Community Impact on Crowdfunding Performance
Yael Inbar
Ohad Barzilay
ABSTRACT
Many digital platforms, regardless of their business domain, follow the common practice of
incorporating social and community features in order to increase their user engagement and expand
their online community. Although this practice is advocated by the literature and clearly makes sense,
its implications are not well understood. In this research, we aimed to close this literature gap,
providing a theoretical framework and empirical evidence regarding the impact of the online
community on platform performance. As a testbed, we studied crowdfunding platforms, that is,
designated websites aimed at enabling entrepreneurs to raise money over the Internet. We used
comprehensive data collected from Kickstarter, the largest crowdfunding platform established to date.
We theorized that online platforms, such as Kickstarter, consist not of a single community but rather a
hierarchy of multiple, partially competing communities. These communities vary considerably with
respect to the interests of their members, their platform participation patterns, and their impact on
platform performance. Our suggested framework incorporates the notion of fluidity of online
communities; that is, online users and digital communities evolve and change over time. As the
interests of the online user change, so does the membership of her immediate community. The
proposed framework allows us to identify such community changes and, consequently, to better
identify pivotal members of online communities and predict their lifetime value as potential backers.
Empirically, we validated our theory by studying the participation patterns of over 6.3 million
Kickstarter users, who have supported more than 150 thousand crowdfunding campaigns over more
than 5 years. We demonstrated the growth of the different community types and estimated their
different impacts on crowdfunding performance over time. Interestingly, we found that some
communities, despite high participation rates, had negative impacts on crowdfunding campaign
success. We discuss managerial and practical implications of our theory and findings.
1
Electronic copy available at: http://ssrn.com/abstract=2524910
INTRODUCTION
Crowdfunding enables entrepreneurs to raise money from a large base of supporters over the Internet
(Belleflamme et al. 2011). In its early days, crowdfunding used social media such as mailing lists or
online social networks to achieve its goals. Today, with the maturing of Web 2.0 technologies and the
success of crowdsourcing (Giudici et al. 2012; Kleemann et al. 2008), there are dedicated
crowdfunding platforms, such as Kickstarter.com, which bring together project owners and potential
backers and facilitate information flow and transactions. Each crowdfunding campaign is centered on
a webpage that describes the project, which usually contains an embedded video, and that keeps track
of the fundraising process.
Crowdfunding was originally positioned as a new funding technology. Its value proposition included
providing entrepreneurs with affordable off-the-shelf tools to manage their fundraising campaign, to
communicate with their backers, and to facilitate money transfer. On the demand side of the market,
crowdfunding technology mitigates part of the perceived risk to backers by the enforcement of the allor-nothing policy (Hemer 2011): A minimum project-financing goal is set and a limited time period is
given for achieving said goal. The sum is transferred to the project owner only if the targeted amount
is pledged within the given period. If the amount is not reached, the project is considered unsuccessful
and the backers (funders) pay nothing.
Online users have been shown to react to weak signals that document the subtle actions of others
(Umyarov et al. 2013). On crowdfunding platforms, by choosing to fund a campaign, users reveal
their preferences (interests) as well as their consumption decisions (the funded product or service),
and this information may impact the decisions of other users (Faraj and Johnson 2011; Miller et al.
2009). At an aggregate level, the visibility of users' actions may affect other users’ decisions via
mechanisms of herding (Li and Wu 2014; Zhang and Liu 2012) and observational learning (Kim and
Viswanathan 2013).
As crowdfunding platforms have gained popularity and embraced social features, additional dynamics
have emerged. Ward and Ramachandran (2010) suggested that peer effects, and not network
2
Electronic copy available at: http://ssrn.com/abstract=2524910
externalities, influence consumption. Funders are looking for social interactions in crowdfunding
platforms (Gerber et al. 2012), and their participation in those platforms represents their belonging to
a community with similar interests. Crowdfunding platforms also manifest reciprocal behavior (either
direct or indirect) between users in terms of mutual backings (Zvilichovsky et al. 2013).
Platform owners may wish to leverage on their user community. Recent studies suggest that those
who are more socially involved in an online community built around a website are more likely to
consume the content of the site (Oestreicher-Singer and Zalmanson 2013). This correlation is
explained via mechanisms of developing a deeper sense of commitment (Bateman et al. 2011) and
perceived ownership (Preece and Shneiderman 2009).
It is not surprising then, that Kickstarter, like many other popular websites, refers to itself as a
community. In its official blog, it states: “A Kickstarter project does more than raise money. It builds
community around your work.” 1 Furthermore, Kickstarter highlights the opportunities that stem from
repeat backers and owners and interconnected communities.2 The company publishes and enforces
“Community Guidelines” governing behavior on the platform3 and holds meetups that take place in
major US cities designed to promote offline connections among its users.4
Crowd-based platforms leveraging on their community (i.e., existing users) introduce a unique
understudied tension. On the one hand, these platforms are positioned as allowing entrepreneurs to
raise money from 'the crowd', the flat and egalitarian mass of the population (Arazy et al. 2014); on
the other hand, online community performance and survival is dependent on a highly motivated and
engaged core of users, which is relatively small (Borgatti and Everett 2000; Kuk 2006). In the context
of crowdfunding, this core may not be large enough or self-sufficient enough (Yang and Wei 2009) to
finance campaigns at scale. The crowdfunding entrepreneur thus faces a dilemma – whether to focus
her efforts on drawing the community pivots to finance her campaign or to address potential backers
1 https://www.kickstarter.com/learn, visited April 29, 2014
2 http://www.kickstarter.com/blog/familiar-faces-interconnected-communities, visited April 29, 2014
3 http://www.kickstarter.com/blog/community-guidelines-update, visited April 29, 2014
4 http://www.kickstarter.com/blog/july-30th-kickstarter-everywhere, visited April 29, 2014
3
outside Kickstarter (e.g., her social network, media).
The literature provides no empirical evidence regarding the magnitude or the impact of the
crowdfunding community on campaign performance; in addition, the community effect on platform
performance at large has not been characterized. Although entrepreneurs acknowledge the importance
of community to the fundraising process (Gerber et al. 2012), it is not clear who core members of a
community are. Unlike other crowd-based platforms, Kickstarter lacks formal hierarchies such as in
Wikipedia (Arazy et al. 2014; Chen et al. 2010; Choi et al. 2010; Sundin 2011) and rating systems
such as in Stack Exchange (Barzilay et al. 2013).
This study addresses the gaps in the literature and advances our understanding of the dynamics of
online communities in general and of crowdfunding platforms in particular. We conducted a
comprehensive empirical study using a unique dataset comprising more than 150,000 crowdfunding
campaigns, conducted using the Kickstarter platform, the largest crowdfunding platform to date. We
estimated the extent to which the online community built around Kickstarter affected the fundraising
performance.
We found that Kickstarter's online community is actually composed of a hierarchy of multiple,
partially-overlapping, competing communities whose members manifest different patterns of
behavior. We differentiate among three community types: (1) ad hoc communities centered around a
single campaign or project, (2) communities of interest centered around a specific category, and (3)
the platform-centered community, whose members are Kickstarter enthusiasts, interested in
crowdfunding and innovation per se. We used typology suggested by Porter (2004) as a prism to
examine the characteristics of each of these community types.
Our rich dataset allowed us to take the novel approach of associating users with different communities
over time. The first backing by a user associated her with the ad hoc community around that
campaign. Subsequent backings (of different campaigns) may manifest a change in user's interests or
preferences and may associate her with a different type of community depending on her choices.
Through these associations, we incorporate an ecological mindset (Lin and Lin 2006; Wang et al.
4
2012), acknowledging the fluidity of online communities and the dynamics of community boundaries
and of user attention (Faraj et al. 2011).Surprisingly, we found that platform attachment acted as a
two-edged sword. We found that users who funded multiple campaigns of different categories
supported more campaigns than category-centered community members did. However, their support
missed the community added value associated with the support of members of the other community
types. Furthermore, this trend increased with users who focused first on one category and only after
some time supported campaigns in other categories. In contrast, we found that backing by categorybased community members had a positive impact on campaign success. In our analysis we controlled
for platform age and the effect of herding on the different community types in addition to other
controls advocated by the literature.
We discuss our results in the light of marketing theories, and compare our community-centered
empirical approach to the alternative social network analysis paradigm. We suggest that our
framework and findings may be applicable on other digital platforms as well.
CROWDFUNDING AND KICKSTARTER
Crowdfunding platforms attract millions of people around the world5 who have become involved in
this new process either as entrepreneurs who seek to secure funds for their ventures or as
investors/backers who wish to contribute money to campaigns of their choosing (with or without
receiving a reward). Crowdfunding can be based on one of several methods of raising money from the
crowd: equity purchase, loan, donation, and pre-ordering/reward based (Ahlers et al. 2012;
Belleflamme et al. 2011; Ingram and Teigland 2013).
Kickstarter, on which we focus in this study, is a reward-based platform that follows the "all or
nothing" business model. A campaign is successful upon achievement of the campaign financing
target in the allocated timeframe. We define success using this metric in our study rather than basing
success on execution or commercial success in later stages. As a for-profit company, Kickstarter
5
http://www.crowdsourcing.org/editorial/2013cf-the-crowdfunding-industry-report/25107 (visited April 21st, 2014)
5
receives 5% of funds collected for each successfully funded campaign.6
Kickstarter divides all campaigns into 13 categories7: Art, Comics, Dance, Design, Fashion, Film and
Video, Food, Games, Music, Photography, Publishing, Technology, and Theater. Currently, the most
popular category8 (in terms of number of projects) is Film and Video (24% of projects), and the
second most popular is Music (20%). The least popular category is Dance, with only 1963 (1.3%)
projects. Surprisingly, this was also the most successful category in our dataset, with a success rate of
70.4%. Another successful category is Theater, with a 64.12% success rate. The most unsuccessful
category is Fashion, with a success rate of only 29.4%.
Research in the domain of crowdfunding include analysis of the motivation to participate in
crowdfunding from the points of view of the backers and the campaign owners (Belleflamme et al.
2011; Schwienbacher and Larralde 2012), funders’ decision-making processes on whether to support
a campaign (Agrawal et al. 2011; Burtch et al. 2013; Kuppuswamy and Bayus 2013), and the key
success factors of crowdfunding campaigns (Mollick 2014).
THEORY AND HYPOTHESES
Emergence and Growth of Online Communities
Online communities are aggregations of individuals or business partners who interact around a shared
interest, where interaction is at least partially supported and/or mediated by technology and guided by
protocols or norms (Porter 2004). Members of an online community can be geographically dispersed
individuals who have no previous acquaintance with each other but who share common interests
(Faraj and Johnson 2011). Many digital networks that facilitate online activity of users do not meet
the usual definition of a community as they do not involve direct interactions among individuals
(Sundararajan et al. 2013). Nonetheless, users on crowdfunding platforms like Kickstarter emulate a
community establishment by leveraging on the co-backing relations of campaigns inducing a quasisocial network (Provost et al. 2009).
6
http://www.kickstarter.com/hello, visited April 29, 2014
On June 11 2014, Kickstarter introduced two additional categories. In order to avoid potential bias we excluded
from our dataset campaigns, which started after the new categories were introduced.
7
8
https://www.kickstarter.com/help/stats, retrieved on June 13, 2014
6
The success of an online platform and, in turn, an online community depends on its ability to retain
active participants over time (Arguello et al. 2006), the community’s sociability and usability (Preece
2001), and strong activity of its pivotal subgroup or subgroups (Maloney-Krichmar and Preece 2005).
Although most of the participants of online platforms are one-time users (for example, 68% of
newcomers to Usenet groups drop out after contributing a single post (Arguello et al. 2006)), the
remainder compensate for its small portion size by active and dominant participation patterns (Kuk
2006). As Kickstarter maintains a successful track record of successfully funded projects, we expected
to find similar dynamics happening on Kickstarter as well. We hypothesized:

H1a: Kickstarter performance relies on large portion of one-time contributors and on significant
participation by a smaller group of repeat backers.
One motivation to participate in an online community is a user’s structural embeddedness in the
community (Wasko and Faraj 2005). Ren et al. (2012) found evidence for such attachment by
strengthening either group identity or interpersonal bonds (with a stronger effect for group identity).
Bateman et al. (2011) showed that users’ behaviors on content sites are directly linked to their
commitment levels: As users increase their engagement with the site, they develop a deeper sense of
commitment to the website. Such socialization processes catalyze engagement around future foci and
drive future participation (Feld 1981).
We assumed that repeat Kickstarter backers form a community, induced by a quasi-social network
(Provost et al. 2009); we expected that over time these users would increase their community
embeddedness and platform attachment and, in turn, increase their participation in funding new
campaigns (Feld 1981). We hypothesized:

H1b: The participation ratio of repeat backers in funding new campaigns increases over time.
Another central component in our research design drew on the concept of fluidity of online
communities. Fluidity is the constant change of boundaries, norms, participants, artifacts, interactions,
and foci (Faraj et al. 2011). An online community evolves over time (Lin et al. 2008; Palla et al. 2007)
as do users and their attentions. Considering the fluidity concept, we expected that the backing
7
patterns of some Kickstarter users would evolve over time. Once a new user has joined the platform
and supported one of the campaigns, she may explore new opportunities offered by the platform.
Furthermore, some users may then fund campaigns that are substantially different from the first
campaign they backed, whereas other users may focus on supporting campaigns of the same area of
interest (e.g., music, food, technology). These considerably different backing patterns are likely to be
a manifestation of the user's change of attention or interest and may be amplified by the highly
flexible or permeable boundaries of the online community (Faraj et al. 2011). We hypothesized:

H2a: Over time, many repeat backers back only campaigns in a specific category. The percentage
of these backers within the community increases over time.

H2b: Over time, an increasing number of repeat backers back campaigns in multiple categories.
The percentage of these backers within the community also increases over time.
Community Hierarchy
Although members' participation and contributions are crucial for sustaining a successful community
(Butler 2001), patterns of use vary (Butler et al. 2013). Several studies in this field have attempted to
classify users of online communities by participation level. Different social and other roles of users of
online communities have also been identified (Kim 2000; Welser et al. 2007). Li and Bernoff (2011)
developed a ladder-type model to create profiles of online behavior, and Preece and Schneiderman
(2009) proposed a ‘Reader to Leader’ framework to examine participation levels. A recent study
(Oestreicher-Singer and Zalmanson 2013) showed that users’ willingness to pay for premium services
on a content website is strongly associated with their level of community participation.
Users' participation in online communities is unevenly distributed, and many communities include a
core subset of members who play key roles in sustaining the entire group (Faraj and Johnson 2011),
effectively creating their own self-sufficient sub-community (Yang and Wei 2009). Online
communities are characterized by subgroups with particular focuses (Faraj et al. 2011), and focused
and non-focused users exhibit different behavior patterns (Ung and Dalle 2010). Similarly, we
assumed that Kickstarter users could be classified into subgroups and that behavior patterns of these
users would differ.
8
However, we considered these subgroups as communities on their own. We hypothesized that the
aggregations of the different archetypes of repeat Kickstarter backers (single backing; multiple
backings in single category; multiple backings in multiple categories) correspond to distinct online
communities whose populations, interactions, and motivations vary considerably from one another.
We used a foci-driven granular perspective to identify communities centered around campaigns,
categories, and the platform. Figure 1 illustrates this idea. Backers who supported a single campaign
are grouped together in an ad hoc campaign-centered community with other backers who supported
only this campaign. Backers who supported multiple campaigns of a single category are group
together in a category-centered community, and backers who supported campaigns of multiple
categories belong to a platform-centered community.
Figure 1. An Illustration of Community Hierarchy.
Users are associated with communities based on their backing pattern.
A typology proposed by Porter (2004) emphasizes five main elements (the five P’s) of virtual
communities: (1) purpose (content of interaction), (2) place (extent of technology mediation of
interaction) – the levels of virtuality of the interaction, (3) platform (design of interaction), (4)
population (pattern of interactions), and (5) profit model (return on interaction) – whether a virtual
community creates tangible economic value. By examining Kickstarter backers through the lens of
Porter's typology we showed that backers' archetypes varied significantly from one another with
respect to the main elements. Therefore, we suggest that Kickstarter should not be considered as a
9
single community but rather as a set of multiple, hierarchical, partially competing communities.
Further, we qualitatively examined the community types using Porter's (2004) typology.
Project-Centered Communities
Project-centered communities are ad hoc communities centered around a campaign or a project9.
Members of these communities are focused on a specific project; many times, it is a physical product,
situated in the offline world. In some cases, members know the campaign owner outside Kickstarter
or heard about the project via their social networks. Project-based community members consider
Kickstarter to be a facilitating technology for the funding process. We found that the vast majority
(95%) created their Kickstarter account specifically to back a certain project.
The Kickstarter platform provides various means of communication for project-based communities.
Backers may post to the campaign page and leave comments on updates of the campaign owners.
Community members use these mechanisms not only to contact the project owner but also to conduct
conversations and send direct messages to other backers using the @username twitter convention.
Campaign owners use the campaign webpage to update the backers during the campaign and also
after it has ended. Some campaign owners use these features to attract backers to their subsequent
campaigns. In some cases the campaign is meant to finance an event happening in the physical world
(such as a show, concert, or exhibition) in which the members of the community meet in person.
Category-Centered Communities
Category-centered communities are communities of interest (Armstrong and Hagel 2000) centered
around a broad theme or topic – in our study, one of the 13 predetermined Kickstarter campaign
categories. The members of these communities are Mavens (Gladwell 2000), and their support of a
campaign signals to their followers that the campaign in question was appealing not only in and of
itself but also in a broader context of other campaigns in its category. These communities are
sometimes extensions of virtual communities outside Kickstarter. For example, gamers maintain user
9
In some cases, an entrepreneur may conduct subsequent campaigns to finance further stages of developments
or future versions of the product. We consider the backers of the subsequent campaigns to be part of the same
project-based community.
11
groups, forums, and websites that update the community regarding trending Game campaigns.
Technology blogs and magazines review interesting campaigns regularly.
The Kickstarter platform provides easy-to-navigate listings of all active campaigns in each category
and recently enhanced its search mechanism to enable focus on campaigns of a certain category or
sub-category.
Platform-Centered Communities
Platform-centered community members are Kickstarter backers who have supported more than one
campaign in at least two different campaign categories. This criterion differentiates them from the
category-centered members. Platform-centered community members' interests are broad and eclectic,
and members may be considered as innovators and early adaptors (Moore 1991; Rogers 2010). Their
interests are aligned with what the Kickstarter platform has to offer – a hub for creativity and
entrepreneurship (Boudreau and Lakhani 2013). They maintain active community membership over
longer periods than do members of the other community types.
The Kickstarter platform provides means for platform-level communication and interaction:
Kickstarter maintains a blog, a Facebook page, and a newsletter to provide the community members
with updates regarding the platform and to promote featured projects. Featured projects ('staff picks',
'trending now') are also shown on the site homepage. We note that these platform-level
communications are moderated by the platform owners and do not promote asynchronous
conversations among the users.
Kickstarter facilitates additional channels for information flow via the design of user profile pages. It
was found that crowdfunding users browse the profile pages of other users before deciding on their
own backing (Burtch et al. 2013). Kickstarter highlights the campaigns that were backed by each user.
Furthermore, if some of the backed campaigns are still live (their funding period has not yet ended)
this fact is emphasized.
We hypothesized:

H3a: Backers who belong to different community types have different participation patterns.
11
Using the fluidity perspective, we argue that Kickstarter users evolve over time. This may occur
within a community, or it may result in a member leaving one community to join another (virtual)
community residing on the same platform. This fluidity manifests changes in users' focus, interest,
and attention.
We expected that over time, some category-centered members would begin to back campaigns outside
their category of interest. We consider this to be a manifestation of changes in interest and attention;
hence we associated these backers with a new community type – the category-diverged community.
Figure 2 depicts transitions between communities as a state automaton10.
Figure 2. User Fluidity across Communities over Time.
10
The criteria by which we classified the communities may seem simplistic. For instance, one may note there
are no backward arrows in the diagram, suggesting that once a user becomes a platform-centered community
member, she can no longer 'go back' to being a category-centered community member. Indeed, for robustness,
we considered several alternative ('relaxed') models; for example, we put thresholds on the minimum number of
backings that triggered a category change. These models yielded similar results.
12
One notable element is that there are two distinct communities, namely platform-centered and
category-diverged. We expected members of these communities to vary considerably in their
participation patterns; hence, we hypothesized that:

H3b: Category-diverged community members exhibit behaviors different from category-centered
community members and from platform-centered community members.
The Impact of Kickstarter Communities on Platform Performance
Next, we sought to estimate the impact of Kickstarter communities on its performance. We expected
that the impacts of the four community types (the original three types and the fluid category-diverged
type) would be different. We analyzed whether one type of community dominates the others. We
expected the difference would be driven by the following two factors: (1) the heterogeneity and
diversity of the community members and their interest, and (2) the persistence of backing behavior,
namely, whether they change their community type from category-centered to category-diverged.
There is no consensus among researchers regarding the effect of user heterogeneity and diversity on
community performance. Members who belong to multiple communities are subject to conflicting
forces. On the one hand, they enjoy the overlap benefits (i.e., diversity of choice, interest, and
stimulation). On the other hand, each member has only limited resources (time, attention, and money)
and may have trouble maintaining active membership in multiple communities over time.
López and Butler (2013) questioned the viability of designs for local online communities that focus
narrowly on single topics, goals, and audiences. Raban et al. (2010) studied the effect of diversity of
an initial seed on the long-term sustainability of online chat channels and found that channels with
more diverse populations were more likely to survive than those with more homogeneous populations.
Similarly, a recent study by Zhu et al. (2013) concluded that membership overlap (members who
belong to multiple communities at the same time) is associated with the survival of the focal
community. Yoganarasimhan (2012) suggested that high clustering around the initiating node,
implying that users belong to a close-knit community, is associated with low performance outcomes.
13
In contrast, other studies (Wang and Kraut 2012) have shown that lower content diversity is
associated with a larger and more connected group of followers. Gu et al. (2014) argue that the desire
to make good quality decisions should steer people away from homophily and toward heterophily.
Wang et al. (2012) showed that sharing members with other groups reduces growth rate, suggesting
that membership overlap puts competitive pressure on online groups.
We drew on the theoretical framework of membership overlap (Wang et al. 2012; Zhu et al. 2013) and
adapted it to the settings of our theoretical framework. In the lens of membership overlap, categorycentered community members do not divide their resources and are able to identify value and quality
in their area of interest. Therefore, their support is more indicative of campaign success than that of
platform enthusiasts. We hypothesized:

H4a: Campaigns supported by a higher percentage of category-centered community members
will have a higher likelihood of raising their stated goal.
We expected that this correlation stemmed from causal relation; that is, the category-centered
community support drives the success. In order to show this, we needed to eliminate alternative
explanations and to resolve potential endogeneity issues. Therefore, we studied the dynamics along
the fundraising period, and investigated the interplay with the different community types over the
course of the campaign. Specifically, we focused on eliminating the herding effect from our
estimations. We elaborate on this aspect in the identification strategy section.
We hypothesized:

H4b: The support of category-centered community members ("homogeneous") has a greater
positive impact on campaign success than the support of platform-centered community members
("heterogeneous", "overlapping community membership").

H4c: The support of persistent community members (project-centered, category-centered) has a
greater positive impact on campaign success than the support of platform-diverged community
members ("non-persistent").
We suggest that H4b and H4c are driven by the "community added value" of the backing; getting
14
support from users who belong to cohesive communities may result in successive support from their
fellow members. This idea draws on a recent marketing theory regarding the network value of
products. It was shown that an increase of the sales of one product in a product network propagates to
an increase of the sales of the products linked to it (Oestreicher-Singer et al. 2013). Hence, the
economic impact of a sale should also account for these future sales. Although Kickstarter
communities do not have on-platform explicit links between their members, we argue that categorycentered communities maintain such links off platform.
DATA COLLECTION AND DESCRIPTION
Kickstarter does not provide an API nor does it provide access to a directory of past campaigns and
users. Furthermore, its web interface does not allow for exhaustive searches. Therefore, to build our
datasets, we developed two dedicated Web crawlers – one for comprehensive, static historical data,
and the other for monitoring the dynamics of live projects.
The first crawler implemented a recursive “breadth-first search” algorithm (Leiserson et al. 2001) that
traversed the links from each campaign page to its backers’ pages and from each backer page to the
pages of the campaigns she backed and created. Crawling was initiated using a publically available
seed comprising 45,000 campaigns (Pi 2012). Recursive iterations from campaigns to backers, and
back to campaigns, were performed until the number of newly discovered campaigns per iteration
converged. The following data was collected by the crawler:

Campaign data: campaign owner, financing goal, financing duration, campaign creator profile,
profiles of all backers (funders), detailed reward levels and reward selections, use of a video,
amount of money pledged, comments, updates, location, category, and sub-category .

User data: personal data (name, location, date account was opened, number of Facebook friends),
campaign ownership data (links to and number of all campaigns created by owner), and campaign
funding data (links to and number of all projects backed by the user).
The data presented in this paper is a result of an exhaustive crawling that ended on August 23, 2014.
The dataset contains the details of 6,632,241 backers and 157,661 completed campaigns, covering
15
97% of the campaigns completed on Kickstarter by that date. Table 1 displays descriptive statistics of
the main campaign attributes that were incorporated into our models.
On June 11, 2014 Kickstarter introduced two new campaign categories11. In order to avoid potential
analysis bias we removed from our dataset all the campaigns that were launched at or after that date.
Hence, our final dataset comprised 146,386 campaigns, to which a total of 6,352,395 backers had
pledged USD 1,168,477,721.
Table 1. Descriptive Statistics – Project Attributes
Variable
Goal (USD)
Min-Max
Mean/Probability
Std. dev
0.01-100,000,000
22,758.06
439,652.41
Duration (Days)
1-91
35.26
14.16
IsSuccessful (Goal Achieved)
0/1
0.45
0.50
0-41,535
2.03
147.85
0-263.14
28.37
39.02
0/1
0.81
0.39
0-105857
107.15
866.16
0-227
8.76
5.48
0/1
0.59
0.49
0/1
0.59
0.49
Owner HadCreated Previous Projects
0/1
0.12
0.33
Owner HadBacked Other Projects
0/1
0.42
0.49
0-176467
31.79
973.72
0-10,266,845
8,135.47
68,879.65
Level of Funding Achieved
(Raised/Goal)
Number of Weeks since the Owner
Joined the Platform
Has Video
Num. of Backers
Num. of Reward Levels
Limits on Number of Backers in one or
more reward category
Has FB Friends in profile
Num. of Comments
Total Money Pledged
In some estimations we split the campaigns according to their size (measured by total number of
backers). We found that the subset of campaigns that had less than 10 backers exhibited no obvious
pattern of distribution of the different community types, unlike other subsets of campaigns. Hence, for
these estimations we show here only campaigns with more than 10 backers12.
For privacy reasons, Kickstarter does not reveal the timing of the backings nor the amount pledged by
each backer. The backers’ list of each campaign is updated only after 10 new backings have been
11
https://www.kickstarter.com/blog/introducing-two-new-categories-journalism-and-crafts (visited at November 4th, 2014)
Moreover, as Kickstarter reveals the campaign backers' profiles only when the number of backers reaches 10 (or when the
campaign is over), the visible list of backers of such campaigns remained empty until the last day of the campaign and thus
the metrics do not represent the dynamics that were present when the campaign was alive.
12
16
made. Furthermore, the list is shuffled on every update. Therefore, merely from observing site
listings, it is impossible to deduce the order in which backers pledged to a particular campaign or the
timing of a particular pledge. In order to overcome this deficiency we developed a second, dynamic
crawler, run in parallel on multiple machines. Every 10.2 hours on average, this crawler recorded a
snapshot comprised of all the data associated with all live campaigns as detailed above. The crawler
was initiated on September 12, 2013 and has been run constantly since. The data presented in this
paper comprise daily snapshots of 37,161 completed campaigns between September12 2013 and
August 23, 2014. The total number of snapshots was 3,348,511.
METHODOLOGY
Our main methodological challenge in this study was related to studying the community impact on
campaign success. Specifically, we wished to distinguish the effects of the different communities
(platform-centered, category-centered, project-centered and category-diverged) on campaign
dynamics. However, relying on observational data makes it difficult to untangle the underlying
forces.
Our central tool for the analysis was the estimation of logistic regression models for predicting the
probability of campaign success. In the following paragraphs, we summarize our empirical approach,
the issues we addressed, and describe our econometric model.
Using the number of backers of each community type in order to predict campaign success is
obviously endogenous; the number of backers is directly linked with campaign success. The more
backers a campaign has – it is more likely to succeed. Hence, instead of using the absolute number of
backers of each community, we evaluated the percentage of each community type among the total
number of campaign backers. This is a zero-sum game; an increase of one percent in the share of
some community type must be compensated via decrease of one percent in the share of the other
community types (combined). Hence, this regression allowed us to estimate the impact of community
composition on the success likelihood of a campaign and also to measure the relative impacts of the
different communities.
17
One may note that the interpretation of the regression odds ratio as "all-else-equal" contradicts the
"zero-sum game" nature of using relative community shares. In addition, having the shares of all four
types as explained variables results in multicollinearity. Hence, we left one of the types out (categorydiverged ratio). This is a common practice used to estimate the impact of composition (e.g.,
Francalanci and Galal (1998) used it to estimate the impact of worker composition on productivity).
Backer perspective. In order to provide empirical evidence for our theory that Kickstarter is
composed of multiple communities, we performed several independent sample t-tests to show that
users who are members of different communities manifest differences in behavior patterns that are
statistically significant.
As our original data set is campaign driven, we compiled a complimentary view of the data in which
the backer is the main point of analysis. We tracked the behavior of each backer from the time she
joined the platform: the number of campaign backed by her, the categories and sizes of the backed
campaigns, the number of days since her last backing (as a proxy for user liveness), her backing
period, the backing frequency during her activity period on the platform, and the success rate of the
campaigns backed by this user.
For backers of campaigns within our dynamic data set we were also able to calculate the timing of the
backings (in days since the campaign started and as a fraction of the funding period), the state of the
supported campaigns at the time of the backing (in terms of the percentage of the funding goal raised),
and the percentage of the backer's total backings that were made before the campaigns backed by her
reached 100% of the funding goal.
Furthermore, we also studied the change of user behavior over time. For users classified as categorydiverged we compared their behavior before and after "being diverged" using paired sample t-tests.
We showed that the changes in behavior were statistically significant.
Identification Challenges
Incorporating campaign dynamics and herding. Another challenge of estimating the community
impact on campaign performance is related to the campaign state at the time when backings were
performed and the presence of herding effect.
18
When using the percentage of community members in order to predict campaign success we
encountered an endogeneity challenge. The naïve logistic regression cannot distinguish whether
community support drove campaign success or whether campaign success attracted the community
members to support the campaign13. Moreover, even when the campaign backing occurred before the
campaign met its goal, it might be the case that a pledge was made as part of herding dynamic that
began before the pledge and/or that the community member was reacting to the herding. To avoid this
issue, only community members who made pledges before the herding effect began can be
considered.
In order to empirically identify such campaign tipping points, we examined the distribution of all
campaigns, according to their percent of goal raised at the campaign end day. We found that there are
almost no campaigns that raised more than 40% but less than 100% of their goal. Put differently, more
than 97% of campaigns able to raise 40% of their target amount succeeded in meeting their goal. This
may suggest that by the time 40% of a campaign goal is reached, herding is already in action.
Therefore, we based our regression models on snapshots of the campaign states at the following levels
of funding (percentage of goal raised): 5%, 10%, 20%, 30%, 40%, 100%, and final state14. The
multiple models allowed us to identify the dynamics of the different communities and their impacts on
the campaign funding process.
Controlling for platform age. In order to accurately measure the forces that affect the performance
of a mature digital platform, such as Kickstarter, one needs to realize that these forces evolve over
time (Faraj et al. 2011). Hence, the dynamics measured today may be different from those three years
ago. Therefore, our empirical challenges were to identify the trends that correlated with the platform
maturity process and to control for these changes over time in our estimations.
In order to identify the trends evident on the Kickstarter platform we leveraged upon our panel data
obtained at monthly resolution. We found that the Kickstarter online community grew over time and
When a campaign reaches its funding goal, the funding process continues until the campaign duration set
beforehand is over. In such cases, the campaigns eventually raise over 100% of the funding goal.
13
14
For unsuccessful campaigns (i.e., those that did not reach the funding goal), the 100% snapshot is similar to
their final state.
19
took increasing part in financing new campaigns. This fact made it difficult to estimate the impact of
community support on campaign success while considering the entire 5 years of the platform
existence (April 2009 – June 2014).
We addressed this concern in two ways: (1) we estimated the community impact on campaign success
only for a recent, limited time interval, in which the community’s relative growth (compared to firsttime backers) was stagnant and (2) we used the fact that community growth was monotonic. When
considering the entire data set in our logistic regression, we controlled for the month in which the
campaign was launched as a proxy for platform maturity.
The Econometric Model
We used a logistic regression model to predict the probability of campaign success given the ratios of
backers from the four community types among the total campaign backers. We controlled for
campaign characteristics, owner attributes, and platform age at campaign launch day. The control
variables were consistent with the main models estimated for predicting Kickstarter campaign success
in previous studies (Burtch et al. 2013; Marom and Sade 2013; Mollick 2014; Zvilichovsky et al.
2013). We used the variable isSuccessful to represent campaign success, defined as attainment of the
funding target within the specific timeframe. The variable has the value of 1 if a campaign achieved
this target.
Formally, we estimated the following:
𝑉(𝑖𝑠𝑆𝑢𝑐𝑐𝑒𝑠𝑓𝑢𝑙) = 𝛼 + 𝛼1 𝑃𝑙𝑎𝑡𝑓𝑜𝑟𝑚𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦 + ∑𝐽𝑗=1 𝛽𝑗 𝐶𝑎𝑚𝑝𝑎𝑖𝑔𝑛 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦 𝑗 +
𝑃
∑𝐾
𝑗=1 𝛾𝑗 𝐶𝑎𝑚𝑝𝑎𝑖𝑔𝑛𝐶ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠𝑗 + ∑𝑗=1 𝛿 𝑗 𝑂𝑤𝑛𝑒𝑟𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠𝑗 +
∑𝑀
𝑗=1 𝜂 𝑗 𝐶𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦𝑇𝑦𝑝𝑒𝑅𝑎𝑡𝑖𝑜𝑠𝑗 + 𝜖
Where:

PlatformMaturity represents the Kickstarter platform age in months at campaign launch. We
observed that community dynamics evolved over time as previously described (Faraj et al. 2011),
and thus we normalized for platform age.

CampaignCategoryj are dummy variables representing 12 of the 13 Kickstarter categories. This
was controlled for because backing patterns may vary across different categories.
21

CampaignCharacteristicsj include the following campaign attributes:
o
Goal is the funding target (in USD) that the campaign owner sought to raise. This variable is
logged in our model due to high variance. Other currencies were converted to dollars using
the exchange rate at the time of the campaign.
o
Duration is the number of days allocated as funding period, after which the success of the
campaign is determined.
o
NumRewardCategories is the number of reward levels offered by the campaign owner. It
was suggested that this number is correlated with campaign sophistication level and hence
may affect the propensity of potential backer to support the campaign.
o
HasLimitedCategory indicates whether at least one reward level is limited to a certain
number of backers. This indicator may serve as a measure of the sense of exclusiveness the
owner is attempting to build around backing the campaign.
o
HasVideo indicates whether the campaign description incudes a video. This measure was
considered in previous studies as a measure of campaign quality (Mollick 2014)

OwnerAttributesj include the following owner attributes:
o
HasFBFriends is a dummy variable indicating whether the campaign description includes a
direct report from Facebook of the number of the owner’s Facebook friends. This indicator
may serve as a control for the owner's social capital outside the platform.
o
OwnersPastCampaignsInfoj includes the variables that indicate whether the owner had
created a campaign on Kickstarter in the past (HadCreated) and whether she had backed
other campaigns before launching the current campaign (HadBacked). These measures were
previously found to have a significant impact on the funding success (Zvilichovsky et al.
2013).

CommunityTypeRatioj is the percentage of a community-type subgroup relative to total campaign
backers: ProjectCenteredPercent, CategoryCenteredPercent, PlatformCenteredPercent, and
CategoryDivergedPercent. In order to avoid multicollinearity we left one of the types out
(category-diverged ratio).
21
𝑒𝑣
The conditional probability of campaign success is thus: 1+𝑒 𝑣
RESULTS
We quantitatively estimated the extent to which Kickstarter really is a community. Namely, we
explored the community features of the platform and estimated the magnitude and impact of these
features on campaign performance. Specifically, we considered the impact of repeat backers and their
participation patterns on the success of crowdfunding campaigns.
The Growth of Kickstarter Communities
The official Kickstarter statistics15 report that 6.3 million individuals have made 15.8 million pledges
since Kickstarter’s inception. This is approximately 2.5 pledges per backer on average. However,
pledges on Kickstarter do not distribute uniformly; the one-time backers behave differently than
repeat backers. Kickstarter's repeat backers account for 30% of all backers (1,904,375 backers).
Repeat backers have supported 5.9 campaigns on average and are in charge of 72% of the pledges on
the platform.
15
https://www.kickstarter.com/help/stats, 13 June 2014
22
Figure 3 displays the mean percentages of new vs. repeat backers across all campaigns per month
since the establishment of Kickstarter (April 2009). We considered all campaigns launched in each
month, and for every campaign we calculated the percentage of new and repeat backers relative to the
total number of campaign backers. Over time, as the platform has matured, new backers have
accounted for a diminishing percentage of all backers, whereas repeat backers have accounted for an
increasing percentage of all backers. Interestingly, we observed that in the most recent months, the
majority of the backings were performed by existing users. This fact is particularly salient considering
that new backers are constantly joining the platform. This trend may be interpreted as reflecting
Kickstarter’s transition from a crowdfunding technology, focused on mitigating concerns of potential
backers, to a crowdfunding community, a reoccurring meeting place for members of shared-interest
Repeat Backers
New Backers
Mean Percentage of Total Campaign
Backers
communities.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1 4 7 11 13 16 19 22 25 28 31 34 37 41 43 46 49 52 55 58 61
Platform Age (Months)
Figure 3. Mean Percentage of New Backers and Repeat Backers on Kickstarter over Time.
We associated Kickstarter backers with sub-communities based on their interests. We associated firsttime backers with an ad hoc project-based community, which was centered on a particular campaign
(or series of campaigns of the same owner). We divided the repeat backers into platform-centered and
category-centered based on whether they supported campaigns in multiple Kickstarter categories or
not.
23
Figure 4 displays the proportions of the three community types out of all Kickstarter backers, over
time. Project-centered type members comprise the highest share of all Kickstarter backers (71% on
June 2014). It is evident that this subgroup proportion has steadily decreased since the platform’s
early days. The percentages of the other two subgroups, platform-centered and category-centered
communities, increased over time (21% and 8% of all Kickstarter backers, respectively, at the end of
the data collection period).
100%
Percent of All
Kickstarter Backers
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7
11 13 16 19 22 25 28 31 34 37 41 43 46 49 52 55 58
Platform Age (Months)
Platform-Centered
Category-Centered
Project-Centered
Figure 4. Percent of Community Members Types out of All Kickstarter Backers over Time.
The Fluidity of Kickstarter Communities
We showed that backing behavior was not arbitrary, but rather a manifestation of user interests and
attentions. Therefore, different backing behaviors are correlated with other characteristics of user
participation and interactions with the online platform. Furthermore, we found that over time some
users changed their attention. In particular, we focused on users who began as category-centered
members who subsequently began to support campaigns in other categories. We refer these users as
category-diverged community members. Members of this quasi-community demonstrated behavior
that was significantly different from that of either category-centered members or platform-centered
members.
Table 2 summarizes the differences among the four backer types. It shows that our theory for
considering these communities as distinct is anchored in empirical data. We performed independent
24
sample t-tests to compare category-centered and platform-centered community members, as well as to
compare category-centered and community-diverged members before they were diverged. We also
compared platform-centered and community-diverged members after their diversion. In addition, we
conducted paired sample t-tests to compare category-diverged members' behavior before and after
they diverged. All results are statistically significant and support hypotheses H3a and H3b.
Platform-centered members supported significantly more campaigns than category-centered members,
and category-diverged members supported more campaigns than platform-centered members. These
differences may be interpreted as due to loss of interest of category-centered members after 3.2
backings on average. On the other hand, the category-diverged members performed their third
backing in a different category, which renewed their interest, and might have exposed them to new
stimulations. This stimulation encouraged them to persist for another 8.4 additional backings, many
more than the average number of backings of platform-centered members.
Category-diverged members were also active for longer times than other types of members. We used
the metric of "Days Since The Last Campaign Backed" as a proxy for user 'liveness' (Fader et al.
2010); when this number is smaller, the community is more likely to have live members.
We also observed that the average personal success rate (ratio of successful campaigns of the total
campaigns supported) is high across all community type members (86%-92%). This confirms the
official Kickstarter reports stating that most of the money pledged by Kickstarter users (88%) was on
successful campaigns.
25
Table 2. Comparison of Different Types of Backers16
Community
ProjectMetric
type
Centered
(std. dev.)
CategoryCentered
PlatformCentered
t-test
P value
Category-Diverged
(Category-centered members who later
supported campaigns outside of the category)
Number (Percent)
4,459,096
backers
(70.2%)
523,256
backers
(8.2%)
1,058,691
backers
(16.7%)
-
310,352 backers
(4.9%)
Days Since The Last
Campaign Backed
509
(375)
385
(309)
281
(265)
***
198
(215)
While Being While Being
CategoryPlatformCentered
Centered
t-test
P Value
Num. of Backings
1.02
(0.136)
3.26
(3.84)
5.46
(13.28)
***
2.45
(3.63)
8.42
(20.76)
***
Personal Success
Rate
86.7%
(33.8%)
90.7%
(25.84%)
89.3%
(25.07%)
***
91.8%
(23.4%)
90.1%
(20.44%)
***
Period Active
in Days
-
301
(250)
400
(319)
***
146
(169)
347
(289)
***
Days before First
Backing as Type
2.9117
(104.17)
179.35
(235.85)
245.47
(264.33)
***
113.04
(174.25)
138.11
(184.85)
***
***- significant at the 0.001 level
16
17
Measured on June 10th, 2014
Measured on a random sample of 841,159 backers.
26
The data shown in Figures 3 and 4 were also analyzed from a campaign perspective. We considered
all campaigns launched in each month, and for every campaign we calculated the percentage of the
four community types relative to the total number of campaign backers. Figure 5 displays the
participation of the four Kickstarter community types in campaigns over time. As the platform
matured, project-centered backers, the vast majority of whom are single-time contributors, accounted
for a deceasing share of all campaign backers; these backers accounted for less than 50% in the last
few months analyzed. Platform-centered backers accounted for an increasing proportion of the total
campaign backers, reaching 30%. The percentage of category-centered backers reached a peak of
9.3% on month no. 38 and accounted for 6.8% in the last month analyzed. The category-diverged
Mean Percent of Total Campaign
Backers
subgroup gradually increased and was 13.9% of backers in the last month analyzed.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7
11 13 16 19 22 25 28 31 34 37 41 43 46 49 52 55 58 61
Platform Age (Months)
Platform-Centered
Category-Diverged
Category-Centered
Project-Centered
Figure 5. Mean Percent of Community Members Types of Total Campaign Backers over Time.
The Kickstarter ecosystem exposes its members to diverse stimulations, different from their original
interest (i.e., different from the category of their first funded campaign). A significant number of
campaign supporters are existing Kickstarter backers who have never supported a campaign of the
same category. Moreover, for most campaign categories this trend increased over time.
27
Figure 6 displays the mean percent of serial backers who back the given campaign's category for the
first time. This measure represents the share of backers who are new to the category but not to the
platform. The figure shows a major difference between the Games category (black) and other
categories. Games is the only category where the percentage of repeat Kickstarter backers who are
backing this category for the first time decreased over time. This suggests that Games backers either
come from within the community (Games category-centered community members) or are exogenous
Mean Percent of Serial Backers Who Back This
Category for the First Time
to Kickstarter (other virtual or physical Games communities).
40%
Art
35%
Comics
Dance
30%
Design
25%
Fashion
Film & Video
20%
Food
15%
Music
10%
Photography
Publishing
5%
Technology
0%
1
4
7 11 13 16 19 22 25 28 31 34 37 41 43 46 49 52 55 58 61
Platform Age (Months)
Theater
Games
Figure 6. Mean Percentage of Serial Backers who were Backing the Given Campaign Category
for the First Time as a Function of Platform Age.
The Impact of Kickstarter Communities on Campaign Performance
We have shown that Kickstarter communities play a significant part in the crowdfunding process. We
wished to identify the impact of the different communities on campaign performance and campaign
dynamics. Specifically, we were interested in the effect of community support on the likelihood of
campaign success. We wished to identify those community members who behave as early adopters
and drive campaign success and those community members who support campaigns only after
financing goals are met.
28
One of the dynamics that may occur during the funding period of successful campaigns is herding (Li
and Wu 2014; Zhang and Liu 2012). After receiving some support, a campaign may reach a tipping
point, which in turn, provides a positive signal to other backers to support the campaign (Kim and
Viswanathan 2013). We found evidence for two such tipping points during the course of the funding
period: the first came after raising some initial portion of the financing goal and the second after
meeting the financing goal.
Figure 7 shows the distribution of all campaigns as a function of the percent of goal raised at the
campaign end day. Almost no campaigns raised more than 40% but less than 100%. Indeed, more
than 97% of the campaigns that raised 40% of their target amount succeeded in meeting the funding
goal. Another spike in the histogram occurs after the 100%; after the campaign has been reached its
goal, the feasibility of the project becomes concrete, mitigating the concerns of some potential
backers who then pledge. The two tipping points may be driven by different mechanisms; the first
tipping point may be a result of on-platform observational and social learning (Kim and Viswanathan
2013). The second may be driven by exogenous sources such as media coverage.
Number of Campaigns
50000
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
Percent of Campaign Goal Funded
Figure 7. The Distribution of All Campaigns based on Percent of Goal Raised at the Campaign
End Day.
In order to estimate the impact of different communities on campaign success, we considered the
timing of the backing and the potential effect of herding. In order to do so, we again used the
campaign perspective. For every campaign in our 'dynamic' dataset, we calculated the percentages of
29
backer types at various points during the funding period. Using this set, which spans 10 months,
allowed us also to control for platform maturity, and in particular the change in sub-community
proportions over time. Table 3 and Figure 8 detail our results. We found that, on average, projectcentered community members (mostly first timers) comprised of 38.7% of early supporters (backing
the project before 5% of its target goal was reached). Their portion of the funders population
increased as the campaign progressed to 52.5% when the campaign goal was met. This implies that
project-centered members are positively affected by the first herding wave, to a greater extent than are
the other types of backers.
Platform-centered and category-diverged member shares decreased over time, which implies that
these sub-types are less prone to herding than the other types. From a community perspective, this
may suggest that the signaling within these two communities is weaker than within the other
communities.
The second tipping point (after reaching 100%) had opposite effects on project-centered and categorydiverged community members, which associates project-centered with the early adopters, and
category-diverged with the late majority (Moore 1991; Rogers 2010).
Table 3. Mean Percentage of Campaign Backers Types over the Levels of the Percentage of
Funding Goal Raised
Percentage
Raised 5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Final
38.7 40.1 44.1 45.7 46.7 47.8 48.2 48.2 48.8 49.8 52.5
48.9
Category-Centered
7.2
6.8
6.9
Platform-Centered
34.2 34.6 32.7 32.0 31.8 31.0 30.7 30.7 30.4 29.8 28.4
29.9
Category-Diverged
19.8 18.2 16.1 15.1 14.4 14.0 13.9 14.0 13.8 13.4 12.3
14.3
Backer
Type
Project-Centered
7.1
7.2
7.2
7.1
7.2
7.1
7.2
7.0
7.1
31
Mean Percentage of
Campaign Backers
100%
80%
Project-Centered
60%
Platform-Centered
40%
Category-Centered Who Turned
Platform-Centered
20%
Category-Centered
0%
5%
11% 21% 31% 41% 51% 61% 71% 81% 91% 111%
Percent Money Raised
Figure 8. Mean Percentages of Campaign Backer Types as a Function of Percentage of Funding
Goal Raised.
We wished to further study the impact of the different communities on campaign dynamics by
considering the quality of the campaign. If some communities are better than others in identifying
good campaigns (campaigns that will become successful), we expected that this fact would also be
manifested in their dynamics. We repeated the measurements described above (results summarized in
Table 3, Figure 8) after division of the dataset into successful and unsuccessful campaigns.
31
Percentages of backers who were platform-centered community members or category-centered
members did not differ between successful and unsuccessful campaigns. The percentage of projectcentered community members, who increased in proportion for all campaigns during the funding
period, was lower for unsuccessful campaigns than for successful campaigns. The opposite was the
case for category-diverged community members; the percentage of category-diverged backers was
higher for unsuccessful campaigns than for successful campaigns. Figure 9 shows platform-centered
and category-diverged behavior side by side. The difference may be because project-centered
community members are better able to evaluate the quality and potential of a crowdfunding campaign
in a certain domain than are backers from other communities. Similarly, category-diverged members
have lower competence to judge a successful campaign than do project-centered backers. This also
confirms our theory: category-diverged members, by definition, are outside the comfort zone of their
Mean Percent of ProjectCentered Backers out of Total
Campaign Backers
original interest.
Project-Centered
60%
50%
40%
30%
20%
10%
0%
5%
11%
21%
31%
41%
51%
61%
Percent of Funding Goal Raised
Mean Percent of CategoryDiverged Backers out of Total
Campaign Backers
Unsuccessful Campaigns
Successful Campaigns
Category-Diverged
25%
20%
15%
10%
5%
0%
5%
11%
21%
31%
41%
51%
61%
Percent of Funding Goal Raised
Unsuccessful Campaigns
Successful Campaigns
Figure 9. Ratio of Project-Centered and Category-Centered Community Members of Total
Campaign Backers during Fundraising Period.
32
Table 4 compares the backing patterns of different community types. Our data show that most of the
backings on Kickstarter are done after the campaign goal has reached. We observed that after
category-diverged members began to support campaigns outside of their original category they had a
significant tendency to support campaigns after the campaign goal had been reached. This implies that
the impact of category-diverged members on campaign success decreases after they have diverged
from their original category.
Table 4. Comparison of Backing Patterns of Different Community Members
Community Type
N (# of backers)
ProjectCentered
CategoryCentered
PlatformCentered
t-test
P Value
(Category
vs.
Platform)
Category-Diverged
While
Being
CategoryCentered
While
Being
PlatformCentered
46,403
46,403
t-test
P Value
1,390,295
230,245
612,754
Backing Day
(absolute)
17.22
16.8
15.9
0.00***
16.2
16.5
0.00***
Backing Day
(percent)
52%
51.8%
48.3%
0.00***
48.8%
48.7%
0.613
All
Campaigns
228%
(586%)
356%
(756%)
287%
(549%)
0.00***
410%
(817%)
337%
(532%)
0.00***
Successful
Campaigns
Only
260%
(623%)
393%
(789%)
318%
(574%)
0.00***
461%
(862%)
374%
(557%)
0.00***
20.7%
(49.98%)
22.6%
(38.1%)
23.3%
(49.2%)
0.007***
23.7%
(50.29%)
21.1%
(27.79%)
0.009***
54.8%
45.3%
47.03%
0.00***
42.12%
37.31%
0.00***
Money
Raised
when
Backed
(std. dev.)
Unsuccessful
Campaigns
Only
Backings Before Goal
Reached
**- significant at the 0.05 level; ***- significant at the 0.01 level
In order to measure the impact of Kickstarter communities on campaign performance, we estimated a
logistic regression model to predict the probability of campaign success given the ratios of the four
community types among the total campaign backers. In order to normalize for the effect of herding we
repeated the regression considering campaigns at key stages of their fundraising period: after 5% was
collected, 10%, 20%, 30%, 40%, and 100%. As we showed earlier, there almost no unsuccessful
campaigns that raised more than 40% of their target goal. We also conducted the regression after the
fundraising period has ended.
33
We controlled for campaign characteristics, owner attributes, and platform age at campaign launch
day. Our model’s control variables are consistent with the main models estimated for predicting
Kickstarter campaign success in previous studies (Burtch et al. 2013; Marom and Sade 2013; Mollick
2014; Zvilichovsky et al. 2013). Table 5 summarizes our results.
34
Table 5. Binary Logistic Regression Model: Predicting the Successful Funding of a Crowdfunding
Campaign on Kickstarter incorporating Subgroup Share Variables at Different Levels of Percentage of
Money Raised (Category-Diverged Subgroup Excluded)
5%
10%
20%
30%
40%
Last/
100%
Final State
Exp(B)
(S.E.)
Exp(B)
(S.E.)
Exp(B)
(S.E.)
Exp(B)
(S.E.)
Exp(B)
(S.E.)
Exp(B)
(S.E.)
Exp(B)
(S.E.)
Logged Goal
0.339***
(0.193)
0.466***
(0.117)
0.569***
(0.095)
0.555***
(0.095)
0.585***
(0.105)
0.194***
(0.042)
0.131***
(0.020)
Original
Duration
0.982**
(0.007)
0.990**
(0.005)
0.988***
(0.004)
0.997
(0.004)
0.991
(0.005)
0.985***
(0.002)
0.988***
(0.001)
Has FB
Friends
0.705***
(0.132)
0.761***
(0.085)
0.819***
(0.076)
0.940
(0.080)
0.996
(0.094)
0.850***
(0.039)
0.856***
(0.018)
Platform
Maturity
0.991
(0.024)
0.993
(0.015)
0.994
(0.014)
0.973
(0.014)
0.968
(0.017)
0.991
(0.007)
0.998***
(0.001)
Weeks On
Platform
1.001
(0.001)
1.002***
(0.001)
1.002***
(0.001)
1.002**
(0.001)
1.002***
(0.001)
1.003***
(0.000)
1.004***
(0.000)
Had
Created
1.280
(0.193)
1.122
(0.117)
0.959
(0.102)
0.813**
(0.102)
0.757**
(0.115)
1.259***
(0.051)
1.294***
(0.029)
Had
Backed
1.079
(0.142)
1.130
(0.093)
1.150
(0.081)
1.097
(0.087)
0.975
(0.103)
1.520***
(0.040)
1.457***
(0.018)
Has
Video
3.853***
(0.478)
1.374
(0.210)
2.093***
(0.153)
1.874***
(0.148)
1.728***
(0.160)
1.706***
(0.061)
1.533***
(0.027)
Num
Reward
Categories
1.013
(0.011)
1.014**
(0.007)
1.011
(0.006)
1.013
(0.007)
1.011
(0.008)
1.048***
(0.003)
1.074***
(0.002)
Has
Limited
Category
1.229
(0.185)
1.293**
(0.109)
1.073
(0.092)
1.076
(0.096)
0.939
(0.114)
1.158***
(0.043)
1.032***
(0.019)
ProjectCentered
Percent
1.004
(0.006)
1.005
(0.004)
1.014***
(0.004)
1.013***
(0.004)
1.014***
(0.004)
1.015***
(0.002)
1.023***
(0.001)
PlatformCentered
Percent
1.016**
(0.008)
1.011**
(0.005)
1.024***
(0.005)
1.014***
(0.005)
1.019***
(0.007)
1.010***
(0.003)
1.013***
(0.002)
CategoryCentered
Percent
1.034***
(0.011)
1.027***
(0.007)
1.030***
(0.007)
1.022***
(0.007)
1.026***
(0.009)
1.041***
(0.004)
1.046***
(0.002)
CategoryDiverged
Percent
-
-
-
-
-
-
-
Category
Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Constant
8.659
(1.823)
6.012
(1.119)
2.2 (0.948)
21.4***
(0.986)
44.5***
(1.136)
140.3***
(0.476)
251.6***
(0.136)
Observations
1561
2972
4156
4700
4952
17460
89362
Log
likelihood:
1602
3750
5046
4715
3747
19714
Cox &Snell
R-Square:
0.107
0.102
0.104
0.103
0.084
0.200
Nagelkerke
R-Square:
0.157
0.137
0.142
0.153
0.148
0.270
88719
0.222
0.312
35
The results should be interpreted as follows. We omitted the category-diverged proportion from the
input variables, hence the odds ratio of one of the other three community types should indicate
whether an increase in the proportion of that community type at the expense of the category-diverged
members increased the success likelihood of the campaign. Because we applied this consideration for
each of the three types we can also deduce the relative impacts of the three types to each other and not
only with respect to category-diverged members.
The results show that category-centered community members have the highest positive effect on
campaign success, and that category-diverged community members have a negative effect (with
respect to the alternative of replacing them with backers of other communities). These results may be
explained via theory of membership overlap: It has been suggested that members belonging to
multiple communities are subject to conflicting forces. On the one hand, these members enjoy the
benefits of overlap (i.e., diversity of choice, interest, and stimulation). However, each member has
only limited resources (time, attention, and money) and may struggle to maintain active membership
in multiple communities over time. Category-centered community members do not divide their
resources among multiple communities and are able to identify value and quality in their area of
interest. Therefore, they are a more indicative index than platform enthusiasts of campaign success.
From a marketing perspective, financing a crowdfunding campaign relies on having a few mavericks,
mavens and social connectors as the product early adopters (Gladwell 2000). Our results suggest that
category-diverged members has a negative impact on campaign success and performance comparing
to all other types because they do not play any of the mentioned roles.
DISCUSSION AND IMPLICATIONS
Social Network Perspective
Crowdfunding dynamics may be considered a case study of broader online phenomena such as
information diffusion, social learning, and herding. Previous studies have investigated these
phenomena either where all users are equally exposed to the same information or in the context of
online networks where explicit associations between the users exist and information channels may be
36
identified. In contrast, we study these phenomena from a community perspective, which differ from
either these approaches. We used community association as a proxy for information flow and peer
effect and were able to show that these dynamics vary among different community types. Our
approach may be particularly useful in the context where explicit associations between the users do
exist but cannot be observed (for example, they exist outside of the platform).
From a social network analysis perspective, the category-centered and platform-centered community
members may be considered as weak ties (Granovetter et al. 1983) or long ties (Susarla et al. 2012) of
the project-centered community members. Such ties are known to be effective brokers of information
(Easley and Kleinberg 2010) and to drive network diffusion. Our proposed framework of community
hierarchy provides an additional, somewhat complementary, qualitative interpretation of the concept
of long ties. We argue that these ties are not only 'long' but also 'tall'; that is, they diffuse the
information to a community of different granularity types, whose members have some broader interest
than the specific campaign in question. The cohesiveness and user visibility levels on these
communities determine their "community added-value" and in turn their impact on campaign success.
Digital Platforms and Two-Sided Markets
Our findings may also be used to better predict a customer's lifetime value (Venkatesan and Kumar
2004). Our results suggest that platform owners should be cautious when they leverage upon their
existing users via cross-sale offers. Diverging users from their original interest may have
counterproductive implications and negative impacts associated with their future platform
participation. In that sense, crowdfunding platforms may incorporate conversion strategies that were
found to be effective on e-commerce sites (Moe and Fader 2004).
Furthermore, many commercial platforms wish to leverage upon their community to increase their
revenues. Kickstarter, however, is part of an emerging platform family, in which this trend is
amplified. Kickstarter is a two-sided market (Eisenmann et al. 2006; Rochet and Tirole 2003). Its
users consist of campaign owners and backers, and the crowdfunding platform serves as an
intermediary between them (Zvilichovsky et al. 2013). As such, not only platform owners are
incentivized to leverage upon their community, but also campaign owners wish to nurture
37
communities of their own. Such dynamics may be found on other digital platforms such as YouTube
(uploading and watching movies), Airbnb (hosting and renting), and eBay (buying and selling).
Community hierarchy may be found on these platforms as well. For example, YouTube channel
subscribers may be classified as an ad hoc community, category-based communities are centered
around YouTube categories such as Music and Sport, and platform enthusiasts are those who
subscribe to channels on different categories.
LIMITATIONS
This study aspires to contribute to the literature in the domain of online communities. However,
supporting a crowdfunding campaign involves real money rather than other types of utility used on
other online communities such as time, effort, or knowledge. Although in economic research, money
may be regarded as yet another form of resource (Marshall 2004), there may be differences in user
perceptions regarding community contributions that involve real money. Hence the findings of this
research may not be directly applicable to online communities outside the domain of crowdfunding.
Nevertheless, studies have shown that commercial websites, such as e-commerce and paid content
platforms, may generate online communities as well (Oestreicher-Singer and Zalmanson 2013).
Like other observational studies, this research faced data limitations and identification challenges.
However, we utilized the large data set to increase our confidence in the reported results.
SUMMARY AND CONCLUSION
In this paper, we introduced a community-oriented estimation approach and used it to study the
community features apparent in Kickstarter and their impact on campaign performance. We explored
the growth of Kickstarter communities that were platform centered, category centered, and campaign
centered and investigated the growing dominance of repeat backers in Kickstarter project financing.
We found that the category-based community is a strategic pivotal subgroup in the overall Kickstarter
community. We suggest that drawing this type of member to support a campaign will increase its
likelihood of success, via signaling to their peers either on Kickstarter or outside. We also investigated
the impact of user fluidity on her backing patterns and consequently on her contribution to campaign
38
success. We found that such users, after detaching from their community, increase their platform
participation, however, their backing lacks community added value and their impact on campaign
success decreases.
How far can we generalize the results of this research? The study described articulates and examines
empirically some of the mechanisms that underlie social behavior in digital spheres to the point where
behavior becomes more predictable.
39
References
Agrawal, A. K., Catalini, C., and Goldfarb, A. 2011. The Geography of Crowdfunding. Working
Paper 16820, National Bureau of Economic Research
Ahlers, G., Cumming, D., Günther, C., and Schweizer, D. 2012. Signaling in Equity Crowdfunding.
Available at SSRN 2161587
Arazy, O., Nov, O., and Ortega, F. 2014. The [Wikipedia] World is Not Flat: On the Organizational
Structure of Online Production Communities. Proceedings of the 22nd European Conference
on Information Systems (ECIS’2014)
Arguello, J., Butler, B. S., Joyce, E., Kraut, R., Ling, K. S., Rosé, C., and Wang, X. 2006. Talk to me:
foundations for successful individual-group interactions in online communities. Proceedings
of the SIGCHI conference on Human Factors in computing systems 959–968.
Armstrong, A., and Hagel, J. 2000. The real value of online communities. Knowledge and
communities, 85-95.
Barzilay, O., Treude, C., and Zagalsky, A. 2013. Facilitating Crowd Sourced Software Engineering
via Stack Overflow. in Finding Source Code on the Web for Remix and Reuse 289–308.
Springer, New York.
Bateman, P. J., Gray, P. H., and Butler, B. S. 2011. Research Note—The Impact of Community
Commitment on Participation in Online Communities. Information Systems Research 22(4)
841–854.
Belleflamme, P., Lambert, T., and Schwienbacher, A. 2011. Crowdfunding: Tapping the right crowd.
International Conference of the French Finance Association (AFFI) 11–13.
Borgatti, S. P., and Everett, M. G. 2000. Models of core/periphery structures. Social Networks 21(4)
375–395.
Boudreau, K. J., and Lakhani, K. R. 2013. Using the crowd as an innovation partner. Harvard
Business Review 91(4) 60–69, 140.
Brandtzaeg, Petter Bae, and Jan Heim. “User Loyalty and Online Communities: Why Members of
Online Communities Are Not Faithful.” In Proceedings of the 2nd International Conference
on INtelligent TEchnologies for Interactive enterTAINment, 11:1–11:10. INTETAIN ’08.
ICST, Brussels, Belgium, 2007. http://dl.acm.org/citation.cfm?id=1363200.1363215.
Burtch, G., Ghose, A., and Wattal, S. 2013. An Empirical Examination of the Antecedents and
Consequences of Contribution Patterns in Crowd-Funded Markets. Information Systems
Research 24(3) 499–519.
Butler, B., Kiesler, S., and Kraut, R. 2013. Community Effort in Online Groups: Who Does the.
Leadership at a distance: Research in technologically-supported work 171.
Butler, B. S. 2001. Membership size, communication activity, and sustainability: A resource-based
model of online social structures. Information systems research 12(4) 346–362.
Chen, J., Ren, Y., and Riedl, J. 2010. The effects of diversity on group productivity and member
withdrawal in online volunteer groups. Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems 821–830.
Choi, B., Alexander, K., Kraut, R. E., and Levine, J. M. 2010. Socialization tactics in wikipedia and
their effects. Proceedings of the 2010 ACM conference on Computer supported cooperative
work 107–116.
Easley, D. and Kleinberg, J. 2010. Networks, crowds, and markets: Reasoning about a highly
connected world. Cambridge University Press.
Eisenmann, T., Parker, G., and Van Alstyne, M. W. 2006. Strategies for two-sided markets. Harvard
business review 84(10) 92.
41
Faraj, S., Jarvenpaa, S. L., and Majchrzak, A. 2011. Knowledge collaboration in online communities.
Organization Science 22(5) 1224–1239.
Faraj, S., and Johnson, S. L. 2011. Network exchange patterns in online communities. Organization
Science 22(6) 1464–1480.
Feld, S. L. 1981. The Focused Organization of Social Ties. American Journal of Sociology 86(5)
1015–1035.
Gerber, E. M., Hui, J. S., and Kuo, P.-Y. 2012. Crowdfunding: Why people are motivated to post and
fund projects on crowdfunding platforms. Proceedings of the International Workshop on
Design, Influence, and Social Technologies: Techniques, Impacts and Ethics
Giudici, G., Nava, R., Rossi Lamastra, C., and Verecondo, C. 2012. Crowdfunding: The New Frontier
for Financing Entrepreneurship? Available at SSRN 2157429
Gladwell, M. 2000. The tipping point: How little things can make a big difference, Little,Brown and
Co., New York.
Granovetter, M. 1983. The strength of weak ties: A network theory revisited. Sociological
theory, 1(1), 201-233.
Gu, B., Konana, P., Raghunathan, R., and Chen, H. M. 2014. Research Note—The Allure of
Homophily in Social Media: Evidence from Investor Responses on Virtual Communities.
Information Systems Research 25(3) 604–617.
Ingram, C., and Teigland, R. 2013. Crowdfunding Among IT Entrepreneurs in Sweden: A Qualitative
Study of the Funding Ecosystem and ICT Entrepreneurs’ Adoption of Crowdfunding.
Available at SSRN 2289134
Kim, A. J. 2000. Community building on the web: Secret strategies for successful online communities,
Addison-Wesley Longman Publishing Co., Inc., New York.
Kim, K., and Viswanathan, S. 2013. The Signals in the Noise: The Role of Reputable Investors in a
Crowdfunding Market. Available at SSRN 2258243
Kleemann, F., Voß, G. G., and Rieder, K. 2008. Un (der) paid Innovators: The Commercial
Utilization of Consumer Work through Crowdsourcing. Science, Technology & Innovation
Studies 4(1) 5–26.
Kuk, G. 2006. Strategic interaction and knowledge sharing in the KDE developer mailing list.
Management Science 52(7) 1031–1042.
Kuppuswamy, V., and Bayus, B. L. 2013. Crowdfunding Creative Ideas: the Dynamics of Projects
Backers in Kickstarter. Working Paper
Leiserson, C. E., Rivest, R. L., Stein, C., and Cormen, T. H. 2001. Introduction to algorithms, MIT
press.
Li, C., and Bernoff, J. 2011. Groundswell: Winning in a world transformed by social technologies,
Harvard Business Press.
Lin, S., and Lin, F.-R. 2006. Towards an Ecological Perspective on the Evolution of Online
Communities of Practice. Proceedings of the 39th Annual Hawaii International Conference
on System Sciences, 2006. HICSS ’06 (Vol. 6) 134a–134a.
Lin, Y.-R., Chi, Y., Zhu, S., Sundaram, H., and Tseng, B. L. 2008. Facetnet: a framework for
analyzing communities and their evolutions in dynamic networks. Proceedings of the 17th
international conference on World Wide Web 685–694.
López, C. A., and Butler, B. S. 2013. Consequences of content diversity for online public spaces for
local communities. Proceedings of the 2013 conference on Computer supported cooperative
work 673–682.
Maloney-Krichmar, D., and Preece, J. 2005. A multilevel analysis of sociability, usability, and
community dynamics in an online health community. ACM Transactions on ComputerHuman Interaction (TOCHI) 12(2) 201–232.
41
Marom, D., and Sade, O. 2013. Are the Life and Death of a Young Start-Up Indeed in the Power of
the Tongue? Lessons from Online Crowdfunding Pitches. Available at SSRN 2255707.
Marshall, A. 2004. Principles of economics. Digireads.com Publishing.
Miller, K. D., Fabian, F., and Lin, S. 2009. Strategies for online communities. Strategic Management
Journal 30(3) 305–322.
Moe, W. W., and Fader, P. S. 2004. Dynamic Conversion Behavior at E-Commerce Sites.
Management Science 50(3) 326–335.
Mollick, E. 2014. The dynamics of crowdfunding: An exploratory study. Journal of Business
Venturing 29(1) 1–16.
Moore, G. A. 1991. Crossing the Chasm: Marketing and Selling Technology Products to Mainstream
Customers, Harper-Collins, New York.
Oestreicher-Singer, G., and Zalmanson, L. 2013. Content or Community? A Digital Business Strategy
for Content Providers in the Social Age. Management Information Systems Quarterly 37(2)
591–616.
Oestreicher-Singer, G., Libai, B., Sivan, L., Carmi, E., and Yassin, O. 2013. The Network Value of
Products. Journal of Marketing, 77(3), 1-14.
Palla, G., Barabási, A.-L., and Vicsek, T. 2007. Quantifying social group evolution. Nature 446(7136)
664–667.
Pi, J. 2012. AppsBlogger. http://www.appsblogger.com/kickstarter-infographic Visited: May 1, 2013
Porter, C. E. 2004. A typology of virtual communities: A multi‐disciplinary foundation for future
research. Journal of Computer‐Mediated Communication 10(1) Article 3.
Preece, J. 2001. Sociability and usability in online communities: Determining and measuring success.
Behaviour & Information Technology 20(5) 347–356.
Preece, J., and Shneiderman, B. 2009. The reader-to-leader framework: Motivating technologymediated social participation. AIS Transactions on Human-Computer Interaction 1(1) 13–32.
Provost, F., Dalessandro, B., Hook, R., Zhang, X., and Murray, A. 2009. Audience selection for online brand advertising: privacy-friendly social network targeting. Proceedings of the 15th
ACM SIGKDD international conference on Knowledge discovery and data mining 707–716.
Raban, D. R., Moldovan, M., and Jones, Q. 2010. An empirical study of critical mass and online
community survival. Proceedings of the 2010 ACM conference on Computer supported
cooperative work 71–80.
Ren, Y., Harper, F. M., Drenner, S., Terveen, L., Kiesler, S., Riedl, J., and Kraut, R. E. 2012. Building
Member Attachment in Online Communities: Applying Theories of Group Identity and
Interpersonal Bonds. Mis Quarterly 36(3)
Rochet, J.-C., and Tirole, J. 2003. Platform Competition in Two-Sided Markets. Journal of the
European Economic Association 1(4) 990–1029.
Rogers, E. M. 2010. Diffusion of Innovations, 4th Edition, Simon and Schuster.
Schwienbacher, A., and Larralde, B. 2012. Crowdfunding of small entrepreneurial ventures. The
Oxford Handbook of Enrepreneurial Finanace, D. Cumming (ed.), (Vols. 1-Book, 1-Section)
369–391.
Sundararajan, A., Provost, F., Oestreicher-Singer, G., and Aral, S. 2013. Research CommentaryInformation in Digital, Economic, and Social Networks. Information Systems Research 24(4)
883–905.
Sundin, O. 2011. Janitors of knowledge: constructing knowledge in the everyday life of Wikipedia
editors. Journal of Documentation 67(5) 840–862.
Susarla, A., Oh, J. H., & Tan, Y. 2012. Social networks and the diffusion of user-generated content:
Evidence from YouTube. Information Systems Research, 23(1), 23-41
42
Umyarov, A., Bapna, R., Ramaprasad, J., and Shmueli, G. 2013. “One-Way Mirrors and WeakSignaling in Online Dating: A Randomized Field Experiment,” in International Conference
on Information Systems, Milan, Italy.
Ung, H., and Dalle, J.-M. 2010. Project management in the Wikipedia community. Proceedings of the
6th International Symposium on Wikis and Open Collaboration 13.
Venkatesan, R., and Kumar, V. 2004. A Customer Lifetime Value Framework for Customer Selection
and Resource Allocation Strategy. Journal of Marketing 68(4) 106–125.
Wang, X., Butler, B. S., and Ren, Y. 2012. The Impact of Membership Overlap on Growth: An
Ecological Competition View of Online Groups. Organization Science 24(2) 414–431.
Wang, Y.-C., and Kraut, R. 2012. Twitter and the development of an audience: those who stay on
topic thrive! Proceedings of the 2012 ACM annual conference on Human Factors in
Computing Systems 1515–1518.
Ward, C., and Ramachandran, V. 2010. Crowdfunding the next hit: Microfunding online experience
goods. Workshop on Computational Social Science and the Wisdom of Crowds
Wasko, M. M., and Faraj, S. 2005. Why should I share? Examining social capital and knowledge
contribution in electronic networks of practice. MIS quarterly 35–57.
Welser, H. T., Gleave, E., Fisher, D., and Smith, M. 2007. Visualizing the signatures of social roles in
online discussion groups. Journal of social structure 8(2) 1–32.
Yang, J., and Wei, X. 2009. Seeking and Offering Expertise Across Categories: A Sustainable
Mechanism Works for Baidu Knows. ICWSM
Yoganarasimhan, H. 2012. Impact of social network structure on content propagation: A study using
YouTube data. Quantitative Marketing and Economics 10(1) 111–150.
Zhang, J., and Liu, P. 2012. Rational herding in microloan markets. Management science 58(5) 892–
912.
Zhu, H., Kraut, R., and Kittur, A. 2013. The Impact of Membership Overlap on the Survival of Online
Communities. Proceedings of the 32nd annual ACM conference on Human factors in
computing systems (pp. 281-290). ACM.
Zvilichovsky, D., Inbar Y., and Barzilay O. 2013. “Playing Both Sides of the Market: Success and
Reciprocity on Crowdfunding Platforms.” In Proceedings of the 34th International
Conference on Information Systems,
43