preparedness lends further support to this
point.
This dissertation also shows that human capital residing in the crowd attenuates the
positive effects of both displayed passion and displayed preparedness. This finding advances
literature on entrepreneurial passion such that it illustrates the heterogeneity among funders and
the heterogeneity in their backgrounds make a difference on how they perceive the pitch. As the
crowds accumulate more domain expertise, influences from the pitch weaken. Combining results
from the mediating effects of knowledge exchange, it is conceivable that crowd-funders with
domain expertise put more weight on the conversation between entrepreneurs and the crowd to
determine whether the entrepreneur can “walk the walk”. Results from the knowledge exchange
section confirmed that the crowd is good at seeing through the pitch to the underlying facts.
A synthesis
This dissertation makes several theoretical contributions to the understanding of
crowdfunding success. First, this study expands financial resource acquisition literature by taking
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a viewpoint of entrepreneurs. Distinct from previous literature focusing on entrepreneurs’
attributes, this study investigates how crowd-funder’ characteristics, i.e., crowds’ social capital
and human capital derived from funders’ funding history, relate to funding success, and how
interactions between entrepreneurs’ pitches and human capital affect funders’ reactions. Findings
from this study are consistent with the emerging evidence from venture capital funding literature
showing that portfolio companies invested by well-connected firms in syndication network
tended to perform better, despite that funding amounts and motivations from VC firms and
crowdfunding platforms are inherently different (Hochberg, et al., 2007). This finding highlights
the persistent facilitating effect of structural embeddedness on network actors’ outcomes and this
study further expanded this set of literature by exploring the mechanism, which will be discussed
shortly.
Second, using a network approach, this study delineates where resources reside in the
crowd, and what network properties of crowdfunding projects relate to funding success.
Transforming the two-mode network consisting of projects and crowd-funders into project
network properties makes it possible to track resource flow in the community. Applying previous
literature’s approach studying collaborative emerging entities such as Wiki pages (Wang &
Zhang, 2012), OSS teams (Grewal et al., 2006; Hahn, Moon, & Zhang, 2008) and user-generated
content (Mallapragada, et al., 2012), this dissertation extends this approach to examine the
crowdfunding context for the first time. Findings of this study demonstrate projects’ social
capital, in the form of network embeddedness, and projects’ human capital, in the form of
number of experience crowd-funders, indeed positively predict the amount of funding they
attract.
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Third, this study is one of the earliest attempts to investigate the mechanism through
which social capital is realized and translated into competitive advantage of network actors. This
study empirically confirms that knowledge exchange within the crowds mediates or partially
mediates the benefits of social capital (and human capital) and the funding success. Results
suggest that information access advantage can fully explain some benefits of social capital such
as junctional embeddedness and human capital, while its partial mediating effect on structural
embeddedness and positional embeddedness invites further exploration for alternative
mechanisms.
The mediating effect of knowledge exchange sheds light on the mechanism through
which crowds achieve productivity. In The Wisdom of Crowds, Surowiecki (2005) argued that
crowds can outperform experts under the right condition. According to Surowiecki (2005), the
right condition should include each individual has their own evaluation of the target so the
information pool is diverse, and everyone’s opinion in the crowd is not dependent upon each
other. Then through certain information aggregation mechanisms, the crowd as a whole can
come up with a decision which is better than an expert’s judgment. Different from this
aggregating-diverse-opinion approach, this study shows that in certain crowdsourcing tasks, the
crowds can benefit from leveraging information from certain individuals to the crowd, especially
those experienced ones. This finding suggests that rather than isolating individuals in the crowd
and preventing them “polluting” each other’s evaluation, utilizing expertise from experienced
crowd-funders can be an alternative approach to harvest productivity from the crowds.
Fourth, the active role crowd-funders play in creating and advancing crowdfunding
project with entrepreneurs jointly resonates with an emerging research stream focusing on “the
active role of customers”. This research stream highlights how consumer interactions can bring
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additional value to the company or the entrepreneur side (e.g., Botan & Taylor, 2004; Ostrom,
Bitner, Brown, Burkhard, Goul, Smith-Daniels, Demirkan, & Rabinovich, 2010; Priem, Li, &
Carr, 2012). Customers’ active role in value creation has been studied from diverse angles, such
as consumer co-creation (von Hippel, 2005), customer participation (Etgar, 2008), collaborative
value creation (Moeller, et al., 2013), consumer online community (Dahlander & Frederiksen,
2012) and brand community (Muniz & O’guinn, 2001), and the core tenet of this research stream
is that consumers should be the focus of value creation. Findings from the current dissertation,
particularly the facilitating role of structural embeddedness and human capital residing in crowd-
funders, the knowledge exchange between entrepreneurs and crowd-funders on funding success
and the similar way crowds react to the pitches as VCs do demonstrate that crowd-funders not
only provides financial resources to projects, but also adds other values through knowledge
exchange process.
Interestingly, the public, and even some scholars usually label crowdsourcing as
amateurism or a hobbyists game (Brabham, 2012). For instance, Jeff Howe’s (2009) opening
chapter is titled The Rise of the Amateur. Brabham (2011) analyzed the discourse around
amateurism surrounding crowdsourcing and suggested that crowdsourcing participants might not
be compensated the way they should be, but their expert knowledge and the large amount of
effort they put into the task should not be overlooked. As Brabham (2012) concluded that “they
[crowdsourcing participants] are largely self-selected experts and what might otherwise call
professionals, who seek opportunities to make money, express themselves, build portfolios for
future employment, and enjoy all the responsibilities and trapping of serious leisure” (p. 407).
Limitations and Future directions
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The nature and the stage of the venture. Although results from this dissertation provide
some important contributions, several limitations should be recognized. The generalizability of
these findings needs further examination. This study examined 3D printing technology-related
projects, a nascent and knowledge-intense industry with an increasingly growing speed, and
reported network embeddedness had a positive effect, generally speaking, on the amount of
funding crowdfunding projects attract. Further, this positive effect exerts its influence initially at
an increasing rate then a decreasing rate. As speculated earlier, it is conceivable that the nature
and the stage of the venture make a difference. Are projects targeting the general public, e.g.,
product design projects, going to exhibit the same effect of network position? If we observe
projects from a relatively established industry, say graphic fictions, would the results still be the
same? All in all, more research is needed to examine how the nature and the stage of
entrepreneurship and the context of raising funds might affect project network properties more
extensively.
Distinctive effects of three types of embeddednesses. This study reported results with
three types of network embeddedness, structural embeddedness, junctional embeddedness, and
positional embeddedness. Consistent with previous literature (e.g., Arranz & Fdez de Arroyable,
2013), this study found that junctional embeddedness’ benefits are translated through knowledge
exchange, and other two types of embeddedness might function through multiple mechanisms in
addition to information advantage. Other possible mechanisms might include signaling effect,
which attract funders because potential funders might interpret high number of funders,
especially experienced ones, as a signal for projects’ attractiveness. Future research will benefit
from further exploring other mechanisms responsible for transforming social capital into
advantages of its possessor.
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Active embeddedness building. This dissertation has revealed the linkage between
crowdfunding projects’ network position and their funding success such that projects with high
level of structural embeddedness tend to attract more funding from the crowd than those with
low embeddedness. A natural question that follows is what practices have entrepreneurs taken to
increase the project’s embeddedness. This question is not simply a practical query; it also has its
theoretical implications because reciprocal embeddedness, how network actors can shape their
position in the network, has been rarely examined (Dacin, et al., 1999). Gulati (1998) discussed
how firms can gain more advantageous network position by forming alliances. Yet, we know
little about how entrepreneurs can selectively appeal to crowd-funders and steer the project to the
center of the community.
Based on the current study’s findings, it seems reasonable that the first step should be to
appeal to experienced crowd-funders, especially those with a long history in the focal community,
e.g., 3D printing technology community. To this end, in addition to refining product quality and
accurately presenting entrepreneurs’ credentials, future research can benefit from examining
effective communication strategies entrepreneurs can conduct. Are experienced crowd-funders
more likely to contribute when entrepreneurs pitch their ideas like a business man or a
technology savvy? Are experienced crowd-funders more likely to contribute when the project
attracts media attention? With these questions answered, entrepreneurs can target their desired
audience more effectively and use resources from experienced crowd-funders to attract other
crowd-funders on the platform.
Alternative relationship between project embeddedness and funding success. This
study has argued that crowdfunding projects’ embeddedness can predict the funding amount such
that the resources accumulated through crowd-funders’ experience will be translated into
121
financial resources. Yet, an alternative explanation exists between project embeddedness and
funding success, i.e., the funding amount may lead to project embeddedness. It is possible that as
projects attract more funding and gain popularity in the community, they accordingly attract
many experienced funders. In this way, early funding success might lead to greater project
centrality. In other words, it is possible that funding success and project network position
mutually influence each other over time. Future studies utilizing longitudinal data can further
explore this relationship and present a more comprehensive picture.
More refined analysis on the public communication process. The current study found
that knowledge exchange practices, including information seeking, information sharing and
suggestion making, mediate the facilitating effects of network embeddedness and human capital
on funding success. As one of the first attempts to explore the mechanisms of network properties,
this finding highlights the significance of public communication process in resource mobilization.
Yet, the current study investigated communications between entrepreneurs and crowd-funders
relatively broadly. It simply recognized whether the comment is exchanging knowledge or not.
More refined analysis of the communication process can bring us a richer lesson. For
instance, are information seeking and information sharing serving the same function in this
process? What aids people to take the first step and start seeking information? Are experienced
funders or inexperienced funders driving the information seeking process? Under what
conditions, experienced funders are more willing to participate in the conversation and contribute
their expertise voluntarily? Future study can greatly benefit from more refined analysis of this
communication process between entrepreneurs and potential funders.
Expanding entrepreneurial passion in online context. As many crowdfunding
activities are occurring online and more and more are expected to appear since the JOBS Act was
122
signed into United States law in 2012, we expect that more pitches will be delivered in the form
of a video or at least include a video, compared to traditional face-to-face presentations. Further,
entrepreneurs are embracing more advanced and sophisticated communication technologies to
make their pitch videos more informative and fun to watch. Based on observations of these 111
pitch videos in this dissertation, entrepreneurs can use emerging technologies to deliver content
in a way that might be challenging to do in face-to-face setting, such as inviting users and experts
to provide testimonies in the video, simultaneously demonstrating machine functions while
talking, and utilizing visuals aids such as animation to facilitate the pitch.
For instance, one team pitching a project to make a 3D printing game featuring 3D
monsters utilized an animated 3D monster in their pitching video and achieved great funding
success. In their pitch, these animated 3D monsters accomplished three functions critical to an
effective pitch. First, their humorous voice and body movements successfully captured potential
funders’ attention. Second, they served as visual aids to demonstrate how the game design
interface functions in a fun and accessible way. Third, because these 3D monsters represent the
finished products of their game design system, they verified claims of the entrepreneur and
conveyed a message to potential funders that these entrepreneurs had developed the product, as
they said in the video.
These advances in pitch technique invite new theory building since scales developed
mainly in face-to-face setting cannot meet these requirements. For example, the existing scale on
displayed passion focuses on the presenter’s body language, expression and voice to assess the
extent to which the pitch is delivered with entrepreneurial passion since entrepreneurs’
description, mostly monologue, is the key element to the pitch in most face-to-face situations.
However, with entrepreneurs incorporating more and more creative communication approaches
123
afforded by emerging technological advances into their pitches, entrepreneurial passion might be
delivered not solely through the presentation per se, but also through other elements such as
avatars. Thus, a new scale developed to account for new pitch techniques afforded by emerging
technological advances should benefit our examination of entrepreneurial passion in the digital
era.
Practical Implications
For entrepreneurs. This dissertation’s findings have important practical implications to
both entrepreneurs and crowdfunding platforms. For entrepreneurs, three important lessons can
be learned. First, appeal to the right crowd-funders. Results from this study demonstrate the
tremendous value experienced funders have. They bring in not only the financial resources the
project needs, but also other resources such as their enthusiasm for the industry, their personal
connections in the community or industry, and domain expertise to improve the project and
attract more crowd-funders. Hence it is worth spending extra effort to engage in activities to
appeal to experienced crowd-funders. For example, promoting the project within the existing
community in which their project belongs to on the crowdfunding platform might be fruitful to
attract experienced crowd-funders.
Second, strategically manage the communication process to utilize resources from
experienced crowd-funders. Recognizing experienced crowd-funders as an asset is the first step.
The second step is to take action to mobilize these resources. Findings from the current study
draw attention to the communication process. In addition to actively promoting the projects and
responding to inquiries, entrepreneurs should also engage experienced crowd-funders. Through
promoting a collaborative norm and acknowledging inputs from experienced crowd-funders,
124
entrepreneurs might be able to “employ” funders whose opinions are more trustworthy among
their fellow funders for free.
Third, prepare the pitch video carefully, especially its content. Findings from this
dissertation suggest that the more passion entrepreneurs display in the video, the more funding
they might attract. More importantly, entrepreneurs also need to show all the “substance” they
have prepared for the project such as researching the market and target consumer and analyzing
potential competitors. In the crowdfunding context, more homework entrepreneurs need to do is
to study the potential crowd-funders. This study demonstrates that potential funders react
differently to the same pitch. Thus, given the same product and the same presenter, crowd-
funders at different crowdfunding platforms might respond distinctly. Some crowdfunding
platforms might honor creativity, while others reward the economic value in products. Taking all
these factors into consideration can increase entrepreneurs’ chance of getting funded.
For crowdfunding platforms. Findings from the current study also highlight two
approaches crowdfunding platforms can adopt to facilitate attracting funds. First, recognize and
retain experienced crowd-funders. This study clearly shows that crowd-funders bring enormous
value to crowdfunding projects. They provide not only funding, but also their expertise
knowledge, effort to create value for entrepreneurship on the platform. They do so by filtering
the quality of the project, leveraging the information asymmetry between entrepreneurs and
crowd-funders, and maintaining the community active. Thus crowdfunding platforms should
recognize experienced crowd-funders as the core asset of the crowdfunding platform, and engage
in proactive activities to acknowledge their contribution and maintain them active on the
platform. When applicable, crowd-funders’ previous experience should be recorded and
accessible to viewers when other crowd-funders desire to weigh the information they receive.
125
Second, promote public communications between entrepreneurs and crowd-funders.
Results from this study show that it is through knowledge exchange that entrepreneurs
communicate their vision to crowd-funders, answer inquiries, and get feedback from crowd-
funders. Crowd-funders can seek information, make suggestions, and co-create the project with
entrepreneurs through conversing. Given the critical role public communications play,
crowdfunding websites should implement mechanisms and advance technological features on
their platforms to promote this process. For instance, educating entrepreneurs and crowd-funders
about the significance of communication in Help section might help. Other possible mechanisms
might include rewarding entrepreneurs or crowd-funders who respond to inquiries and making
public communications more user-friendly with advanced information communication
technologies.
126
Conclusion
It is hard to over-estimate the significance of receiving financial capital for organizations
to survive or thrive. Crowdfunding provides a much needed alternative for small businesses to
acquire funds. Yet, what crowdfunding can offer exceeds money alone. According to Crowdfund
Capital Advisors, a leading consulting firm specialized in crowdfunding, companies with
successful crowdfunding campaigns experience increased revenues, create more jobs, and gain
benefits from the crowd during and post campaign including “knowledge, experience, strategy,
marketing, product position and demand”
11
, in addition to money.
Many professionals donate their time and expertise to help entrepreneurs realize their
dreams on crowdfunding platforms. Numerous people lend $10 or $20 dollars to strangers on
crowdfunding websites and choose not to collect interest when they can. Many people contribute
money to projects that benefit the public interest such as sustainable local farms. Crowdfunding
clearly shows its potential to advance innovation, battle economic inequality, and increase public
engagement in a way the traditional VC cannot achieve. As one of the first attempts to
understand this complex process, this study helps us to understand how resources residing in
crowds can be transformed into financial capital. This dissertation also highlights value crowd-
funders can provide in addition to money: their expertise.
11
http://crowdfundcapitaladvisors.com/news/press-releases/174-crowdfund-signals.html
127
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Abstract (if available)
Abstract
This dissertation explores factors explaining crowdfunding success by examining how resources residing in crowds can be transformed into financial capital. It aims to answer the following two questions from an entrepreneur’s perspective: What attributes should I look for in potential investors to increase my chance of being funded? What is the best way to pitch my idea given certain funder characteristics? ❧ Results from this study suggest that projects with high social capital in the form of network embeddedness, including structural embeddedness, junctional embeddedness, and positional embeddedness, are likely to attract more funding. Further, the rate of facilitating effects of network embeddednesses on attracting funding increases initially and then decreases. Human capital residing in the crowds in the form of the number of experienced crowd-funders predicts funding success as well. What’s more, the amount of knowledge exchange within the crowd is the underlying mechanism translating benefits of social capital (and human capital) into funding success. Specifically, knowledge exchange can fully explain benefits of junctional embeddedness and human capital, and can account for partial effects of structural embeddedness and positional embeddedness. Displayed passion and preparedness in entrepreneurs’ pitches both aid entrepreneurs to attract more funding. Yet, displayed preparedness fully mediates the relationship between displayed passion and funding success, meaning that it is only through the pitch content or its substance that crowds are willing to contribute. Lastly, human capital within the crowd attenuates the positive effects of both displayed passion and displayed preparedness in the pitch on funding success.
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Asset Metadata
Creator
Lu, Li
(author)
Core Title
How social and human capital create financial capital in crowdfunding projects
School
Annenberg School for Communication
Degree
Doctor of Philosophy
Degree Program
Communication
Publication Date
09/16/2014
Defense Date
08/12/2014
Publisher
University of Southern California
(original),
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(digital)
Tag
crowdfunding,crowdsourcing,entrepreneurial passion,human capital,OAI-PMH Harvest,social capital,structural embeddedness
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application/pdf
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Language
English
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Electronically uploaded by the author
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Advisor
Fulk, Janet (
committee chair
), Jian, Lian (
committee member
), Monge, Peter R. (
committee member
)
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llu2@usc.edu
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https://doi.org/10.25549/usctheses-c3-480141
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etd-LuLi-2952.pdf (filename),usctheses-c3-480141 (legacy record id)
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480141
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Lu, Li
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The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...
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Tags
crowdfunding
crowdsourcing
entrepreneurial passion
human capital
social capital
structural embeddedness